\n",
+ "\n",
+ "In this exercise we will implement linear regression and see it work on data\n",
+ "\n",
+ "Files included in this exercise:\n",
+ "\n",
+ " - ex1data1.txt - Dataset for linear regression with one variable\n",
+ " - ex1data2.txt - Dataset for linear regression with multiple variables\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Simple Python function
\n",
+ "\n",
+ "We will warmup by creating a function which returns an n x n identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def warmupexercise(x):\n",
+ " A = np.identity(x)\n",
+ " return A"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can run the function with an input of 5 to create a 5 x 5 identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 1.]])"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "warmupexercise(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Linear Regression with One Variable
\n",
+ "\n",
+ "In this part of this exercise, we will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and we have data for profits and populations from the cities.\n",
+ "\n",
+ "We would like to use this data to help you select which city to expand to next. The file ex1data1.txt contains the dataset for our linear regression problem. The first column is the population of a city and the second column is the profit of a food truck in that city. A negative value for profit indicates a loss. \n",
+ "\n",
+ "We now load the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read comma separated data\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')\n",
+ "X, y = data[:, 0], data[:, 1]\n",
+ "\n",
+ "m = y.size # number of training examples"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex1/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/ex1/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..f6a45f5
--- /dev/null
+++ b/ex1/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,683 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
**Exercise One : Linear Regression**
\n",
+ "\n",
+ "
Introduction
\n",
+ "\n",
+ "In this exercise we will implement linear regression and see it work on data\n",
+ "\n",
+ "Files included with this exercise:\n",
+ "\n",
+ " - ex1data1.txt - Dataset for linear regression with one variable\n",
+ " - ex1data2.txt - Dataset for linear regression with multiple variables\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n",
+ "import matplotlib.patches as mpatches \n",
+ "import matplotlib.lines as mlines # for creating a legend\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Simple Python function
\n",
+ "\n",
+ "We will warmup by creating a function which returns an n x n identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def warmupexercise(x):\n",
+ " A = np.identity(x)\n",
+ " return A"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can run the function with an input of 5 to create a 5 x 5 identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 1.]])"
+ ]
+ },
+ "execution_count": 138,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "warmupexercise(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Linear Regression with One Variable
\n",
+ "\n",
+ "In this part of this exercise, we will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and we have data for profits and populations from the cities.\n",
+ "\n",
+ "We would like to use this data to help you select which city to expand to next. The file ex1data1.txt contains the dataset for our linear regression problem. The first column is the population of a city and the second column is the profit of a food truck in that city. A negative value for profit indicates a loss. \n",
+ "\n",
+ "We now load the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read comma separated data\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')\n",
+ "X, y = data[:, 0], data[:, 1]\n",
+ "\n",
+ "m = y.size # number of training examples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.1 Plotting the Data
\n",
+ "\n",
+ "Before starting on the task it is useful to visualize the data. Since we are dealing with only two variables, we can do this with a scatterplot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotData(X,y):\n",
+ " red_x = pyplot.plot(X, y, 'ro', ms=10, mec='k')\n",
+ " pyplot.title('Figure 1: Scatter Plot of Training Data')\n",
+ " pyplot.xlabel('Population of City in 10,000s')\n",
+ " pyplot.ylabel('Profit in $10,000s')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "In this exercise we will implement linear regression and see it work on data\n",
+ "\n",
+ "Files included with this exercise:\n",
+ "\n",
+ " - ex1data1.txt - Dataset for linear regression with one variable\n",
+ " - ex1data2.txt - Dataset for linear regression with multiple variables\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n",
+ "import matplotlib.patches as mpatches \n",
+ "import matplotlib.lines as mlines # for creating a legend\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Simple Python function
\n",
+ "\n",
+ "We will warmup by creating a function which returns an n x n identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def warmupexercise(x):\n",
+ " A = np.identity(x)\n",
+ " return A"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can run the function with an input of 5 to create a 5 x 5 identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 1.]])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "warmupexercise(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Linear Regression with One Variable
\n",
+ "\n",
+ "In this part of this exercise, we will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and we have data for profits and populations from the cities.\n",
+ "\n",
+ "We would like to use this data to help you select which city to expand to next. The file ex1data1.txt contains the dataset for our linear regression problem. The first column is the population of a city and the second column is the profit of a food truck in that city. A negative value for profit indicates a loss. \n",
+ "\n",
+ "We now load the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read comma separated data\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')\n",
+ "X, y = data[:, 0], data[:, 1]\n",
+ "\n",
+ "m = y.size # number of training examples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.1 Plotting the Data
\n",
+ "\n",
+ "Before starting on the task it is useful to visualize the data. Since we are dealing with only two variables, we can do this with a scatterplot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotData(X,y):\n",
+ " red_x = pyplot.plot(X, y, 'ro', ms=10, mec='k')\n",
+ " pyplot.title('Figure 1: Scatter Plot of Training Data')\n",
+ " pyplot.xlabel('Population of City in 10,000s')\n",
+ " pyplot.ylabel('Profit in $10,000s')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "In this exercise we will implement linear regression and see it work on data\n",
+ "\n",
+ "Files included with this exercise:\n",
+ "\n",
+ " - ex1data1.txt - Dataset for linear regression with one variable\n",
+ " - ex1data2.txt - Dataset for linear regression with multiple variables\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n",
+ "import matplotlib.patches as mpatches \n",
+ "import matplotlib.lines as mlines # for creating a legend\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Simple Python function
\n",
+ "\n",
+ "We will warmup by creating a function which returns an n x n identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def warmupexercise(x):\n",
+ " A = np.identity(x)\n",
+ " return A"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can run the function with an input of 5 to create a 5 x 5 identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1., 0., 0., 0., 0.],\n",
+ " [0., 1., 0., 0., 0.],\n",
+ " [0., 0., 1., 0., 0.],\n",
+ " [0., 0., 0., 1., 0.],\n",
+ " [0., 0., 0., 0., 1.]])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "warmupexercise(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Linear Regression with One Variable
\n",
+ "\n",
+ "In this part of this exercise, we will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and we have data for profits and populations from the cities.\n",
+ "\n",
+ "We would like to use this data to help you select which city to expand to next. The file ex1data1.txt contains the dataset for our linear regression problem. The first column is the population of a city and the second column is the profit of a food truck in that city. A negative value for profit indicates a loss. \n",
+ "\n",
+ "We now load the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read comma separated data\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')\n",
+ "X, y = data[:, 0], data[:, 1]\n",
+ "\n",
+ "m = y.size # number of training examples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.1 Plotting the Data
\n",
+ "\n",
+ "Before starting on the task it is useful to visualize the data. Since we are dealing with only two variables, we can do this with a scatterplot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotData(X,y):\n",
+ " red_x = pyplot.plot(X, y, 'ro', ms=10, mec='k')\n",
+ " pyplot.title('Figure 1: Scatter Plot of Training Data')\n",
+ " pyplot.xlabel('Population of City in 10,000s')\n",
+ " pyplot.ylabel('Profit in $10,000s')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "Here we will build a logistic regression model to predict whether a student gets admitted into a university given the results of two exams. Our training set consists of samples of applicants' scores on two exams and an admissions decision."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Visualizing the data
\n",
+ "\n",
+ "Before we begin on the algorithm we load and visualize the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load data\n",
+ "# The first two columns contains the exam scores and the third column\n",
+ "# contains the label.\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex2data1.txt'), delimiter=',')\n",
+ "X, y = data[:, 0:2], data[:, 2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotData(X,y):\n",
+ " # New figure\n",
+ " fig = plt.figure()\n",
+ "\n",
+ " # Find indeces of positive and negative examples \n",
+ " # Then plot them seperately (Don't try to plot then label after)\n",
+ " pos = y == 1\n",
+ " neg = y == 0\n",
+ "\n",
+ " plt.plot(X[pos,0],X[pos,1],'k*', lw=2, ms=7)\n",
+ " plt.plot(X[neg,0],X[neg,1],'yo',mec='k',ms=7)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "First we construct the sigmoid function defined as:\n",
+ "$$h_\\theta(x) = g(\\theta^Tx)$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(z):\n",
+ " \"\"\"\n",
+ " Compute sigmoid function given the input z.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " z : array_like\n",
+ " The input to the sigmoid function. This can be a 1-D vector \n",
+ " or a 2-D matrix. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " g : array_like\n",
+ " The computed sigmoid function. g has the same shape as z, since\n",
+ " the sigmoid is computed element-wise on z.\n",
+ " \"\"\"\n",
+ " # convert input to a numpy array\n",
+ " z = np.array(z)\n",
+ "\n",
+ " g = 1 + np.exp(-1*z)\n",
+ " g = np.reciprocal(g)\n",
+ "\n",
+ " return g"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sigmoid of 0 is 0.5\n",
+ "Sigmoid of 100 is 1.0\n",
+ "Sigmoid of -100 is 3.7200759760208356e-44\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Check a few values of sigmoid\n",
+ "print(\"Sigmoid of 0 is \",sigmoid(0))\n",
+ "print(\"Sigmoid of 100 is \",sigmoid(100))\n",
+ "print(\"Sigmoid of -100 is \",sigmoid(-100))\n",
+ "\n",
+ "# sigmoid of 0 should be exactly 0.5\n",
+ "# sigmoid of large positive numbers should be close to 1\n",
+ "# sigmoid of large negative numbers should be close to 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a working sigmoid function, we can now implement a cost function which returns the cost and gradient for cost defined as:\n",
+ "\n",
+ "$$\\begin{align}\n",
+ "J(\\theta) & = \\dfrac{1}{m} \\sum_{i=1}^m \\mathrm{Cost}(h_\\theta(x^{(i)}),y^{(i)}) \\\\\n",
+ "& = - \\dfrac{1}{m} [\\sum_{i=1}^{m} y^{(i)} \\log(h_\\theta(x^{(i)})) + (1 - y^{(i)}) \\log(1-h_\\theta(x^{(i)}))] \\\\\n",
+ "\\end{align}$$\n",
+ "\n",
+ "and derivative:\n",
+ "\n",
+ "$$\\frac{\\partial}{\\partial \\theta_j} J(\\theta) = \\dfrac{1}{m} \\sum_{i=1}^{m} (h_\\theta(x^{(i)}) - y^{(i)}) x_j^{(i)}$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup the data matrix appropriately, and add ones for the intercept term\n",
+ "m, n = X.shape\n",
+ "\n",
+ "# Add intercept term to X\n",
+ "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def costFunction(theta,X,y):\n",
+ " \"\"\"\n",
+ " Compute cost and gradient for logistic regression. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " The parameters for logistic regression. This a vector\n",
+ " of shape (n+1, ).\n",
+ " \n",
+ " X : array_like\n",
+ " The input dataset of shape (m x n+1) where m is the total number\n",
+ " of data points and n is the number of features. We assume the \n",
+ " intercept has already been added to the input.\n",
+ " \n",
+ " y : arra_like\n",
+ " Labels for the input. This is a vector of shape (m, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed value for the cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " A vector of shape (n+1, ) which is the gradient of the cost\n",
+ " function with respect to theta, at the current values of theta.\n",
+ " \"\"\"\n",
+ " ## Initialize some useful values\n",
+ " m = y.size # number of training examples\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ " h = sigmoid(X.dot(theta))\n",
+ " logh = np.log(h)\n",
+ " tempLog = np.log(1-h)\n",
+ " yTrans = y.transpose()\n",
+ " Xtrans = X.transpose()\n",
+ " tempTrans = (1-y).transpose()\n",
+ " \n",
+ " \n",
+ " J = ((-yTrans).dot(logh))\n",
+ " J = J - tempTrans.dot(tempLog)\n",
+ " J = J * (1/m)\n",
+ " \n",
+ " diff = np.subtract(sigmoid(X.dot(theta)),y)\n",
+ " grad = Xtrans.dot(diff)\n",
+ " grad = grad * (1/m)\n",
+ " \n",
+ " # =============================================================\n",
+ " return J, grad\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now test our cost function with varying initial thetas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at initial theta (zeros): 0.693\n",
+ "Expected cost (approx): 0.693\n",
+ "\n",
+ "Gradient at initial theta (zeros):\n",
+ "\t[-0.1000, -12.0092, -11.2628]\n",
+ "Expected gradients (approx):\n",
+ "\t[-0.1000, -12.0092, -11.2628]\n",
+ "\n",
+ "Cost at test theta: 0.218\n",
+ "Expected cost (approx): 0.218\n",
+ "\n",
+ "Gradient at test theta:\n",
+ "\t[0.043, 2.566, 2.647]\n",
+ "Expected gradients (approx):\n",
+ "\t[0.043, 2.566, 2.647]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize fitting parameters\n",
+ "initial_theta = np.zeros(n+1)\n",
+ "\n",
+ "cost, grad = costFunction(initial_theta, X, y)\n",
+ "\n",
+ "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+ "print('Expected cost (approx): 0.693\\n')\n",
+ "\n",
+ "print('Gradient at initial theta (zeros):')\n",
+ "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n",
+ "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n",
+ "\n",
+ "# Compute and display cost and gradient with non-zero theta\n",
+ "test_theta = np.array([-24, 0.2, 0.2])\n",
+ "cost, grad = costFunction(test_theta, X, y)\n",
+ "\n",
+ "print('Cost at test theta: {:.3f}'.format(cost))\n",
+ "print('Expected cost (approx): 0.218\\n')\n",
+ "\n",
+ "print('Gradient at test theta:')\n",
+ "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n",
+ "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working cost function, we can implement gradient descent using a built in optimization function scipy.optimize.\n",
+ "\n",
+ "To use this function we need to pass in:\n",
+ "\n",
+ "- The initial values of the parameters we are trying to optimize\n",
+ "- A function that, when given training set and theta, computes the logistic regression cost and gradient with respect to theta for (X,y)\n",
+ "- jac: which is an indication if we would like the function to return the jacobian (gradient) as well\n",
+ "- method: which is the method/algorithm we would like to implement\n",
+ "- options: options specific to our chosen algorithm (chosen iterations in our case)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at theta found by optimize.minimize: 0.203\n",
+ "Expected cost (approx): 0.203\n",
+ "\n",
+ "theta:\n",
+ "\t[-25.161, 0.206, 0.201]\n",
+ "Expected theta (approx):\n",
+ "\t[-25.161, 0.206, 0.201]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set options for optimize.minimize\n",
+ "options= {'maxiter': 400}\n",
+ "\n",
+ "# see documention for scipy's optimize.minimize for description about\n",
+ "# the different parameters\n",
+ "# The function returns an object `OptimizeResult`\n",
+ "# We use truncated Newton algorithm for optimization which is \n",
+ "# equivalent to MATLAB's fminunc\n",
+ "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n",
+ "res = optimize.minimize(costFunction,\n",
+ " initial_theta,\n",
+ " (X, y),\n",
+ " jac=True,\n",
+ " method='TNC',\n",
+ " options=options)\n",
+ "\n",
+ "# the fun property of `OptimizeResult` object returns\n",
+ "# the value of costFunction at optimized theta\n",
+ "cost = res.fun\n",
+ "\n",
+ "# the optimized theta is in the x property\n",
+ "theta = res.x\n",
+ "\n",
+ "# Print theta to screen\n",
+ "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n",
+ "print('Expected cost (approx): 0.203\\n');\n",
+ "\n",
+ "print('theta:')\n",
+ "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n",
+ "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have an optimal theta, we can use it to get a decision boundary."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mapFeature(X1, X2, degree=6):\n",
+ " \"\"\"\n",
+ " Maps the two input features to quadratic features used in the regularization exercise.\n",
+ "\n",
+ " Returns a new feature array with more features, comprising of\n",
+ " X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc..\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X1 : array_like\n",
+ " A vector of shape (m, 1), containing one feature for all examples.\n",
+ "\n",
+ " X2 : array_like\n",
+ " A vector of shape (m, 1), containing a second feature for all examples.\n",
+ " Inputs X1, X2 must be the same size.\n",
+ "\n",
+ " degree: int, optional\n",
+ " The polynomial degree.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " : array_like\n",
+ " A matrix of of m rows, and columns depend on the degree of polynomial.\n",
+ " \"\"\"\n",
+ " if X1.ndim > 0:\n",
+ " out = [np.ones(X1.shape[0])]\n",
+ " else:\n",
+ " out = [np.ones(1)]\n",
+ "\n",
+ " for i in range(1, degree + 1):\n",
+ " for j in range(i + 1):\n",
+ " out.append((X1 ** (i - j)) * (X2 ** j))\n",
+ "\n",
+ " if X1.ndim > 0:\n",
+ " return np.stack(out, axis=1)\n",
+ " else:\n",
+ " return np.array(out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 184,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotDecisionBoundary(plotData, theta, X, y):\n",
+ " \"\"\"\n",
+ " Plots the data points X and y into a new figure with the decision boundary defined by theta.\n",
+ " Plots the data points with * for the positive examples and o for the negative examples.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " plotData : func\n",
+ " A function reference for plotting the X, y data.\n",
+ "\n",
+ " theta : array_like\n",
+ " Parameters for logistic regression. A vector of shape (n+1, ).\n",
+ "\n",
+ " X : array_like\n",
+ " The input dataset. X is assumed to be a either:\n",
+ " 1) Mx3 matrix, where the first column is an all ones column for the intercept.\n",
+ " 2) MxN, N>3 matrix, where the first column is all ones.\n",
+ "\n",
+ " y : array_like\n",
+ " Vector of data labels of shape (m, ).\n",
+ " \"\"\"\n",
+ " # make sure theta is a numpy array\n",
+ " theta = np.array(theta)\n",
+ " \n",
+ " # Plot the data (note: first collumn is x-intercepts so we can ignore it)\n",
+ " plotData(X[:,1:3],y)\n",
+ " \n",
+ " if X.shape[1] <= 3:\n",
+ " # Only need 2 points to define line, so we choose the two endpoints\n",
+ " plot_x = np.array([np.min(X[:, 1]) - 2, np.max(X[:, 1]) + 2])\n",
+ " \n",
+ " # Calculate the decision boundary line ( given form y = theta0*x0 + \n",
+ " # theta1*x1 + theta2*x2, we just solve for y)\n",
+ " plot_y = (-1. / theta[2]) * (theta[1] * plot_x + theta[0])\n",
+ " \n",
+ " # Plot and adjust axes\n",
+ " plt.plot(plot_x, plot_y)\n",
+ " \n",
+ " # Setup legend\n",
+ " plt.legend(['Admitted', 'Not admitted', 'Decision Boundary'])\n",
+ " plt.xlim([30, 100])\n",
+ " plt.ylim([30, 100])\n",
+ " \n",
+ " else:\n",
+ " # Setup grid range\n",
+ " u = np.linspace(-1, 1.5,50)\n",
+ " v = np.linspace(-1,1.5,50)\n",
+ " \n",
+ " z = np.zeros((u.size, v.size))\n",
+ " # Evaluate z = theta*x over the grid\n",
+ " for i, ui in enumerate(u):\n",
+ " for j, vj in enumerate(v):\n",
+ " z[i, j] = np.dot(mapFeature(ui, vj), theta)\n",
+ " \n",
+ " z = z.T # important to transpose z before calling contour\n",
+ " # Plot z = 0\n",
+ " plt.contour(u, v, z, levels=[0], linewidths=2, colors='g')\n",
+ " plt.contourf(u, v, z, levels=[np.min(z), 0, np.max(z)], cmap='Greens', alpha=0.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plotDecisionBoundary(plotData, theta, X, y)\n",
+ "plt.xlabel(\"Exam 1 score\")\n",
+ "plt.ylabel(\"Exam 2 score\")\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a decision boundary we can create a function to predict whether a given student (a single sample of two exam scores) will be admitted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(theta, X):\n",
+ " \"\"\"\n",
+ " Predict whether the label is 0 or 1 using learned logistic regression.\n",
+ " Computes the predictions for X using a threshold at 0.5 \n",
+ " (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " Parameters for logistic regression. A vecotor of shape (n+1, ).\n",
+ " \n",
+ " X : array_like\n",
+ " The data to use for computing predictions. The rows is the number \n",
+ " of points to compute predictions, and columns is the number of\n",
+ " features.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " p : array_like\n",
+ " Predictions and 0 or 1 for each row in X.\n",
+ " \"\"\"\n",
+ " # Number of training samples\n",
+ " m = X.shape[0]\n",
+ " \n",
+ " # initialize p\n",
+ " p = np.zeros(m)\n",
+ " \n",
+ " temp = sigmoid(X.dot(theta))\n",
+ " for i in range(m):\n",
+ " if temp[i] >= 0.5:\n",
+ " p[i] = 1\n",
+ " \n",
+ " return p\n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "For a student with scores 45 and 85,we predict an admission probability of 0.776\n",
+ "Train Accuracy: 89.00 %\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict probability for a student with score 45 on exam 1 \n",
+ "# and score 85 on exam 2 \n",
+ "prob = sigmoid(np.dot([1, 45, 85], theta))\n",
+ "print('For a student with scores 45 and 85,'\n",
+ " 'we predict an admission probability of {:.3f}'.format(prob))\n",
+ "\n",
+ "# Compute accuracy on our training set\n",
+ "p = predict(theta, X)\n",
+ "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Regularized Logistic Regression
\n",
+ "\n",
+ "In this part of the exercise, we will implement regularized logistic regression to predict whether microchips from a fabrication plant pass quality assurance. \n",
+ "\n",
+ "First we visualize the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load data\n",
+ "# The first two columns contains the exam scores and the third column\n",
+ "# contains the label.\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex2data2.txt'), delimiter=',')\n",
+ "X, y = data[:, 0:2], data[:, 2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plotData(X,y)\n",
+ "plt.xlabel(\"Microchip Test 1\")\n",
+ "plt.ylabel(\"Microchip Test 2\")\n",
+ "plt.legend([\"y=1\", \"y=0\"])\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to create a more complex boundary, we will now map the features into all polynomial terms of x1 and x2 up to the sixth power. This results in a conversion of our vector of two features becoming a vector of 28 features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note that mapFeature also adds a column of ones for us, so the intercept\n",
+ "# term is handled\n",
+ "X = mapFeature(X[:, 0], X[:, 1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now compute the cost function and gradient for our newly mapped features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def costFunctionReg(theta, X, y, lambda_):\n",
+ " \"\"\"\n",
+ " Compute cost and gradient for logistic regression with regularization.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " Logistic regression parameters. A vector with shape (n, ). n is \n",
+ " the number of features including any intercept. If we have mapped\n",
+ " our initial features into polynomial features, then n is the total \n",
+ " number of polynomial features. \n",
+ " \n",
+ " X : array_like\n",
+ " The data set with shape (m x n). m is the number of examples, and\n",
+ " n is the number of features (after feature mapping).\n",
+ " \n",
+ " y : array_like\n",
+ " The data labels. A vector with shape (m, ).\n",
+ " \n",
+ " lambda_ : float\n",
+ " The regularization parameter. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed value for the regularized cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " A vector of shape (n, ) which is the gradient of the cost\n",
+ " function with respect to theta, at the current values of theta.\n",
+ " \"\"\"\n",
+ " # Initialize some useful values\n",
+ " m = y.size # number of training examples\n",
+ " temp, n = X.shape\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ "\n",
+ " h = sigmoid(X.dot(theta))\n",
+ " logh = np.log(h)\n",
+ " tempLog = np.log(1-h)\n",
+ " yTrans = y.transpose()\n",
+ " Xtrans = X.transpose()\n",
+ " tempTrans = (1-y).transpose()\n",
+ " \n",
+ " tempTheta = theta[0]\n",
+ " theta[0] = 0\n",
+ " J = ((-yTrans).dot(logh))\n",
+ " J = J - tempTrans.dot(tempLog)\n",
+ " J = J * (1/m)\n",
+ " J = J + (lambda_ / (2*m)) * np.sum(np.square(theta))\n",
+ " theta[0] = tempTheta\n",
+ " \n",
+ " diff = np.subtract(sigmoid(X.dot(theta)),y)\n",
+ " grad = Xtrans.dot(diff)\n",
+ " grad = grad * (1/m)\n",
+ " for i in range(1,n):\n",
+ " grad[i] = grad[i] + (lambda_ / m)*theta[i]\n",
+ " \n",
+ " \n",
+ " # =============================================================\n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 175,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at initial theta (zeros): 0.693\n",
+ "Gradient at initial theta (zeros) - first two values only:\n",
+ "\t[0.0085, 0.0188]\n",
+ "------------------------------\n",
+ "\n",
+ "Cost at test theta : 3.16\n",
+ "Gradient at test theta - first two values only:\n",
+ "\t[0.3460, 0.1614]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize fitting parameters\n",
+ "initial_theta = np.zeros(X.shape[1])\n",
+ "\n",
+ "# Set regularization parameter lambda to 1\n",
+ "# DO NOT use `lambda` as a variable name in python\n",
+ "# because it is a python keyword\n",
+ "lambda_ = 1\n",
+ "\n",
+ "# Compute and display initial cost and gradient for regularized logistic\n",
+ "# regression\n",
+ "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n",
+ "\n",
+ "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+ "print('Gradient at initial theta (zeros) - first two values only:')\n",
+ "print('\\t[{:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+ "\n",
+ "\n",
+ "# Compute and display cost and gradient\n",
+ "# with all-ones theta and lambda = 10\n",
+ "test_theta = np.ones(X.shape[1])\n",
+ "cost, grad = costFunctionReg(test_theta, X, y, 10)\n",
+ "\n",
+ "print('------------------------------\\n')\n",
+ "print('Cost at test theta : {:.2f}'.format(cost))\n",
+ "print('Gradient at test theta - first two values only:')\n",
+ "print('\\t[{:.4f}, {:.4f}]'.format(*grad[:4]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a working cost function, we can now apply linear regression to fit our parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 187,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train Accuracy: 83.1 %\n",
+ "Expected accuracy (with lambda = 1): 83.1 % (approx)\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "Here we will build a logistic regression model to predict whether a student gets admitted into a university given the results of two exams. Our training set consists of samples of applicants' scores on two exams and an admissions decision."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Visualizing the data
\n",
+ "\n",
+ "Before we begin on the algorithm we load and visualize the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load data\n",
+ "# The first two columns contains the exam scores and the third column\n",
+ "# contains the label.\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex2data1.txt'), delimiter=',')\n",
+ "X, y = data[:, 0:2], data[:, 2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotData(X,y):\n",
+ " # New figure\n",
+ " fig = plt.figure()\n",
+ "\n",
+ " # Find indeces of positive and negative examples \n",
+ " # Then plot them seperately (Don't try to plot then label after)\n",
+ " pos = y == 1\n",
+ " neg = y == 0\n",
+ "\n",
+ " plt.plot(X[pos,0],X[pos,1],'k*', lw=2, ms=7)\n",
+ " plt.plot(X[neg,0],X[neg,1],'yo',mec='k',ms=7)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "First we construct the sigmoid function defined as:\n",
+ "$$h_\\theta(x) = g(\\theta^Tx)$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(z):\n",
+ " \"\"\"\n",
+ " Compute sigmoid function given the input z.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " z : array_like\n",
+ " The input to the sigmoid function. This can be a 1-D vector \n",
+ " or a 2-D matrix. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " g : array_like\n",
+ " The computed sigmoid function. g has the same shape as z, since\n",
+ " the sigmoid is computed element-wise on z.\n",
+ " \"\"\"\n",
+ " # convert input to a numpy array\n",
+ " z = np.array(z)\n",
+ "\n",
+ " g = 1 + np.exp(-1*z)\n",
+ " g = np.reciprocal(g)\n",
+ "\n",
+ " return g"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sigmoid of 0 is 0.5\n",
+ "Sigmoid of 100 is 1.0\n",
+ "Sigmoid of -100 is 3.7200759760208356e-44\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Check a few values of sigmoid\n",
+ "print(\"Sigmoid of 0 is \",sigmoid(0))\n",
+ "print(\"Sigmoid of 100 is \",sigmoid(100))\n",
+ "print(\"Sigmoid of -100 is \",sigmoid(-100))\n",
+ "\n",
+ "# sigmoid of 0 should be exactly 0.5\n",
+ "# sigmoid of large positive numbers should be close to 1\n",
+ "# sigmoid of large negative numbers should be close to 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a working sigmoid function, we can now implement a cost function which returns the cost and gradient for cost defined as:\n",
+ "\n",
+ "$$\\begin{align}\n",
+ "J(\\theta) & = \\dfrac{1}{m} \\sum_{i=1}^m \\mathrm{Cost}(h_\\theta(x^{(i)}),y^{(i)}) \\\\\n",
+ "& = - \\dfrac{1}{m} [\\sum_{i=1}^{m} y^{(i)} \\log(h_\\theta(x^{(i)})) + (1 - y^{(i)}) \\log(1-h_\\theta(x^{(i)}))] \\\\\n",
+ "\\end{align}$$\n",
+ "\n",
+ "and derivative:\n",
+ "\n",
+ "$$\\frac{\\partial}{\\partial \\theta_j} J(\\theta) = \\dfrac{1}{m} \\sum_{i=1}^{m} (h_\\theta(x^{(i)}) - y^{(i)}) x_j^{(i)}$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup the data matrix appropriately, and add ones for the intercept term\n",
+ "m, n = X.shape\n",
+ "\n",
+ "# Add intercept term to X\n",
+ "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def costFunction(theta,X,y):\n",
+ " \"\"\"\n",
+ " Compute cost and gradient for logistic regression. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " The parameters for logistic regression. This a vector\n",
+ " of shape (n+1, ).\n",
+ " \n",
+ " X : array_like\n",
+ " The input dataset of shape (m x n+1) where m is the total number\n",
+ " of data points and n is the number of features. We assume the \n",
+ " intercept has already been added to the input.\n",
+ " \n",
+ " y : arra_like\n",
+ " Labels for the input. This is a vector of shape (m, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed value for the cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " A vector of shape (n+1, ) which is the gradient of the cost\n",
+ " function with respect to theta, at the current values of theta.\n",
+ " \"\"\"\n",
+ " ## Initialize some useful values\n",
+ " m = y.size # number of training examples\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ " h = sigmoid(X.dot(theta))\n",
+ " logh = np.log(h)\n",
+ " tempLog = np.log(1-h)\n",
+ " yTrans = y.transpose()\n",
+ " Xtrans = X.transpose()\n",
+ " tempTrans = (1-y).transpose()\n",
+ " \n",
+ " \n",
+ " J = ((-yTrans).dot(logh))\n",
+ " J = J - tempTrans.dot(tempLog)\n",
+ " J = J * (1/m)\n",
+ " \n",
+ " diff = np.subtract(sigmoid(X.dot(theta)),y)\n",
+ " grad = Xtrans.dot(diff)\n",
+ " grad = grad * (1/m)\n",
+ " \n",
+ " # =============================================================\n",
+ " return J, grad\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now test our cost function with varying initial thetas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at initial theta (zeros): 0.693\n",
+ "Expected cost (approx): 0.693\n",
+ "\n",
+ "Gradient at initial theta (zeros):\n",
+ "\t[-0.1000, -12.0092, -11.2628]\n",
+ "Expected gradients (approx):\n",
+ "\t[-0.1000, -12.0092, -11.2628]\n",
+ "\n",
+ "Cost at test theta: 0.218\n",
+ "Expected cost (approx): 0.218\n",
+ "\n",
+ "Gradient at test theta:\n",
+ "\t[0.043, 2.566, 2.647]\n",
+ "Expected gradients (approx):\n",
+ "\t[0.043, 2.566, 2.647]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize fitting parameters\n",
+ "initial_theta = np.zeros(n+1)\n",
+ "\n",
+ "cost, grad = costFunction(initial_theta, X, y)\n",
+ "\n",
+ "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+ "print('Expected cost (approx): 0.693\\n')\n",
+ "\n",
+ "print('Gradient at initial theta (zeros):')\n",
+ "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n",
+ "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n",
+ "\n",
+ "# Compute and display cost and gradient with non-zero theta\n",
+ "test_theta = np.array([-24, 0.2, 0.2])\n",
+ "cost, grad = costFunction(test_theta, X, y)\n",
+ "\n",
+ "print('Cost at test theta: {:.3f}'.format(cost))\n",
+ "print('Expected cost (approx): 0.218\\n')\n",
+ "\n",
+ "print('Gradient at test theta:')\n",
+ "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n",
+ "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working cost function, we can implement gradient descent using a built in optimization function scipy.optimize.\n",
+ "\n",
+ "To use this function we need to pass in:\n",
+ "\n",
+ "- The initial values of the parameters we are trying to optimize\n",
+ "- A function that, when given training set and theta, computes the logistic regression cost and gradient with respect to theta for (X,y)\n",
+ "- jac: which is an indication if we would like the function to return the jacobian (gradient) as well\n",
+ "- method: which is the method/algorithm we would like to implement\n",
+ "- options: options specific to our chosen algorithm (chosen iterations in our case)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at theta found by optimize.minimize: 0.203\n",
+ "Expected cost (approx): 0.203\n",
+ "\n",
+ "theta:\n",
+ "\t[-25.161, 0.206, 0.201]\n",
+ "Expected theta (approx):\n",
+ "\t[-25.161, 0.206, 0.201]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set options for optimize.minimize\n",
+ "options= {'maxiter': 400}\n",
+ "\n",
+ "# see documention for scipy's optimize.minimize for description about\n",
+ "# the different parameters\n",
+ "# The function returns an object `OptimizeResult`\n",
+ "# We use truncated Newton algorithm for optimization which is \n",
+ "# equivalent to MATLAB's fminunc\n",
+ "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n",
+ "res = optimize.minimize(costFunction,\n",
+ " initial_theta,\n",
+ " (X, y),\n",
+ " jac=True,\n",
+ " method='TNC',\n",
+ " options=options)\n",
+ "\n",
+ "# the fun property of `OptimizeResult` object returns\n",
+ "# the value of costFunction at optimized theta\n",
+ "cost = res.fun\n",
+ "\n",
+ "# the optimized theta is in the x property\n",
+ "theta = res.x\n",
+ "\n",
+ "# Print theta to screen\n",
+ "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n",
+ "print('Expected cost (approx): 0.203\\n');\n",
+ "\n",
+ "print('theta:')\n",
+ "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n",
+ "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have an optimal theta, we can use it to get a decision boundary."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mapFeature(X1, X2, degree=6):\n",
+ " \"\"\"\n",
+ " Maps the two input features to quadratic features used in the regularization exercise.\n",
+ "\n",
+ " Returns a new feature array with more features, comprising of\n",
+ " X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc..\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X1 : array_like\n",
+ " A vector of shape (m, 1), containing one feature for all examples.\n",
+ "\n",
+ " X2 : array_like\n",
+ " A vector of shape (m, 1), containing a second feature for all examples.\n",
+ " Inputs X1, X2 must be the same size.\n",
+ "\n",
+ " degree: int, optional\n",
+ " The polynomial degree.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " : array_like\n",
+ " A matrix of of m rows, and columns depend on the degree of polynomial.\n",
+ " \"\"\"\n",
+ " if X1.ndim > 0:\n",
+ " out = [np.ones(X1.shape[0])]\n",
+ " else:\n",
+ " out = [np.ones(1)]\n",
+ "\n",
+ " for i in range(1, degree + 1):\n",
+ " for j in range(i + 1):\n",
+ " out.append((X1 ** (i - j)) * (X2 ** j))\n",
+ "\n",
+ " if X1.ndim > 0:\n",
+ " return np.stack(out, axis=1)\n",
+ " else:\n",
+ " return np.array(out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotDecisionBoundary(plotData, theta, X, y):\n",
+ " \"\"\"\n",
+ " Plots the data points X and y into a new figure with the decision boundary defined by theta.\n",
+ " Plots the data points with * for the positive examples and o for the negative examples.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " plotData : func\n",
+ " A function reference for plotting the X, y data.\n",
+ "\n",
+ " theta : array_like\n",
+ " Parameters for logistic regression. A vector of shape (n+1, ).\n",
+ "\n",
+ " X : array_like\n",
+ " The input dataset. X is assumed to be a either:\n",
+ " 1) Mx3 matrix, where the first column is an all ones column for the intercept.\n",
+ " 2) MxN, N>3 matrix, where the first column is all ones.\n",
+ "\n",
+ " y : array_like\n",
+ " Vector of data labels of shape (m, ).\n",
+ " \"\"\"\n",
+ " # make sure theta is a numpy array\n",
+ " theta = np.array(theta)\n",
+ " \n",
+ " # Plot the data (note: first collumn is x-intercepts so we can ignore it)\n",
+ " plotData(X[:,1:3],y)\n",
+ " \n",
+ " if X.shape[1] <= 3:\n",
+ " # Only need 2 points to define line, so we choose the two endpoints\n",
+ " plot_x = np.array([np.min(X[:, 1]) - 2, np.max(X[:, 1]) + 2])\n",
+ " \n",
+ " # Calculate the decision boundary line ( given form y = theta0*x0 + \n",
+ " # theta1*x1 + theta2*x2, we just solve for y)\n",
+ " plot_y = (-1. / theta[2]) * (theta[1] * plot_x + theta[0])\n",
+ " \n",
+ " # Plot and adjust axes\n",
+ " plt.plot(plot_x, plot_y)\n",
+ " \n",
+ " # Setup legend\n",
+ " plt.legend(['Admitted', 'Not admitted', 'Decision Boundary'])\n",
+ " plt.xlim([30, 100])\n",
+ " plt.ylim([30, 100])\n",
+ " \n",
+ " else:\n",
+ " # Setup grid range\n",
+ " u = np.linspace(-1, 1.5,50)\n",
+ " v = np.linspace(-1,1.5,50)\n",
+ " \n",
+ " z = np.zeros((u.size, v.size))\n",
+ " # Evaluate z = theta*x over the grid\n",
+ " for i, ui in enumerate(u):\n",
+ " for j, vj in enumerate(v):\n",
+ " z[i, j] = np.dot(mapFeature(ui, vj), theta)\n",
+ " \n",
+ " z = z.T # important to transpose z before calling contour\n",
+ " # Plot z = 0\n",
+ " plt.contour(u, v, z, levels=[0], linewidths=2, colors='g')\n",
+ " plt.contourf(u, v, z, levels=[np.min(z), 0, np.max(z)], cmap='Greens', alpha=0.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plotDecisionBoundary(plotData, theta, X, y)\n",
+ "plt.xlabel(\"Exam 1 score\")\n",
+ "plt.ylabel(\"Exam 2 score\")\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a decision boundary we can create a function to predict whether a given student (a single sample of two exam scores) will be admitted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(theta, X):\n",
+ " \"\"\"\n",
+ " Predict whether the label is 0 or 1 using learned logistic regression.\n",
+ " Computes the predictions for X using a threshold at 0.5 \n",
+ " (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " Parameters for logistic regression. A vecotor of shape (n+1, ).\n",
+ " \n",
+ " X : array_like\n",
+ " The data to use for computing predictions. The rows is the number \n",
+ " of points to compute predictions, and columns is the number of\n",
+ " features.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " p : array_like\n",
+ " Predictions and 0 or 1 for each row in X.\n",
+ " \"\"\"\n",
+ " # Number of training samples\n",
+ " m = X.shape[0]\n",
+ " \n",
+ " # initialize p\n",
+ " p = np.zeros(m)\n",
+ " \n",
+ " temp = sigmoid(X.dot(theta))\n",
+ " for i in range(m):\n",
+ " if temp[i] >= 0.5:\n",
+ " p[i] = 1\n",
+ " \n",
+ " return p\n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "For a student with scores 45 and 85,we predict an admission probability of 0.776\n",
+ "Train Accuracy: 89.00 %\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict probability for a student with score 45 on exam 1 \n",
+ "# and score 85 on exam 2 \n",
+ "prob = sigmoid(np.dot([1, 45, 85], theta))\n",
+ "print('For a student with scores 45 and 85,'\n",
+ " 'we predict an admission probability of {:.3f}'.format(prob))\n",
+ "\n",
+ "# Compute accuracy on our training set\n",
+ "p = predict(theta, X)\n",
+ "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Regularized Logistic Regression
\n",
+ "\n",
+ "In this part of the exercise, we will implement regularized logistic regression to predict whether microchips from a fabrication plant pass quality assurance. \n",
+ "\n",
+ "First we visualize the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load data\n",
+ "# The first two columns contains the exam scores and the third column\n",
+ "# contains the label.\n",
+ "data = np.loadtxt(os.path.join('Data', 'ex2data2.txt'), delimiter=',')\n",
+ "X, y = data[:, 0:2], data[:, 2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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kH1SwoXdGobV1J3PmKK2tO7nuuneZNm1yWQQ1mgIJkaCf4vuH9YsX19DcPJR77hGam4eyeHFNwcP6II+dLeluzCeeeCIrV67kF7/4BStXrhww3ifoUULcI+KT8UvhhZEKP1eCHCGEnYssjpgCCZEwnuLzGdZnm1Y96imDdDfm5cuX8+STT7J+/XqefPLJrCLQgxwlxD0iPhm/FF7cpu+CHiFEbReMBaks66W6lLoXVr4UmweOl3y9nr7+9a/runXrVFV17dq12tTUFLywMSHIVOpBpcLPh6A9B+PimRgGWDr3eBD1U7yXUijfmu+0SbGNEvwiaKNvnKbvitnmWCxE6sZbrsQlcrjYXE5T0T9t0t7eTnV1NX19fbH2eoqSMIJBg0qFn0w2rrNBBxvGyZU4KmwEUoT4VQo2bnPW+VJMXk9RUipG32xHUXG1OZYSNgIpQvwMgPOrCFOUlGNm33woBaNvLqOosEYIcZlRiAIbgRQRQdgs4jRnnS+lZM/wa3SZikxu5K+9Vk17e3sg5/WTXEdR5T5CCBpTIEVEEH72pXTzLQWCjOLONKWzefMeXnnllcCjxwsln1FUNsGGRn6YAikiSsVmYRxKGB5xqYJB58+HlhZ4770PUNXYe+JFnVLHOBhTICHhVzoFMxjnRqYpoSCni3IlrCju5Cmdq69ezCWXzIpV9HgmzHU2ZqQKDinVJapAQj+DB8s5AC4fMgVJxi2A8r777tOKigqtrq7WwYMH64oVK1Q1mLTz2Zw3rsQxGLfUIU0gYeQ39TCXKBTIrl27rG5BBGSKUI9rzY50UdxBK7o4RY9nS380fUtLi+/R9MahpFMgNoUVMKXie19sZJoSimPSPzjUI+7pp5/O2i5SyHRcMXrimWE8HpgCCZhS8L0vRjI5HMTVGSHZI2779u1ZK7pCvLfMEy89QaWCLxXSKhAR+YSIrBeRrSJyp4h8xLPtd+n2ywUROU9EXhGRLhG5PsX22SKyQ0Q2ucuVnm1NIvKauzT5IU8QBOU1ErcfdpwM0v1kcjgoBmeEbBRdKeQziyvlXiwqK1LNazlTXvz/wJeBY4HrcWqP17nb/phuv2wXYDBOLfQxwBCc+ubjk/rMBhan2PcYoNt9Pdp9f/RA54zCBhJE7eY4GhHjZpBWzexwUCzOCAPZJ3p6erSpqUmrqqoU0KqqKp09e3ZgBvdywWyXB0OuRnRgU9L6F4DXgAnAH9Ltl+0C/Ffg1571ZqA5qU86BXIZsMSzvgS4bKBzloIXVtx+2HE1SJcK2Si6YvOiGoigvc6yoZxStWdDOgWSyQYySESO8oxUngC+CnQAfljZjgf+4lnf7rYl848i8ryIPCQiJ+S4LyJylYhsFJGNO3bs8EHs3PEznULcjPJ+GKTjOP0VF7KxTxTDdFwuhFVTPRNmu8yOTArkfwKf8Dao6ibgi8AaH84tKdo0aX0NUKuqnwKeAPqz5GWzr9Ooepeq1qtq/fDhw/MWtlD88hop9Ift983aD4N0HG4YxUwxelGlIk72HIt4z460CkRVV6jqIcZyVd2mqpf7cO7twAme9dFAT9K53lbVPnf1fwNnZbtvqVLoDzuIm3W+T8BxumEUM6XiRRUn92qLeM+SVPNaYSw4qeS7gToOGNE/kdRnpOf9RcAzesCIvhXHgH60+/6Ygc4Zh5K2hZKvUT5IW0W+BmkzABvJRGXPSWV3iaOzSlQQx0h04HzgVRxvrBvdtluAC9z3rcBLrnJZB3zcs+8VQJe7XJ7N+UpBgajm98OO68261AzARmFEFRWfzovQIt4d8lYgwMRs2ophKRUFoprfDzuON+tiTKNhBEfY7tXmRZgdhSiQQ1x2gWcH2i+OSykpkHyI4826WOIxjNIkriPzuJFOgaQtaSsiZ+PEagwXkW94Nh0FHDagccWIHXEs/eqVobGxkcbGxgilMcqNfi/C9vZ2qqur6evri0Vam2IhkxvvEThR6BXAcM/yAU48iFFklIq3TtRY3EppUWpxNGGSdgSiquuAdSJyr6p2A4iIANWqmjoQwTDKAK8r9J133hm1OEaBxHFkXiyIM72VoYPIfcB1wF5gI86o5FZV/UHw4vlLfX29bty4MWoxjCKlqqoqZdLKyspKEolEBBIZRjiIyLOqWp/cnk0699NU9T+BrwCP4QTtzfZXPMOIP3EKdDNKh7hl1s6FbBTIEBGpAC4EfqaqHwD7ghXLMNITlQ0irnVEjOKl2FPGZ6NA7gb+jBPx/bSInAjsDFQqw8hAlLmzzOBq+EUikWDatMlcd927tLbuZM4cpbV1J9dd9y7Tpk0uipHIgApEVX+oqqNUdbLrD7wd+HzwohnGwcQhd1apJC40oidumbXzYUAFIiLDRWSJiPzCbfo4MDNYsQwv5jbqEAcbhLlCG35RCinjs5nCWgY8zYHst68B/29QAsWNOBi4LN25g9kgjFKiFFLGZ6NAjlPVDlzDuaruAT4MVKqYELWBKw5TNrkS9GjJbBBGqVAKKePTBhJ6eF9EjsEt2CQiE4D3ApUqBngNXAfmKHeyfj1MmzaZbdt6UxaFSiQSrF69mi1btjB27FimT5+ed/Go7u5umpubeeCBB0gkElRVVXHppZfS2tqa/wcLmKCD7CzoyygVKisrWbPmMaZNm8wjj+xl3Lj36eo6gq1bK1iz5rG87xthkk0gYT1wO051wudwSsderE51wqIil0DCjo4OFi2aS2vroQ5nzc1DmT9/CTNnHmwK6uzsZNq0ydTVHfpjyKd8LcCKFSu44oorGDJkCH19fSxbtoxZs2bldawgsSA7w8iP/ofO7u5uxowZU9BDZ1CkCyTMlExxoqo+o6obRaQROBWnlOxmNxakpMnVwJXviGUgvFM2N9xwA0uXLo2lAinG0ZJh+EGhsw795a6LkUw2kP3zD6r6gao+p6qbykF5QO4GrqBc8orFbdQM3EY5ErWdNGqyMaKXJbkauIJyySsmt1EzcBtBEUdX9lIIBCyUTApkjIg8nG7x4+Qicp6IvCIiXSJyfYrtC0Rks4g8LyJPishJnm0fisgmd/FFHi/9Bq7Fi2tobh7KPfcIzc1DWby4JqWBqxRc8gqlWEZLRvERR1f2UggELJS0RnQReQ24Mt2Oqvp0QScWGYxTD/2LONHtncBlqrrZ06cR+L2q7hKReUCDql7qbtupqkNzOWc+2XizNXDt3r2b2tqRSTYQZ8SyeHFN3jYQwyhn4uyc0dLSwquvfo85cw69h95zj3DKKbdw0003RSCZ/+RsRAfeK1RJDMDZQJen1shKnISN+xWIW5Okn2eA0K3H2Rq4SsElzzDiRpydM8aOHcujjx5BqtSAXV1HMHVq6c86ZJrC2hbwuY8H/uJZ3+62pWMO8EvPeqWIbBSRZ0TkK0EImCsTJkxg69Ye5s9fwimn3ML8+UvYtq03bxdewyh34uycUQqBgAWTqlB6GAtOWdy7PetfAxal6TsLZwRyuKdtlPs6BkfZjU2z71U4hbA2nnjiibnUkTdUtaenR8855xzt7e2NWhSjTGloaNCKigpduHChVlRUaGNjY9Qi7WfDhg06YkSNTpw4VGfNEp04caiOGFGjGzZsiFo0XwE2aor7a5ReWNs5kF8LnEJVPcmdROQLwI3ABara19+uqj3uazfwFHBGqpOo6l2qWq+q9cOHD/dP+iIjXy+WOBovjfIizs4Z5T7rMGAkemAndopUvQpMAv6KY0SfqaovefqcATwEnKeqr3najwZ2qWqfiBwL/A64UD0G+FSUc0nba665hiVLljB37tysUozE2XhpGEa4FFLSFhGZLiI/EJH/JSIX+SGQqu7FqbX+a+Bl4AFVfUlEbhGRC9xu/xMYCjyY5K57KrBRRJ4D1uHUaM+oPMqVfBMyxiF1umEY8SabeiB3AlcDLwAvAnNF5Md+nFxVH1XVj6nqWFX9vtv2XVV92H3/BVUdoaqnu8sFbvtvVfU0Vf0v7us9fshTiuSrCOJsvDQMIx5kMwI5F5iiqveq6r3A+UBDoFIZvlGIIohrZHkco5INoxzJRoG8AnitVicAzwcjjhEE+SqCuBovzbBvJGMPFdGQTTr3p4EJwAa3aQKO0XoXQP+0UjFQrkb0pqYmLr/8choaGli3bh3Lly+PdU6tdJhh30hHrk4iRm6kM6Jno0DOzbRdg41W95VyVSClQm9vb9qoZLPNlCf2UBEOeXthqerTmZZgxDWMQzHDvpGMeQtGS1oFIiLr3df3ROQ/Pct7IvKf4YloGAeIq2HfiAZ7qIiWtApEVT/rvh6pqkd5liNV9ajwRDSMA8TVsG9Ehz1UpCeRSNDR0UFLSwsdHR2+1yjJlI13P27q9RHe/qr6Z18lMYwsWL58+f73jY2NNDY2RiiNEQf6HyoaGhqor68/6DdSznR2djJt2mTq6pzs4I8+egQLFlzLmjWP+ZZqJRsj+nzge8AbwD63WVX1U75IECJmRDcMo1gopNZ6IpGgrm6Ub/WJCkll8k3gFFX9hBv1fVoxKg/DMIxiodBa62FVS8xmCusvwN99OZthGIaREW+t9QMKYCfr18O0aZOzGj1s2bKFcePeT7lt3Lj36e7u9kXWTF5YC0RkAdANPCUizf1tbrthGIbhM36MHsaOHUtX1xEpt3V1HcGYMf5US8w0hXWku/wZeBwY4mk70pezG4ZhGAfhx+ghrGqJaaewVPVffDmDcQiFGMcMwyht/Ki1XllZyZo1jzFt2mQeecTxwurqOoKtWytYs+Yx3+432aRzf1xEajzrR4vIr305exlSqHEsLljyOsMIBr9GD2FUS8zGiD5cVd/tX1HVv4nIcb5JUEb4YRyLC96MuPkkr+vt7WXGjBmsWrXKooYNw4Ofo4eqqipmzpwZmKzZuPF+KCL7w31F5CQgmjq4RU5YrnVBkm+Fw2QsJbthHIw3avy1117j5Ze7Y19rPRsFciOwXkRWiMgK4DdAc7BilSZ+u9ZFMY1UaPI6vxSQURzYVGd2pJraPvXUMZx88sncdNNNzJw5M5azE9lk4/0VcCawyl3OUlVfbCAicp6IvCIiXSJyfYrth4vIKnf770Wk1rOt2W1/RUSm+CFP0PjtWhfFU3yhyesse2p5YSPNgfFObbe27mTOHKW1dSfXXfcu06ZN9j1/lZ9kMwIB+DROGdsGYKIfJ3bza/0YmAqMBy4TkfFJ3eYAf1PVccAPgdvcfccDM4BPAOcBd7rHizV+GceifoovJHmdZU8tD6L+jRYTxTy1nY0X1q046Uw2u8s3RaTVh3OfDXSpareqfgCsBC5M6nMh0J8Z7SFgkoiI275SVftUdSvQ5R4v1vQbxxYvrqG5eSj33CM0Nw9l8eKanIxjUT/FF5oRN18FZNMhxUPYv9Ggs84GSVhR40GQzQjkfOCLqrpUVZfiPPF/yYdzH4+TJqWf7W5byj6quhcnpcqwLPcFQESuEpGNIrJxx44dPohdGH641kX9FL98+XIaGhoAJyNuruVx81VANh1SPIT5Gy121/iwosYDQVUzLsDzwDGe9WOA5wfaL4vjfhW427P+NWBRUp+XgNGe9S04CuTHwCxP+z3APw50zrPOOktLhYaGBq2oqNCFCxdqRUWFNjY2Ri1SYFRWViqO599BS2VlZdSiGRkI4ze6a9cuHTGiRlta0HXrDiwtLeiIETWaSCR8P6ffJBKJ2H8GYKOmuKdmMwJpBf4oIstEZDnwLPCvBWsuZ9Rwgmd9NNCTro+IVAAfAd7Jct+SphQKK2U77RD1lJ2RH2H8RovZftCPX1PbUZAxkNC1N6zHMZxPAAT4jqr6MQndCZwsInXAX3GM4skRLw8DTcDvgIuBtaqqIvIw0CEiPwBGAScDG3yQKdZ4U6BMmTKFiRMdf4ZiLKyUS7Gb/umQ9vZ2qqur6evrM8N7ERBG8a9ith946Z/aXr16Nd3d3UydOqYoUhxlVCDuzfpnqnoWzs3cN1R1r4hcB/waGAwsVdWXROQWnOHSwzhTUytEpAtn5DHD3fclEXkAx6i/F7hWVT/0U764EUZ1sbDIJyLfa3i/4YYbWLp0KbNmzQpXcCN2+JE3Ki4EHTUeBNmkMnlGRCaoqu8WKVV9FHg0qe27nve7cWwlqfb9PvB9v2UKgmddmTYAABnKSURBVEKTJ5ZSChTIPO3wyCPOtEPyH8nKlhqpmD59OgsWXMv69RxSec/PrLP5UA5JU7NRII3AXBH5E/A+zjSWqlUlzAo/Rg753HDjTD7TDlYL3UhFWFlnc6WUZgwykY0CmRq4FCWKXyOHUpnn7aeUph2M6Imb/aDUZgwykY0X1kjgHVX9k6r+CccWYdbLLPDLQ6So/cRTEFaxG6N86LcfxCFvVCl4hmVLNiOQNpxcWP28n6LNSIFfI4c4z/PmQ1ynHQzDD0ptxiAT2SgQcQNJAFDVfW5MhjEAfk3VhHHDDdvgF7dpB8Pwi3KaohWPbkjdQeSnwFM4ow6Aa4BGVf1KsKL5T319vW7cuDG08+3evZva2pFJc6HOyGHx4pqc50L7b/Ld3d2MGePfDTfZ4OdVTqVk8DOMMPD7fx8HRORZVa0/pD0LBXIccAfweZwUEk8C/01V3wxC0CAJW4FA/G/OiUSCurpRJfVjN8qbOFS7jPv/PlfSKZABp6JcRTEjEKnKgLhP1ZSai7BhFFpu2Q/i/r/3i7QKRET+u6ouFJFFpChhq6rfCFSyEiLOEablZPCLgjg8DZcLVVVVB+VTa2tro62tjcrKShKJRCTyxPV/7xeZ3Hhfdl834iRQTF6MEqDUXITjhqWgDw9Luhk+aRWIqq5xX5enWsIT0QgSi8k4GL+KVllFvkMJuiCYHzVIrGhZjqTK8e4a1h/OtKTbL85LKdUD8ZMNGzboiBE1OnHiUJ01S3TixKE6YkSNbtiwIZDz9fT06DnnnKO9vb2BHL8Q5s2bp4MGDdJ58+YVdJyenh5tamrSqqoqBbSqqkpnz54dy88cFn5d20wUWoMkDBmLEdLUA0nrhSUiO3Cq/t0P/B4nB5ZX8TwdjEoLjii8sHIhyuRrQbkIp+Kaa65hyZIlzJ07NzIjZzLJ8+f9FDJ/vmLFCq644gqGDBlCX18fy5YtK8sMwkFc23Q0NTVx+eWX09DQwLp161i+fHlWFTPDlLEYkyym88LKNAIZjFO+djnwR+B/AJ9I178YljiPQMIeBURBPpUFwxqtBDFiKKeqkZkohtFYWDIW6/+cNCOQrG68wOHAbGAHMD+bfeK4xFWBlEJZzmzI508a5pTCfffdpxUVFVpdXa2DBw/WFStWFHS8r3/967pu3TpVVV27dq02NTUVLmSR4ve1DYKgZSzm/3k6BZIxmaKIHC4i04GfANfiBBSWTiawmFAuyddyMXJGYYT2Fq0Skf3r+bJ8+XIaGhoAJwV9NlMpYRCFodjvaxsEQctYiv/ztArErX/+W5ykif+iqhNUtUVV/xqadGVCOcViZPsnjcIlsxTqzGdDFK7FxXBtg5axFP/nmSLRv4aTefdjwDec8ujAgYJSR+V7UhE5BlgF1ALbgEtU9W9JfU7Hyb91FPAh8H1VXeVuWwacC/zd7T5bVTflK08Q5GIoK8bka/kaArOtLBhFHfRSL1oVZaBdWNe2kMDNoGUsxv/5gKSa1wp6ARYC17vvrwduS9HnY8DJ7vtRQC9Q464vAy7O9bxh2UByNZQlEomimhsNyxBoRmh/KQZjdqHE2Q232P7nXkhjA4kqLfuFQIP7fjlOtt/veDuo6que9z0i8iYwHHg3HBHzI59qZMVUHyPMamtWB91fohjVhUXc0pikopj+59mSTUXCIBihqr0A7utxmTqLyNnAEGCLp/n7IvK8iPxQRA7PsO9VIrJRRDbu2LHDD9kzkq+hrD/52vz5SzjllFuYP38J27b1xipzZ29vL2eeeSa1tXtCMQT6YYS2yOKDKQZjdj4USxqTYvif50JgIxAReYLUpW9vzPE4I4EVQJOq7nObm4HXcZTKXTijl5QWQVW9y+1DfX195tz1PlCIoSzuyddaWlp45ZVXmDkz9WWMoyEwDplZ40SpjuqKaXQV9/95LgSmQFT1C+m2icgbIjJSVXtdBZGytoiIHAU8Atykqs94jt3rvu0TkXuBb/koekEUi6EsFyN48vTApjTuCnH6fMUwpREFpewo4B1d3XDDDSxdurQsI//DJKoprIeBJvd9E/Dz5A4iMgRYDdynqg8mbRvpvgrwFeDFQKXNgWJITtjZ2Uld3SgWLZrLq69+j0WL5lJbO5LOzs6U/ZOnB7q6iPXng+KZ0jD8oxhchUuOVJb1oBdgGE5lw9fc12Pc9nrgbvf9LGAPsMmznO5uWwu8gKM4fgIMzea8cfXCCpN8o2G9UbqDBg3So4+ujuXn81IM0c+GUQyQTyR6UKjq26o6SVVPdl/fcds3quqV7vufqOphqnq6Z9nkbvu8qp6mqp9U1Vmqeuh8UYTE2VCWr5HfOz0waNAgTjutPpafz0upGowNIy5E5cZb8sTVUJavkT+V8TWOn89LqRqMDSMumAIpM/I18hej8bUYZTaMYiIqI7oREcVg5DcMoziwEUiZUYrRsMkUY8Eeo3Qop99f2oqEpUjcKxKGSZgVCMOks7OTadMmU1d3qHKMm5HfKD1K9feXriKhKRCjZEgkEtTVjUrK0+VMzy1eXONrni7DSKaUf3/pFIjZQIySoRQL9hjFQzn+/kyBGFmTSCTo6OigpaWFjo6Og1KFxIGBXJSfe+65QBIrWsJGA0qzYNRAmAIpQqK4keea/iQKxo4dS1fXESm3dXUdwbPPPhtIJb4oKvylIo6KLO4PHX4y0O9vzJh45InzE7OBFBlRGOmKZW539+7d1NaOTClnSwt88MHB/QtNrJicsNGv4+bLNddcw5IlS5g7d24sMg+XqkE5HZl+f3H6n+RDOhtIJLmwolrCyoUVFPnmsSqU9vZ2nThx6EHn7F8mThyq7e3tgZw3H1LlIRs+/CP6pS99yfdKfHGp8FdZWanAIUtlZWWocniJ6rcaNXHOg1cIxCkXlpEfURnpimluNzkP2dy5i1i48Eccfvjh9PX1UVVVxQcffOBLrYj+GhR79uyhurrat+PmShwzD5ejQRninQcvCEyBFBFR3ciLbW63Pw/ZlClTuP76f2LJkvlUVf2U8eP3MWSI08evxIpxSNgYF0XmpZgeOvym//d30003MXPmzKKdtsoGUyBFRFQ38mJMf+Kt3d7aupMrr4RFi+Bb30pQU1PF8ccf78t54lKDIg6KzEuxPXQY+WGpTIqI6dOns2DBtaxfzyFGuiBv5MWY/iTTFMojj8DUqVN9OU9cEjbGLfNwVL9VI1xMgRQRUd7I++d2+9OfTJ0a7/Qn5TaFEhdF1k8xPnQYuWMKpMiI8kYe1xonqSiW2vSlTLE9dBi5E0kciIgcA6wCaoFtwCWq+rcU/T7EKV0L8GdVvcBtrwNWAscAfwC+pqofJO+fTCnEgRjZUco++YYRNuniQKIagVwPPKmqt4rI9e76d1L0S6jq6SnabwN+qKorReTfgDlAW3DiGsWGTaEYRvBENQJ5BWhQ1V4RGQk8paqnpOi3U1WHJrUJsAP4qKruFZH/CtysqlMGOq+NQMqPYkpb39vby4wZM1i1alWkLrhG7pR6DZBYRaID7yat/y1Nv73ARuAZ4Ctu27FAl6fPCcCL2Zy32CPRjdJm3rx5OmjQIJ03b17UokRCT0+PnnPOOaFH8hdKqUafeyFNJHqQSuIJ4MUUy4U5KJBR7usYHFvJWGB4CgXyQgY5rnKV0MYTTzwxmKtrGAUQx1QkfpKtYghSgQalnMolZUs6BRJYIKGqfkFVP5li+Tnwhjt1hfv6Zppj9Liv3cBTwBnAW0CNiPTbb0YDPRnkuEtV61W1fvjw4b59PsPwizimIvGTgbIVV1VVISK0tbWxb98+2traEJH91yMMGfKlXFO29BNVJPrDQJP7vgn4eXIHETlaRA533x8LfAbY7GrDdcDFmfY3jGIhjqlI/CBbxRCkAvVbOSWnp/+P//iPsoo3SiYqBXIr8EUReQ34oruOiNSLyN1un1OBjSLyHI7CuFVVN7vbvgMsEJEuYBhwT6jSG4bPxC0VSTpyqTmSrWIIUoH6qZxS1cRZvPh/sWlTamN5OaRsiUSBqOrbqjpJVU92X99x2zeq6pXu+9+q6mmq+l/c13s8+3er6tmqOk5Vv6qqfVF8DqM0iaIIkjen1po1a9i7d29kRZgyff5cpoJyUQxBKVC/lFNybrU5c5TW1p0sWLCLrq7drFt3cP+ySdmSyjBSqot5YRkDEbVHTVzPP2TIkLwM/Q0NDVpRUaELFy7UiooKbWxsPGj7rl27tL29XU8//XS98cYbNZFI6Nq1a7Wpqcm3zzSQDNmQqSbO2WdX61FHVZelF5alMjEMF+9T5gGj6E7Wr4dp0yYHHr0e5/PfccdHmDLlEv793/+dRCJBVVUVl156Ka2trRmPmSnJo7di4Sc/+T5PPtnF3Xf/mDVrHmPZsmW+fS4/Ek1myq32sY8lOO+8f+aUU04pu5QtpkAMwyVzBl/HoybIXGC5nD+IoMPM5/+QoUOH7p8K6uvry2oqKF2SxzCVpR+JJgfOrXZK0eSJ8xOrB2IYLlFn8M3l/EG4pQ50/l/96leAP3aKYnN/LcaaOGFgIxDDcIk6g28256+qqjrIqN3W1kZbWxuVlZUkEolAzz969GjuvfdeX2qORK2sc8Vyq6XGRiBGbAnbGyrqp8xszh9kzMRA5++3I4AzFVSInaIYKxaWW73zrEhlWS/VxbywioeovJHi6gXlPf99992nFRUVWl1drYMHD9YVK1aEen4/SCQSZZECpFQgjRdWJNl4o8Ky8RYHiUSCurpRkdXyiDqD70Dnb2xsZP369fzrv/4rN9xwA5/73OdYu3ZtaOf3C68XVvKUUFk/1ceQuNUDMYy0RO0NFXXlxYHOH3T987A+v1UsLH5MgRixo9gMrGETt/rnhRC1sjYKw4zoRuwoRgOrYZQjpkCM2BG1N5RhGNlhU1hG7DCfe8MoDkyBGLHEDKyGEX9MgRixxQyshhFvzAZiGIZh5IWNQAzDKBr6gxy3bNnC2LFjbVozYkyBGEZI2M2vMJIj1x999AgWLLjWItcjJJJUJiJyDLAKqAW2AZeo6t+S+jQCP/Q0fRyYoao/E5FlwLnA391ts1V100DntVQmRlRY2o7CiDq9TbkTt1Qm1wNPquqtInK9u/4dbwdVXQecDvsVThfwmKfLt1X1oZDkNYy8ibrSoFeOYh0BRZ3exkhNVEb0C4H+fAzLga8M0P9i4JequitQqQwjAOJQPKmzs5O6ulEsWjSXV1/9HosWzaW2diSdnZ2Bn9sPLL1NPIlqBDJCVXsBVLVXRI4boP8M4AdJbd8Xke8CTwLXq2pfqh1F5CrgKnCS0BlG2ER984vLCKgQoi72ZaQmsBGIiDwhIi+mWC7M8TgjgdOAX3uam3FsIhOAY0ia/vKiqnepar2q1g8fPjyPT2IYhRF1bq84jIAKxdLbxJPARiCq+oV020TkDREZ6Y4+RgJvZjjUJcBqVd3jOXav+7ZPRO4FvuWL0IYRANOnT2fBgmtZv55DDMBh3PyiHgH5gaW3iSdRTWE9DDQBt7qvP8/Q9zKcEcd+PMpHcOwnLwYlqGEUStQ3v1KZ/rH0NvEjKjfeYcADwInAn4Gvquo7IlIPXK2qV7r9aoH/A5ygqvs8+68FhgMCbHL3OfTfkYS58RpRElWlw927d1NbO9JcYI28SefGayVtDaMMsDgUoxDiFgdiGEaI2PSPEQSmQAyjTLDsxobfWDZewzAMIy9MgRiGYRh5YQrEMAzDyAtTIIZhGEZelJUbr4jsAP4U4imPBd4K8Xy5YvIVhslXGCZfYYQp30mqekguqLJSIGEjIhtT+U7HBZOvMEy+wjD5CiMO8tkUlmEYhpEXpkAMwzCMvDAFEix3RS3AAJh8hWHyFYbJVxiRy2c2EMMwDCMvbARiGIZh5IUpEMMwDCMvTIEUiIgcIyKPi8hr7uvRKfo0isgmz7JbRL7iblsmIls9204PWz6334ceGR72tNeJyO/d/VeJyJCw5ROR00XkdyLykog8LyKXerYFcv1E5DwReUVEukTk+hTbD3evR5d7fWo925rd9ldEZIof8uQo2wIR2exeqydF5CTPtpTfcwQyzhaRHR5ZrvRsa3J/D6+JSFNE8v3QI9urIvKuZ1ug11BElorImyKSslCeONzhyv68iJzp2Rb4tTsIVbWlgAVYCFzvvr8euG2A/scA7wDV7voy4OKo5QN2pml/AJjhvv83YF7Y8gEfA052348CeoGaoK4fMBjYAowBhgDPAeOT+lwD/Jv7fgawyn0/3u1/OFDnHmdwyLI1en5f8/ply/Q9R3D9ZgOLU+x7DNDtvh7tvj86bPmS+s8HloZ1DYFzgDOBF9NsPx/4JU5BvYnA78O6dsmLjUAK50Jguft+OU6J3UxcDPxSVXcFKtUBcpVvPyIiwOeBh/LZP0sGlE9VX1XV19z3PcCbOBUpg+JsoEtVu1X1A2ClK6cXr9wPAZPc63UhsFJV+1R1K9DlHi802VR1nef39Qww2sfz+yJjBqYAj6vqO6r6N+Bx4LyI5bsMuN9nGdKiqr/BechMx4XAferwDFAjIiMJ59odhCmQwhmhqr0A7utxA/SfwaE/xu+7Q9EfisjhEclXKSIbReSZ/uk1YBjwrqrudde3A8dHJB8AInI2zlPjFk+z39fveOAvnvVUn3t/H/f6/B3nemWzb9CyeZmD87TaT6rv2W+ylfEf3e/tIRE5Icd9w5APd/qvDljraQ7jGmYinfxhXLuDsIJSWSAiTwAfTbHpxhyPMxI4Dfi1p7kZeB3npngX8B3glgjkO1FVe0RkDLBWRF4A/jNFv5z9vn2+fiuAJlXd5zYXfP1SnSpFW/LnTtcnm30LIevji8gsoB4419N8yPesqltS7R+wjGuA+1W1T0SuxhnNfT7LfcOQr58ZwEOq+qGnLYxrmImofnuHYAokC1T1C+m2icgbIjJSVXvdG9ybGQ51CbBaVfd4jt3rvu0TkXuBb0Uhnzs1hKp2i8hTwBnAv+MMjyvcp+zRQE8U8onIUcAjwE3usL3/2AVfvxRsB07wrKf63P19totIBfARnGmHbPYNWjZE5As4CvpcVe3rb0/zPft98xtQRlV927P6v4HbPPs2JO37VNjyeZgBXOttCOkaZiKd/GFcu4OwKazCeRjo93ZoAn6eoe8hc6nuTbPf3vAVIKXnRZDyicjR/VM/InIs8BlgszqWuXU4dpu0+4cg3xBgNc6874NJ24K4fp3AyeJ4oA3BuYkke9t45b4YWOter4eBGeJ4adUBJwMbfJApa9lE5AxgCXCBqr7paU/5PfsoWy4yjvSsXgC87L7/NTDZlfVoYDIHj9hDkc+V8RQcY/TvPG1hXcNMPAx83fXGmgj83X2QCuPaHUyQFvpyWHDmvZ8EXnNfj3Hb64G7Pf1qgb8Cg5L2Xwu8gHPj+wkwNGz5gE+7Mjznvs7x7D8G5wbYBTwIHB6BfLOAPcAmz3J6kNcPx9PlVZwnyxvdtltwbsoAle716HKvzxjPvje6+70CTA3gNzeQbE8Ab3iu1cMDfc8RyNgKvOTKsg74uGffK9zr2gVcHoV87vrNwK1J+wV+DXEeMnvd3/x2HDvW1cDV7nYBfuzK/gJQH+a18y6WysQwDMPIC5vCMgzDMPLCFIhhGIaRF6ZADMMwjLwwBWIYhmHkhSkQwzAMIy9MgRhlgYioiKzwrFeIkw32F+76BZIiK6uP579ZRFIGOYrIb3M4zmo3C2yXiPxdDmSF/XSO8nzejSFIte0T4mQ/7hOR/5bLcY3ywiLRjXLhfeCTIlKlqgngizhxOQCo6sOkCCZLhRu0KHognUpBqGrWN39VvciVoQH4lqp+Oc/Tfh54CyfZYjJv4WSgvTjFNsPYj41AjHLil8CX3PcHZQUQpz7FYvf9CPdJ/zl3+bSI1IrIyyJyJ/AH4AQRuUxEXhCRF0XkNs+xzhORP7j7Puk5/3gReUpEukXkG57+O93XBhH5jXvuzSLybyKS9X9URCaIyNMi8qyI/FJERrjt/+Qe7zkR+YmIjAWuBL6davSiqm+o6kZgb4rTGMZ+bARilBMrge+601afApYCn0vR7w7gaVW9SEQGA0NxUlqcghPde42IjMLJ33QW8DfgMXEys/4fnNxO56jqVhE5xnPcj+PU6jgSeEVE2tSTF83lbJyaIn8CfgVM50A6/bS46TVux4mkfktE/h+gBbgK+O/ASar6gYjUqOq7InI38Jaq/migYxtGOkyBGGWDqj4vTuXAy4BHM3T9PPB1d58Pgb+7uYX+pAcSOU4AnlLVHQAi0o5TCOhD4Dfq1AJBVb11HR5RJ7Fhn4i8CYzASVXhZYOqdrvHvB/4LFkoEOBU4BPAE84MG4M9x34J+ImI/Bz4WRbHMoysMAVilBsPA/8fTtbSYTnu+77nfarU2f3t6fID9Xnef0jq/1/yvtnmGhLgeVVNNaKagpPS/ULgJhH5ZJbHNIyMmA3EKDeWAreo6gsZ+jyJUwoWERksTir5ZH4PnCsix7rTXJcBT+Nkbj3XzcRL0hRWNpztZokdBFwKrM9yv83A8eIU3EJEhrjeVIOB0aq6Fvg2TiXHauA9nKk0w8gbUyBGWaGq21X19gG6fRNoFKeo1rM4U0PJx+nFKWa1Dicz6x9U9efulNZVwE9F5DlgVY4i/g64FSe78FacNPYD4k6NXQz8wD3vH4F/wBnldIjI8zjG/9tU9T2ctPmXiMgfk43oIjJaRLYD3wBuFpHtIlKd4+cwygDLxmsYMcEH11zDCBUbgRiGYRh5YSMQwzAMIy9sBGIYhmHkhSkQwzAMIy9MgRiGYRh5YQrEMAzDyAtTIIZhGEZe/F8jb+Itqk+dTgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plotData(X,y)\n",
+ "plt.xlabel(\"Microchip Test 1\")\n",
+ "plt.ylabel(\"Microchip Test 2\")\n",
+ "plt.legend([\"y=1\", \"y=0\"])\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to create a more complex boundary, we will now map the features into all polynomial terms of x1 and x2 up to the sixth power. This results in a conversion of our vector of two features becoming a vector of 28 features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note that mapFeature also adds a column of ones for us, so the intercept\n",
+ "# term is handled\n",
+ "X = mapFeature(X[:, 0], X[:, 1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now compute the cost function and gradient for our newly mapped features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def costFunctionReg(theta, X, y, lambda_):\n",
+ " \"\"\"\n",
+ " Compute cost and gradient for logistic regression with regularization.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " Logistic regression parameters. A vector with shape (n, ). n is \n",
+ " the number of features including any intercept. If we have mapped\n",
+ " our initial features into polynomial features, then n is the total \n",
+ " number of polynomial features. \n",
+ " \n",
+ " X : array_like\n",
+ " The data set with shape (m x n). m is the number of examples, and\n",
+ " n is the number of features (after feature mapping).\n",
+ " \n",
+ " y : array_like\n",
+ " The data labels. A vector with shape (m, ).\n",
+ " \n",
+ " lambda_ : float\n",
+ " The regularization parameter. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed value for the regularized cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " A vector of shape (n, ) which is the gradient of the cost\n",
+ " function with respect to theta, at the current values of theta.\n",
+ " \"\"\"\n",
+ " # Initialize some useful values\n",
+ " m = y.size # number of training examples\n",
+ " temp, n = X.shape\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ "\n",
+ " h = sigmoid(X.dot(theta))\n",
+ " logh = np.log(h)\n",
+ " tempLog = np.log(1-h)\n",
+ " yTrans = y.transpose()\n",
+ " Xtrans = X.transpose()\n",
+ " tempTrans = (1-y).transpose()\n",
+ " \n",
+ " tempTheta = theta[0]\n",
+ " theta[0] = 0\n",
+ " J = ((-yTrans).dot(logh))\n",
+ " J = J - tempTrans.dot(tempLog)\n",
+ " J = J * (1/m)\n",
+ " J = J + (lambda_ / (2*m)) * np.sum(np.square(theta))\n",
+ " theta[0] = tempTheta\n",
+ " \n",
+ " diff = np.subtract(sigmoid(X.dot(theta)),y)\n",
+ " grad = Xtrans.dot(diff)\n",
+ " grad = grad * (1/m)\n",
+ " for i in range(1,n):\n",
+ " grad[i] = grad[i] + (lambda_ / m)*theta[i]\n",
+ " \n",
+ " \n",
+ " # =============================================================\n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at initial theta (zeros): 0.693\n",
+ "Gradient at initial theta (zeros) - first two values only:\n",
+ "\t[0.0085, 0.0188]\n",
+ "------------------------------\n",
+ "\n",
+ "Cost at test theta : 3.16\n",
+ "Gradient at test theta - first two values only:\n",
+ "\t[0.3460, 0.1614]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize fitting parameters\n",
+ "initial_theta = np.zeros(X.shape[1])\n",
+ "\n",
+ "# Set regularization parameter lambda to 1\n",
+ "# DO NOT use `lambda` as a variable name in python\n",
+ "# because it is a python keyword\n",
+ "lambda_ = 1\n",
+ "\n",
+ "# Compute and display initial cost and gradient for regularized logistic\n",
+ "# regression\n",
+ "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n",
+ "\n",
+ "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+ "print('Gradient at initial theta (zeros) - first two values only:')\n",
+ "print('\\t[{:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+ "\n",
+ "\n",
+ "# Compute and display cost and gradient\n",
+ "# with all-ones theta and lambda = 10\n",
+ "test_theta = np.ones(X.shape[1])\n",
+ "cost, grad = costFunctionReg(test_theta, X, y, 10)\n",
+ "\n",
+ "print('------------------------------\\n')\n",
+ "print('Cost at test theta : {:.2f}'.format(cost))\n",
+ "print('Gradient at test theta - first two values only:')\n",
+ "print('\\t[{:.4f}, {:.4f}]'.format(*grad[:4]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a working cost function, we can now apply linear regression to fit our parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train Accuracy: 83.1 %\n",
+ "Expected accuracy (with lambda = 1): 83.1 % (approx)\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "Here we will use logistic regression and neural networks to recognize handwritten digits (ranging from 0 to 9). To start we will extend our previous implementation of logistic regression to one-vs-all classification, beginning with a visualization of the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import sys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 20x20 Input Images of Digits\n",
+ "input_layer_size = 400\n",
+ "\n",
+ "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n",
+ "num_labels = 10\n",
+ "\n",
+ "# training data stored in arrays X, y\n",
+ "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+ "X, y = data['X'], data['y'].ravel()\n",
+ "\n",
+ "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+ "# This is an artifact due to the fact that this dataset was used in \n",
+ "# MATLAB where there is no index 0\n",
+ "y[y == 10] = 0\n",
+ "\n",
+ "m = y.size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+ " \"\"\"\n",
+ " Displays 2D data stored in X in a nice grid.\n",
+ " \"\"\"\n",
+ " # Compute rows, cols\n",
+ " if X.ndim == 2:\n",
+ " m, n = X.shape\n",
+ " elif X.ndim == 1:\n",
+ " n = X.size\n",
+ " m = 1\n",
+ " X = X[None] # Promote to a 2 dimensional array\n",
+ " else:\n",
+ " raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+ "\n",
+ " example_width = example_width or int(np.round(np.sqrt(n)))\n",
+ " example_height = n / example_width\n",
+ "\n",
+ " # Compute number of items to display\n",
+ " display_rows = int(np.floor(np.sqrt(m)))\n",
+ " display_cols = int(np.ceil(m / display_rows))\n",
+ "\n",
+ " fig, ax_array = plt.subplots(display_rows, display_cols, figsize=figsize)\n",
+ " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+ "\n",
+ " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+ "\n",
+ " for i, ax in enumerate(ax_array):\n",
+ " ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n",
+ " cmap='Greys', extent=[0, 1, 0, 1])\n",
+ " ax.axis('off')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Randomly select 100 data points to display\n",
+ "rand_indices = np.random.choice(m, 100, replace=False)\n",
+ "sel = X[rand_indices, :]\n",
+ "\n",
+ "displayData(sel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have an understanding of the data we are working with, we can implement a cost function for linear regression."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# test values for the parameters theta\n",
+ "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n",
+ "\n",
+ "# test values for the inputs\n",
+ "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n",
+ "\n",
+ "# test values for the labels\n",
+ "y_t = np.array([1, 0, 1, 0, 1])\n",
+ "\n",
+ "# test value for the regularization parameter\n",
+ "lambda_t = 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(z):\n",
+ " \"\"\"\n",
+ " Compute sigmoid function given the input z.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " z : array_like\n",
+ " The input to the sigmoid function. This can be a 1-D vector \n",
+ " or a 2-D matrix. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " g : array_like\n",
+ " The computed sigmoid function. g has the same shape as z, since\n",
+ " the sigmoid is computed element-wise on z.\n",
+ " \"\"\"\n",
+ " # convert input to a numpy array\n",
+ " z = np.array(z)\n",
+ "\n",
+ " g = 1 + np.exp(-1*z)\n",
+ " g = np.reciprocal(g)\n",
+ "\n",
+ " return g"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def lrCostFunction(theta, X, y, lambda_):\n",
+ " \"\"\"\n",
+ " Computes the cost of using theta as the parameter for regularized\n",
+ " logistic regression and the gradient of the cost w.r.t. to the parameters.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " Logistic regression parameters. A vector with shape (n, ). n is \n",
+ " the number of features including any intercept. \n",
+ " \n",
+ " X : array_like\n",
+ " The data set with shape (m x n). m is the number of examples, and\n",
+ " n is the number of features (including intercept).\n",
+ " \n",
+ " y : array_like\n",
+ " The data labels. A vector with shape (m, ).\n",
+ " \n",
+ " lambda_ : float\n",
+ " The regularization parameter. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed value for the regularized cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " A vector of shape (n, ) which is the gradient of the cost\n",
+ " function with respect to theta, at the current values of theta.\n",
+ " \n",
+ " \"\"\" \n",
+ " # convert labels to ints if their type is bool\n",
+ " if y.dtype == bool:\n",
+ " y = y.astype(int)\n",
+ " \n",
+ "\n",
+ " ## Initialize some useful values\n",
+ " m = y.size\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ " h = sigmoid(X.dot(theta))\n",
+ " logh = np.log(h)\n",
+ " tempLog = np.log(1-h)\n",
+ " yTrans = y.transpose()\n",
+ " Xtrans = X.transpose()\n",
+ " tempTrans = (1-y).transpose()\n",
+ " \n",
+ " J = ((-yTrans).dot(logh))\n",
+ " J = J - tempTrans.dot(tempLog)\n",
+ " J = J * (1/m)\n",
+ " J = J + (lambda_/(2*m))*np.sum(np.square(theta[1:]))\n",
+ " \n",
+ " diff = np.subtract(sigmoid(X.dot(theta)),y)\n",
+ " grad = Xtrans.dot(diff)\n",
+ " grad = grad * (1/m)\n",
+ " grad[1:] = grad[1:] + (lambda_/m)*theta[1:]\n",
+ " \n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can run our cost function on some test inputs to be sure it is running correctly. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost : 2.534819\n",
+ "-----------------------\n",
+ "Gradients:\n",
+ " [0.146561, -0.548558, 0.724722, 1.398003]\n"
+ ]
+ }
+ ],
+ "source": [
+ "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n",
+ "\n",
+ "print('Cost : {:.6f}'.format(J))\n",
+ "print('-----------------------')\n",
+ "print('Gradients:')\n",
+ "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working cost function, we can implement ove-vs all classification by training multiple regularized logistic regression classifiers, one for each our our K classes. Note that this classification will work for any value of K, not just our case where K = 10."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def oneVsAll(X, y, num_labels, lambda_):\n",
+ " \"\"\"\n",
+ " Trains num_labels logistic regression classifiers and returns\n",
+ " each of these classifiers in a matrix all_theta, where the i-th\n",
+ " row of all_theta corresponds to the classifier for label i.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input dataset of shape (m x n). m is the number of \n",
+ " data points, and n is the number of features. Note that we \n",
+ " do not assume that the intercept term (or bias) is in X, however\n",
+ " we provide the code below to add the bias term to X. \n",
+ " \n",
+ " y : array_like\n",
+ " The data labels. A vector of shape (m, ).\n",
+ " \n",
+ " num_labels : int\n",
+ " Number of possible labels.\n",
+ " \n",
+ " lambda_ : float\n",
+ " The logistic regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " all_theta : array_like\n",
+ " The trained parameters for logistic regression for each class.\n",
+ " This is a matrix of shape (K x n+1) where K is number of classes\n",
+ " (ie. `numlabels`) and n is number of features without the bias.\n",
+ " \"\"\"\n",
+ " # Some useful variables\n",
+ " m, n = X.shape\n",
+ " all_theta = np.zeros((num_labels, n + 1))\n",
+ "\n",
+ " # Add ones to the X data matrix\n",
+ " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ " \n",
+ " for c in range(num_labels):\n",
+ " initial_theta = np.zeros(n+1)\n",
+ " options = {'maxiter': 50}\n",
+ " res = optimize.minimize(lrCostFunction, \n",
+ " initial_theta, \n",
+ " (X, (y == c), lambda_), \n",
+ " jac=True, \n",
+ " method='TNC',\n",
+ " options=options) \n",
+ " all_theta[c,:] = res.x\n",
+ "\n",
+ " return all_theta"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run oneVsAll optimization with lambda = 0.1 to get a prediction for theta\n",
+ "lambda_ = 0.1\n",
+ "all_theta = oneVsAll(X, y, num_labels, lambda_)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working oneVsAll classification, we can use the resulting theta to predict what an input should be classified as."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predictOneVsAll(all_theta, X):\n",
+ " \"\"\"\n",
+ " Return a vector of predictions for each example in the matrix X. \n",
+ " Note that X contains the examples in rows. all_theta is a matrix where\n",
+ " the i-th row is a trained logistic regression theta vector for the \n",
+ " i-th class. You should set p to a vector of values from 0..K-1 \n",
+ " (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) .\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " all_theta : array_like\n",
+ " The trained parameters for logistic regression for each class.\n",
+ " This is a matrix of shape (K x n+1) where K is number of classes\n",
+ " and n is number of features without the bias.\n",
+ " \n",
+ " X : array_like\n",
+ " Data points to predict their labels. This is a matrix of shape \n",
+ " (m x n) where m is number of data points to predict, and n is number \n",
+ " of features without the bias term. Note we add the bias term for X in \n",
+ " this function. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " p : array_like\n",
+ " The predictions for each data point in X. This is a vector of shape (m, ).\n",
+ " \"\"\"\n",
+ " m = X.shape[0];\n",
+ " num_labels = all_theta.shape[0]\n",
+ " p = np.zeros(m)\n",
+ "\n",
+ " # Add ones to the X data matrix\n",
+ " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "\n",
+ " all_theta_T = all_theta.transpose()\n",
+ " temp = sigmoid(X.dot(all_theta_T))\n",
+ " for i in range(m):\n",
+ " iTempMax = np.argmax(temp[i,:])\n",
+ " p[i] = iTempMax\n",
+ "\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Set Accuracy: 95.14%\n"
+ ]
+ }
+ ],
+ "source": [
+ "pred = predictOneVsAll(all_theta, X)\n",
+ "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Neural Networks
\n",
+ "\n",
+ "We have now implemented multi-class logistic regression to recognize handwritten digits. However, because this is only a linear classifier, logistic regression cannot form more complex hypotheses. \n",
+ " In this portion of the exercise, we will implement a neural network to recognize handwritten digits using the same training set as before. The neural network will be able to represent more complex models to from non-linear hypotheses. In this exercise we will implement parameters from a neural network that has already been trained. Our goal is to implement the feedforward propagation algorithm to use our weights for prediction.\n",
+ " Our neural network is shown in the following figure. it has 3 layers and takes as input our pixel values of digital images. This gives us 400 input layer units (plus our extra bias unit outputting +1). Our network parameters are stored in ex3weights.mat. We begin by loading them into Theta1 and Theta2.\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# training data stored in arrays X, y\n",
+ "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+ "X, y = data['X'], data['y'].ravel()\n",
+ "\n",
+ "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+ "# This is an artifact due to the fact that this dataset was used in \n",
+ "# MATLAB where there is no index 0\n",
+ "y[y == 10] = 0\n",
+ "\n",
+ "# get number of examples in dataset\n",
+ "m = y.size\n",
+ "\n",
+ "# randomly permute examples, to be used for visualizing one \n",
+ "# picture at a time\n",
+ "indices = np.random.permutation(m)\n",
+ "\n",
+ "# Randomly select 100 data points to display\n",
+ "rand_indices = np.random.choice(m, 100, replace=False)\n",
+ "sel = X[rand_indices, :]\n",
+ "\n",
+ "displayData(sel)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup the parameters we will use for this exercise\n",
+ "input_layer_size = 400 # 20x20 Input Images of Digits\n",
+ "hidden_layer_size = 25 # 25 hidden units\n",
+ "num_labels = 10 # 10 labels, from 0 to 9\n",
+ "\n",
+ "# Load the .mat file, which returns a dictionary \n",
+ "weights = loadmat(os.path.join('Data', 'ex3weights.mat'))\n",
+ "\n",
+ "# get the model weights from the dictionary\n",
+ "# Theta1 has size 25 x 401\n",
+ "# Theta2 has size 10 x 26\n",
+ "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+ "\n",
+ "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+ "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+ "Theta2 = np.roll(Theta2, 1, axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now implement in predict() the feedforward computation which computes predictions for each training sample."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(Theta1, Theta2, X):\n",
+ " \"\"\"\n",
+ " Predict the label of an input given a trained neural network.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " Theta1 : array_like\n",
+ " Weights for the first layer in the neural network.\n",
+ " It has shape (2nd hidden layer size x input size)\n",
+ " \n",
+ " Theta2: array_like\n",
+ " Weights for the second layer in the neural network. \n",
+ " It has shape (output layer size x 2nd hidden layer size)\n",
+ " \n",
+ " X : array_like\n",
+ " The image inputs having shape (number of examples x image dimensions).\n",
+ " \n",
+ " Return \n",
+ " ------\n",
+ " p : array_like\n",
+ " Predictions vector containing the predicted label for each example.\n",
+ " It has a length equal to the number of examples.\n",
+ " \"\"\"\n",
+ " # Make sure the input has two dimensions\n",
+ " if X.ndim == 1:\n",
+ " X = X[None] # promote to 2-dimensions\n",
+ " \n",
+ " # useful variables\n",
+ " m = X.shape[0]\n",
+ " num_labels = Theta2.shape[0]\n",
+ " \n",
+ " X = np.concatenate([np.ones((m, 1)), X], axis=1) # Add collumn of ones to X\n",
+ " z2 = Theta1.dot(X.transpose())\n",
+ " z2 = z2.transpose()\n",
+ " a2 = sigmoid(z2)\n",
+ " a2 = np.concatenate([np.ones((a2.shape[0], 1)), a2], axis=1) # Add collumn of ones to a2\n",
+ " z3 = Theta2.dot(a2.transpose())\n",
+ " a3 = sigmoid(z3)\n",
+ " a3 = a3.transpose()\n",
+ " p = np.argmax(a3, axis=1)\n",
+ "\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Set Accuracy: 97.5%\n"
+ ]
+ }
+ ],
+ "source": [
+ "pred = predict(Theta1, Theta2, X)\n",
+ "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Neural Network Prediction: 0\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
\n",
+ "Here we will use logistic regression and neural networks to recognize handwritten digits (ranging from 0 to 9). To start we will extend our previous implementation of logistic regression to one-vs-all classification, beginning with a visualization of the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import sys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 20x20 Input Images of Digits\n",
+ "input_layer_size = 400\n",
+ "\n",
+ "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n",
+ "num_labels = 10\n",
+ "\n",
+ "# training data stored in arrays X, y\n",
+ "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+ "X, y = data['X'], data['y'].ravel()\n",
+ "\n",
+ "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+ "# This is an artifact due to the fact that this dataset was used in \n",
+ "# MATLAB where there is no index 0\n",
+ "y[y == 10] = 0\n",
+ "\n",
+ "m = y.size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+ " \"\"\"\n",
+ " Displays 2D data stored in X in a nice grid.\n",
+ " \"\"\"\n",
+ " # Compute rows, cols\n",
+ " if X.ndim == 2:\n",
+ " m, n = X.shape\n",
+ " elif X.ndim == 1:\n",
+ " n = X.size\n",
+ " m = 1\n",
+ " X = X[None] # Promote to a 2 dimensional array\n",
+ " else:\n",
+ " raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+ "\n",
+ " example_width = example_width or int(np.round(np.sqrt(n)))\n",
+ " example_height = n / example_width\n",
+ "\n",
+ " # Compute number of items to display\n",
+ " display_rows = int(np.floor(np.sqrt(m)))\n",
+ " display_cols = int(np.ceil(m / display_rows))\n",
+ "\n",
+ " fig, ax_array = plt.subplots(display_rows, display_cols, figsize=figsize)\n",
+ " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+ "\n",
+ " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+ "\n",
+ " for i, ax in enumerate(ax_array):\n",
+ " ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n",
+ " cmap='Greys', extent=[0, 1, 0, 1])\n",
+ " ax.axis('off')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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99dYhswFhc+7JtKDmW265JXQa7KhNcXktdE2qzMDkfffdd6rfVatwXavlTKus8+13p512Cl2e1ZwrgZ4Tg001WUXf4o877jgASWuhNjDlXlXN+5DrR8ehVmzdK/hbp/uT3n9MvNF7lrXX9LvyQI+t64vWM91f9ttvv5CfeeaZkI855hgAyQSWRRddNGRa7DQZQC2SP/zwA4Bko2i1gumzAq+3m3oaY4wxZprFDzrGGGOMqVua7LrKchExmJUuECAZbKRuKLYTWG655UK3xhprhPzTTz8BSLYjUNeJlpuuFbRE+ZgxYwAA5513Xuiuv/76kGlSHT9+fOg02CrP8en8qWn1999/B5A0MWpTNTUXMjAuK0CX86YlvLXmBUu/awBe3uZ0rh8tJa61GdLaGej41MzKIGtds+oSy3Msek46f3TDaIBmVsO74cOHAwBGjx4dOg1CZ/0ZnbNVV101ZLph2XwXSLru8qhvRXeZ1tPimgWS7WRoUn/ggQdCp00Bd9111wbHr0V3Tjl0r1U3AwPjdZy1EoDcGLhm9JzVdUOXuoYGaM0Z7rvVbFHCfVXdxdq2QcMEuBcNGzYsdFrniQ109V5XNxF/H/JwzWnYBFsxAKW19uijj4ZOW3honTT+1uvvt4YpcC40nEPdkPxefX7Q+SuX5JOGLTrGGGOMqVv8oGOMMcaYuqXJris1IalpkObSRx55JHRqWtPupU899RSApLlLoWuEpdyBZNfU7bbbbqrnlSdqNtPaDVrTZ+DAgQCSpvM77rgjZHYVVnOmlsBeccUVK3fCU6DXSV0TzHRQE7iaXpk9A5Sy3bROjprRx40bByBpBlUzOr8376wIHStdG5odoC0OtN0IM1S0BLp2uma2j7omq7X+slxX3333HYBk1py61tQMvdFGGwFImobVDUQ3gWYqqWs1rZ2A3hd5ZtDpmlV3qrok6ebSOhzqWua4a71OjqJZU3R56/gU3mvqQmxNriuSVvsIKNUHGjBgQOh0fzr++OMBJLu35+Ga1HuRLRpY4wcADjnkkJD1t+DZZ58FkMw00jo5vL90f9EMJP5/HnOq11nXD2tr6e/chAkTQtbfDc6Pfl5dj0T3H4X3ZZaLqjlZZ7boGGOMMaZuaVEdHX3i2meffQAkg4a1joo2i2R9AH0z1CBYBiurRaFr164Nvr+IeiWKPlmyESBQeiKlZQcovWUApbczfaPUipOsT5LH+HTOeJ2B0hO7WmbU4jZixIiQ+/fvDyBpBdA3EloV1PKmlU3zfCNR9Ph8y9Bqnb179w5ZK+uyMrfWgdBrwblWi1613ph1zeg9MXLkyAZ/q2+cahGgXs9Z63ykjSWtqWk14fxpPQ2uQyC5vmjd0gDyTTfdNOTWYt3Q/UUbQDLw9tVXXw2dNltkfaSsOl1FoOuPlHszz0p8oUXr4osvDt1WW20VMq13eY9Zz++rr74CkAzqHzx4cMhqHaas10SD/fv06QMA2GCDDVK/K8/1mzUn3Hd0T9Hf+sUXX7zBZ/Q8085Z9zL9f55DJesF2aJjjDHGmLrFDzrGGGOMqVumm5oZbNKkSY22kaU13VPT8XvvvRcy3QQa4Kh1OOaZZx4A2fU4Wmq6m2GGGaYDgMmTJ1fMBqjjpmm1V69eodOaCeuvvz4AYM899wzdJptsEjLH3dxxtmnTZjoAmDhx4lQPoKZBlt3WAN0bb7wxZG0QybngPAHAKqusEjLN6BogqNenpfPXtm3b6QDgn3/+afL6VBebBs5rsPFHH30EIFl7hXWiAGCzzTYDkAyGraQ5efrpp2/y+FoTHF+59alw/WhSg7Yo0QaQDIbXAHMNXGVgZF4uAK7Ppuyfaeg9o+fft29fAMAKK6wQuoceeihk3pd5uRi5f5abPz3/sWPHAki2ElHXd1qwqd6rOj7WqtFgVnUt03Xe3PFz/pqyf7KRs9a7Yb2qKf+W9WMOPvjg0KlrNW/XW6XWZ63C9anYomOMMcaYusUPOsYYY4ypWyrmukojK+sjLZo6rQ5HXqblPFxXCsetkfZ///13yDS5qum1kq65xrqulLRMHK2NoNkE/Js555wzdFqCny6dvDIFmuO6IlmR/GpGZ30Ida3OMsssDT6T1/q06yob3VN0z6DrACjV0WFtKCCfthRZtMQ1oLXJtDs7O0IDwMILLwwg2Q5DXf95Z8U1x3XFcAVtIaDuYD1n3lcLLLBA6JgJCZRqyqy00kqh03u1pfPbWNeVwn1F9/k///wz9W+5FnV9KrW8PlsDdl0ZY4wxZprCDzrGGGOMqVtydV3VKnm7rkhW2Wpe87xcH81xXaXRlLLb5YpDVZKWuK6yUDN7GtUsuGbXVePIWp+Us1oI5E0ergFdn0WPr7GuK6WlReDSPpfX+JvjuiKN2TPz3v/LYdeVMcYYY0wd0aIWEGbqVNPKkQet/fybQtEl8k3T8fpsPRRtxagW09KabE3YomOMMcaYusUPOsYYY4ypW6YajGyMMcYY05qxRccYY4wxdctUg5EnTJhQl+aedu3aTQcAf//9d12Ob8YZZ5wOqP/5a2l6cq0yraR/VnN9tmnTJmRasf/5559cvovrM+/yFUVRqfIVtQrvv3rfP+t9f1Fs0THGGGNM3eL08hxI6xvlWKhpDy0YpgXfiir0RtKKt6mcd6+5aqHnf+ONN4Y866yzAgC22GKL0GkvvtY+bmNMElt0jDHGGFO32KLTArLe0i+//HIAyY7Xe+65Z/VOzBQKrQPakXn8+PEha6fltm3bAsjfiqBrlRYb7a48efLkkNlVubVaOdgB/JVXXgndkUceGfLZZ58NIPv+nRbRa6FyXnFMJhtef73/mtI6Q63Enr//hy06xhhjjKlb/KBjjDHGmLolV9dVVjAmUXNxWndeNbvVomlZTeMffPBByMcffzwAYJVVVgnd7rvvHjJNkrU4pmqSFQyra4HunyKuVblOxHqeamb+4osvAAA33HBD6K644oqQe/bsGXK/fv0AJN1clULPb9KkSSFff/31AJIBuurGGjZsGABgwQUXDF2t34s6VroJ+/fvH7pNNtkk5IMOOqjB52txTOUo1x27HGnXDAB++umnkLt06dKkY1aSprhrWuP8ZblOv/nmGwDAW2+9Fbpff/21wef1M3p/c84AYLXVVqvMyabQ0k70WftrHnu+LTrGGGOMqVv8oGOMMcaYuqXiris1Qf39998hv/jiiyHTZDfvvPOG7o033gj5zTffBAD07ds3dHPNNVfIeZj5y5Fmprv//vtDHjBgQIP/33bbbUNurRksU0OvSVPGxM+p6fbLL78M+fnnnw95p512avC3ecPz0+yFH374IWSaiV977bXQvfzyyyE/+eSTAID3338/dHPOOWfIHTt2rPAZl8jKmLnoootCPv/88wEkzd2dOnUKmWPp0KFD6DRTjHNdRA2gLPT+osl/+PDhoVOZ16iIfaQS8Pw1U07Ra5EG17d+/sILLwz5448/Dvmee+4BUMye9dtvv4WsrlWOjxmLQDLDlTD7Dii+plna74e6pq688sqQn332WQDA2LFjU4/Vrl07AEDnzp1D16NHj5BfeOGFkCvtusra8/VeSru+utbomtM9/8MPPwx57733BpDcc1q619iiY4wxxpi6pWIWHT5lf/3116Hj2zgAvPfeeyGz74y+eUycODHkCRMmAEg+BTLAFwDmnntuANV9o+RTqp6zPkVfcsklIa+33noASk+mQPOtH7UI3yjTgoaB8uPjNVSLyCGHHBKyBsHyqX777bdvwRmXJy0wbujQoaE77LDDQqalRNessvDCCwMA9t1339AdfvjhIS+55JIhV2oNp70x3nXXXSGfd955IXft2hUAsPPOO4fuscceC/mAAw4AACy//PKh0zo066+/PoBSvR2g+LfkP/74I+TrrrsOALDVVluFTsfSGi05eq/RunjEEUeEbumllw751FNPBZBdQ4XHeuSRR0LHawYA9913X8jNDThtKjq+b7/9FgCw4447hu7VV18NeeaZZwaQtOKod2COOeYAkLw+XLMAMMMMM4Sc57rN2vOvuuoqAMCJJ54YOrWobrzxxgCS+8QGG2wQ8nzzzQcgOY60OlmVJM0KqMkMrB0HlNbdTDPNFDr+pgOlefvqq69Ct+KKK4a8xx57VOisS9iiY4wxxpi6xQ86xhhjjKlbKua6omlOA6E02IoBVEC66V/NfDRtae2R2WefPeTTTjutwWfyNp3zu37//ffQqbm3ffv2ITPwU037aebErDoCHEvRLi49J3XZ/fzzzwCABx54IHTqWqJpWdHAQAab77bbbqHbZ599Qj700END5hqq1vyqvOiii4Zuu+22C3mhhRYCUGoOCSRNzzTDqk7NzLoWKjUumq41wO/iiy9O/U62I1F3nLYoOemkkwAAQ4YMCZ26YWlavvTSS0NXRLC9muu1jtXIkSMBALfcckvq39ZSEPXU0HPWYFy6EdU1yT2xMcf6/vvvAQBHH3106DbffPOQV1555ZCrda10zdBdfcYZZ4RO3RwfffQRgKRrTvflhx56CACwzTbbhE5dsxtttFHIWQHdlUZDG+i60j3vhBNOCJmhGVlrttzvQx6JG2kuTL3nZptttpCZ7KB7nrqL6WZkKxYgmeyRh7vUFh1jjDHG1C1+0DHGGGNM3dIi15WamGha03z4tP8HSq6pY445JnQvvfRSyDQzah79o48+GvLBBx8MoGTiA/LPpKA5kDUAAOCpp54KWU2PyyyzDIDyWQ/jxo0L3Y8//tjg80V0V85yV2lZeJq8P/3009Bphl1anRy6q4BSto/WeNC1oGtl1VVXBZCcX3WDtQQ9Dt1xAHDzzTcDSJq+1Y2alnWWNlc6jjzWZ9r9pW0n3n333ZA33XTTkOmG0nPSOj8DBw4EULr2AHDccceFfNtttwEA1l133dDtsssuIefZMTkr0+rcc88Nme0eunfvHrpy909aCxL9TLVcOFnu+NNPPz3ku+++G0DS9aT3D+dV16TWNDvqqKMAJO/vM888M2QNM6hWhpqOlS5hZh8BybEwg0fn/6abbgr5jjvuAAAsu+yyodO2CHnOpZ7nZZddFjLdOUDJ5a8uQp0Lnl8tuVh5LuqO0jWp7ZDWXHPNRh1Tr9Vyyy0XMkMf3ALCGGOMMaYR+EHHGGOMMXVLxVxXdMMMHjw4dGqaWmyxxUJmoSEt4jV69OiQ2QJAMw00wvvzzz8HAMwzzzwtOf1mkVVKfKmllgq5XIYVMwT69OkTuocffjjkk08+GUDSHJ1VzrxSpHWUZfYKAPTq1Svk+eefH0DJhQEk3Yycd/28urbosho0aFDosjIo2Pqj0tlJAPDdd9+FrAX92K5E1yczrYD07rpNKZiYB/z+e++9N3S6ZrR7N03DWS4mFvTUTCu9bmzNwi7nQNLNx88Dlb8WauJX17HKdD1mFbRMO5YWNOO60EySvIsj8r7TYzN7CACuueaakJnheMopp4ROi7Olua4064htHfTzWqSzVlwmeh6aTUjXlBY21JCJXXfdFUCyyKW2S8jTjayZxNr2SFsYMStT75M83b2VROdEs07LZbKlZf2NGjUqdNoCg39byWtii44xxhhj6paK1dH566+/ACTfjBQNNmKwLS0zQLLmB8kKxqXVSI9ZrbLe2ghR35hZ9r8xsL6HljXXN/H+/fsDSFrB1CJSqTeStNoxjz/+eOgOPPDAkNdee+2QWe5b33gVBh7rOa+xxhoh8+ldLSq//vpryGpJqRRcS/rGdc4554Ss159vzCussELodE2lNf0sAr0/+Ear9SgWWWSRkHUuyp03/18tHjvssEPIt99+OwDgwQcfDJ3WPtIg5krVKUkrQa81u9RixbdLHWda4LZagbTOCeu0bLbZZqHTppd5JAnwmO+8807oNNiYZf+BUjsPtTIptBSMHz8+dDo+3l86Z0rR9bu4htWKpTW7mDiwxRZbhI61aYDSvqlNP/MOquY10+/Ue+7pp58Oma1hdH67desWcq3sL+Uod35pAf5AyXuhVkj1iOQxV7boGGOMMaZu8YOOMcYYY+qWirmuNLAqDi6uneHDh4fM+giffPJJ6NSNwWNlmYgZhHbssceGTsv152F65THHjBkTOq03oaZloqv6bqQAACAASURBVOevdVoYxLv66quHbuuttw6Znd4//vjjlp72VNHze/311wEku4jvv//+IWv3+LQ6B3pdGMSqdVb69esXMmsuqJlevyuPcv00o+o8qOsiLXAzq04Mz7VDhw6hKzqYkIH76prTYEFdq40lzV0HlMzzGjSursdcSrj///nRoFNt8aBuVrqxde2oy4utBdTdodeNf6vXLI92M+oapLtGExS0zpa6AxkErskaq6yySsjsZH7ttdeGTgNj+Xl2+QaKWb96TTWw+IADDgCQHF/v3r1DZtdvXd9pdWiK6FKva27bbbcNmQkcQMn1qB3Jdd/l75q2PSpiLC0lK/GDSSjaAkOTWfJYi7boGGOMMaZu8YOOMcYYY+qWFrmu1ITLjqSanaHl6BW6LtR0mVbWX82Aafn7GrWdNzxXrTehZdXTOrGntb0ASuX41ZynGVisE6Flw/Nwx6lpkWZ8dUHod2qdA7rp1MR45513hszWEJp18Mwzz4TMmhbaPTvN9FxJeEw117NLN5BsQUL0nLXmCOs8MTsOAJZYYomQiyibz+/XVg4tXTN6f+r6pssqjy7J5fjiiy9C1rYpmnXFcev5jxgxImRmIGnWoGbLsFO9ro9Kua70OOqa4b3+3HPPhU73xNdeey1kurTVdaN7BWs+abuarl27NpDzrAc0JWnfpTq9/9daay0AybYCei8yq05dP7Xo2tHxqWtxyJAhAJJ1krSOF7P+rr/++tBphl2tZ2OlrQFtR8P7lvcZkH+mny06xhhjjKlbppvak9SECRMa/ZjFJ3KtYKkBnG+99VbIaTUH0hok6lO6WkQYzKXHbwrt2rWbDgD+/vvvRo8vrfIlg+aA5BvJBRdcACBZDVOhJYQVIgHgrLPOCpl6rTys16rc0++MM844HVB+/vScOT8awKiNPPXtl4HLOqcaLMmxaDVhDazTuSRNeaLn/E2cOLHJrwFZAe5pb4S//PJLyGq9ufrqqwGUmpMC6U0/m/uW0rZt2+kAYNKkSVM9gL458fy1UepXX30VsgZes45V1lswz1+Pr+NjZWQeB0gGk3bq1KnBeSkzzDBDo9anwnPR89Cmifr2T0uHfrc2NX355Zd5HqHTwEg2uNR12hSLAdfn5MmTG4xPkzY0QHi99dYDkKwNphZPDYxnMoAGS2viAutA6V71yCOPhMzvaO76bNOmTYP7j/Oja073F66JLGtEWgNVtUjp/sLEE635pdb9lloHeP81ZX02BV4rvT6azMH50znX/aelllSuz3L7S3NJ82jobzV/S9RLoPdFSy1W3F8UW3SMMcYYU7f4QccYY4wxdUvF6ujQtKvN09ScrW4Y1sLQdgqPPvpoyHTtqAlSTZM9evQAkAzWy7sOBM9F8/1ZzwEA9tprr5DZVG7o0KGh0yBRmua0tpCalk8//XQATXNXNQc1x9OcPXDgwNCpiVSDyWhaVdeNlphPa8SZJhddal7HlxZAxwB7INlglSXM1XT+2WefhdylSxcAxQRIqjtDg13VDUeTeVYwKOvIvP3226FjI0igZGbeb7/9QqfuqjyCJXmu6u7V8Wm7A9Y3+vbbb0PH2lRAaa/Q+1cbQPIez2P+9NronsDaTLrO1PVb7l5R1x1dzueee27otK1KnkHIt956a+jU3c1gW61nlBXgzfO///77Q6dJEmzmXEQwfCXgWPU3a8kllwyZ9xXrPQHAEUccETLbDdVqUDLnVeeM7mIAOO644wDk2/x3SlrnSjHGGGOMaQR+0DHGGGNM3VIx1xVRc2/Hjh1D1tLm/BvNOlDXDVEzp9YpYU2BIkx3Oj7tuKo1EWgGZysEIFnCnF3b1Ry55pprhsz6M3mUnc+C1zItkwcATjvttJBZzjzNXQUU3w6BpHVnz6rDotkuaf+v2T4sza+1SbQdRBEuOZrxtbbIqFGjQr700ktD/u233wCUWgUAwLPPPhsys+nYpRxIulHphtbv0nsxj/HTtbbNNtuETls4qOt4xRVXBJDMAFIzOd1Dev+pSzrPfUXvKbogAOCiiy5qcJ5Z9xGvxdixY0OndZ7YdZ1dsqckz/X59ddfhzxy5MiQV155ZQBJF5pmdZ5zzjkhP/HEEwCS87fjjjuGzAykvF37LUX3H11TaVmN+lvH+mpaO4eZdrVKmuv/rrvuClkzALt37w4g6XrM281vi44xxhhj6paK1dEp+0UpVZA1cO3QQw9t8Lf6lqVPh2uvvTaA5lsOmlNHpxwazMkgSQYVA8nAZL5Rr7POOqHTmiBzzz03gOa/pTS2jk4a+pTN8wSSFrcNN9wQQNJil/cbvdLYOjppb1T6lqvB4Msuu2yDz2ltC31TpfVKLR6s5gq0/O2kOXV0KD/55JOh22OPPULWBpysD6N1ZHSuWfGb8wwk1zJr9aTVPmkMzamjQ/Q7NcCYVgCgFLCsVtIVVlghZFb2zstiOrU6OkraW3DWeeh9+ddffwFIBlAzQB4oWZdXWmml0FXyjTmtjg7P780334y/Y70lABg9enTi3IHkntm+ffuQaTHWYGqdS35XXpa3ltTR0XnS2kYDBgwImZZw/f26+OKLQ2YdMg3G1jpQLR13pero6PrVcbNZ8+abbx46JrAApTpzlaydo7iOjjHGGGOmKfygY4wxxpi6pWquK4UmKzVtDho0KGSaNFmPBki6dtLqgDSFPFxXCk16aprUOiusabL44ouHTk23LTXjtcR1pahpMq3pZlF1HJrjuqKsQeNaYl8Dj2mG1QBr1m4CgAMPPBBA0pyeVTOoOTTWdaVwfOqiuPfee0NWN/HTTz8NIGlu1qaDW2yxBYBkWwRt4cHvaO44W+K6UtLaxihZc5L3um2s66op6Pj++OMPAMlgX21Wm2fTTiDddTW18wRKbWP4L5CsvcRgZaBUJ6eoRpYtcV1pbTetg3P22WeHTPe5tktio2AAOPbYYwEkfx/1uC2d10q5rnSutSYeayZpMsRjjz0WMutH5TWndl0ZY4wxZprCDzrGGGOMqVsKcV3RDaJ1dLbddtuQafK88cYbQ6ftBlqaQZC364pkRaWnuRkqaWaulOuqVmlO9/K0jrqaiaR1LIhmBWgGIOt3VNJdpTTHdUWyurNPmDAhZG2dQHR8rNmRVVOppVTKdVWr5OG6UjgvWe6MvOtYTc11paTtf2n1ZKaEYynKNV6prCvtvq511JiVpLWBNBuUrT/yzgpsqetKwxk0247ZfldffXXotF0M5zUv16pdV8YYY4yZpvCDjjHGGGPqlkJcV2lowS+WwGYrBCBpWq9U1HnerquisOsqmyx3Yjmq2X29Ja4rJS3rbEqZVHN8dl21bhrrumqttMR1pZS757LI+/7LI+tq/PjxIQ8ZMgRAsi3LrLPOGnLeLkm7rowxxhgzTVEzFh0NrEurQ5NHMJYtOq2Tllh0WgOVsujUKrbotG5s0WndVMqio6h1h7/l+vtdzcByW3SMMcYYM03hBx1jjDHG1C1TdV0ZY4wxxrRmpp/af9Z7jEC9+5jrfXz1vj7rfXxen60Tr8/WzbSyPhW7rowxxhhTt0zVomOMMfVKuTpD1ex4bppO1vy1lrlq7eevNKdmUF4tdNKwRccYY4wxdYstOsaYaQo2I9Q6H9rUlU1PtanrLLPMEnJezRabitYuSavyndWUtTVaDBSOa9KkSaGbOHFiyGxKm9XgtlbQRrs6J9oFoJbR9cVGpSo/9dRToeOcAECvXr0AlJqXAskGoXnMlS06xhhjjKlb/KBjjDHGmLolV9dVVgNF6rOClmi6UnNrLZoem0LaWLOaSlKv5ky9FvVCuflvTTSnaV/RNOX+TFuLOo6mjL9a6Dlpi5kvvvgCAHDttdeGbsyYMSE/+uijAIB99tkndBdccEHIamYvAo7rt99+Cx0bIQMlN84ff/wRuq5du4ZMN1xrdWFx3Z144omh+/rrr0O+7rrrACTdjUXfc7pmfvrpJwDA0UcfHTo9P67LGWaYIXQ6V0WPheie8eGHH4bcs2dPAEDbtm1TP3fbbbcBAI455pjQ9e3bN+S0ZIAWn2vFjmSMMcYYU2P4QccYY4wxdUvFXFdp5m6NhH/ttddCfuaZZwAAH330UejUDLbGGmsAAPbaa6/QtWvXLuTW4sbJck0x2+PNN98MHc2ZAPDCCy8AADbZZJPQrbrqqqnHqhUzZlNIWytZbpAixtdY16qipmXKWeeubpQ80e9Py0D57LPPQnf77beH/P777wNI3mcrrrhiyIcddhiApGm6ltYhx6rnr1khNJMPHz48dGlj0f1L57cI15WuRe4fZ511VugefPDBBv//ww8/hK53794hn3LKKQCSWWW1NH9p6PrluIYOHRq6lVdeOeRacaNm/RZeeOGFAID77rsvdFtuuWXI/F3U37xOnTqFPNNMMwEo/ndQ14z+Vu29994AgHHjxoVO7xm6uQYOHBi6RRddNOTtttsOQGWz5mzRMcYYY0zdMtWmnuV6YegTK5/Yvvzyy9AdcMABIY8dOzbkxRZbDACw9NJLh+7jjz8Omfn3tOwAwOWXXx7ywgsv3OBcmvLEl3cvE14LrZOgb48MlqPlZsq/5dP/6quvHrp77rkn5LnmmivktKf6SvW6ynpzLWfRSCPt6fzhhx8OnQaD0mIAlN469Tvz6NWSVnNE63TQCgkAH3zwQYP///bbb0MeMWIEgGQwqNZpufrqq0PefPPNASSvX6V6CemYdH2dccYZAIC77747dD169Ai5S5cuAIBffvkldPr2SeuPvkU3JbA1j/svbX1dccUVoTvzzDND5lxkWdb4/4ceemjq58u9aVarl9Cvv/4asl5/7itqEZ9vvvlC5v46zzzzhK4p1oFq9brKsuzSktWvX7/QHXLIISFffPHFAJpfm6VS61O/X70Xq6yyCoDk+lOZn/v7779DRysJAJxwwgkAkr8DTbn/8l6fvNZZ15yB87rPq0eDe02537ks3OvKGGOMMdMUftAxxhhjTN3S5KjIrOZdTz/9NICSWRwAllxyyZAvueSSkBlYNdtss4Xu2WefDfnFF18EkKyD8NZbb4Wc5roqgqxr8dVXXwEAzj///NBpsCddU2o63nPPPUOm6+Obb74JnbpBOnbs2OJzz0LNreqG/Ouvv0Ked955ASTNiTqX5QLT77rrLgBJ16aa2fMMJkxztwLA+PHjQ9Z5IzfeeGPINLNmzT/dbVq7ZM455wyZri+g5LrKAzVna+Aq3aBXXnll6DSYkK4ZHdMSSywRMoPoV1pppQqfcdNQF5K6Bq+55hoAwIABA1L/nyZ11anrgP+v95xey6wkg2qj95wGFqftD1tvvXXIc8wxB4Dar6OT5Xqlm1vnoXv37iEziFddy0WgLUZ0/+B11xYIp556asicH03guf7660N+4403AJTWOVAKB9HjF0W5+2P++ecHkAxd0dAAt4AwxhhjjGkCftAxxhhjTN3SZNeVmvs1U4a1KbRewy677BKymlZpWlPTntbp2GGHHQAkzZHbbrttyLVS80FNdJo1tuuuuwJIlsXW7q0bbbQRAGD55ZcP3bLLLhsys120xHvnzp1DzqN+AudVzfXHHntsyC+99FLIdF1pVoC6MVZbbTUASXfVyy+/HDJrfmgmiJYA15omukZaAs3gatbVrCN1c3Aud9ttt9BphgBN45tuumnotOMw16e6q7QmRlrNnUqh9+eoUaNCvuOOO0LmWNdff/3QqZn/999/BwCcc845odOxMhuwyBpHU37/K6+8EvLJJ5/c4G/1+i+33HIAkq5jugOAUn0hLcFfK3uOouPTe1Ez5IjWaeG4iq7DUg4dn2Y1vvrqqwCStVc222yzkCu1ZzQHdYEyBAMAbrrpppDnnntuAMDgwYNDp791XGtrrbVW6DQDmb+Pxx13XOrx//Wvf4VchBsr7V5Ja8GimcQ6Pp6/W0AYY4wxxjQCP+gYY4wxpm5ptOuKZkQ1C95yyy0h77fffgCAPfbYI3RqNitnTmRZa6BUAnrYsGGpx6LLqGjTuY6JRaoA4L333gOQNJfr+Jh189hjj4Wuffv2IR944IEAgCOPPDJ06pKolDkyLWulf//+obv33ntD1kw3mkz182qGZIYZsweA0jUBShkURxxxROg0a0CzYSoF502vubqj1M3G0uSaCdeccvlZf6f6Sq9hnRN1F2rWCrM5HnnkkdDpWmaJfWb/AaVMCQDYeOONG3wmb9JcjzfffHPImlXGv9U51awjZquoOf3www8P+dNPPwWQdPcU3bFcSctq1L2S63f33XcPXXOLOxZB2lyra4auDy3o2NzieZWC951mp6q7WPXs2r3MMsuELi0rUNecuumYrfX555+Hju5mAJh11lmbN4gKwfnTvUjDXOha1j31qKOOCplhAJV0rdqiY4wxxpi6pckWHQ1a1ABcBtY2tyGjvh0yMOmGG24IndYUYDBkEcF0Oj59itYWF2ltCzSwmMHGeqyff/45ZFpPFlxwwdBV6u1Zn7L1+tGSc/bZZ4euW7duIWsdB851VlM3vknrWunZs2fIs88+OwBg3333DV0e1gG9vnzLYyAjkJw/fbugJUQDBNWiQUtV2vGBYgNXdU7VIqFNH2+77TYAyTpVW2yxRchvv/12g//XZIEi3ph5rfUtVveHH3/8scHfbrPNNqHTOiW0tH7//fehoxUHKNWh0aB4tf7kue/o/ZnWlkRl1usCgD59+oTcoUMHAEkrl1qXiwzWbQwct9a2Yu0toJTYcfDBB4dOrR9FBlmrFVU9HlozhvXDylnpdR9J2184z0AyALloTwevv7a9oMcHKNVn02QQTczJY/5s0THGGGNM3eIHHWOMMcbULY12XdEcpuZcLeFPl4eWvdcA03IBmGoapmlVWxBoYBcD68p1Ec4DNSFq0Nett94aMt0jWkfhzjvvDJkmWTVdandk1kmopAkvra3CE088ETLbHmjQsZYt1zo/nJ8pOoqHTJeHtv14/vnnQ77//vsBJM2teZjT9fw4fjV3q+vq3XffDZntTDQYW+eabhAG5QJJM3KRLQJ0zai7TV1XRGuvaLAgTe7aEVprelTLdZXmGrzoootC984774SswZysU6Xdy7Wm0Z9//gkAOOmkk0KnLWj22WcfAMnaUHmPOa1jtdbhUjM/9xJdv9r9eamllgJQCioHkokBWh8oDV2/1XJzpe1PF154YcgaGsA6ZVpbrFbqHLEGE5CcS0024f5Ybn/XeVA3HmsKLb744i0615ai56e/32znwHkCkns9g+WrGSBvi44xxhhj6hY/6BhjjDGmbmmy60rp0aNHyF9//TUAYP/99w+d1oHRv2XXXa3toa6N5557DkApuwpIZsvQTMtS2kDx5cy1jgPbVahpT8t981qyngAAHHTQQSHTDFhJcx5Nw7/++mvqd9K0ryZyrfOgJuy0tZDWDuPqq69O/S62iKhm9g7PWdeMtjjQ9fPdd98BSLpztAUGawapa07rfDCDoIjslqysx7Q6MGpO1rYBHL+65pqbTdkSdE3RDax1kPQ81LVGN3BWbRVmaL3++uuh0wzBddZZB0B625pKoteUmW5aj0v3RN0r6ZLS66Ml9Fmna7311gudZuCx1pm6o9Wdxe70QOla5I2uT+71rMcFJN2wRx99NIBkJlnR+z/JqrekdXAae//osT755JOQWUeomq6rtNpN33zzTcjaooN1qvT+UTck6+TpnKVdk0ruObboGGOMMaZuaXJTz7QATyA9QOzSSy8N+fLLLw+ZT7f6lqLHZTNJDYzVOh+ssqtv50WQFkAHlIJttXKnBkuefvrpAIBevXqFLo/Kx2nodV5llVVCZmDmCiusELpyVhw9Zw28O+WUUwAk35K18iwDl/N+C9P5SXvTynr7YuCgBtNrU0u+vdAyBSQDX2npqfWmkHr/cc0CwLrrrgug1PwSqN75Z73FseaN1qNS1GLBN13923HjxoXMIGutA8YEACBZEyrtXFqCBm1qtXC+5WqjWw0G15pBnDdt6qh/y7d/nVNNhnjhhRcAJANkWdsKAHbeeeeQaRXKY09Si5Q2E2ZihFoJTjvttJBZk6aWKjxzLxs9enTodK7Vo1EOfm7kyJGhGzRoUMgMLNdqwlrzqlLXRe9F1n5jIDEADB06NGT1FNC6qskuan3ivZSVtMHv1d+Hllp3bNExxhhjTN3iBx1jjDHG1C1Ndl0paW4sNdGpa0RrQrB0uboGtN1Bmmtj7bXXDvnhhx9u8F1FB0uqyZImRW3rwEZuQMllVS13lR5fzdVqeuQ1K+euAtKb7j300EMhM6B5+PDhodNgwjyDdHUdqGuGNZnUndapU6eQWVYeKJlhNVhX1y/rRKi7Tt0kdFNqbaGiXVf6/TSNM+gfSAY7sjWHNqIt4vx1rh5//HEAybWjwY4axPvAAw80OJbWlGGtE70XNHGCNZPyDkDWFg4MMNVr3q9fv5DXXHPNkBkGoAHI6iZhLTOtacYAXqDURkPdRXotFllkkZDznHc9tjYFfvTRRwEkz1/DALhvFu26Smt7oK2K9PdB3dhpgb0qs3WCuqa0tQSTWDbYYIOWDaAMek7cHzQERcMxNtxww5AHDBgAAJh33nlDp3slkwH083qt2I6FAfpAsimvHrexa8AWHWOMMcbULX7QMcYYY0zd0iLXlUIzpLqb1Fyn3UmZ2aNmJ5V5DDXHajl2drLVz+jf5mluVXeTRpqfeOKJIdP0xuwxIOm6KtL0qtemuVlPNDOOGjUqdJpBttdeewEAVl111dBVa6xqAtU6D3RNqDlU3Wlac4XZHto24I033giZdZx0TZ9xxhkhMwOiaNO6oteFLRCuvPLK0LFtAADstttuDT5TxFj0XuM1zbq3OSYguS7T4Lg0U0vdNSSPfUTvOXXtc/1o9pN2vO7WrVvIdKk25V7WmjM61+XOsdLXQOeU7jogmc3D3w1mbwKl2mtTnl+tQDePulV0HWoGGe8lda1ruyD+lrz//vuh09Y1vXv3bvD9ebuW6dqfccYZQ6euJ23RwfPXdaYtdpjtqO4sha5ldVOrG/uwww4Lmde93Pht0THGGGNM3TLd1J6EJk2aVGgEpVqEnnrqqZBZs0eDDrXmh1p3Mo47HQBMnDix0ePjW6C+TfTt2zdkDdJiHQqtI6TWgbwr5rZt27bJ4yuHvt3zTWSnnXYKndYkYYPEvCpXc3zl1qeubQaGs2ElkKwzwjoRQDLIlej6okVy9913D502TWyp9YPrs5L3n94TrLi7zTbbhE6tO5zXvN6cp3b/ZdWm+s9//gMA2GWXXVL/Ni0ZQd84tc7IEkssASBZebdjx44ht3TcjV2faU2Jy40JKN5S2Jz1mVYbRavlauA1A0+vv/760GmQdt7jb8nvA70NANCzZ8+Q1dLDOjNa2+mVV14JmdYrrby+2Wabhcwkh+Zeh8auzzT0nmEFZCC5f7I+VFbT7QUWWABAsg6ZWtS5V2211Vah0/1XA+fTnl84f4otOsYYY4ypW/ygY4wxxpi6paZdV+pC0KaebJqprhF1I2iwEo+h42ysaTLNjD5kyJCQta2BngtLX9NEB1Q3gK5SrqssM/qwYcMAlOqtAMB5550XMhu7agBZJWmO6TWt3LiaWzUwLq3ORZppNa+gzTxcV3quJ5xwAoBko9m33norZLZxySvAsTn3H89fg/4vu+yykLVmEV1WG220Uej69OkTMt0IWrtL95qWjrslroHWQHPWJ+8ZDVrVRqxa0+fmm28GUGqLAVS3QW5zXFdpTZO1bYOGNrDmFlutAMnEhh133BEA0L1799TvKmJ9prkex48fH7ImA7CmnN6/uv+yBZQ2OtUgZ5KVrFRu/HZdGWOMMWaawg86xhhjjKlbatp1paYvNV2xvoJG7au76oILLgi5Uq4rfl5Lxavp/5xzzgn5iCOOaHDO1aRSris1N7K2AVBqx6GR8JrBRDN1Xq6PSrkGdHxZ2T6kKabTllIp15WO77vvvguZ86auVbojgVKGYNGuK4VjUXeougnS7lXNtNKsnTQzfCXHatdVQ3jN1QWVNX9swVGt2mhT0pz1SfSeyxor9xJtO6MZxvzNyivcoSXrMyucQWWtlZQGx5+361+xRccYY4wxdYsfdIwxxhhTt1SsBUQeqDlLzWHMGtES6VpcSM2ALXEfpXVn1yJQm2++ecia4VF0p+o8UDMsC+ade+65odNrXosl2tMouvBa3ug6VDP58ccfD6BU2BIAOnToEHItXheek2ZHaZG/NLKK7NXj/Vnr8JqrO0ozGRXOVWucJ11n+pulBWPTqERrnmqg55k1P7V4/rboGGOMMaZuqelg5CwY8KVPzPoUWe6NtCXBZvqdWYFnRb+J5FFHR8fEmjPaKDCr3HceONizcaRZJIGSVUTXrN4z5QKzW0pL7r/WgNdn68brs3XjYGRjjDHGTFP4QccYY4wxdctUXVfGGGOMMa0ZW3SMMcYYU7dMNb188uTJdWnuadOmzTQRbFbv46v3YLp6H98///xTl+Obfvrpa+L+SwtAB0pB6mx+2lR4/02YMKEu569du3bTxPqs9/1FsUXHGGOMMXVLzRQMTEtPrtf4Ib5pZaXx1uL4dX5I1vnV0nk3Fp2LtLGmUW78rfE61APl5q8WCyJWEpbAmDBhQugGDRoUcvv27QEAe+yxR+jyLinQUtLmtN7nMWsdt/b9hWsta3x5FIy0RccYY4wxdUvNWHR++umnkFkmXLsP1/obRzn0/Fmo7bfffgudFiJkp+W2bduGroiy2vpE/fPPP4dMf7+e3xTd4RvoavHtQ+dk4sSJId97770AgLvuuit0P/74Y8hdunQBkGyB8a9//SvktOvTGt8+tVy/vn1xLFpwsGh0Ljl/AwcODN2AAQNCXn755at3YlVC54dr9fLLLw/dOeecE/Jmm20GANh9991TP1/0vZr2xv/nn3+GzHWnvw+tHR3rpEmTAAC///57WdjSpQAAIABJREFU6t/OOOOMAEq/E0Dy96EWfyv1940FZ3X/pA4ATj31VADJ+W3p/mmLjjHGGGPqFj/oGGOMMaZuKdR1RRMdAKy00kohr7nmmgCA6667LnRZbpJaRs11aoY86aSTAAA33nhj6LR79Iorrgig1GUaALp37x5ynm4QNaE+9NBDIR922GEhL7roog3OSU2n7G6upnG9FrUyfzrW119/PeRDDz0UQNIErPJHH30EAHjjjTdSj8v1e8EFF4SukmbYPEgb65tvvhm6jz/+OOSll14aALDEEktU6ezS0fn7/PPPQz788MMBAD/88EPodP0OHToUADDvvPOGrhbnpBw6ft1fTjjhBADALbfcEroFFlgg5DPPPBNA0jVZ9PjT+up99tlnoTvwwAND5nnfc889oaM7Byh+LI1F98Fnn3025Pvvvx8AcP3114eO4QAAsO666wIALrvsstBpJ/ha3F81NIC/ay+//HLoNthgg5DpptRwgBafS8WOZIwxxhhTY/hBxxhjjDF1SyGuK5q0Ro8eHTrNQKKZrhajxxsDx/frr7+Gbu+99w75scceAwAst9xyoaM7CwD+85//AAC222670D366KMhd+3aNeRKZWPxWv/111+hGzJkSMjffvttyL/88gsA4NVXXw2dVl694YYbAACLLLJI6NZee+2Qa8W0quehriW6EdXcqjLHqpkCs846a8i33XYbAGD22WcPHd0FQO1kuOj9pfN+5513Aki63saOHRvyscceCwA466yz8j7FqaIuiptuuilk7iXq7lbXTWvP1qEbePz48aHr06dPyMOGDQOQzMrp3bt3yHQ91sp9CCRd259++ikAYLfddgudupb32WcfAI2vd1Vr8L77448/QtevX7+Qub/8+9//Dp266R5//HEAyXuyY8eOIdfKvOqcvvvuuyHTNffiiy+GrnPnziGzYnclM41b50oxxhhjjGkEhQYj6xtZawkgyyKtTg6DAgHgu+++C/n9998HkHyK1cBA1rmgZQAAnnvuuZDVElSpp16+BWhQ33HHHReyWie+/PJLAKU3L6Bk5QFKwWSvvfZa6NSiUyvom49ayXittbLsSy+9FPIyyywDAJhnnnlCp9YDVqF95JFHQnfwwQeHrPNe5NuXWuE0sPqoo44CkFwLs802W8jt2rWrwtmlo2+JX3zxRcgaeMt7Ue+pXr16hUzrWxG1qZqL7i8M0lUrMGsHAcCCCy4IADj55JNDt8MOO4TMa1j0nptVx4pzOXLkyNB169YtZFpHdR22prnkPT/zzDOH7sEHHwyZ86P764gRI0LWIPtahPOqHo2ePXs2kOeff/7QaU0uft6VkY0xxhhjGoEfdIwxxhhTtxTiuqJJivVWgNqsA9AUNDCOJkcNIFY3FOvQqLlOx8xjaTChBrvmiZ6HmouvvvrqkGmSpAsLAPbaa6+QWXOl1oMFdazqEtH6KoRzpp/L+vziiy8OALjwwgtDN3jw4JBPOeWUkHmNqrnmea50oQKlAHKglAygY2KAIFCMy4PmbD0PvabffPNNyLymOo+sTaXUetNEvf7qOmVNoDFjxoROa1pdc801AEpBx0ByLEW7rIhef91L+vfv3+Bvdf7mnHNOAOl7puqLnr9yqOtO3VisicRWCEDy+jAYW38/a2VOgZLLWOvEaQsdNpPNOuc85q22f4mMMcYYY1qAH3SMMcYYU7cU4rqiyU7rsGgkeWusn6PnzNYWWrZbM6XoskrrCA0Ad9xxB4BkJs+qq66a+rd5kpUVxzokSy65ZANd1udrnXKd1suNReeS2Vp6HK25U4RJPe2eUheBZu2sttpqAIAPP/wwdJrBtNNOOwFIulbyznrh9f36669Dp7VzdHxzzz03gGQLGXUNMAPygw8+CF379u1DZmuLIrJ6dBy6J2qdFdYk0TpbWvOINYNaUyaStgPg/bP11luHTseva3HKzwClfTOtrUQtwPtGz0mzaplNp1mrRx55ZMh0adVSWyTdC9iORfcXzbqia19bQOWNLTrGGGOMqVv8oGOMMcaYuqUQ1xVNqlrki0XmgNbpukpDzYmaYcVCbVpQScvpM2siLVMLKN4kzXGpO0ZbCPD/55hjjuqeWJXRdaoFE59++mkASXfJxhtvHHLRXaN5fk8++WTo1LVK188777wTOhYRBEqunSLOXQsbalsSvdc22WQTAMAqq6wSOpbNB0quAe3OzkweANh2220BAKeddlroNCs0j/2Jx9R7W91RzzzzTMg0/R9yyCGh0xYXRe8PjUXXj3bvZsFDzVrUFgd0eTzxxBOh06xQFlzdb7/9QqeulTzdPFmZYLpmPvroIwDJIoFnn312yNxXtcjjiSeeGDKzcYsODdAx6Zq78sorASTdiVp8Vn8Lq4UtOsYYY4ypWwqto8N6AaqrB/71r38BSAYj33zzzSHzSfz2228Pnf7tww8/DABYffXVG3ymFuCbigYQap0H/v/6669f3RObCmlv4fqWp6TV4VA5raaLvn0ysFffyHQuq0VWMObzzz8PIFnbQq1MbDGgFqktttgiZK7VvN/M9Py5V1x66aUNzgNIthDg59QixdojQKmZIu/TKT9PS/OoUaNCN2DAgJDXWGONkCt1X6Y1etRGiDp/DNJdc801U4/FdZ01/5SL2FP0nuM6A0qNjoGSRaZTp06h03Pl+t1///1Dp2v5lVdeAQDsuuuuodO5zhMd37hx40I+44wzQqYlSluYaDIH9089lq71Wvkt0PWl1n3+fun6/Pnnn0PmWKpp2bZFxxhjjDF1ix90jDHGGFO3FFpHR811rTEAOa1jOQD89NNPAJLBWH379g2ZJj2WwgaAgw46KGTWR6gVEyWQHCvHddVVV4VOA6uPOOIIAEnTc1qQXt7j03Pmd+k8aU0W1bNrtwagppmOtU7LtddeG/I222wDIHl9tFN4kS0UgFLXYO1Orm5knp92v2YALFBQMOH/XzPaCkXXlNa8oRtKa4/89ttvIetcEr0+vP9ef/310GnNL3VdVQp+v7qA1fWmbkTW1NIx6/joxtE6JeoG4brWY5arI1Up9DqPHj06ZN0/eH31/PS6cH/RANhzzz03ZLpOilinOj7WawJK7jagdP/tuOOOodMWFwxSfuihh0K3zjrrhLz33ns3+K4i0O/Xmj9sATT77LOHTpMxWKfrkksuCZ26FvPYH23RMcYYY0zd4gcdY4wxxtQthbiuaEbdcMMNQ6cZSK0lA0vNwXfddVfIdEMttNBCoVMzLMc633zzhU7NzLVYB0PHyq7Xas5XN8+ee+4JIOka0fGx07R+plKdzvU4dCECpdoOmh2g3XU1g4rnpW03lllmmQbfpbWPtBz/5ZdfDqD4TIms7uqsEzNkyJDQMVMFKLX2oAuuKPT86U7KqhejLRzYtVvXwltvvdXk79fPp7UdqCQcyyOPPBI6zVTp2rVryKx5pJlKupbpclN3EGuvAKX6Qttvv33ottxyy5DzaC1AN4ceT9ecunZXWmmlBn+rrivWT9I6Q2uvvXbImuFaLTg+dZd169YtZJ0rumlmm222Bp8HgK+++goAcOedd4ZO3WBci7X0OzF48OCQuT7ZyghI7rv8fRg+fHjodC3mgS06xhhjjKlbCrHo8O2WlgGgmMCxpsAnbn3Le+qpp0LWYGIGcWplTj7FAqX6HKxAC9SmFSsrsI4VY7UaMN+igdLby0UXXRQ6faJnELBWq22pRYef1wrNhx12WMjDhg0DkHxb7dGjR8gdOnQI+Z577gEADB06NHRaxZTfpW+hrP0BlKx3Ra/prMqlDIxk0CCQDBy/+OKLASSr0RYdGM9rvvzyy4dOr68G/jNwWu9JbZrI9ZdVR4nXaq211godqy0DlQsCTQuWf++990Kn56eV4/v06QMgGQCqFi1aUjWAXBu03nfffQCStWfUuq5BztWqIqzB8GwKqeev1tP11lsPQLK2ExtdAqUgWLUo54GuP86f7gk6vs6dO6fqic6vzivRsRR9L6ahvwW0COv+oWuZlnK1WOaNLTrGGGOMqVv8oGOMMcaYuqVQ1xWbmwFJ0zr/v5bcOTSdv/3226Hbd999Q6Y5GSjVedAAxoUXXjhkBmltsMEGodO/rZVxq7lRm+6xKaSauD/55JOQaWZWE6uOifWT1HSfVa6+sfDzagJ+9NFHQ6bLSus5/Pvf/079Wx5LzdBpbih1FygMbFZ3XBFzqmtKzeGHHnoogKQbT2sCsc5TLZnIOSca9H3//feHTNckUApS1Qagui7S3KQ6P9yLtGnmYost1uD/W0pasHjPnj1Dp00rtYEpA3M1QPnYY48NmS7JH374IXRjx44NmW4Q3b+0PlEe886x6n2u56+wzceYMWNCp01NWXPm4IMPDp0Gm3MtaAB2S+eM560uUnWnMYBa16deR5XTArM1GefFF18EkHSt615Vi+hYuG/q/qNu8oEDBwIAbr311iqdnS06xhhjjKlj/KBjjDHGmLqlENcVycpeYM2SrAj2aqHnx67CvXr1Ct2mm24a8gknnBAyzdDMHgCSpnXWJ6lU7ZhKou4qNQeff/75IbM+jM6Junaoz5q/7t27A6iua4ffpdknanpWlyRN3ptvvnnoNIOL10UzPY477riQWQejd+/eDb4fqHxtEj2+fo/WOdJ2CMy2YXYZkOw0XEv1OQhN/+ou1HopagY//vjjASQzrdSNQdLakgDAbrvtBgBYd911U/82T1jjBgA22mijkHWueF+pu/joo48OmfWjNJNJx0/32A477BC6lrqOG4u6cHT/VDf+iBEjACTnT2tSsf6OutvU9cowgUq64Hh99Jpq25eRI0cCABZZZJHQLbXUUqnHYn0jra2jewXr62g4xLzzzhsy99qiW0AoWt+KrT209tGJJ54YMjMndc5z3/9zPboxxhhjTIEUatFh8z0g+UTHYDN9itfKtdUirbKqWjk0GE6D5VjRUoMht95665Bp/VHrSa0Efuo5PfDAAyG/+eabIbOyZ1adCFqstPmhWnf41lpJixbPRdeM1obh24U2EtRrzkZzQOntI6u2CN9Ixo8fHzqt0kqLwrLLLhs6fTuvlMVE7x9WntZqs/qW/9lnn4XMYHnWIwFqJwC+HHqeWllWa+ZwLWq1VrXk0RKgjQTVYscqwdWsbM3jawX1fv36hawNEjUgluiaSrPuaU2gnXbaCUAxlbv1e3RMap1jxe5BgwaFTsdy3nnnAUiuX51LUsk1zWNpI2oNEL7pppsAJAOIu3TpErLuq+PGjQOQDDDXwOwBAwYASFZ71vmtJUsOOeaYY0JeYYUVACQD+NU6essttwBIVsbPe/3ZomOMMcaYusUPOsYYY4ypW6abmnlv8uTJudiz+Z1XXXVV6LROy2WXXQYgP9NWmzZtpgOAiRMnTnV8ai6lmXH33XcPnQbLqemUJk11bbHRGVAaf17murZt2zZqfGlo7QOt46E1NxjsRxcIUAowBoAFF1wQQHaJ/ZbWSeL4Jk2aFAegOVev6aeffhryBx980OD/VdYGnvPMM0+D70wLXFVzsgb+cq1oYKk2cC037zPMMEOD8U353UAywP3CCy8EkKxXoWXjNVie67OosvIc3z///JPL/sJ1p00ttaYM3ZDqJtL7l9e4uetz+umnb/b9p+hcZ91LjSVt3bf0/pswYULF5i9trCeddFLotEEvfx+yAqhb6rJq165dg/XJY+p5ajgFa4vxXyAZzqB1nNjaQt3lbLQLlAKas+qQtdR1xfWZtr80hazr/8477wBItoXQvZD7Tl5JD9xfFFt0jDHGGFO3+EHHGGOMMXVLIa4roqYrrcNC03JemSCNdV0pNNNpF241R6rLh2ZwzTTKMkPmQUtcV4qes9aP4Fi1joWSdx2WNNdVGjon5TK89Jwbe/5qutXvor45xwSm7rpSFwa7UAPAGWecASBZb0Nr+6TVhCkq0y9v1xXROU8z9+t9WMlrUSnXVa2Sh+tKSWu3oOT9+5DmuiJZtZd4zvo7puev64u/C5rJqcfKe/+slOtK0fuLe5Tq0lo85YVdV8YYY4yZpvCDjjHGGGPqlkJdV4kTqVIJcqB5riuSZQ5Pi/ovqghbpVxXSprrpyjXR2NdV62VqbmulLQWEFlrrpJZKS2lWq6rorDrqjJkuZvz3nem5rpqCuWyo4q6D/NwXdUSdl0ZY4wxZpqi0BYQStFvmY2lVlo1VJtpddy1jN4ztdiI05iW0Nr3nNbymzYtYIuOMcYYY+oWP+gYY4wxpm6ZajCyMcYYY0xrZqoxOtXMuqomzLry+Fon08r46j1rp97HV+9ZLfV+/+U1fywqyJ54QLLQZ0v7mpVjWpk/xa4rY4wxxtQtNZN1Ve+k1d/JqwR9EWjNCMpZdSSKbEGQVcJd4Xm1tEtwrZM2Zypn1d5pLe7ucuNTqtmipVI0Zf5a+/5SjrTxFz1mvf5qpfn5558BAPvvv3/orrvuupA7d+7c4POmZdiiY4wxxpi6xRadHFGLwaRJk0L+9ddfAZSafwLAzDPPHDJrohT9RlIOfUvROi5sADpx4sTUz3Gs2tSuWuicfPPNNyHfcccdIR955JFVPadqkFZFWdfXX3/9FTIb1+r8ZDUgTDt+0es2rUr0L7/8EvJvv/0GINmIdY455giZYy16HFmkWUwfffTRkG+66SYAQJ8+fUK3+uqrh1yPNZd0/XLfad++feiKsM6m3XMAMGbMGADABx98kPr/pvL46hpjjDGmbvGDjjHGGGPqlpp2XVWz0WcloRlSzal9+/YN+ZprrgEA7LzzzqHbe++9Q15rrbUAADPOOGPoasmMTpfVl19+Gbqbb745ZJrOP/nkk9DNOeecIV9xxRUAgO222y71+HnOtbor7rvvvpDfeOONkNNcO62RrPvnvffeA5B0dzz11FMhv/zyywCAZZddNnS6PinTRQmU3F0AMM8884RcrWuopn8Ge954442he//990Omm+rJJ58M3YYbbhjyueeeC6B29x+eF+cRAA4//PCQOX69/4YPHx4yXTqtdX1z/H/88Ufott9++5Dpkh41alTo9L4vAnXj002ua07Ty2tprU2NWm1amoYtOsYYY4ypW/ygY4wxxpi6pVB7XlPqXKRlddSS6VVN55MnTwYAXHTRRaEbPHhwyDPNNBMA4J577gndvffeGzLrK5x//vmhK9qNpeOjy+Loo48O3SOPPBIyXVKnnnpq6FZaaaWQ559//tzOMwuuH2a8AUl3zb777lv1c8obvWe+++67kPfYYw8AwDvvvBO6ueeeO2TOz9tvvx264447LuROnTol/g5IujG32GKLkPNcq7omP//885APPfRQAECPHj1CR3cUUHKtvfbaa6HbaaedQmbWHccJFJ+plLb/0UUMAD179gx5kUUWAZDMurrzzjtDPvDAAxscs5bcDOWg6/yll14K3X/+85+QV1ttNQDF18HS79esW7qGTz/99NBpBmst/a5NDVZ4BtLrxKmu6DpctugYY4wxpm6pmkVHn27TenloMONXX32V+HfKz3fr1g1Ask5CEW9cek76/QyCvPjii0O39tprhzzbbLMBAJZaaqnQ6dvJVVddBQDo0KFD6E466aSQs56U80S/89NPPwWQPOezzjor5F69egEA2rRpE7pyb1d5zx/P/8cffwzdc889FzIDpLPQ68yx6DpOs0jqmFSu1pumfs9ll10W8tixYwEkLW6HHHJIyLyvNID34IMPDplBnksssUToOnbsGHK11qR+j1p0aMlYd911Q6drkW+iWjtnwoQJDf6/aLLWCa0XX3zxRejUusPzv//++0NHKwIA7LXXXgCStZFq3aKj+w+tkwMGDAjdDDPMEDItzTrnRVhJdP5YuwkAvv/+ewDJ+yfNulZLFilev7PPPjt0uv8vtthiIfO6b7vttqFbY401Uo9bLWzRMcYYY0zd4gcdY4wxxtQtFXNdpZUlV1nNwQxyHDlyZOheeOGFkEeMGAEA+Prrr0OnpksGU6rpcpZZZgm5CHeOthOgm0lrI1x77bUh04yppkt1qWyyySYASi4sANhss81CXmWVVUKmSbEI07PWptCxtG3bFkDSXKzzTzeD1nFZYYUVQmYwIZDeoLAl6Dzp+ae1o8hyt9LN8eqrr4ZOy7kz4HnzzTcP3eKLLx5yEW7Wxx9/PGQGq6q7aq655gqZ86Zzsueee4bMa0EXrOqAYlxX66yzToP/1/Wn15znqnWuahHdXzSI/qijjgKQnBN103Atq+vgrrvuCpnJEkW0YGkuei3oktPaQFtvvXXIG2ywQfVOrJG88sorIS+99NIAkvdcmmu8HM35THPh/aMJNhpuosHgnCutrXbDDTeEvNVWWyWOWQ1s0THGGGNM3eIHHWOMMcbULU12XWWZ82mG0uwFrVNx9dVXh0wzF6PPgaRpcqGFFgIALLnkkqHTDIMHHngAAHDGGWeETjuB52k61/FrWW89F5rEtcXDggsuGDLdODrmzp07h8w6FzRRA0k3ycorr9z8ATQTvaZs5zD77LOH7s033wyZLhut3aIZaJy/3XffPXSagZZHfQ+6MbTVQ5cuXUJWNxa/X02zt956a8hDhgwBkDQ96/yNGzcOQNKcq+On60jPK2/oDgWAK6+8EkDSjadZUxy3thgYOnRog79V11zR3ZebYwZXd47WqSqyXYCufbqYgGTNFWa4sF7QlJ/jPbPwwguH7q233gqZbvJZZ521QmedD7qmdK5OPPFEAMk9Y9CgQSHzt6CITKssd9Lo0aNDZkiDrjn9XFoLGpW5PlWXhxtLj8nf+lNOOSV0upfqXv/6668DSLpb1XW+zTbbALDryhhjjDGmIvhBxxhjjDF1S6NttDSHqblszJgxITODZtiwYaHTSHPtNEuXx3777Re69dZbL2R2ddUiS6uvvnqDc6pmplFaWWstwnX77beHzO7jLCWvnwfSCybqWJihoyb0osuC6/lx/jSr7JZbbgmZ7kvtHq3F2fr16wcg2XFYr2seY+X5ayafdt/W76dLla04gOScHXHEEQCSmXDMNNNjqetKC+5p6w+a2fNYy3rM448/PuR3330XQLKFh87FY489BiCZ1aLrlxlO2h1bi3eqG69WSHMJPfTQQ6HbdNNNQ+a6LuKe03XG7FMg6TpkBidbyQDprguGAADATz/9FDJdkuraqiXKuY6JulG0HUmRBR+z2j589NFHIXPf0Kw3XWvMStZ9QkNCuC+p6z1vOC5tBTN+/PiQL7zwwpD5u6j7gLrOW7rXNSe0wRYdY4wxxtQtjbbo8C21f//+odMAMAY26pvtfPPNF7JaN9j0UYPJ0sqR61OuBs7xjVsD7BZYYIGQ83gT4/g1gPrkk08OWd9oWUdHdVmBY2mk1cYpok5OVlAbrztbCQClAFygFHiuwdTaNJNBvDqmvN+eORZdkxpMrMF0XKsabK0tIlizSd8c0wLrunfvHrKOVdctW4PkMb86f/pGz3N9+umnQ6fWJ96LW265Zeh0Lmlx1PnXdi1s0VJLpCURqEWPVmSgZFXRYOC84Xf+/PPPodOaJWp9WX/99QGU3x+y6lhpkGitkFZzTQP4tebWscceCyBphayVth1qGVYrzocffhiyBpYT3ROYxKJtg3Qud9llFwDJRspsVDvl31YqMJnj+vbbb0PXu3fvkNU6yrWs12LFFVcMOa3FRVodrqwWS83ZK23RMcYYY0zd4gcdY4wxxtQtTS4YofVs1JzEss4adKR1UrRFQ1pZfzUT04ylpmU1mdEk9ssvvzT19JtEmjlV2zJoMLK2o+jRoweA8u6qLNPcs88+20CnNRfyqDOThpoe77333pAPOOAAAMkS+hqYzMBB7VirFNG2gtdM68Wo60VLyLPOzSWXXBK6mWeeOeQ0M3lap1/9jLppNYhfzdOVQAPY1Vzes2fPkJkkoNdC76V99tkHQDLAUF3LXBefffZZ6Gqp+3U5cz2DebXjss5/NV1WU6JrQ+uUHHPMMSGz3UNWHRKO/8knnwyd7h90gxXdHVvRveaHH34AANxxxx2h0/W1/PLLA0iu9SLaqqSh1/TPP/8MWe8fupk0wPiwww4LmXXU+vTpEzr9LTnttNMAABdccEHo9PenUvOa9vvXq1ev0LEeGpBcX0SDlU899dSQmayj+6i27WAnek1geuaZZ0L++OOPQ1522WUBADvttFPotB0KsUXHGGOMMXWLH3SMMcYYU7c02nVF09m5554bOu1+zJoN6qLKct2Uy7ChyUwzQfQz7JqsHaG1ZkGlOimr6Y5ZZSyfDwBrrrlmyOqm43dmfTePq6ZXbfFw0003AQC6du0aOs0KUSrtMshyfajpnDUrtIs6OwoDJZdjVvf6IqBpnGZvINkCQWtSMMNKs670/MuZhnkNNRPpxRdfDFlN0pWC49PvpAkcSM4l649o7RitSaIZaCTtXtbu5Uq13FhZbSc4V2rC1po/vIfzMPc3hbT747rrrgud1pHROmNp6LVg5tZtt90WOu1Er611agVdX9xLNGtJ7xmGSRRdW6wpaE04tuDQrE9187BFAl04QHKsvK+1Npa2I9LPteReTGtLob/56rrWFkDMvNZz1jpIlPV3Wvda7iu6P6ubT2VmNutvsbqk4/wbaIwxxhhj6oRGW3T4dKcWG7U48OmtuW/uaklgQN75558fOg0222uvvQAk63Xk8RaZVgVZ61xosG1TmorySVYDuzUYjUG+J5xwQujybgTJsWqA8TnnnBOyBqvS4qTNRTVYjA3sNtpoo9BVK4A6C36nvu2wOSlQeksEShYrvc66FtLqPOj/801cLQYarN2pU6cGx2oOaQHQZ511Vuj0jUsDO2kd0Gtx2WWXhcz6OayQDJTeMoHSvag1iVTOMzBU9wm9fzRJgFYtrcb96aefhszA44033jh0RVsHuA60Tpfur2lWmCwr1MMPPwwg2VRZrXvcw4ses84EThJ9AAAgAElEQVTl559/HjLrzOhb/p577hkyg/yLthKnoddUGzlrYO3dd98NIFmZXPciBpFr7ThagYBSnTK1CFXKi5EF19q6664bOq1zxEarQClxSL0suifQCqMWG63txN8aXR96LTTwmNYdtX6mYYuOMcYYY+oWP+gYY4wxpm5pch0dNc211PSZFax6+eWXA0gGEGrgMWuCaLuJlpaILgdNY3qeSy+9dMjqBuC5ZJW4pmmdpcyBZM0MBpmpia4pLSSaA10v6g7QRoKrrrpqyKutthqApGmRZemBUruBLNdPEXAuNChQTad33nlnyAzcZL0HIBl4y3GraVbnj6X71dw8ZMiQkHXdVsp9wDWnpdh1fakbkX+r9WK0ASRrYdFEDiTrzBCd0zQ3WiXhd2kgoo5P65TQJbD33nuH7p133gn5pZdeavAZncsi0WundZj0/CjrnvP666+HzBY06u7Sa8Ug7SJcV7pO9P7QBsB0XWiLHd1ra9FlRXRv7tChQ8hs6wCU3KxaW0uDabn/a4KAunbY+kITY/T3JY8WEGkJNnrOWrOJ4R0a5qF7Dds1aR06DZkguk9qzS916aY1yE7DFh1jjDHG1C1+0DHGGGNM3dJk11UlUbOTuhG0qzKhuwQoZXjk7c5RaDpXU6CaE9WcytLf2sJCXQrXX389gKS5Wbt7H3fccQ2+q1pmZnVHaVnvtE7JqtNy5zTJ6rGKzvAg6m7R9aduQma7qWtDXafUayaaZlVst912AIDddtstdHPPPXfITanJ01h4nM6dO4du1KhRIWsLFWZ46HfrXLGm0IgRI0Kn3c9pktc5V9M0syqAys075027PD/++OMhv/vuuyG/9957AJKmc4X3nXZULjorkOPTTEbNOj388MND5vrTNTVs2LAGxxo4cGDo1PRfpOtH15nuiZotyGwlzbSqxXYPaWR1lO/bt2/I3Dc001azrpgVp7WPtEUCQzf0PtNrUq2aUHpva80qrkvtqK7wumy22WahSzvnrPswbf7L3bO26BhjjDGmbinEosOnN63mqG8ffDvTtxDWzgFKQXh5v3npE+vqq68OIJnP379//5C1jgzrl2gwtVp3+Malb/xah4BP9NV88+JTMgPFAGDXXXcNWeus8O1Ea/ukBVMrRTd9TBufBqtqg0/WudEAyHXWWSdkWi/UyqNvX3y7yQq2z+ONi9YpnTOt06FWUlbZ1msxbty4kFlzR62oWtOE56+1abSasgY+V8qiw/Wjb756/meffXbIrKg7ePDg0Kn1h81a99hjj9Bp4G+11mpa5Vmtd6M1dVh7RfVqcdT6JrwWarEq2grCNaP1TtQ6p+uEdZy09lStWITLkWWF12QG7qW6J+ha4DHU4pwW+F/0nCpZlqypUc05tUXHGGOMMXWLH3SMMcYYU7dMNzUz0+TJk3Ox4dIk9/bbb4fuqKOO0u8FkCzLr/9frmlmOdq0aTPd//+eqR4gzfWgJmQt8c+2B0DJ9aQtIrRp5Oabbw6g5A4Dki0kWmqSbOz40lATqZZlp7kfKJWY/7/snXXYFdX79ZfXq4DdBQZ2i4UtInaAAYiYgIkImNjdYqOoKHaLHSi2YmEXdgsWWIhfUX/X9f617rPGZw5PnTlznmF9/uG+bp5zzuzZe/bM3Kmui0suuSTkAw88EEB2ZcmbMj7+vo5PA4i16SXnfZVVVgndbLPNVue7GtOotjFwfNOmTWvw+DguDZAeOnRoyKNGjQqZ86rHr+Oj60mDJdV1x89p7Qut86JurrTz0rp160aPj3Oi36cByNoUl9eXBma/8MILIffq1QsAMGbMmNBpzZnmXn8c399//92s608DvHWsdI1rMKq6qajPKlmjVatWjb7+OC5N4OjevXvIupc899xzAJJtRarp5uD115T5U9LcUZWkqd/flPlrSXD+FFt0jDHGGFNY/KBjjDHGmMKSi+uKZjY1d6dlGGmJc62j0FwzbFNcHzxmNRGq60OzCeiy0awIrTNAvZrIs3B9NHf+1PWkc8Wxqk6zCvi5rLJXmjO+tEwXIL2UeLn5ybpORVNcV0THpMevazWt3YF+jhlIun7T1qeeh8bUfGqK6yoN3RMUznG5djVsA6LuOt1rmrtum+O6Usq1kOFc6XFmtZek0RzXhx6brkkdi7rk8qBSrqtaxa4rY4wxxpgC4QcdY4wxxhSWXFxX8eNlTN+kKUWIGkJzXB/ljrkxx591QbJKua6UtLGWc1e0xPHVEs1xXSmNWatKWlZZJee0Uq6rxpA2/qzHZ9fH9FF3qZJ3cUC7rlo2dl0ZY4wxZoYi16ae1bRyVIqWeMyVYEYdd0vGc1bC56L2yNtyY2YcbNExxhhjTGHxg44xxhhjCst0g5GNMcYYY1oytugYY4wxprBMNxi5mul1mv7JiqdaLTmLpnRFTx/0+FomM0r6519//VXI8bVp08brswUzo6zPos+fYouOMcYYYwpLrunlasX566+/Qr777rsBADvttFPo2H8HcHqoaTxcM00topdGuSKJWffCqhZN7WVVFFrS+Otby7V4zMZUC1t0jDHGGFNYasai88ADD4R82mmnAQC6du2a+rct0aKj3ctJNTsOVxLGUNVXwr1c9+g84PrRY/71119Dvvfee0MeO3YsAODNN98MXbt27UIeMmQIAGCjjTaq8/1FgGNhl3oguVbVulpE0lpE/PbbbyHPOeecdf42D/S3dX54rNp9ftZZZ63egRlTYfT+Ue6+Mz1s0THGGGNMYfGDjjHGGGMKS66uKzW3PvrooyHPP//8AEouEqBluqvU3Pb888/X+f9ll1025AUXXDDkWhmrmgjVTP7uu+8CAD7++OPQ6ViXWWYZAMBiiy0WugUWWCDktO7YWcPj//rrr0O3//77h/zUU0+FzGNdeumlU///jTfeAADcd999oevYsWPIXNctyZ2VVt5hxIgRoXvllVdCvuaaawAAs802W+hqZc1Wgv/3//4fAGD06NGhGzZsWMi33XZbyHRj5e2avf/++0M+8MADAQB9+vQJ3fnnnx9yHsdazWuhpazFapyTPM+Fjk9LxfD6qu9zb731Vuj0/qj3lYauZVt0jDHGGFNY/KBjjDHGmMKSi+uKpqmpU6eGbsKECSEfeuihAJKZAi3FHAmUxqdmtd69e4fMbJ+ePXuGbuTIkSGrS6/aqFlx0qRJIdNdofI333wTOp2r+eabDwCw6KKLhq5Hjx4hH3TQQQCSWSHVMqeru03X1DnnnBPyVlttBQBo27Zt6K6//vqQzz777Dq6tdZaK+SW4rIqZ1p+8cUXASRdNN9++23IN954I4CSi6RW0bVcruZR2t/SNTlo0KDQ7bjjjiHnvS+l7S/qZmO23MSJE6t7YNOBx1wuezZtz6tvzvS7dP7SsuZqCYYElBv/tGnTAJTPqqsPzfBt3bp1nd/KGo5P19/3338fMvdKnV+dPz4LDBgwIHQXX3xxyEsssUTIdl0ZY4wxZoYnV4vOI488Ejq17myzzTYAksGweQf7NQWt0/L333/XkT/66KPQ6Rt1HhYBnmu14hx++OEh33nnnSFvv/32AIDzzjsvdBtvvHHITzzxBADgjDPOCJ0GQ5L+/fuH3KpVq5CzePvgd3bu3Dl0m266acj6RsFzoetP3y7eeecdAMDNN98cuq233jpkvv3naZmbHhyXHt8xxxwT8k033QQgWa1c1+SZZ54JANhnn31Cl7eVI42ffvop5HnmmSdkjr/cG/OoUaPq/P9+++0Xsr4x18oc61qlrNdUHugxMQmA1kIAmDx5csjvv/9+yNzreZ0BwGeffVbn/3XNDR48OOSBAwcCSI4/7/uHnotffvkFAHDttdeGbty4cSHzHM0xxxyh03sJ0euMViAguVfxWs16ner4aFE84IADQrfSSiuFTIuOfkbv/5y/xRdfPHQrr7xyyE2ZS1t0jDHGGFNY/KBjjDHGmMJSNdeVmql+//13ACUTOQCss846IddKbYqmwrF+8sknofvnn39CZp2S5ZZbLnR5NIVMM92fddZZoXvwwQdDprsKAC677DIAybYIahrdfffdAQCrrbZa6Pbee++QGczbpUuX0HXo0CH1uyoFz29Dyodz3elxaM2YvfbaC0Cy9tOUKVNCrsVgZHXN8fpT07nWzGEAI+tZAcCff/4ZMs9lrbhtgGTNLbqEaQIHkoHjCy+8MIDkORk/fnzIrI+krleteVUr49Y9Q93gXOO9evWq+jEpeq2xjpgGsGsygrqZ+Lm11147dKuvvnrIrG/FoHgAuOqqq0LmuJsStJoVuj7ZWua4444L3Zprrhky3fysVwYAf/zxR8icdyZ9AMm2JLqvZulGLrfP3X777QCAJ598MnRbbrllnc/p5zXxgaEP6rrU8TXl+rNFxxhjjDGFxQ86xhhjjCksVXNdqQmNJnMtxz98+PCQabrM29zYXJ577rmQNSqeqLsm70wOnms9Zo161zoGrC+jmWIK52/FFVcMnZpT6SbR77z00ktD1myDSq2B5rqTdP3S5aHmVB1rWouLPNxZ5erk0OR/wgknhE4zWC666CIASXclszeAkptA6yhpVkTeWVeffvopAODtt98OnXZiT6uzMmbMmJC/++47AMnaSLWYAaru8C+++KLO/6trOQ90H+vevTsAYN111w3dXHPNFTLdpYrq1PVDXnjhhZA//PDDkGvFdaxrRjMYH3roIQDJ/fWGG24IedVVVwWQvGek3RPSMkWBptffaSz6m+pmOvbYYwEkr5999923zvHp/f+kk04KebfddgOQbPWg5yJtLdR7rI3+hDHGGGNMC8EPOsYYY4wpLJm6rtS09fPPP4dMl4Vm+Cy55JIhp3V/TjMd520iV/RYaVLWTAg1IdLkqKbHWhoLmXfeeVPl+lwzLASm7kiaa4GSm0TdBVowbMMNNwy5VtwEOlfsqqsd2bUgVq10L9drhpkMQMm0rK6BK6+8MuRddtmlznepm4HjUndXHqRlkgGlQpZ0lwBJNw7PC1s9AMnxH3XUUQCA9u3bh65W1iFQOv7XX389dGraZ3FEdY3kge4PXGvLL798kz6va/mrr74CkHSXqOuYru8823P89/e1uOjVV18NAHjggQdCp65fupnVRaNy2v2vnFxpdB60IOeuu+5a528uueSS0M0999wh//bbbwCAPfbYI3SrrLJKyKeccgqAZNayfl5bC7kFhDHGGGNmeDK16OjTrTbtZLCfBhulfU4DuPTtnzUH9I2lliwiPBYNgFT4FKqBebUIgzqBZIl1Pn1rAKC2uPj8888BlOpFAMkncr7d6fkpd66aQ5rFSd9I6gsW1v/XtcggOn0Lq5UWCGrl0GBhDTzmORg6dGjodt555zrfpW9LGvjKwPlFFlkkdNUas86f1vY59dRTQ2a5/CFDhoROA9xpEWBzWSBp8TnkkEMAJM9lrdTOAUrngPV+gGQdp44dOwLIvwWE0pTaS+UsomyRoFY81u4CShatPKxwuj71+tMWOJwXbaujFm8e9+yzzx46HZ9aj0nW1x/nQvd5TVDQpr+05HMdAslkCCY7aDsQbaFDi7N6eU488cSQmzJWW3SMMcYYU1j8oGOMMcaYwlJx15WaG9WcqoGpe+65J4Bk2weFn9MS7DR3AcDTTz8NID2AOS903OxOq+46Nbexfo62gMgbmunZ3gAoBc0BwK233hpyWosEdd1wfP369QudBqsxWPSZZ54JXdaBuzQpaxfgxx9/PGSticP6D2wVACQD78aOHZv4OyB5/Dw/1QxG5vi0C/CRRx4ZspaTZ2BkmrtKoYsZAK677rqQ6XqspouO61OvKbqYAODhhx8OeamllgKQbAuga5Fj0bL6ur65FvLeUxRdS3SjqrtAXQNsl6DB5rXk2m8o6gZiACtQahegrkXWngHS6yRlDY9V3anq2qE7Hyi5UTVBQMMwuJcy6QEA7r///pDp5tJg+azXKs+ptmrQe3rfvn1D3mmnnQAAP/74Y+ieeuqpkM8999w63697MdtFqLs9LRi7MdiiY4wxxpjC4gcdY4wxxhSWiruu1NyoZck1A4fmL82E0KwOmpY1D793794hM+uqlsyxOm7Wz9E6APr/dIlo9948xpKWdXTwwQeHrmvXriFrOX3W7FAT4oILLhgy52ehhRYKnWZV0Qyp5tZKjT+tVQMAfPzxxwCSmTjaXVfrkLD1AV1s/4XZFOW6QzfGZcXjba6bi58fN25c6EaPHh2yug5pWk47DqC0Vq+55prQ0R0LlLJaskbPyQ8//AAgmSmlLQC0hDxdOyNHjgyduqboBtGsMa3zpS6BWkHPBd2v6hpQeC3q+m+JlMvaffbZZwGUWgUAyQzIarkcy9V5SzuOHXbYIWSuYa0Ho64ruhzVXTxw4MCQGbrRp0+f1GOp1F6q30mX3GmnnZb6O+oa32effQAks3Y1A43rc7PNNgvdNttsU0fW2l3NzaCzRccYY4wxhSXTOjoajLzMMsuEzCBifQqeNGlSyHfddReA5BtJjx49QmZ9gVqqVqpP7y+//DKAZOCrBlOxom7aW0Be8Olcz/nSSy8dsgZOp70xpFXmVJ1WoaV1SC0+KjfnjaScZeSOO+4AALz00kuhO+aYY0JmbRUAeOSRRwAkm1rq2wUtchrgmvZGVa7RXhaNShmErAG6eh71jZJznFatGyhZYm+55ZbQ6VjOOeccANnXDtJjuvfeewEkLYevvPJKyLq/0DqnwcYaRMlgZbWo6l6Vdn7yRs8/K49PnDgxdLq/rLDCCnV0ajGvdbiuNcBa549rXS2TuhbLNRuuNLrmuX70mrzwwgtT/5YWUV3fWqeL1hP1gqgVlUk8Wewpih4za/+oFfumm24KWRt0jh8/HkDymtp2221DvuCCCwCUrkP9fv3dSo6pdu60xhhjjDEVxg86xhhjjCksmdbR0QCkL7/8MmS6dtTEePbZZ4dMk+yll14aOjV91ZLLKg2ajNMakQLAfPPNB6A2gwU1wPO5554LWYNAefzl5oHj/+yzz0J34IEHhkzT+9FHHx06bfZXKZeBmuvZAFFdUNq8Uutw3HDDDQCAww47LHQaDNmlSxcAyWBQNZ2zDou6LukOA4AtttgiZDZLbVJZc1lf7733Xp3j1Easm2++eZ3P6/pTM/Pxxx8PIFn7Q4+5W7dudX4/CzePficD4zUAU11PCk3/GiDJOQOAESNGAEiOX2vO1JLLKg0en65vDaCm66rW98n60HYDdF0CpSBebQRZrWQOdQfymgOAvffeG0ByH9AWPzqWtm3bAki2HVE3Fd3rH3zwQei0xQkbmFZzfnmtsOEmAPTv3z9kdb0NGjQIQLLO04033hhy2v0j62vOFh1jjDHGFBY/6BhjjDGmsFTcdaXmqA022CDkyy67LOSePXsCSJrLac4DSiXqN9xww9ClZfXUEmrSVDcM0fNCk6tGmlcrU0BRNyNNh1q2XFs0aJ0DZr7o51WmyVJbEGhNpc6dOwNIurOaW+I7Df1Oum7eeeed0GlHYHWT0s2qY9KsB7petc4QzclAaS2r60fdeOoSpBm3KWtaj4+ZDmpC1vWlZmR2klfXMs3NenxqWtd2LNVqkaDnhOdUdbpOVB42bBiAZCaIlqun+zKLOk7VIC2rT9t10H2ZhTu4GnBces3oXNLlXKlMzaaia05d30Tbxqgbmy1k6MIHgPnnnz9k1iHT2lBsiwCU9rVquq54fmeZZZbQqbtUWzjQZaxZm9zngHzWoi06xhhjjCksmVp0tNrjY489FjKfZO+8887QabAkn2j1u2rxjUvfqDXY7MUXXwRQ/u2T1oFaqqPDY6G1BUjWnHn11VdD5pO8VpPVmiZswKqVMTXwl1YjDZat1FN+uXXCis9aIVgrb2udmZVWWglAqfYOkB44qJYZDUykdWWNNdYInTal1Yq8zXkr03PGOlN6zK+99lrIW2+9dcizzTYbgOSa1blkfRKtM6RvrHkEuab9pl4/Ohes73HUUUeFjgG6QD7W0+aiew33T7USaM0gVpGuZlPZSsJ5HTNmTOh0zrbaaisAyQDyaq1JPQ6txnzttdcCKL//pAXeqk7XMi02aj0pdy+pNrqm2AEASCYJnH766QCS95K8LYq1c6c1xhhjjKkwftAxxhhjTGGZaXouob///rti/qI0M6rWsSgXWJgFrVq1mgnIbnz33HMPAGDffffV3wz50UcfBVAq5Q1U1pzelPHRdKoBdNriQF1TDHL8/vvvQ6fl6OmyHDBgQOj0XDS3hUdDx5fWtLScu5EBxkDJNbfEEkvU+bzKao7VwHqeQ3XdppU4V1m/n+P7559/Gjx//Lw232QjUyAZhM2aPlozadSoUSHT5aYB2JU0Pc8yyywzAcBff/3VrOtP9w8NfKdJ/fbbbw8d3XVA9vtLmzZtKr6/qBvj+eefBwBst912oWNbGaA0vxogn8X+0pj1WR9p7Qy0dpe6Ybl/alPMSo6voeuzmq7BSoZucH02ZX/RZIf9998/ZJ2Lc889t/kH2Qw4f4otOsYYY4wpLH7QMcYYY0xhqZrrqpbI2nVFM+rFF1+c+reDBw8GUD6qvrk0Z3zqDtA6Flpfh64BHbPWVDjxxBMBJDueV7LTbnPGl+bOAtLr+NSX9VeujhDdgOU+X+4YSHNcA5q9oXOppn2an//3v/+FTl0faeOvJJVyXSnqhmRrBLpIq00WritdJxzf77//Hjp1jc4xxxyV+tlUsnBd6brlXO62226he+utt0JmzafevXuHbuGFFw65ues2i/VZSzTFdUXKuev1WuNazCtT2q4rY4wxxsxQ+EHHGGOMMYXFrqsMSMvwUVpKVpmakxuTYcA1lVXbjqznL2+yzmpJm8tqFiHLwjWQNr68Cqtl4bpSamV/yXp9qrv85JNPDrl79+4AgCuuuCJ02mLArqvp0xzXlaL3h1pq0WTXlTHGGGNmKGzRKSAeX8smizfmWmJGeWP2+mwatBRosOtXX30VMutbsTkrUFkr1oyyPou+vyi26BhjjDGmsPhBxxhjjDGFZbquK2OMMcaYlszM0/vPovsop02bVrXxlStOR1gErBK0bt266uOrJhzfv//+W8jxzTzzzDNEjEfRx1f0GIiijy/r9an3BC0ey+KeWWXPzSjrU7HryhhjjDGFZboWnbzRPH0lr/oYjSWtLQQA3HXXXQCAqVOnhq5v374ht5TxzahwXuurI5GHW7hce4k0uVzti7SO6i2RtLYcQPq8tMRrrtz85rn+qkF9dYRqffw8bs0qu/rqq0Pu2bMnAGDJJZcMnbZeKArl1m8W16ItOsYYY4wpLLladMrFrTBeZdKkSaFTH+Y888xT57tq8YlXmyq+9957IR900EEAgLPOOit0Lf3tuejU11RR1y+bKur8Z/12ye8vZ7nQppd//vkngGQjyFlnnTVkXmt6TelbVq2vVZ4DHf+YMWNCHjFiBACgR48eoevVq1fItW7d4fn/448/QqcNWtlgUee01kmzRNa3lnXMur457jZt2oSu3LWYx1rmuN5+++3QnXTSSSGvt956AICllloqdLV4f2sqHD+bCwNJ7wbrI+mcNfs3K/ZNxhhjjDE1hh90jDHGGFNYcnFd0SSlpqvXXnstZJqZL7300tAtuuiiIQ8cOBAAsMUWW4ROA7fyNj2nmZaPPPLIkNmUbr/99gtd3secN/WZkPMILEwL8ASAiy++GABw7LHHhm7ppZcO+dlnnwUAtGvXLnRZm555rHpNPfbYYyE/9NBDIT/11FMAgBVWWCF0ei1tvvnmAICVVlopdOoG4FjydgEoaYH/t956a+iGDRsW8qGHHgoAGD9+fOha0vVHN6k2utS9cq+99gIAXHnllaFTN2UtBunq/HF9/fLLL6FTNw/nlesYAL755puQV1xxRQClEAEAOPDAA0Mul+SSJfqb06ZNAwBccskloVt22WXryC1pTdaHzi9d54cddljoHn/88TqynpPm7p+26BhjjDGmsPhBxxhjjDGFJRfX1UsvvQQgaW7VrAianhdYYIHQaYbLoEGDACTN7dddd13I8803X8h5mP9oprvwwgtD99FHH4VMvWaStfSo+nI1S9Kg6VzHrC6XyZMnh8w1oKb3rElzw6gbhK4rzQpgpgQAzD333ACq6yLgMas5X831mqFC18dzzz0XOv3cwgsvDADo0qVL6HbaaaeQd9hhBwDJ8eeRlVWuDseDDz4IADjjjDNCd+ONN4bMudJ11pLgutJMP82w+uyzzwAkq623bt26zudrCV0/H374IQDg1FNPDd0TTzwRMkMC1J3KTEcA+OSTTwAk99+tttoq5OWXXz7kLPdd3Qd1fNdffz2ApGuZ4QxAac8rkutKzwU70T/88MOh06wrZlur66rZv1+xbzLGGGOMqTGqZtHRt4iXX34ZAPD555+HTgPH+PS9wQYbhO75558PeddddwWQfBqfbbbZUn+rWujb7ffffw+gVAEZADp16hQyA1dr8c2qIfDtWd8oGWAHAN999x2ApBVhwoQJIX/77bcAgHvuuSd0b775ZshqqWMQ+oABA0LXtm3b5g2ggej8vPvuuyH/+OOPAICFFloodBpsPueccwJIVsOuFjw2IBkMr9cH503fcvVvx44dCyC5fh955JGQ+aa9//77hy6PAE+14ugb4fDhwwEAxxxzTOh0L+Gb8oILLhi6lm5RVevNuuuuC6D2rTiKHh/Xn1rc1KLIudQ5u+OOO0IeN24cgOT1p/tTlpSrhn/zzTeHzJo5q6yySujOOeeckHl9Fsmio+eFe+mvv/4aug4dOoScxf3RFh1jjDHGFBY/6BhjjDGmsOQSjNy7d28AQL9+/UI3//zzh8w8ew1A08AymmnVdK7BeNUyQ5cLurzvvvsAJGs73HnnnSHTjVDr5vJyAcZ0E6i5mAHmAPDiiy8CSJqetQ4SXSZ0QQKl2iYA0LFjx5Bpfs+7QSYDHAK+AiUAACAASURBVIHSuVDX1TLLLBNyHiZn/ibdwkD5YHfWrxg8eHDoNBicwZJ6zalr65RTTgGQrF213Xbb1TmWrNH1qXW4uD9069Yt9XNpwfAtibSmljoWugE0gL/Wx6quf9YX69OnT+r/c6/XBBVNFuBYtQ7U4osvHnKW61OPU9fk4YcfHjKTZa644orQ6f5Y63PVUPSe8cUXX4R82WWXAUiu39122y1ktniy68oYY4wxpgH4QccYY4wxhaVqris1o9OMqOZy1k4AgGuuuQZAMlJ94403Dpndh7fffvvQ5WHuU9Ocdic/++yzAZRKkQO11aJiepSr/fDOO++EfPrppwMA3njjjdDtu+++ITNDSk2PmimVVhtH/1azFfKsgzRlypTQqeuK/9+5c+fQsXYOUL1sKzX9MtPt6aefDp2eu2233TbkIUOGAEhmzbHjNVBybek1p92V2eLi2muvDZ2eC3UjV9rlqGPW/WPo0KEhb7311gCS7nA9F2kZYnqctZihpMc8ceJEAMnrT/fX999/H0Cyo7fOdS2OT6H7R485Da0DpftTmmtZa+5kQVoLlpEjR4asWXHnn38+AGCttdYKXdo+Vy50gPNXy/cRIHn8mjXNMIf27duHbpdddgmZ94VK3tNt0THGGGNMYfGDjjHGGGMKS6auKzVdaUE4loB+4YUXQkd3FQDMO++8AIAjjjgidHvuuWfINEnmUZCtHJdffnnIP/30E4BkCXM1ndaiyZFzpebuq6++OuTjjz8+5L59+wJIjlnNxPWZxvn/+lt5o2uV80MXKZAsbsnjZ/ag6qqJmrOZAcdilQCw2GKLhXzuueeGTJeAmobTMgg32mijkM8666yQ6QbTc6KdptUNxmu0Um0hNKtFO1r/9ttvIe+9994Akq4P3Ss0g4you41uID0/ebt79Pyx+J1mHemewq70LSnrKo1y7kS6abU7e1q7C3WH6P6bxbngtaitVG644YaQ9f7FbMBy9wGucT1Ova65Vueaa67Q1dI9hWtVrzO6u4HSudLQDt7zgWzGYouOMcYYYwpL1Sw6at1gMy8NFtS3L7YO2HTTTUPHRoNA8um92uhxvvrqqyFrTRm2MOjRo0foyjUgTCPPmjFqpVErgJYoZ50LfUvKO4C4ueiccP1p0z0tIb/66qsDSJZwz2PO9Ji//vprAMlgyG222SZkDQbn/NS3DtXittpqq4XMwHNtyqvtPLRdR6UsOfwePc86P6uuumrIHKs20r3gggtC5tuxtkhQiySDRLXOU5YB1pWmWk1VK4laJ2nJUIuUWjT69+8PIGkl0Kae3KsYlK7fWUn0PPOedNttt4VO98G99torZLVKEr1uX3/9dQDAQw89FDpNMmAdtqOPPjp0WY+1MfC8aNuY22+/PWTeyzXBQa3AmVjcKv6NxhhjjDE1gh90jDHGGFNYMnVdqYn3xhtvDJndaddZZ53QaU0E1hlQc5+aBNVlUG3UXKm1G9SdxnLWLGUNJF07aUHUWgeDZtysTZBpnXavu+660LELNwBMmjQp5HvvvRcAsPLKK4euXbt2IaeV8K5Fc385Ez/XGltZAMnAP7ZAUHN53uPjmtHj0GNuCvpd6kZgsK8Gg3755Zch67pNM9M3Bc4V28MAyWBodYPT5Tp69OjQbb755iGztYAGa6ubjqX5uQ8BwAknnFDnWIB85p1B2J9++mnoll122ZDZvbwWKbcetF3CqFGjACTdMcOGDQuZLhF1DWnNp65duwIouXiA7PdS7v/atkjnRF2rXDParkVb4Hz88ccASq12gPR2H3p/6dSpU8h5dK3X4+O1dNddd4WO4QAAsMceewAA1lhjjdBlHe5gi44xxhhjCosfdIwxxhhTWKrmutKoatbhUHOVZjjQpKW1B9jxVOW8y5prpoBmZdCkqKbl+++/P2SWaFd3lbp+6LJTXRbj0+/kudTaOdpd98knnwz5tNNOA5A8fq25stlmmwEoZUcASTNrrWRlpWVaAaVO8+qO1O7ka6+9NoDk/NdSTSdS0e6/MtZx48YBSGaKZJ3pw7Go60Ndh2PGjAmZLlddy5o1xvWn50eP/8QTTwQADBo0KHTaAkTrf+SR4UI3stYpUTdlc12WlULXDK8lbfWjWXOa4cmaTBdffHHodN4p65xp6MPgwYMBJFuUlKtjVql1y7Wk+4jW8dH9j6079J7H2kBAaX2pC1K/l3vx5MmTU/9fx1qt+6LONeeY2WNA8v6/ww47AKhunSdbdIwxxhhTWBpt0dEn4LTKo+WeIFWf9vSmwYBLLbUUgKTFgI0gVZ+3ZUDHpHVW2BRRx6lVTPkmygrKQPLtmG8sRx11VOiyDoDkd6633nqhW3PNNUNW6waPVWtbaJVrBjRrYJ7WUVhwwQUB5D9/+hbCRnNAKQhZ13fPnj1DZh2IvOtVKGnHUi4oOG39pFk39I3rm2++CZkVX3VNauB6ltYdfVtVK5sGRg8cOBBAMlhe11rauUobC+slAcnx6/dmuQbKNTBlEKvOT97XEtFj/uGHH0KmxebBBx8MXZrFByhZx3VMKm+11VYAklaSu+++O2TWdNLaSFrZW4OUK7WXciwaFP/444+H/Oijj4bMwGq9J2jleXoyll566dCpxWbnnXcGkAxW1ntltUirIwSULGnffvtt6NQ6yiDzaq5ZW3SMMcYYU1j8oGOMMcaYwtJg1xXNVOqi0WAyBqBqDn99pik1fakZlsGOGgBIc2VDvrdaqOk0zWSrx7n88suHTPeQmlvVtcD6C2mNJrMiralnuWByut60LQfdjQDw3nvvASjVSwJqx82j5nI1B6ubkGt8iSWWCN0hhxwSMl1aea9DnR+audW1oyXyX3nllZAZOK5zoueFa+CDDz4InQaL0s2nbs4DDjggZHX5Veoccaw65i233DLkxRdfPORnnnkGAHDzzTeHbvvttw9Zr1uS5ib6+eefQ6e1u6o17+VcAwz2LFeTJs92JBqAqu4KtsthjTEA6NixY8i6vtigVd0x2kKoX79+AJIuKL1W+V1XXXVV6Hr16hWy1txpThKBnmded5dccknotOmvur55fejx698ussgiAJKuLa0jx3PJoGv9/f8eV5bonjF+/PiQGbqg86duYD4jVDOBwxYdY4wxxhQWP+gYY4wxprA02nXF8tQAsOOOO4ZMk6SaC9UdpdBkrm0FbrrpppCvueYaAMDcc88dun322SdkmszycB2ouU1LlHfv3j1kzTAiWlPnq6++ApCM0GdtGqBUZ6CaJmj+1vDhw0OnWVfsyA6UXDtvvvlm6I444oiQWZp/xIgRoaM5Fshn3rh+df60rLyua5qBhw4dGrr55psv5LzbPRA9jyyrzmsHKNXrAEptG4CSm27DDTcMnWad0QytmSITJkwImaZndUNUa37V3abZT3379g35wgsvBJDMBNSaJPPOO2+d79KaNMzg0kzPvNevoi4Dou7CSrXdaAy8vk4++eTQaVsHuru17YFmJbF2DlA672eeeWbodP2mtTvRTt50k6gbXlswZOFG57FoiMUDDzwQsmZV8brUMevn2rZtCyDprtSsv0022QQAcOSRR4YuC3dxGuUyAdVlx+PebrvtQqeu4zzCGGzRMcYYY0xhmWl6b6d//fVX/Cef5DRASgO8GACoQYFsrvZf2OxL36716ZtPrOedd17oNJi3uU+sbdq0mQkApk2b1qxXc31z+vXXX0OmVUSDqTWYkRYbfUtU61Va4GVjaN26daPHx99Sy5lWe9Y3jpEjRwJIBqBpYCgry7Zv3z50lXzL4Pj+/fffBo+Pc8Wq1EDSSvXjjz+GzLd/Ng8ESlYAIPs3kplnnnkmAPj7778bPD5en5wbINmIUgOveS40cUCTDPRNjWjgZI8ePQAkK9eWS0JIq6nTqlWrRo8vjXJ1WGjV0mrJui+xTo5eX++++27IbJDIelhA/XWIFI7vn3/+qdj41OLEfZVBqUCyCjmbkWa1TmeZZZY64+OxdunSJf5OqxUzMFXnSevc6L3koIMOAlB/BWqdhzQrV7m/LVcRW461yetTv0/XjHovWHFerfwK74uaANKtW7eQN9100zr/35j9tTnrUy1HWu1fuxhwrrUp8korrRSyroEs4PwptugYY4wxprD4QccYY4wxhaXBriui5jjWOwBKgX90YQHJOhba9DEtGJDuKqBUU0DN4ZU0w1bKdaWkmSzLtW1IaypYyQDXpriuiAYI3nfffSGraZTBchpsxkasQPYtOprjutIAaj1+dd3QJKtrVsk6GLkpriuiLmCtc3XGGWeE/NFHHwFIrk81/bPOiJ6fDh06hLzCCisASLrzGkOlXFdKmgti4sSJIWtNIAaBatPcRRddNGTWZNFz0pg5r5TrSsek+x/dkxqAy9oyQCkwNCsXQZrrisc6evTo+LtRo0aFzHOp9bY0tEGbrvK78gr6bo7rqhz13R/qI60dRlP3oeasT62No0k3GizOxAC6UIGk6zvrebXryhhjjDEzFH7QMcYYY0xhabTrKvHhlE7m6gJQ15aartIi5NNq7mTlIsjCdVVLNMd1pZRzF9L0qhH4+rdZu3aa4rpKM4dPmTIlZD3mtBYB1ayd0xTXFY+vnLtFOx0zA6uc6ZzXql6zaXOtc94YM3wWriuFx6LnQmX+f5o7WeU8XAPl0PPLrBzNgNX9U7NxsiDNdUXq6xKvrg9dU9VsB1AfWbiuaonmrM9ydXQ0K5BZjRp6Us39064rY4wxxsxQ+EHHGGOMMYWlWa6r1C8U01a5rKM0qmnasuuqYdTnjsirFUJTXFek3PpM6xSf1/iak3VVrhhaOXl639GYImuNIWvXVd5k4bpSeP7L7a9Zr9vpua6UtHVSK+1TpoddVw2jvnt9XnNt15UxxhhjZiga3NSzoVTzzcJkSxHnr+jrs5y1pbnjbq4Vx1SOvN+YG0qtH59pHi1pL7VFxxhjjDGFxQ86xhhjjCks0w1GNsYYY4xpydiiY4wxxpjCMt1g5KKn1xV9fE1Jv24JMP266OUBij5/WaVf5w3TW5tSnqMlwPIcec+fVlZWmltlmfNX9P2l6Pc/xRYdY4wxxhSWiqeXmxmHhqQym5ZBfQUFtYeRyZe0XoFptKT036bw9NNPh8z+ewCw8cYbA/CaNSVs0THGGGNMYbFFxzQavvFr91pFu9aa2kUtA7/88kvIJ598MgBg8uTJobvmmmtC9vxWH7XITJkyBUCpCz2QXoJfu8+rTOuHWjxq3eKj4/vzzz8BAIMHDw5d3759Q+7UqRMAW3SyRvePNCtwOcsj56Wa688WHWOMMcYUFj/oGGOMMaaw5Oq6UnOXBpNRr6at//u//6vegVWIxvQHqnXTscK5uuWWW0L37rvvhnzeeecBAFq1ahW6ljS+osN1OXXq1NANGDAg5DvuuAMAcNBBB4VOr888YCpxQwLg07rPt8T1p6b/l156KeQjjzwSAPDee++lfo57ZceOHUO3ww47hMxg3VVXXTV06tqqxXOlqeSjR48GAKy11lqh07Vai/cKPaecV72m9Jib24uuUvOXlqCg80AXKpB0c3N8EyZMSD2m9u3bAwAWWGCB1N/KYv3ZomOMMcaYwuIHHWOMMcYUllxcVzTZ/fzzz6G78sorQ/7ggw8AALvuumvott9++5DzMK3W54ZSMzNN51qhM+2Y9TvVJFiLpuM0dMxvvvlmyNOmTQOQzM5pKWMqR9r865jSzLx6fpprmq4kPL7bbrstdPfee2/I++yzDwDgzDPPDJ26IbPMZknLrgGAiy66CADwxRdfhE7Pb9u2bUPecccdAQDt2rUL3fzzzx8y9x/9rVrJ0NFj0qwqnYvx48cDAOabb77Qde7cOeR5550XAPDggw+G7txzzw35wgsvBADst99+oTv11FNDznt9Ep3f3377LeRHH30UAHD00UeHrk2bNiHXoutqlllmCZlr+OKLLw7dcccdFzJdOuXmgedFx/nHH3+ErG7IhtZcqg+6pm699dbQPfXUUyF/++23IXMN67Wq+8fqq68OILnm1M2aBbboGGOMMaawVM2io0+WX375JYDkE/ndd98d8sorrwwA2HvvvVM/X60ndn274m+qlUb/f+LEiSGPGTMGAHDTTTeFjmMGSmOZddZZQ3fVVVeFzDoQtQrfNPQt+NNPPw35888/B5AMFqyVN+bGoGtO3645ltlnnz10WlOIf6tz/vDDD4e87LLLhtyrVy8AVagjIWNhkODw4cNDt80224Q8bNgwAMnxVWv+9DzMMcccIf/4448AgBtuuCF0enxqEWXNn0UXXTR0Sy+9dMhdunQBAPTs2TN0ah3J06Khv61vwQceeGDIhx56KABglVVWCR2tOEDJeqD766RJk0I++OCDASSv2VpE91fdSxdaaCEAwHLLLRe6Wtxf9Pi5fgHgnHPOAQDceeedoTvqqKPqfF7XggYu//TTTwBKSQNA0tLyyCOPhDz33HPX+a7poZancePGhbz77rsDSFpulMUWWyxkWte22mqr0L322mshM7Be17QeM6/bSs6pLTrGGGOMKSx+0DHGGGNMYcnUdaXm8ieffDJkml7VDKZ1HmiGvueee0KnNR8WWWQRANmYK9PcVQBw/fXXAwCef/750KmZ75VXXgn566+/rvNdacGsWnb/tNNOC5kmyQUXXDB0eZtm0wI333rrrdD98MMPIbO+x5prrlmlo8sGDaYbMmRIyFyfSy21VOg++eSTkL/77rs6unXWWSfkJZdcMmSe16zdJbqW6dr57LPP6uiAkstIP5N1nYs09Hd4/nltAcAzzzwTsh4f3Yg6Pp1Lupbvv//+0NFdB5RcInkHteqYunXrVuf/y+0JPG908QDA4osvHvIGG2wAoHydk7zhfUPvD7fffnvIXKvl6tDUCnr/O/7440O+7rrrAABbbLFF6NRNm/Z5rVlz2GGHAQDuuuuu0On9sTH12/5LOdcxUXfvzjvvHLK6UelS1uPfd999Q6brjUkrQPm9plLYomOMMcaYwuIHHWOMMcYUloq7rtKyOwBg4MCBITMb5fzzzw9dv379QmaE/aBBg0KnbgKWQM+CtFLdQMkMrrVH1HWlf8vMLK3doWbAhRdeGEApOwkAXn311Trydttt18RRVJ60mjFau0KzVlZcccXE37UENGvn5ZdfBpA00+pYWadF6wTpXK233noAklk/mmGn6yZLl2RapiNQcsPuv//+oVtjjTXqHJOak//555+Q9VxkiZ4bul7UxVaupg5dGi+++GLo6C4ASu4vbaugdWZY3ySPrLNyNMU1o9ff999/HzLbtVSzBH9TUNcMa68Apf1FM2BrET0+3d/putY6OsyOAkpznZZpBZSyoXgfAYDLL7885Lnmmivkxs6lHvPyyy8f8rPPPlvnOPX60Do+dAlfe+21oWNtPKB0rao7VvfKLLBFxxhjjDGFxQ86xhhjjCksFXddqYlViwBqBD0LVmkktpat3nbbbQEkTXMaoV0t1HTIjr80+wPJInHrr79+yCyU17Vr19BpOfqvvvoKANCnT5/Q6fi09H2tkOaGUnOlmjG1eFkto+6Op59+OmSWxmeRLAA46aSTQqZpWN0ZKqeZi/PupK1uGrZe0YKc6rrjWEaMGBG6tHYC6k6oVtaYuoPV9aJwrXbo0CF0uv+wOKe64DSrZ/DgwQCS48vbddUYuK61SCAzXQHgiSeeAJB0zeaN7rV0s44cOTJ0WlCV2Yza9qNW2s3onqJhDh9//HHIJ554IoBkwcP6XJOa4Uo3pHZsz7qFAjOd9T6gWcN77rlnyMxM1j1F55fnSIvs6vW30047AUiGezQ3q84WHWOMMcYUlopZdPiUpm/52vRL374YTKwBmvrENueccwJIPqXrmxyfFDVAMgv0LY5l4x977LHQqRVGS8xzrPr0q0/6DHZlozQgWeeCLTBqNZiXb0z6RK9vx5RrJahR0XOqa1WtiwxWVSvVzTffHDJbPKywwgqh22STTULmuq6l8Y8ePTpkWirUyqjwuj3rrLNCp9YB/r9aPKpFQyxjnGNtGqzj1zdNosH01Qq2rg/dM3SsadYlXddsgHn11VeHbtSoUSHTkqO1u2pprXKPVSuFWs8Z7D927NjQ6bWYx1g4V2p50aaV2tanf//+dT6vx8z1qRYPWoGAUmKOto1Qi0mlrI9p15quyb///jtkraPG49f9U4Oced/URsIPPPBAyJz/Y445JnRap6cp1h1bdIwxxhhTWPygY4wxxpjCUjHXVZppS01XalplsK26fvRzLO2utQPycOOo6Y7mbA1wLPe3NK3pmLSOBTvNqjm2ffv2ITPwq5bMyWn1hbT2itaJoGsnb9dbmutQx6GuQ223QTfG+++/Hzqt2ULTrJpetbsyAyc1wL5ac1kuWFBryrB0u7qTdXwMctT1qW5k1ryoxdorQMmMry0evvnmm5A5f2oC7927d8h0I+fRVkDPqboDNFiT7hHdX1Sm65WtWACgc+fOITOwXoNhsw4DSEPHqvcCuva1xYAGw3N/1NAIdV1Vi7Q6ReoC1+tH9weGYeg5V9cT90+tPafr98YbbwSQT4sg/R3dPzRxgXuJuh71+FkHSGvrPP744yEziUn3FK2f1apVq5Abuu/YomOMMcaYwuIHHWOMMcYUlorX0dGMhiWWWCJkNT3uscceAJJZG2p6fe211wDkY04tB01kTTVnq2mOdSw0u0NbCMwzzzyJ36w1aLLVrDlmegAlk7uWEK8Wuo7YRRwouaGYPQck24o89NBDIdM0yuw/INm2gWMdMGBA6DSDK0+XXbmsMoUZgmqG1jolPG+77bZb6OhuBUol+PVc12KdmbS2JeX+X908U6dOBZBPbRbtzn7AAQeETHcOkFyLaXAu6OIBki0w6JrTrJk80DnRvZ7rVuuJ6X2FLp1XXnkl60OcLrr+6UZTd4zu6bq++DeaSaxzRTeQ1vY64YQT6nxv3vcH/f0111wzZNaRS2vhApRq0qlrT++P7M6u7nbWTgKSzw0NbQNii44xxhhjCkvFg5H1yVtz/7fZZpuQ+XaoVoCVVlop5F9//RVAMpiTtWX0t2oVvqlogJ1W+STLLLNMyNrAlJ+v1XHy+Gh5AkpzBpTeXvjkXk30LevHH38MmdaXHXbYIXR77bVXyMsuu2zIXMMazKv1k04++WQAyTeye+65J2Su2zysHPqbWjlWq4zyvOj60nNFi4Hq9LrmW1Qtrc+0wNYPP/wwdHqstB6odVbfOKtVB0nPKRttajVubYqseyHR49M3W54LDbYfOnRoyKySrBb3vC1yar3h27smOGy66aYhc3x5BIsruuY+/fRTAEkrIINugWQVcs6bWsRVplVPA6xZrRsoXZ+1dP01Zi74txpUrM8HrFmmTV21KbEmwTQUW3SMMcYYU1j8oGOMMcaYwlLxYGRFzXFqeqSsptvPP/885OHDhwNIln3WppkNDUDKC9ZE0LLWWhqc/691BtSMl7dJtj5oMlVzv5pxWQeiXDBoliZXXRsatPbwww8DKDWkBJJNDbWOBZt2qutDW3wwmJ5Bc0AycDlvNwDR60tN6mwqq2iw43PPPQcg6a475JBDQl511VUB1M44geT8MdhT6+jo9bXhhhsCAN5+++3QbbnlliGz/lcW+4xeB5999lnIdG1rAH1ao1WgFESsrmOtucL6Ko888kjobrjhhpAZ2KzudA0dyGNeNTCabgp1/WidI7bzyLqRZX3oPs3A/XXXXTd09e15+v/aAJRusOOOOy50ev3med2Va2vUmBYlaaEZGmzP0Ie55547dNoouin3D1t0jDHGGFNY/KBjjDHGmMKSqeuqvk7DahrWmgjMdqGJGUianmvJZE7UNPf7778DSJb91joRzHZQd0EtRdDXB49VM+G03ccbb7wBIJl1pvNXLfScMquKblEg2ZFby+0z62OhhRYKHd01QKn0ua7DWlyTOv5dd9015LPPPhtA8lyom5l1ZPr27Rs6zaBk/ae8x1yuhQBL5Oucarl6msT1+LNenzxW/U3dHy6//HIASRO9ugbUTUw3zuabbx46dfNzLOqO69OnT8js+n3OOeeETusoVat+kH63ZjB269YNAHDLLbeEbp999gmZc/3ss89mdmwNQeeSNcNYY6oh6Jq98sorQ15nnXUAAFtssUXo8r4/cP2qi1Hr3Oj6Y4uYtLZIADBlyhQAybYmF110UcjMNtT1q5lW7l5ujDHGGCNkatEpB58O1aJzxx13hMyne23qVouUq+zJgFcNptOn0E6dOgEAFl544dDl/cTeGHis+has1VoZTKjnJI8qswrfvjTAU+vMtGvXLmS+PZQLJqz1YHGix7zffvuFzDcxWnaApEWHNTu0tpNW8c7bkpOGzgnr/+hcM0AeKAUp6xujJktkMb+cC11TWluFb8rrrbde6HR/0Jo31KvFR4+Z36XVdmnlAkpNW7VOjVq/tMFwlmtd16fuD+eddx6A5JxoxeEePXoASI4vjzWpc8nfb8hxMHCeCRJAMhiX9WO0dlLeew6PWZtz9urVK+SuXbuGfPDBBwNI3t81sYHyRx99FDq1bnFejzrqqNBpskdTkgRs0THGGGNMYfGDjjHGGGMKS9VcV2mBgxoAp2Y8mtm1DkotunbKua7oGlBzuZqeF1tsMQBJM6fWAWkpqDmRtWWAkkkyz+aW5WiJLqimomPVmhRs8Pjxxx+HToPJGbhdro5LLZJW30NN3Hr8Sy65JICSiwRItsio1l7D4wCAM844Y7p/m5bYUd/61TGrm2fPPfcEkHRd6f6Ux3Wh42MSgLpblca4iWoR3iseffTR0Om9bu211wZQm+Mrl4Ch7RoYRK7XpO4lvNepu1zvH/vuuy8AoEOHDqFr7pq0RccYY4wxhcUPOsYYY4wpLLm4rn7++WcAwIgRI0KndUqOOOIIAMlMnlo045VrcUCXjprO1TQ3ZMgQAEl3HSLPnAAAIABJREFUVS265srBY9X5UTcAsz7UNFmL8zcjoeef61PL1ae59FrSmlR4LWrtGNa2AkpZIVqbJo/1WV+dsUqiexHr1Gy77bZV+/3GwGOp9VY/jUEz5Fi/SzOt+vXrFzKzrWrJtc7rgzVygOT1pW5Quql0Ten9nXWCtIXHcsstFzKzeXX8zV2ftugYY4wxprD4QccYY4wxhaVqris1Pc0333wAkiXo1STGglW17u7QMambZvvttweQzCTTgmtpprmWiI5fiwey+Fetz9+MSlFcA7r+1I16+OGHA0hmmnHPAbw+6UbRPamWXFdFRNca23xoWxVmWv33b2sFHpNmJ15wwQUV/34gm33JFh1jjDHGFJaZpvck//fff2f6mF+uTkfWT7StWrWaCajs+DQY+c8//wRQaoUAJOtY8O0yq7coju/ff/8t5GvazDPPPBMATJs2rZDja9269Qwxf//8808m42OQf7lg36ytF7PMMstMAPDXX38Vcv7atGmT6fzlDecv6/2l3P2Plv6s6pBxf8n6/p4XvP8ptugYY4wxprD4QccYY4wxhWW6ritjjDHGmJbMdLOuih4DUXQfc9F9sEUfn9dny4TzV/T90/PXMuH8TZ06tSbHx9gkLairGcoaz5QWxzT77LM7RscYY4wxMw5Vq6NjTEtD3xbs4jXGmGzQFhm//PILAOCmm24K3c477xxyU1q32KJjjDHGmMJii46pOPp0ngatI7VqJeHxszkpkPQX8//LWXxqdVzTQ+eM4yrXVC+r+h7NQY+Jc6Vj+ueff0JuifNjSqStP89py0Ovz2nTpoV89NFHAwDuvffe0HXu3DnkJZZYovG/1YTjM8YYY4xpEfhBxxhjjDGFpeKuqzQT8n+hmbG+QCKbI1sOOpdsgVFufmmynH322UNXS3M9duxYAMCNN94Yuo4dO4a85557Aki6trSpq5Z2r0V4jeq1+tNPP4X866+/AgCWXnrp0Om1XCtNB/X41c329ttvAwC++uqr0HXt2rXO52ppzTWGNNepzknauPRv+flabSqctj51/U2cOBEA8MQTT4SuR48eIfNaLOd6NfnC9cegYwAYNGhQyM888wyApOtq9dVXD7kp69YWHWOMMcYUFj/oGGOMMaawVMzGTnPU5MmTQzds2LCQ2bEbANZYYw0AwKqrrho6dWPwu9QFoKbLNNdXSzdNpmUSlDNN50FaJpXqJkyYEPJxxx0HAPj6669Dp/M377zzAgDOP//80DWlNkJz0eN/6KGHQh4wYACAkgsHAEaPHh3yww8/DCCZKbD88suHfPjhhwMA2rdvH7pquQnKZUfptfTzzz8DAG699dbQ3X777SH/8MMPAIBtttkmdIceemjIzHrQ81dftdIs0N/RfYduqkmTJoXuvffeC3nZZZcFULuumzT0XNMl98orr4ROXXNt2rSp8xk9P6+//joAoEuXLqnfn8deqnNJ17e6U1966aWQr7nmGgDAm2++GTq9v3BeO3XqFLo555wz5LzvFdwL9Tj+/fffkNNcd0qa67LW17Kur99//x0AsN9++4VO3ZCjRo0CAGyyySaha+74bNExxhhjTGGZblPP+np96BMln0j333//0N1xxx0hzzLLLCHz7bJ169ap/7/aaqsBSL6lbLTRRiEvtNBCAICFF1449Vjqe/prTq8rfTJVub4n8rRgQD33ah3gd/3xxx+hm2uuuULWc5VGpXoJ6fj07YpvkvqWeO6554ZM646ek7Tv3XvvvUN30kknhazzmmbdaU6vK7UsPf744yH369cv5P/9738Aksevc8W3x++//z50WqeFlkq1kqjFp743yqb0uuJ3lqthxLd4ABg6dCgA4JFHHkk9Jp4jvY50Tnr16gUAOPbYY0M3xxxzpH5XGpVan3pN6fUzcOBAAMAtt9wSunXXXTfkESNGAEjOSSWtiJXqlVTu+ttll10AAGuuuWboLrroojqf0/Nz8cUXh8xxb7fddqFrzPiz6HWlY73vvvsAAH369AmdWmyWWWYZAMCCCy4Yuk8++STkDz/8EEDynBxwwAEhl9uXSKXmr5xFhsHUb7zxRui4JgFgyJAhAJIWN13fDOLlOIFSAC+QPG9t27YFkLwmq9XrSvfazz//PGQmc6jF9eabbw6ZiR9NvSbd68oYY4wxMxR+0DHGGGNMYalYMDLN3F9++WXoWMoZADbffPOQ33rrLQBJc+MLL7wQ8ssvvwwAePrpp0On7pqVV14ZANCzZ8/Q7bjjjiEvtdRSIVeqZga/R01wHAcA7LDDDiHTzEgTJZB087z//vsAgL/++it0GizJz/PvAODSSy8NWd149ZlhG4uaG9W0qnPJ+VF3TatWrep8l86Znn+aqa+++urQbbbZZiHTNQJUzqXA39SgtyOOOCLkKVOmhDz33HMDALp37x66bbfdNmSag8vNL914GsCrgb8MxgYqP77ffvstdDfccEPIGvjNIGuday2rzmBl/S41M9MNosHWBx54YMh5BEbqWmvXrl2d/+eaBUpzceKJJ4Yu72Dc+jjrrLNC5lww6B9Ir92k++c999wT8t133w2gNlt5AMDGG28MALjrrrtCp3s616rO+amnnhryO++8A6B0nQLZzynPpa4jDT1QNypdanov0WOdZ555AABffPFF6JgAAQDXX389AODdd98Nne7FupfyWqjmmua+ogkq++yzT8jcf+iiBEr3dCCb/cMWHWOMMcYUFj/oGGOMMaawNMt1peYwui60HgnrOfxXpmlS3S5Tp04N+eOPPwYAfPrpp6F76qmnQh4/fjwA4Iwzzggdc++BpEmaLoeGmu7KmbBZx0EzhbSEdZprTseUhp6T2WabLeRvv/0WQKmeBFDKBAKyMTnT9P3qq6+Grm/fviHrXPBv9VzR3QMA888/P4Ck62P99dcPmS45/U6dP80GYTn3pphe9TyxNsxRRx0VOq4zAFhrrbVCZuuHJZdcMnSaIUh3E7MD//tbzLriOgeAa6+9NuTDDjussUOpFx7T8OHDQ6fXh5r5eazMfgCAM888M+TPPvsMADBmzJjQXXbZZSFzXjWrRdF1U64NTKXR39l0000BANddd13oNEOuVl02QNIFpbVjNIOV63PRRRcNnbpAafq/8sorQ8dMLaCUrZR3bS5Fj2WBBRYAkHQXK5zLCy+8MHTqBj/ooIMAAFtuuWXq92cB9yd1jZ9wwgkhaxgC0T1F3Vy77rorgGRbmbXXXjvkgw8+GEDy+ta9SOVquaz0XkCXvu4DWlPtwQcfBACsssoqocva3W2LjjHGGGMKix90jDHGGFNYKt5muVzhMO30TNOxmmkZaQ6Uinutt956odOobbocttpqq9B98MEHIWshOHWDNAR1F9HEBpTK+tMF8l9ee+21kOnGYqsLIJmhwoJXdPEASTPlTjvtBCCZwcay9UA2ZlhmWO2+++6h++6770LWFh2cl86dO4duiy22qHOsmjWgxdlOO+00AMlMssceeyxkdVMym625JlgWyaNbEEiatvX8M8NDXRya1UDKmVtXWGEFAMBee+0VOs0g0dLndPk1ZU7VXEw3xyWXXBI6zYTT65KmfXWh6f/TjdehQ4fQ6bliBpC6tk4//fSQtes510UWazYtkw8onX/NJNO1XIuuKx6TuvO14KRm0vC603OqrjvuW5o1qS5Nfk7Xb7VcjOVIK66q5+LFF18MuX///gCSrm91vdK1o+7aLNafnjNe3/vuu2/o9Pj1b7mXaPaltkOiy3vrrbcOnRaHpBtZXV+adabFZbN0Cek1x0xNANhjjz0AJDMdNQOQY9GsYz1O7luVdLvZomOMMcaYwlJxi05DnpzTntTSmhHqE6MGjvKNUmuXaDDw8ccfP93fSoO/pVYGbWtAi4wGOmuAmAYGMthPrQQKj0mP7YorrgiZ5fp1HIsttljIzXlKT2uuBpTGpRYP/Vv9fR6rvjGnteBYffXVQ6dvNPomQvTt57bbbgtZ6xM1BD1mrf1Ci4fOGZsDAsnAaR5LWm2ShsA3SbWYqPVR325ovWsKes7ZlkPfktQKpcHEPKc6J/W1MNEWCgyyHDduXOj0WlTrjlr6skT3HbarYFAykHy75Lhr0bKje4KOSd/S064fTXwYOXIkgKTlUPcMBvHyzRsoH9icJeVa6NB6c/nll4dOrYe8VjWZQ2uyMRhWkwkqhe4JGmB85JFHAkgmjaRZiYFSnTBtmqsWf7X0kwsuuCBk1sRSy/dKK60UcpZWnLS2T0DSosRrTa2Imozy9ttvA0gGU+u6Z32oSgZV26JjjDHGmMLiBx1jjDHGFJaKu66aiprRaXrTAF8GUAKldgmbbLJJ6NTNpKa/hpphaRrr0aNH6Oacc86Qe/fuDSDZRbdczR3+Zrnu1zR/prnjgFJ3dq1zor/VHNOyml614y2DgbWej5p+tSYJXVb1mUj1/1Wmy0x1Gmx9yimnTPd7p4euI9ZbAkq1YbQU/iKLLBIyzzlQOdO9BgNrYOSdd94ZcmNdV2o6VncF3Z167HpONbCfx6Lnvz43jv4t3Rx6fagbVMu5V4u0ml4a7KnXD4PkNUEizR1UTXj8en1qHZJ+/fqFzGtR3YLPPfdcyKNHjwaQXH8ajE3XSRZrvj50nWlto8GDB4fMYGombQDJJAm6QfRa1+uagfO8JoDkWm2OG0TPk3ZPP+aYYwAkk2I6deoUss4V3ZC6JhVea+quO+ecc0JmTSQG3etnqokGy7MtBVByWWlg9rPPPhsy6wTpnqTrgiEFWqeruW5mW3SMMcYYU1j8oGOMMcaYwpKL6yotq0o7uTLrhtkDQDKa/dhjjwWQrFOiUe1NMePRnKnm0kMOOSRkmizVdNkYc6+aWdm9ddCgQaHTEuAct7pWsjAt63ni+DVrR2sXaR2Hhp5fHbOaqZkhpCZkrbOz+OKL1zmupqDnlMes2WNNncuGklYbBEhmizQWvWa0zpLWGSFagl7HTZdqueNLQzu1c91qnSStA6JZV3l0Audv6pyqa+qmm24CAJx00kmhUzdtHm6AtN/WrEW6RoBSp3itfTRlypSQmXWmeyLrgAGlfUXnplrzpHuCZlUxHAEota7o2rVr6NgWAiitVR3zk08+GTJdlk3NmpweuqY0RIK1e/Q6Kre/8FzrXOuxcn/Uth0bbbRRyHRj6W9lPX/cd5gxBSQzkDVDmG2SNBNO3bDMBqQLCwCGDBkSMl1y5UJDmnT8zfq0McYYY0wNUzWLTlrNDr5ZAcB5550XMhuAbbjhhqHTty8GMWmApwb+Ngd9cmzum50+caulhJWBx44dGzrWRgBKVVDzfBsGygdz1nde0qqsTpgwIWRW0UyrvQM0z7qin9WmcbRoaICy1q5IeztqzHHo5zl+/S21iGgQfWPRefjqq6/qfH+a5QIAOnbsGHLbtm3rHLNaTHn8WmFc37hYa6ldu3ah04rM+vadZ+NIHZ8GHjMYV48zj2utPvT4u3fvHjIr52rNLw3GvffeewEka6ukJXvkgf62Wpm02a42CE77HNeqrslbbrklZAa+qsWrUvcHRddMU75frTh6LR9wwAEAklZYbVrKLgLVnEeOT604anHUYHIe18knnxw6DZzm/qcB9toAWa0/lcIWHWOMMcYUFj/oGGOMMaawZOq6UnOp1tlgEJrmyWvNB5oxNRhY69ek1ampRdTNwDouQMkMqU3btFw7z1vW5vQ0t4LOmZpTNbBXTcJE2w0w8Fjr9LBEOlCq/6Lzt84664TcnJomas7VoObddtsNAHDooYeGTtekunY4Pl1zae44nR91/bAm0dFHHx06rbnR2Eazio5P61CwxYSWhdfaKtqAla0RtK2A1rmgy0Qb2GqzWwamDxgwIHTqZs7TXVUOPW/LLbccgGRQeC26rvSYdP0x2Pjdd98Nna4pNhNWd13Wbo60YPa0YNJyrvE017HqGM4AlFoE3H333aEbNmxYyBx/ni666cHzom1n1I3HmkfaCFgDy/O47/FcvvXWW6G7+eabQ9am3C+88AKAZIC4uiMZuD1w4MDQ6V7C8+OmnsYYY4wxDcAPOsYYY4wpLBVzXaWVs1Zzo9aBeOihhwAAG2ywQeg0QptuBI1Kr1UzZENRNxDdAJppoBlkWY5VzYHquuC5VheilvXWOkfaWoCoG4h1ILSOgv4/XUNae6F///51jgVo3rlQ0zdrv3z00UehU3OpmtFZGl/NxZphwLWu5d5ZGwkouY/UxKymaa2/0VjzrLqF9JhZIl/bimjtoh9//DFk1qlKa0sClManLkTtdM1rNc2dXAuk1elSeKw6/lrsZF6uTgpdA8xeBJJuUrqRs3bHpbVb0RpK2n162223BZB04WoLCnWNc1zffPNN6DTDavLkyQCSGUBa86tarv/GoGuRrnt157PjN1ByCdHFDOQfpsG1qLXdNBNMs/6eeOIJAMk9v0uXLiHzvqd7atY1nWzRMcYYY0xhaZZFJ60OhzYsHDp0aMj69sEGlppHr80k06oQtyR4XvQtX2tedOvWDQDQoUOH0FVrrPpmoG8MbIqq1XTVCqCBf6SctYVP5BrsydolQKkipgZjl6up0xz0zWDWWWcFAFx22WWh0wDO+++/P2QG4T799NOh08DAtONUixyDXdVip8079U24OW8v+lk2o9UKqi+99FLI2iCQQarlqrgy2FWPWd++aAksV/k1D+uI/iYtpmpRVlrivqLHzLdnDUZno9X//m214G/q2mY9H6D09q+NaNWyqfcH7lFqBdFrlVYjrTOj1Iolp9x1wMr3ajHXRsaso1bNysf1wf1N90GVtU5Yz549ASQbZK+44ooh03pcTS+NLTrGGGOMKSx+0DHGGGNMYZlpeiaxadOm1fnPci6GM888EwBw4YUXhk5ro9BdBQBrr702gGQAZDXNra1bt54JAP75559M7IE032qLBwbjASU3kLpuKhlsNssss8wEAH///fd0x5c2lw8//HDoHnjggZDVDTJp0iQAyUafGsTMmirqGtO/pRupqXPeqlWrBo0vDR2zmsa1DhBdHxpMrYHHacc9++yzh8wGl2quTWvqVw6Orynrs1yjzqaYvst9vrlm9Iauz8age8mnn34KIHl9qWmd7ss+ffqkfldzx8f5S9s/G0M51yID69dff/3QaTBu1oGr3D91/tICv9UdxfPPuQGSwfJah4pBzBrsru1cGCSf1T0ji/kbMWJEyAys1gSdI444ImS6ibJyV3H+pk6dWrH9Mw09/mre32efffY6PkNbdIwxxhhTWPygY4wxxpjC0mjXlWaXaLn5LbfcEkAy6l5z65lpBJSyPvKKJM/adUXzntYG+vDDD0O+9tprASTdHZU8F01xDaTVHtGO69p9/KeffgKQdM3ouqBpWd1Zak6vlGugkq6PNJdPc7OHmppV0BzXVUsgC9eVzhXdkMwkBJKl69lpXVuEVDIDJAvXh8JsHXWH3H777SEvueSSALJzF6S5rtLQvSTNzVHu+NL+Vucn6/tGU+aPc6Vjuu6660LW7t4HHngggOT61P0zazdPc1xXLQG7rowxxhgzQ+EHHWOMMcYUlka7rtQ19e2334ZM06m6K3r37h2ylv7Ou51DFq4rNbey4Jy666644oqQmXWW1XmolGugXAZPGmnrKCsTcxauq1rCrqvmkeZ6TMtgynp9Ntd1pejx0w2s7irdX5nhmdX+0lDXVUulKfPH+6IWWe3UqVPI6rofN24cgGQLlzyyju26MsYYY4wpAI226ChqxdA6FkSfYmup7HoWFp20dhjvvPNO6NZaa62Q085VJcn6jTlvbNFp2cwo67OSFh2Fe43uI7qP51FHp0g0JxhZEzi0ZtCcc84Zcvv27QHkd0+0RccYY4wxpkD4QccYY4wxhWW6ritjjDHGmJaMLTrGGGOMKSzTjYr966+/CmnuadOmTabBgnkzowQLenwtk7zHxyQKDebVVOzmpmVzfEXfP4s+Pl9/LROOT7FFxxhjjDGFJds8ZzPDUF9xQY0Fc1yYqTZqvfn6668BADfeeGPott9++5A7dOgAoLZKYjSGtIKJvv5qm8YUZ22p6zJPbNExxhhjTGGpmkVH36j4ROon03Sa0m4hD9LK6gOl7tF//vln6LRTe5s2bQDk3wokK7jW08r2A9nOn/6mFvSsb03p/3NeWvr1qWOaMGFCyP379wdQatUCAP369avegVUInV9dX3///TcA4I8//giddseee+65ASTb+RT1WqxldH3+8ssvIQ8fPhwA8NRTT4WOHc8BoGfPnlU4umJhi44xxhhjCosfdIwxxhhTWKrmunruuedCbteuHQBgySWXrNbP1ySNCTor54bIw43F31dz97Bhw0LmXD/99NOhU3Pr0UcfDQBYZpllQlcr7jigcW6eNN348eMBAFOnTg3dGmusEbLOZaXGzd+n2xAAJk6cGDL7r+nf6DGrm2OFFVYAAMw333yha0lurLS+Q7vsskvIn3zyCQDg5ZdfDt2iiy4acq27cTi+jz76KHS33npryO+++y6A5PW3yCKLhLz//vsDALbbbrvQrbjiiiHnPdf1XX+klvaMpqD7gO6fXJfqrrrrrrtC7tKlC4Bkx/q85yyNcgHWDQ3NqOSYbNExxhhjTGHxg44xxhhjCst0e101t/KlmubWXnvtkDfccEMAwCWXXJL6OR6THpt+V3PJozJyfRkwqvvtt98AAEcccUTodtttt5A7d+5c53N6rrKujMyxfP7556Fbf/31Q15zzTUBAAsssEDoHn/88ZBpRh87dmzo5pprrpDrM1lmUdkzzR2o51TdGb/++mudz+v89u7dGwCwxBJLhO6aa65J/d2066+h49NjZobbwIEDQ/fCCy+EPG3atJA5Fs3EmTRpUsi33XYbAKBbt26hU5dYc8m6MiuziejCAYC11lorZLpOTzvttNTPN9clknVl5Ndeew1A0rWhbizutZrpqOeC+8tWW20VOq0ppJ9LOxeVqoxczp3B9anXnB5HWlZjJd35WVdG5vq85557Qnf++eeHfMsttwAAllpqqdCx9hNQclm1bt06dI0Zc9b7J/dC3TN+/PHHkN98800A6fsoAGyyySYAgMUWW6xJx+LKyMYYY4yZoahaMPLmm28e8iuvvAIAmDJlSug08PGRRx4BkLQCDB06NORaD0Lj063WDtLAVI77/fffD50+3bJ+wqhRo0LHcwYAzz//fMi0hORxTnR8V111Vci0BOgxXX311SEfc8wxdXRqvaoW5d4IH3zwQQDADTfcEDqtc/Htt98CSFpxGCAIlIKRu3fvXuEjTqLH/P333wMA7r777tBttNFGIevxT548GUDyLWvWWWcNmZa4Wr/OFJ2LNIuoBuMyMF4/U+sByPp2fO211wIAPv3009CpJe/II48EAMw555yh07V87LHHAgDmn3/+0On8V6vOE+v9AMDDDz8cMq1LGiy+8MILh7zjjjsCSFoc1WKXZuXOG11rP/30EwDg+OOPD51eq0zS0DlffPHFQ06zOOdBuTpqtDhedtlloeM9HShZl8tZ4VjniZYfILlWmzJuW3SMMcYYU1j8oGOMMcaYwlI115UGZj7wwAMAknUu1PRFl5W6a/KuHZOGHpOWU2fNEq0ddNNNN4X84YcfAkgG82qdEwaJarCommlnm222kPM8F2paTZPnmGOO0GnTRLoh33nnndTPq0usWmb0119/PeQTTzwRQHJON9hgg5DPO+88AMAbb7wROnWT0PTao0eP1N+qVH0I/R5eX2oiXm211UJWN8G9994LABgyZEidYwZKZmI1t6tcizU7dJ0wmPPFF18MHd09QKlpZzl3VS26Plq1ahUyXR4aDrDtttuGzLmkixVIrguy+uqrh6zXXJZuPP0drfPTt2/fkDfeeGMAwKqrrhq6zTbbLOQddtgBQHLP1HtFx44dATSudkvW6F7Cdcl6TkCyjk7a9VUr11w51+OFF14Y8gUXXACg5EIGkvsL6+fR3Q4knwXmnXdeAKVWQZXAFh1jjDHGFBY/6BhjjDGmsFTNdfX777+HTNPxPPPMEzo1l2o5+lqjXKbGV199FTKzibR2irpmll12WQDAOuusEzp1g7ATMVtlAMBee+0Vspqx88gW4Tm4//77Q3fGGWeEvPTSSwMA1ltvvdBpVgXnV83R6qarlplWzbDq5mA2i5Zd13L5NNned999oVPTNLNetI5Q1m4Q/n6nTp1Sf/Ozzz4LWWumEL0+6UbQ7zr44INDbtu2bZ3vzwN1g7z66qsh07XTq1ev0O28884hp62vtE7gOqdKHuPW32R9ER2fwqxOdSc89thjIbPOzr777hu6rPcRnl/WewJKLkYg2SKFGWJaz0fnmq57db0+88wzIfO6o4sZKLU1AfLZX3TczKbVTE3WlgNqx02Vhl4nes8bOXJkyMwQ07Yrhx56aMjM9txjjz1Cx2sOKGUF1lfPqVHH3axPG2OMMcbUMH7QMcYYY0xhydR1pe6aRx99NGQWUitXpIoZJF988UXo8o6a5++rWVEL9+2zzz4hM9p8iy22CN2ee+4ZMs2Uak7WrAFGqGvBpeWXXz7kcqXRqwV/U02vWk6fWVUs7AWUMpmAUraImjbzdgfosdB0rgUNNQOE5dq1CNvpp58ecv/+/af7W1mia0MLAqrrk25SvaY064FrUQu2qWuApnft+J2HO0Bd3CeffHLIzCY699xzQ5fmGtXv0muZLkm62IFkCxb9rmrNq7rR+Jua1aItHrgWNetz3XXXDZkun0pmtdQHz/UPP/wQuieeeCJkvX7oGlV3hq5Pfu66664LnbpeuRdrpm8e+4uuL82wYlFPdd1pJm0tF6/U61yLGDKTWvVasFIztNj6iYUTAWDFFVcMmfeHShb0tEXHGGOMMYWl4hYdfQrT2iTvvfdeyP369QOQfMrTYK2ff/4ZQPIp+JtvvgmZQbpZP6WnNSrTNz+GES5cAAALU0lEQVRtqqccdthhAJIBnFqCnowZMyZkPRd8E9G3MB1r3kGgPC8MOgaA5ZZbLmTWaeG/QLKBIIMEG9PIM2s0cJhvF1p7RC0+b7/9NgDgoIMOCt3+++8fMt/48xiTvvmrFZVl2YFSiXmt/aM1d2gJ0vHfeeedIdN6cvnll6f+bpboNanXjwaT33rrrQBKzQ+B5Fzw7fCKK64InVoU2C6DlgWg1KgWSFp61OpQabQRq9YB+u677wAk91S1vrG+jFqUL7300pBZxySP9amWCx4HkLSe0lKndca03cVbb70FoHSfAIDBgweHTOuyNr3MY6y6VnX+aJ3Xtg/1XT+1YuXRe49aBFdZZZWQea71WUDnj81MdS0cfvjhIbMdVCXnzBYdY4wxxhQWP+gYY4wxprBk6rr68ssvQ9bu3cypHzBgQOi0Oy0D1tRcp8F2DHbKwpxXLuiZZvxDDjkkdJrnr4HDW265ZZ3vUtcUg+iefPLJ0GmwMcuBpwUgVpNyLS4YJK7BgDSnAyWT5N577x06rbNDl1Xe7ipFx0eTrLpTP/roo5BZf+SUU04JnQbW5zkuvSa4DgFg+PDhIdMNp2XZ09baNttsEzp149D1yKBsIBl4mMVa5fFpAOOZZ54ZsroBOnfuXOc4NJiVdXa0nhDbCgDA7rvvDqDUxR5Idn/PMjFC98+PP/445LPOOqvOsWiwrdahocuHLh4gff+sJlyX6iK+/vrrQz7ppJNCpst75ZVXDp26/rnXaGiAJkPQZZX3/qKux3HjxoXMZBRdk2wLBJTuleoaorsZSK6RPNHrS/cdXh/aouOYY44Jma7JXXfdNXS85oDStV7JfaQ2zpgxxhhjTAb4QccYY4wxhaXiris1F2omjubJ08ys3Xc1apsR+NrxW7+rWh2t1d1Gd5LqNFNDs4p4fOoOUNcOsz3UtKcuMdZs0YyOarmuyrnb1PXBbBstAa5jZWl61tMBkmbYPDMI0jLpAOCpp54KWesbEXbUBYBBgwYBSLpb9VzliV5/au7Xcv/8G11TadlDWi+GbQfygsc6duzY0Gkmh5b7pxtR15m6uW6++WYASdeVuvleeuklAMk6Xuo6yNIlot+tWUm33XZbyNw3llpqqdCpa5H1nbRFgq7prbfeGkD1MuUUHZ9mbWq7HP6NukO1nQWvW3WH1OL+ovcK7dTNbDlti6PXItea1hzSmjvMAKyVTKz/wnWl3ekff/zxkJlVpbXVNEMui3HZomOMMcaYwpKpRUeryWqVTgar6lO4vl107NgRQLJppDYAzcK6kVb5WGsfsA6J1mvQYNu0iq0aAKjBcqwJ1Lt379Bp00g+0VYzAJlvSfoWy+aUQPKJvE+fPgCSTUc12HillVYCkGw+mndgYFoAqQYIamVrvklr00Rt8Mkg5bzrGdVHuWDBhqLnLE3Oulq5fj8tTrSsAslg1fXXXz9k7iVae0sD53lda7C1VunlWtZgc13r1XqT1mBxBlgrOr9qfeP6rZWg1XLonqBv9DxuDQDXeedeqeenVq5FPY455pgjZLXO9ezZE0ApKB5IBi7zXLC5LpC0COXZJUDv0+WOgw2E1WKj9wJ6QqplJQVs0THGGGNMgfGDjjHGGGMKS6ZNPdV0quX+ad7Tpp9q8mODNjVtzT///Kl/WylohlMTopZV57F+/fXXoRs9enTIU6ZMCZn1cbTRmdZkYTNMDZBkgBZQPdO4mh5pOlQT8QsvvBAyGzkCpSBybWqpga9du3YFUNmmbE0hzd2irjkN0GWAJgBccMEFAJLBdA899FDIDEyuFXN5OXT8jTlWfk6DKTUImG6+ck15K4V+J49JWzFoWwp1rbJFg5rOdX0ycJwuWCDZboDJEHTBAtmsXzXXzzzzzHV0SlqdEj0/WrOMblYNkFfXWx5ByPWRNte6/0yYMCFkuj60hUCWrTiairprNHGBx6rhGDrvrCP0zjvvhE6TcfIMA9DrRPcHNgoGSjXl9PjZaBcAtt9+ewDVvT/YomOMMcaYwuIHHWOMMcYUlkxdV0p9pm01PY4fPx5Aurm2Id/VFPidmr2g3X9fffVVAMl6FprJoeZgRttrbYsRI0aEzBLgtdS9m6g7UUvMd+rUKeTJkycDSHZ/1gwDNdPWCpxfdbf9+uuvIWt3Z6K1g9RMzvoetei60uuoXM0guknKZU1wXLpmteYMs9XUNJ+164DXl9aG0bL5/fr1C5ltBrR2la7PK6+8EkCydo7W4eH6raQ5nef6zTffDJ1m8u23336J3wZKXeSB0pwBwMSJEwEka+Pcd999IbO+lWaIagZPmusrb3R9sk7QVVddFTpdawxpyDP7qCHo+WW4AgAce+yxAJL3Bw2ZOPvsswGUsrOAZNZWte4VOifMINZjUtfVb7/9FjLHrVnV2mme9YEWXXTRCh9xeWzRMcYYY0xhqZpFJw19Ite3a74xaqO6rOFTqD7FarAij0XfcvWJVoMdWedBK5e2b9++zm/pE38eb1dpAYDaXHTkyJEh8y0EKDW41KaBF110UcgMrK4VKxVQGqu+WWhzQw0yZ7NKDSBnNWgAWGihhQDU1vh4LEcddVTotPJqt27dQmb9Jq32rFW6GUSvTTu1aS2vhWoGgHJ8Cy64YOg0GPnZZ58NmRYN1vMAgB49eoTM49faLWm/lQWadKDWYQb76z6hFqu0irtqBdCaQgym3nHHHUOXd4Pg+tB9l4GtWnvt6KOPDnndddcFUJsByIqe5+7du4fMwPn/394d66QSRGEc/2zUjhexszKh8RGgMuEFiJWJnQ2xMfFNoMDGxIfhSZTqFjdn+PbegSDrsMv4/1WTjQmcLCzjOTNnnp6e0jX/XD8/P0uSptNputbF/fPXiUyjH8TtvX38tyKy335Qq58cEBnXY25QIaMDAACqxUQHAABU62xXGuzz87NojszTlX6A2XA4lNRcAOupsbap5cvLyzNJ+vr62hlfbjGnp9typR9pk2bcVpoqnXq8uLg4k6T1er33C0V8vgDS+/x4ye7m5kZSs7R3d3eXxrGgu1Sc5+fne8WXW8DuKfD5fJ7921hE9/b2lq5FbxZpkzLvOj4X7z/KipL08vKSxr5YNRac+8J7X+wahy167wsvTcYi+kPjPyS+nG2LrXNHVOS+t6XvX+756e/DF9u+vr5K2vQQk5ox+WL48Xgsqfl8jIOSpWYZvYR4frb9ffD4fBPE/f29pOYRLdGbTNqUPkqVGCO+tp9P57HGEUG+MN0X5kb5slS5qs3zxZ8ZHx8faTwajdL49vZWkrRYLNI1P6C1dMkx4nNkdAAAQLWY6AAAgGp1Wrry1LLv+ojV2n6id6TDpPZpvH1LV+47PRu63tVwSOkqeJyeTvaUeqTRPZ1+zNLcIaWrGHsr+djdIDXT4A8PD5Kkq6urdK2P8eV4utt3mPmuqdiN5HF46Wo2m0lq9nTxPiZt4/+p0lVf7SpdOU/hxxEy+/RBitKh7xrze1J6N2CJ0pUflzOZTCQ1j/Dw/knHiq/U5zPuq3/nPKYY97E07qXXx8fHNF4ul2kcx+V4uf+Yv4mUrgAAwK/CRAcAAFSr09LVNrky0U+mvg4pXZ2SNqUrty113nXDwzalj9zunH+dcnwuV7r7jlLxU7r6X26nmOu6uahrU7ry75+XkWOnrbRp5Pn+/p6u+UnfpRvNlS5dda3N988/e6vVKo1jJ5kkXV9ft3p/bVG6AgAAv0qnR0Bs0/V/LPjL78Mx23WX1KdjG0rrUxYAu5Xu7dMXHt9gMEhj7/MUfXK890otz59T5xlHP3bE9fFekdEBAADVYqIDAACqtXMxMgAAwCkjowMAAKrFRAcAAFSLiQ4AAKgWEx0AAFAtJjoAAKBaTHQAAEC1/gByL+0/CT1uNgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Randomly select 100 data points to display\n",
+ "rand_indices = np.random.choice(m, 100, replace=False)\n",
+ "sel = X[rand_indices, :]\n",
+ "\n",
+ "displayData(sel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have an understanding of the data we are working with, we can implement a cost function for linear regression."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# test values for the parameters theta\n",
+ "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n",
+ "\n",
+ "# test values for the inputs\n",
+ "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n",
+ "\n",
+ "# test values for the labels\n",
+ "y_t = np.array([1, 0, 1, 0, 1])\n",
+ "\n",
+ "# test value for the regularization parameter\n",
+ "lambda_t = 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(z):\n",
+ " \"\"\"\n",
+ " Compute sigmoid function given the input z.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " z : array_like\n",
+ " The input to the sigmoid function. This can be a 1-D vector \n",
+ " or a 2-D matrix. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " g : array_like\n",
+ " The computed sigmoid function. g has the same shape as z, since\n",
+ " the sigmoid is computed element-wise on z.\n",
+ " \"\"\"\n",
+ " # convert input to a numpy array\n",
+ " z = np.array(z)\n",
+ "\n",
+ " g = 1 + np.exp(-1*z)\n",
+ " g = np.reciprocal(g)\n",
+ "\n",
+ " return g"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def lrCostFunction(theta, X, y, lambda_):\n",
+ " \"\"\"\n",
+ " Computes the cost of using theta as the parameter for regularized\n",
+ " logistic regression and the gradient of the cost w.r.t. to the parameters.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " theta : array_like\n",
+ " Logistic regression parameters. A vector with shape (n, ). n is \n",
+ " the number of features including any intercept. \n",
+ " \n",
+ " X : array_like\n",
+ " The data set with shape (m x n). m is the number of examples, and\n",
+ " n is the number of features (including intercept).\n",
+ " \n",
+ " y : array_like\n",
+ " The data labels. A vector with shape (m, ).\n",
+ " \n",
+ " lambda_ : float\n",
+ " The regularization parameter. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed value for the regularized cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " A vector of shape (n, ) which is the gradient of the cost\n",
+ " function with respect to theta, at the current values of theta.\n",
+ " \n",
+ " \"\"\" \n",
+ " # convert labels to ints if their type is bool\n",
+ " if y.dtype == bool:\n",
+ " y = y.astype(int)\n",
+ " \n",
+ "\n",
+ " ## Initialize some useful values\n",
+ " m = y.size\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ " h = sigmoid(X.dot(theta))\n",
+ " logh = np.log(h)\n",
+ " tempLog = np.log(1-h)\n",
+ " yTrans = y.transpose()\n",
+ " Xtrans = X.transpose()\n",
+ " tempTrans = (1-y).transpose()\n",
+ " \n",
+ " J = ((-yTrans).dot(logh))\n",
+ " J = J - tempTrans.dot(tempLog)\n",
+ " J = J * (1/m)\n",
+ " J = J + (lambda_/(2*m))*np.sum(np.square(theta[1:]))\n",
+ " \n",
+ " diff = np.subtract(sigmoid(X.dot(theta)),y)\n",
+ " grad = Xtrans.dot(diff)\n",
+ " grad = grad * (1/m)\n",
+ " grad[1:] = grad[1:] + (lambda_/m)*theta[1:]\n",
+ " \n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can run our cost function on some test inputs to be sure it is running correctly. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost : 2.534819\n",
+ "-----------------------\n",
+ "Gradients:\n",
+ " [0.146561, -0.548558, 0.724722, 1.398003]\n"
+ ]
+ }
+ ],
+ "source": [
+ "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n",
+ "\n",
+ "print('Cost : {:.6f}'.format(J))\n",
+ "print('-----------------------')\n",
+ "print('Gradients:')\n",
+ "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working cost function, we can implement ove-vs all classification by training multiple regularized logistic regression classifiers, one for each our our K classes. Note that this classification will work for any value of K, not just our case where K = 10."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def oneVsAll(X, y, num_labels, lambda_):\n",
+ " \"\"\"\n",
+ " Trains num_labels logistic regression classifiers and returns\n",
+ " each of these classifiers in a matrix all_theta, where the i-th\n",
+ " row of all_theta corresponds to the classifier for label i.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input dataset of shape (m x n). m is the number of \n",
+ " data points, and n is the number of features. Note that we \n",
+ " do not assume that the intercept term (or bias) is in X, however\n",
+ " we provide the code below to add the bias term to X. \n",
+ " \n",
+ " y : array_like\n",
+ " The data labels. A vector of shape (m, ).\n",
+ " \n",
+ " num_labels : int\n",
+ " Number of possible labels.\n",
+ " \n",
+ " lambda_ : float\n",
+ " The logistic regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " all_theta : array_like\n",
+ " The trained parameters for logistic regression for each class.\n",
+ " This is a matrix of shape (K x n+1) where K is number of classes\n",
+ " (ie. `numlabels`) and n is number of features without the bias.\n",
+ " \"\"\"\n",
+ " # Some useful variables\n",
+ " m, n = X.shape\n",
+ " all_theta = np.zeros((num_labels, n + 1))\n",
+ "\n",
+ " # Add ones to the X data matrix\n",
+ " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ " \n",
+ " for c in range(num_labels):\n",
+ " initial_theta = np.zeros(n+1)\n",
+ " options = {'maxiter': 50}\n",
+ " res = optimize.minimize(lrCostFunction, \n",
+ " initial_theta, \n",
+ " (X, (y == c), lambda_), \n",
+ " jac=True, \n",
+ " method='TNC',\n",
+ " options=options) \n",
+ " all_theta[c,:] = res.x\n",
+ "\n",
+ " return all_theta"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run oneVsAll optimization with lambda = 0.1 to get a prediction for theta\n",
+ "lambda_ = 0.1\n",
+ "all_theta = oneVsAll(X, y, num_labels, lambda_)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working oneVsAll classification, we can use the resulting theta to predict what an input should be classified as."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predictOneVsAll(all_theta, X):\n",
+ " \"\"\"\n",
+ " Return a vector of predictions for each example in the matrix X. \n",
+ " Note that X contains the examples in rows. all_theta is a matrix where\n",
+ " the i-th row is a trained logistic regression theta vector for the \n",
+ " i-th class. You should set p to a vector of values from 0..K-1 \n",
+ " (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) .\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " all_theta : array_like\n",
+ " The trained parameters for logistic regression for each class.\n",
+ " This is a matrix of shape (K x n+1) where K is number of classes\n",
+ " and n is number of features without the bias.\n",
+ " \n",
+ " X : array_like\n",
+ " Data points to predict their labels. This is a matrix of shape \n",
+ " (m x n) where m is number of data points to predict, and n is number \n",
+ " of features without the bias term. Note we add the bias term for X in \n",
+ " this function. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " p : array_like\n",
+ " The predictions for each data point in X. This is a vector of shape (m, ).\n",
+ " \"\"\"\n",
+ " m = X.shape[0];\n",
+ " num_labels = all_theta.shape[0]\n",
+ " p = np.zeros(m)\n",
+ "\n",
+ " # Add ones to the X data matrix\n",
+ " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "\n",
+ " all_theta_T = all_theta.transpose()\n",
+ " temp = sigmoid(X.dot(all_theta_T))\n",
+ " for i in range(m):\n",
+ " iTempMax = np.argmax(temp[i,:])\n",
+ " p[i] = iTempMax\n",
+ "\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Set Accuracy: 95.14%\n"
+ ]
+ }
+ ],
+ "source": [
+ "pred = predictOneVsAll(all_theta, X)\n",
+ "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Neural Networks
\n",
+ "\n",
+ "We have now implemented multi-class logistic regression to recognize handwritten digits. However, because this is only a linear classifier, logistic regression cannot form more complex hypotheses. \n",
+ " In this portion of the exercise, we will implement a neural network to recognize handwritten digits using the same training set as before. The neural network will be able to represent more complex models to from non-linear hypotheses. In this exercise we will implement parameters from a neural network that has already been trained. Our goal is to implement the feedforward propagation algorithm to use our weights for prediction.\n",
+ " Our neural network is shown in the following figure. it has 3 layers and takes as input our pixel values of digital images. This gives us 400 input layer units (plus our extra bias unit outputting +1). Our network parameters are stored in ex3weights.mat. We begin by loading them into Theta1 and Theta2.\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# training data stored in arrays X, y\n",
+ "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+ "X, y = data['X'], data['y'].ravel()\n",
+ "\n",
+ "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+ "# This is an artifact due to the fact that this dataset was used in \n",
+ "# MATLAB where there is no index 0\n",
+ "y[y == 10] = 0\n",
+ "\n",
+ "# get number of examples in dataset\n",
+ "m = y.size\n",
+ "\n",
+ "# randomly permute examples, to be used for visualizing one \n",
+ "# picture at a time\n",
+ "indices = np.random.permutation(m)\n",
+ "\n",
+ "# Randomly select 100 data points to display\n",
+ "rand_indices = np.random.choice(m, 100, replace=False)\n",
+ "sel = X[rand_indices, :]\n",
+ "\n",
+ "displayData(sel)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup the parameters we will use for this exercise\n",
+ "input_layer_size = 400 # 20x20 Input Images of Digits\n",
+ "hidden_layer_size = 25 # 25 hidden units\n",
+ "num_labels = 10 # 10 labels, from 0 to 9\n",
+ "\n",
+ "# Load the .mat file, which returns a dictionary \n",
+ "weights = loadmat(os.path.join('Data', 'ex3weights.mat'))\n",
+ "\n",
+ "# get the model weights from the dictionary\n",
+ "# Theta1 has size 25 x 401\n",
+ "# Theta2 has size 10 x 26\n",
+ "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+ "\n",
+ "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+ "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+ "Theta2 = np.roll(Theta2, 1, axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now implement in predict() the feedforward computation which computes predictions for each training sample."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(Theta1, Theta2, X):\n",
+ " \"\"\"\n",
+ " Predict the label of an input given a trained neural network.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " Theta1 : array_like\n",
+ " Weights for the first layer in the neural network.\n",
+ " It has shape (2nd hidden layer size x input size)\n",
+ " \n",
+ " Theta2: array_like\n",
+ " Weights for the second layer in the neural network. \n",
+ " It has shape (output layer size x 2nd hidden layer size)\n",
+ " \n",
+ " X : array_like\n",
+ " The image inputs having shape (number of examples x image dimensions).\n",
+ " \n",
+ " Return \n",
+ " ------\n",
+ " p : array_like\n",
+ " Predictions vector containing the predicted label for each example.\n",
+ " It has a length equal to the number of examples.\n",
+ " \"\"\"\n",
+ " # Make sure the input has two dimensions\n",
+ " if X.ndim == 1:\n",
+ " X = X[None] # promote to 2-dimensions\n",
+ " \n",
+ " # useful variables\n",
+ " m = X.shape[0]\n",
+ " num_labels = Theta2.shape[0]\n",
+ " \n",
+ " X = np.concatenate([np.ones((m, 1)), X], axis=1) # Add collumn of ones to X\n",
+ " z2 = Theta1.dot(X.transpose())\n",
+ " z2 = z2.transpose()\n",
+ " a2 = sigmoid(z2)\n",
+ " a2 = np.concatenate([np.ones((a2.shape[0], 1)), a2], axis=1) # Add collumn of ones to a2\n",
+ " z3 = Theta2.dot(a2.transpose())\n",
+ " a3 = sigmoid(z3)\n",
+ " a3 = a3.transpose()\n",
+ " p = np.argmax(a3, axis=1)\n",
+ "\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Set Accuracy: 97.5%\n"
+ ]
+ }
+ ],
+ "source": [
+ "pred = predict(Theta1, Theta2, X)\n",
+ "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Neural Network Prediction: 0\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
\n",
+ "In this exercise we will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.\n",
+ "\n",
+ "
\n",
+ "In this exercise we will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.\n",
+ "\n",
+ "
\n",
+ "In the previous exercise, we implemented feedforward propogation for neural networks and used it to predict handwritten digits with given weights. Here we will implement backpropagation to learn the parameters ourselves. We begin by bringing in some useful functions from our previous exercise."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+ " \"\"\"\n",
+ " Displays 2D data stored in X in a nice grid.\n",
+ " \"\"\"\n",
+ " # Compute rows, cols\n",
+ " if X.ndim == 2:\n",
+ " m, n = X.shape\n",
+ " elif X.ndim == 1:\n",
+ " n = X.size\n",
+ " m = 1\n",
+ " X = X[None] # Promote to a 2 dimensional array\n",
+ " else:\n",
+ " raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+ "\n",
+ " example_width = example_width or int(np.round(np.sqrt(n)))\n",
+ " example_height = n / example_width\n",
+ "\n",
+ " # Compute number of items to display\n",
+ " display_rows = int(np.floor(np.sqrt(m)))\n",
+ " display_cols = int(np.ceil(m / display_rows))\n",
+ "\n",
+ " fig, ax_array = plt.subplots(display_rows, display_cols, figsize=figsize)\n",
+ " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+ "\n",
+ " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+ "\n",
+ " for i, ax in enumerate(ax_array):\n",
+ " ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n",
+ " cmap='Greys', extent=[0, 1, 0, 1])\n",
+ " ax.axis('off')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(z):\n",
+ " \"\"\"\n",
+ " Compute sigmoid function given the input z.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " z : array_like\n",
+ " The input to the sigmoid function. This can be a 1-D vector \n",
+ " or a 2-D matrix. \n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " g : array_like\n",
+ " The computed sigmoid function. g has the same shape as z, since\n",
+ " the sigmoid is computed element-wise on z.\n",
+ " \"\"\"\n",
+ " # convert input to a numpy array\n",
+ " z = np.array(z)\n",
+ "\n",
+ " g = 1 + np.exp(-1*z)\n",
+ " g = np.reciprocal(g)\n",
+ "\n",
+ " return g"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(Theta1, Theta2, X):\n",
+ " \"\"\"\n",
+ " Predict the label of an input given a trained neural network\n",
+ " Outputs the predicted label of X given the trained weights of a neural\n",
+ " network(Theta1, Theta2)\n",
+ " \"\"\"\n",
+ " # Useful values\n",
+ " m = X.shape[0]\n",
+ " num_labels = Theta2.shape[0]\n",
+ "\n",
+ " # You need to return the following variables correctly\n",
+ " p = np.zeros(m)\n",
+ " h1 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), X], axis=1), Theta1.T))\n",
+ " h2 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), h1], axis=1), Theta2.T))\n",
+ " p = np.argmax(h2, axis=1)\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can visualize our data using our old function displayData"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# training data stored in arrays X, y\n",
+ "data = loadmat(os.path.join('Data', 'ex4data1.mat'))\n",
+ "X, y = data['X'], data['y'].ravel()\n",
+ "\n",
+ "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+ "# This is an artifact due to the fact that this dataset was used in \n",
+ "# MATLAB where there is no index 0\n",
+ "y[y == 10] = 0\n",
+ "\n",
+ "# Number of training examples\n",
+ "m = y.size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Randomly select 100 data points to display\n",
+ "rand_indices = np.random.choice(m, 100, replace=False)\n",
+ "sel = X[rand_indices, :]\n",
+ "\n",
+ "displayData(sel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that, as before, our data consists of 5000 samples of 20 by 20 pixel grayscale images of a digit. Each pixel is represented by a floating point number indicating grayscale value, and the grid of pixels is \"unrolled\" into a 400-dimensional vector. Each training sample is a row in our data matrix X, leaving us with the 5000 by 400 matrix X. We also have a 5000 dimensional vector y consiting of labels for the training set. The following figure provides a representation of our neural network model.\n",
+ "\n",
+ "\n",
+ "\n",
+ "It has 3 layers - an input layer, a hidden layer and an output layer. Recall that our inputs are pixel values\n",
+ "of digit images. Since the images are of size $20 \\times 20$, this gives us 400 input layer units (not counting the extra bias unit which always outputs +1). The training data was loaded into the variables `X` and `y` above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup the parameters you will use for this exercise\n",
+ "input_layer_size = 400 # 20x20 Input Images of Digits\n",
+ "hidden_layer_size = 25 # 25 hidden units\n",
+ "num_labels = 10 # 10 labels, from 0 to 9\n",
+ "\n",
+ "# Load the weights into variables Theta1 and Theta2\n",
+ "weights = loadmat(os.path.join('Data', 'ex4weights.mat'))\n",
+ "\n",
+ "# Theta1 has size 25 x 401\n",
+ "# Theta2 has size 10 x 26\n",
+ "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+ "\n",
+ "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+ "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+ "Theta2 = np.roll(Theta2, 1, axis=0)\n",
+ "\n",
+ "# Unroll parameters \n",
+ "nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now implement our neural network's cost function to return the cost. Recall that our regularized cost function is represented by $$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m}\\sum_{k=1}^{K} \\left[ - y_k^{(i)} \\log \\left( \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) - \\left( 1 - y_k^{(i)} \\right) \\log \\left( 1 - \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) \\right] + \\frac{\\lambda}{2 m} \\left[ \\sum_{j=1}^{25} \\sum_{k=1}^{400} \\left( \\Theta_{j,k}^{(1)} \\right)^2 + \\sum_{j=1}^{10} \\sum_{k=1}^{25} \\left( \\Theta_{j,k}^{(2)} \\right)^2 \\right] $$\n",
+ "\n",
+ "and our regularized gradient as $$ \\begin{align} \n",
+ "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} & \\qquad \\text{for } j = 0 \\\\\n",
+ "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} + \\frac{\\lambda}{m} \\Theta_{ij}^{(l)} & \\qquad \\text{for } j \\ge 1\n",
+ "\\end{align}\n",
+ "$$\n",
+ "\n",
+ "Note that we will *not* be regularizing the first column of $\\Theta^{(l)}$ which is used for the bias term. Furthermore, in the parameters $\\Theta_{ij}^{(l)}$, $i$ is indexed starting from 1, and $j$ is indexed starting from 0. Thus, \n",
+ "\n",
+ "$$\n",
+ "\\Theta^{(l)} = \\begin{bmatrix}\n",
+ "\\Theta_{1,0}^{(i)} & \\Theta_{1,1}^{(l)} & \\cdots \\\\\n",
+ "\\Theta_{2,0}^{(i)} & \\Theta_{2,1}^{(l)} & \\cdots \\\\\n",
+ "\\vdots & ~ & \\ddots\n",
+ "\\end{bmatrix}\n",
+ "$$\n",
+ "\n",
+ "Note that for this cost function we will need the sigmoid gradient function as well as a function to randomly initialize theta, since a zero initialization would not lead to a helpful solution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoidGradient(z):\n",
+ " \"\"\"\n",
+ " Computes the gradient of the sigmoid function evaluated at z. \n",
+ " This should work regardless if z is a matrix or a vector. \n",
+ " In particular, if z is a vector or matrix, you should return\n",
+ " the gradient for each element.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " z : array_like\n",
+ " A vector or matrix as input to the sigmoid function. \n",
+ " \n",
+ " Returns\n",
+ " --------\n",
+ " g : array_like\n",
+ " Gradient of the sigmoid function. Has the same shape as z. \n",
+ " \n",
+ " Instructions\n",
+ " ------------\n",
+ " Compute the gradient of the sigmoid function evaluated at\n",
+ " each value of z (z can be a matrix, vector or scalar).\n",
+ " \n",
+ " Note\n",
+ " ----\n",
+ " We have provided an implementation of the sigmoid function \n",
+ " in `utils.py` file accompanying this assignment.\n",
+ " \"\"\"\n",
+ "\n",
+ " g = np.zeros(z.shape)\n",
+ "\n",
+ " # ====================== YOUR CODE HERE ======================\n",
+ "\n",
+ " g = np.multiply(sigmoid(z), (1-sigmoid(z)))\n",
+ "\n",
+ " # =============================================================\n",
+ " return g"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def randInitializeWeights(L_in, L_out, epsilon_init=0.12):\n",
+ " \"\"\"\n",
+ " Randomly initialize the weights of a layer in a neural network.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " L_in : int\n",
+ " Number of incomming connections.\n",
+ " \n",
+ " L_out : int\n",
+ " Number of outgoing connections. \n",
+ " \n",
+ " epsilon_init : float, optional\n",
+ " Range of values which the weight can take from a uniform \n",
+ " distribution.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " W : array_like\n",
+ " The weight initialiatized to random values. Note that W should\n",
+ " be set to a matrix of size(L_out, 1 + L_in) as\n",
+ " the first column of W handles the \"bias\" terms.\n",
+ " \"\"\"\n",
+ " epsilon_init = 0.12\n",
+ " W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n",
+ "\n",
+ " return W"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def nnCostFunction(nn_params,\n",
+ " input_layer_size,\n",
+ " hidden_layer_size,\n",
+ " num_labels,\n",
+ " X, y, lambda_=0.0):\n",
+ " \"\"\"\n",
+ " Implements the neural network cost function and gradient for a two layer neural \n",
+ " network which performs classification. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " nn_params : array_like\n",
+ " The parameters for the neural network which are \"unrolled\" into \n",
+ " a vector. This needs to be converted back into the weight matrices Theta1\n",
+ " and Theta2.\n",
+ " \n",
+ " input_layer_size : int\n",
+ " Number of features for the input layer. \n",
+ " \n",
+ " hidden_layer_size : int\n",
+ " Number of hidden units in the second layer.\n",
+ " \n",
+ " num_labels : int\n",
+ " Total number of labels, or equivalently number of units in output layer. \n",
+ " \n",
+ " X : array_like\n",
+ " Input dataset. A matrix of shape (m x input_layer_size).\n",
+ " \n",
+ " y : array_like\n",
+ " Dataset labels. A vector of shape (m,).\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " Regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed value for the cost function at the current weight values.\n",
+ " \n",
+ " grad : array_like\n",
+ " An \"unrolled\" vector of the partial derivatives of the concatenatation of\n",
+ " neural network weights Theta1 and Theta2.\n",
+ " \"\"\"\n",
+ " # Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices\n",
+ " # for our 2 layer neural network\n",
+ " Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n",
+ " (hidden_layer_size, (input_layer_size + 1)))\n",
+ "\n",
+ " Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n",
+ " (num_labels, (hidden_layer_size + 1)))\n",
+ "\n",
+ " # Setup some useful variables\n",
+ " m = y.size\n",
+ " K = num_labels\n",
+ " J = 0\n",
+ " Theta1_grad = np.zeros(Theta1.shape)\n",
+ " Theta2_grad = np.zeros(Theta2.shape)\n",
+ "\n",
+ " # Forward Propogation\n",
+ " y_mat = np.identity(num_labels)[y,:]\n",
+ " a1 = np.concatenate([np.ones((m, 1)), X], axis=1) # Add collumn of ones to X\n",
+ " z2 = Theta1.dot(a1.transpose())\n",
+ " z2 = z2.transpose()\n",
+ " a2 = sigmoid(z2)\n",
+ " a2 = np.concatenate([np.ones((a2.shape[0], 1)), a2], axis=1)\n",
+ " z3 = Theta2.dot(a2.transpose())\n",
+ " a3 = sigmoid(z3)\n",
+ " a3 = a3.transpose()\n",
+ " p = np.argmax(a3, axis=1)\n",
+ " \n",
+ " # Unregularized cost function\n",
+ " log_h = np.log(a3)\n",
+ " prod1 = np.multiply(y_mat, log_h)\n",
+ " prod2 = np.multiply((1-y_mat), np.log(1-a3))\n",
+ " for i in range(m):\n",
+ " for k in range(K):\n",
+ " J = J + prod1[i,k]\n",
+ " J = J + prod2[i,k]\n",
+ " J = -(J/m)\n",
+ " temp = 0\n",
+ " \n",
+ " # Regularization term\n",
+ " for i in range(Theta1.shape[0]):\n",
+ " for j in range(1,Theta1.shape[1]):\n",
+ " temp = temp + (Theta1[i,j])**2\n",
+ " temp = temp * (lambda_/(2*m))\n",
+ " J = J + temp\n",
+ " temp = 0\n",
+ " for i in range(Theta2.shape[0]):\n",
+ " for j in range(1,Theta2.shape[1]):\n",
+ " temp = temp + (Theta2[i,j])**2\n",
+ " temp = temp * (lambda_/(2*m))\n",
+ " J = J + temp\n",
+ " \n",
+ " # Backpropagation\n",
+ " d3 = a3 - y_mat\n",
+ " d2 = np.multiply((d3.dot(Theta2[:,1:])), sigmoidGradient(z2))\n",
+ " Delta1 = d2.transpose().dot(a1)\n",
+ " Delta2 = d3.transpose().dot(a2)\n",
+ " Theta1_grad = Delta1/m\n",
+ " Theta2_grad = Delta2/m\n",
+ " \n",
+ " # Regularized Backpropagation\n",
+ " Theta1[:,0] = 0\n",
+ " Theta2[:,0] = 0\n",
+ " Theta1 = (lambda_/m)*Theta1\n",
+ " Theta2 = (lambda_/m)*Theta2\n",
+ " Theta1_grad = Theta1_grad + Theta1\n",
+ " Theta2_grad = Theta2_grad + Theta2\n",
+ " \n",
+ " # ================================================================\n",
+ " # Unroll gradients\n",
+ " # grad = np.concatenate([Theta1_grad.ravel(order=order), Theta2_grad.ravel(order=order)])\n",
+ " grad = np.concatenate([Theta1_grad.ravel(), Theta2_grad.ravel()])\n",
+ "\n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now initialize lambda and check our cost function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at parameters (loaded from ex4weights): 0.383770\n",
+ "This value should be about : 0.383770.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Weight regularization parameter (we set this to 1 here).\n",
+ "lambda_ = 1\n",
+ "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n",
+ " num_labels, X, y, lambda_)\n",
+ "\n",
+ "print('Cost at parameters (loaded from ex4weights): %.6f' % J)\n",
+ "print('This value should be about : 0.383770.')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex5/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/ex5/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..fc2d779
--- /dev/null
+++ b/ex5/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,845 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 5:\n",
+ " Regularized Linear Regression and Bias vs. Variance
\n",
+ " \n",
+ "
Introduction
\n",
+ "In this exercise, we will implement regularized linear regression and use it to study models with different bias-variance properties. To start, we will import necessary modules, implement some useful functions from previous exercises, and load our data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def trainLinearReg(linearRegCostFunction, X, y, lambda_=0.0, maxiter=200):\n",
+ " \"\"\"\n",
+ " Trains linear regression using scipy's optimize.minimize.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset with shape (m x n+1). The bias term is assumed to be concatenated.\n",
+ "\n",
+ " y : array_like\n",
+ " Function values at each datapoint. A vector of shape (m,).\n",
+ "\n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ "\n",
+ " maxiter : int, optional\n",
+ " Maximum number of iteration for the optimization algorithm.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " theta : array_like\n",
+ " The parameters for linear regression. This is a vector of shape (n+1,).\n",
+ " \"\"\"\n",
+ " # Initialize Theta\n",
+ " initial_theta = np.zeros(X.shape[1])\n",
+ "\n",
+ " # Create \"short hand\" for the cost function to be minimized\n",
+ " costFunction = lambda t: linearRegCostFunction(X, y, t, lambda_)\n",
+ "\n",
+ " # Now, costFunction is a function that takes in only one argument\n",
+ " options = {'maxiter': maxiter}\n",
+ "\n",
+ " # Minimize using scipy\n",
+ " res = optimize.minimize(costFunction, initial_theta, jac=True, method='TNC', options=options)\n",
+ " return res.x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def featureNormalize(X):\n",
+ " \"\"\"\n",
+ " Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+ " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+ " working with learning algorithms.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+ " and n is the number of dimensions for each example.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " X_norm : array_like\n",
+ " The normalized input dataset.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of size n corresponding to the mean for each dimension across all examples.\n",
+ "\n",
+ " sigma : array_like\n",
+ " A vector of size n corresponding to the standard deviations for each dimension across\n",
+ " all examples.\n",
+ " \"\"\"\n",
+ " mu = np.mean(X, axis=0)\n",
+ " X_norm = X - mu\n",
+ "\n",
+ " sigma = np.std(X_norm, axis=0, ddof=1)\n",
+ " X_norm /= sigma\n",
+ " return X_norm, mu, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotFit(polyFeatures, min_x, max_x, mu, sigma, theta, p):\n",
+ " \"\"\"\n",
+ " Plots a learned polynomial regression fit over an existing figure.\n",
+ " Also works with linear regression.\n",
+ " Plots the learned polynomial fit with power p and feature normalization (mu, sigma).\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " polyFeatures : func\n",
+ " A function which generators polynomial features from a single feature.\n",
+ "\n",
+ " min_x : float\n",
+ " The minimum value for the feature.\n",
+ "\n",
+ " max_x : float\n",
+ " The maximum value for the feature.\n",
+ "\n",
+ " mu : float\n",
+ " The mean feature value over the training dataset.\n",
+ "\n",
+ " sigma : float\n",
+ " The feature standard deviation of the training dataset.\n",
+ "\n",
+ " theta : array_like\n",
+ " The parameters for the trained polynomial linear regression.\n",
+ "\n",
+ " p : int\n",
+ " The polynomial order.\n",
+ " \"\"\"\n",
+ " # We plot a range slightly bigger than the min and max values to get\n",
+ " # an idea of how the fit will vary outside the range of the data points\n",
+ " x = np.arange(min_x - 15, max_x + 25, 0.05).reshape(-1, 1)\n",
+ "\n",
+ " # Map the X values\n",
+ " X_poly = polyFeatures(x, p)\n",
+ " X_poly -= mu\n",
+ " X_poly /= sigma\n",
+ "\n",
+ " # Add ones\n",
+ " X_poly = np.concatenate([np.ones((x.shape[0], 1)), X_poly], axis=1)\n",
+ "\n",
+ " # Plot\n",
+ " plt.plot(x, np.dot(X_poly, theta), '--', lw=2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Regularized Linear Regression
\n",
+ "In the first half of this exercize, we will implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. We begin by visualizing the dataset which is split into a training set (X,y), a cross validation set (Xval, yval), and a test set (Xtest, ytest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex5data1.mat, where all variables will be store in a dictionary\n",
+ "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n",
+ "\n",
+ "# Extract train, test, validation data from dictionary\n",
+ "# and also convert y's form 2-D matrix (MATLAB format) to a numpy vector\n",
+ "X, y = data['X'], data['y'][:, 0]\n",
+ "Xtest, ytest = data['Xtest'], data['ytest'][:, 0]\n",
+ "Xval, yval = data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# m = Number of examples\n",
+ "m = y.size\n",
+ "\n",
+ "# Plot training data\n",
+ "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n",
+ "pyplot.xlabel('Change in water level (x)')\n",
+ "pyplot.ylabel('Water flowing out of the dam (y)');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we implement a regularized linear regression cost function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n",
+ " \"\"\"\n",
+ " Compute cost and gradient for regularized linear regression \n",
+ " with multiple variables. Computes the cost of using theta as\n",
+ " the parameter for linear regression to fit the data points in X and y. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset. Matrix with shape (m x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " theta : array_like\n",
+ " The parameters for linear regression. A vector of shape (n+1,).\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " The value of the cost function gradient w.r.t theta. \n",
+ " A vector of shape (n+1, ).\n",
+ " \"\"\"\n",
+ " # Initialize some useful values\n",
+ " m = y.size # number of training examples\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ "\n",
+ " h = X.dot(theta)\n",
+ " J = h-y\n",
+ " J = np.square(J)\n",
+ " J = np.sum(J)\n",
+ " J = J / (2*m)\n",
+ " tempTheta = theta[0]\n",
+ " theta[0] = 0\n",
+ " J += (lambda_/(2*m))*np.sum(np.sum(np.square(theta)))\n",
+ " theta[0] = tempTheta\n",
+ " \n",
+ " grad = (1/m)*X.transpose().dot(h-y)\n",
+ " grad[1:] += (lambda_/m)*theta[1:]\n",
+ " \n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at theta = [1, 1]:\t 303.993192 \n",
+ "Gradient at theta = [1, 1]: [-15.303016, 598.250744] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Test case for cost function\n",
+ "\n",
+ "theta = np.array([1, 1])\n",
+ "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
+ "\n",
+ "print('Cost at theta = [1, 1]:\\t %f ' % J)\n",
+ "print('Gradient at theta = [1, 1]: [{:.6f}, {:.6f}] '.format(*grad))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next we run train our linear regression model using this cost function and graph the resulting line of best fit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# add a columns of ones for the y-intercept\n",
+ "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "theta = trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n",
+ "\n",
+ "# Plot fit over the data\n",
+ "plt.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)')\n",
+ "plt.plot(X, np.dot(X_aug, theta), '--', lw=2);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Bias-Variance
\n",
+ "An important concept in machine learning is the bias-variance tradeoff. High bias models are not complex enough for the data and tend to underfit, while high variance models over fit the training data.\n",
+ "\n",
+ "In this portion of the exercise we attempt to diagnose bias-variance problems by plotting training and test errors on a learning curve. \n",
+ "\n",
+ "We begin by creating a function to return a vector of errors for the training and cross validation set, then plotting it on a graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def learningCurve(X, y, Xval, yval, lambda_=0):\n",
+ " \"\"\"\n",
+ " Generates the train and cross validation set errors needed to plot a learning curve\n",
+ " returns the train and cross validation set errors for a learning curve. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The training dataset. Matrix with shape (m x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each training datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " Xval : array_like\n",
+ " The validation dataset. Matrix with shape (m_val x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " yval : array_like\n",
+ " The functions values at each validation datapoint. A vector of\n",
+ " shape (m_val, ).\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " error_train : array_like\n",
+ " A vector of shape m. error_train[i] contains the training error for\n",
+ " i examples.\n",
+ " error_val : array_like\n",
+ " A vecotr of shape m. error_val[i] contains the validation error for\n",
+ " i training examples.\n",
+ " \"\"\"\n",
+ " # Number of training examples\n",
+ " m = y.size\n",
+ "\n",
+ " # You need to return these values correctly\n",
+ " error_train = np.zeros(m)\n",
+ " error_val = np.zeros(m)\n",
+ "\n",
+ " # ====================== YOUR CODE HERE ======================\n",
+ " \n",
+ " for i in range(1, m+1):\n",
+ " X_train = X[:i, :]\n",
+ " y_train = y[:i]\n",
+ " Theta = trainLinearReg(linearRegCostFunction, X_train, y_train, lambda_=0.0, maxiter=200)\n",
+ " error_train[i-1] = linearRegCostFunction(X_train,y_train,Theta,0)[0];\n",
+ " error_val[i-1] = linearRegCostFunction(Xval,yval,Theta,0)[0];\n",
+ " \n",
+ " # =============================================================\n",
+ " return error_train, error_val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "# Training Examples\tTrain Error\tCross Validation Error\n",
+ " \t1\t\t0.000000\t205.121096\n",
+ " \t2\t\t0.000000\t110.302641\n",
+ " \t3\t\t3.286595\t45.010231\n",
+ " \t4\t\t2.842678\t48.368911\n",
+ " \t5\t\t13.154049\t35.865165\n",
+ " \t6\t\t19.443963\t33.829962\n",
+ " \t7\t\t20.098522\t31.970986\n",
+ " \t8\t\t18.172859\t30.862446\n",
+ " \t9\t\t22.609405\t31.135998\n",
+ " \t10\t\t23.261462\t28.936207\n",
+ " \t11\t\t24.317250\t29.551432\n",
+ " \t12\t\t22.373906\t29.433818\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n",
+ "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n",
+ "\n",
+ "plt.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n",
+ "plt.title('Learning curve for linear regression')\n",
+ "plt.legend(['Train', 'Cross Validation'])\n",
+ "plt.xlabel('Number of training examples')\n",
+ "plt.ylabel('Error')\n",
+ "plt.axis([0, 13, 0, 150])\n",
+ "\n",
+ "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+ "for i in range(m):\n",
+ " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the resulting figure, we can see that both the taining and cross validation errors are high when the number of training examples is increase (specifically the training error increases to math cross validation). This reflects a problem of high bias in our model. That is to say, our model is too simple and unable to fit our data set well. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
3 Polynomial Regression
\n",
+ "The problem with our model was that it was too simple for the data and resulted in underfitting (high bias). In this portion of the exercise, we will address this problem by adding more features to produce a more complex fit to the data. We begin by creating a function to map the original training set into its higher powers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def polyFeatures(X, p):\n",
+ " \"\"\"\n",
+ " Maps X (1D vector) into the p-th power.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " A data vector of size m, where m is the number of examples.\n",
+ " \n",
+ " p : int\n",
+ " The polynomial power to map the features. \n",
+ " \n",
+ " Returns \n",
+ " -------\n",
+ " X_poly : array_like\n",
+ " A matrix of shape (m x p) where p is the polynomial \n",
+ " power and m is the number of examples. That is:\n",
+ " \n",
+ " X_poly[i, :] = [X[i], X[i]**2, X[i]**3 ... X[i]**p]\n",
+ " \"\"\"\n",
+ " X_poly = np.zeros((X.shape[0], p))\n",
+ " X_poly[:,0] = X[:,0]\n",
+ " for i in range(1,p):\n",
+ " X_poly[:,i] = np.power(X.transpose(), i+1)\n",
+ "\n",
+ " return X_poly"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now apply this function to our training set, test set, and cross validation set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p = 8\n",
+ "\n",
+ "# Map X onto Polynomial Features and Normalize\n",
+ "X_poly = polyFeatures(X, p)\n",
+ "X_poly, mu, sigma = featureNormalize(X_poly)\n",
+ "X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1)\n",
+ "\n",
+ "# Map X_poly_test and normalize (using mu and sigma)\n",
+ "X_poly_test = polyFeatures(Xtest, p)\n",
+ "X_poly_test -= mu\n",
+ "X_poly_test /= sigma\n",
+ "X_poly_test = np.concatenate([np.ones((ytest.size, 1)), X_poly_test], axis=1)\n",
+ "\n",
+ "# Map X_poly_val and normalize (using mu and sigma)\n",
+ "X_poly_val = polyFeatures(Xval, p)\n",
+ "X_poly_val -= mu\n",
+ "X_poly_val /= sigma\n",
+ "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the ability to map polynomial features, we can train our model via linear regression and plot to see how it fits our data. We will also plot a learning curve for lambda = 0 to see if we still have a bias/variance problem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Polynomial Regression (lambda = 0.000000)\n",
+ "\n",
+ "# Training Examples\tTrain Error\tCross Validation Error\n",
+ " \t1\t\t0.000000\t160.721900\n",
+ " \t2\t\t0.000000\t160.121511\n",
+ " \t3\t\t0.000000\t59.071634\n",
+ " \t4\t\t0.000000\t77.997728\n",
+ " \t5\t\t0.000000\t6.448961\n",
+ " \t6\t\t0.000000\t10.831639\n",
+ " \t7\t\t0.000000\t27.916727\n",
+ " \t8\t\t0.000064\t21.128258\n",
+ " \t9\t\t0.000147\t30.474290\n",
+ " \t10\t\t0.021425\t50.335502\n",
+ " \t11\t\t0.032329\t55.153697\n",
+ " \t12\t\t0.036300\t37.781163\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lambda_ = 0\n",
+ "theta = trainLinearReg(linearRegCostFunction, X_poly, y,\n",
+ " lambda_=lambda_, maxiter=55)\n",
+ "\n",
+ "# Plot training data and fit\n",
+ "plt.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+ "\n",
+ "plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
+ "\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)')\n",
+ "plt.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+ "plt.ylim([-20, 50])\n",
+ "\n",
+ "plt.figure()\n",
+ "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n",
+ "plt.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
+ "\n",
+ "plt.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
+ "plt.xlabel('Number of training examples')\n",
+ "plt.ylabel('Error')\n",
+ "plt.axis([0, 13, 0, 100])\n",
+ "plt.legend(['Train', 'Cross Validation'])\n",
+ "\n",
+ "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n",
+ "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+ "for i in range(m):\n",
+ " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the resulting figures, we can see that our curve fits the data extremely well. In fact, it fits it too well. Along the samples it follows perfectly, however it fails to follow the trend along the extremes. We can also see this in the learning curve, as while the training error is extremely low, the cross validation error (the error we would realistically expect to see) is still high. This imply we now have an issue of high-variance, or overfitting. To address this, we can add a regularization term. In order to choose an effective lambda, we automate the process by testing a sequence of lambdas and choosing the one with the least error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def validationCurve(X, y, Xval, yval):\n",
+ " \"\"\"\n",
+ " Generate the train and validation errors needed to plot a validation\n",
+ " curve that we can use to select lambda_.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The training dataset. Matrix with shape (m x n) where m is the \n",
+ " total number of training examples, and n is the number of features \n",
+ " including any polynomial features.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each training datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " Xval : array_like\n",
+ " The validation dataset. Matrix with shape (m_val x n) where m is the \n",
+ " total number of validation examples, and n is the number of features \n",
+ " including any polynomial features.\n",
+ " \n",
+ " yval : array_like\n",
+ " The functions values at each validation datapoint. A vector of\n",
+ " shape (m_val, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " lambda_vec : list\n",
+ " The values of the regularization parameters which were used in \n",
+ " cross validation.\n",
+ " \n",
+ " error_train : list\n",
+ " The training error computed at each value for the regularization\n",
+ " parameter.\n",
+ " \n",
+ " error_val : list\n",
+ " The validation error computed at each value for the regularization\n",
+ " parameter.\n",
+ " \"\"\"\n",
+ " # Selected values of lambda\n",
+ " lambda_vec = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n",
+ "\n",
+ " error_train = np.zeros(len(lambda_vec))\n",
+ " error_val = np.zeros(len(lambda_vec))\n",
+ "\n",
+ " for i in range(len(lambda_vec)):\n",
+ " lambda_ = lambda_vec[i]\n",
+ " Theta = trainLinearReg(linearRegCostFunction, X, y, lambda_, maxiter=200)\n",
+ " error_train[i] = linearRegCostFunction(X,y,Theta,0)[0]\n",
+ " error_val[i] = linearRegCostFunction(Xval,yval,Theta,0)[0]\n",
+ "\n",
+ " return lambda_vec, error_train, error_val"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now plot a cross validation curve of error vs lambda which allows us to select which lambda paremeter to use."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "lambda\t\tTrain Error\tValidation Error\n",
+ " 0.000000\t0.036300\t37.781163\n",
+ " 0.001000\t0.112707\t9.842030\n",
+ " 0.003000\t0.170997\t16.309292\n",
+ " 0.010000\t0.221517\t16.944779\n",
+ " 0.030000\t0.281841\t12.830156\n",
+ " 0.100000\t0.459318\t7.586964\n",
+ " 0.300000\t0.921783\t4.636755\n",
+ " 1.000000\t2.076199\t4.260602\n",
+ " 3.000000\t4.901376\t3.822923\n",
+ " 10.000000\t16.092273\t9.945554\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
Programming Exercise 5:\n",
+ " Regularized Linear Regression and Bias vs. Variance
\n",
+ " \n",
+ "
Introduction
\n",
+ "In this exercise, we will implement regularized linear regression and use it to study models with different bias-variance properties. To start, we will import necessary modules, implement some useful functions from previous exercises, and load our data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def trainLinearReg(linearRegCostFunction, X, y, lambda_=0.0, maxiter=200):\n",
+ " \"\"\"\n",
+ " Trains linear regression using scipy's optimize.minimize.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset with shape (m x n+1). The bias term is assumed to be concatenated.\n",
+ "\n",
+ " y : array_like\n",
+ " Function values at each datapoint. A vector of shape (m,).\n",
+ "\n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ "\n",
+ " maxiter : int, optional\n",
+ " Maximum number of iteration for the optimization algorithm.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " theta : array_like\n",
+ " The parameters for linear regression. This is a vector of shape (n+1,).\n",
+ " \"\"\"\n",
+ " # Initialize Theta\n",
+ " initial_theta = np.zeros(X.shape[1])\n",
+ "\n",
+ " # Create \"short hand\" for the cost function to be minimized\n",
+ " costFunction = lambda t: linearRegCostFunction(X, y, t, lambda_)\n",
+ "\n",
+ " # Now, costFunction is a function that takes in only one argument\n",
+ " options = {'maxiter': maxiter}\n",
+ "\n",
+ " # Minimize using scipy\n",
+ " res = optimize.minimize(costFunction, initial_theta, jac=True, method='TNC', options=options)\n",
+ " return res.x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def featureNormalize(X):\n",
+ " \"\"\"\n",
+ " Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+ " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+ " working with learning algorithms.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+ " and n is the number of dimensions for each example.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " X_norm : array_like\n",
+ " The normalized input dataset.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of size n corresponding to the mean for each dimension across all examples.\n",
+ "\n",
+ " sigma : array_like\n",
+ " A vector of size n corresponding to the standard deviations for each dimension across\n",
+ " all examples.\n",
+ " \"\"\"\n",
+ " mu = np.mean(X, axis=0)\n",
+ " X_norm = X - mu\n",
+ "\n",
+ " sigma = np.std(X_norm, axis=0, ddof=1)\n",
+ " X_norm /= sigma\n",
+ " return X_norm, mu, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotFit(polyFeatures, min_x, max_x, mu, sigma, theta, p):\n",
+ " \"\"\"\n",
+ " Plots a learned polynomial regression fit over an existing figure.\n",
+ " Also works with linear regression.\n",
+ " Plots the learned polynomial fit with power p and feature normalization (mu, sigma).\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " polyFeatures : func\n",
+ " A function which generators polynomial features from a single feature.\n",
+ "\n",
+ " min_x : float\n",
+ " The minimum value for the feature.\n",
+ "\n",
+ " max_x : float\n",
+ " The maximum value for the feature.\n",
+ "\n",
+ " mu : float\n",
+ " The mean feature value over the training dataset.\n",
+ "\n",
+ " sigma : float\n",
+ " The feature standard deviation of the training dataset.\n",
+ "\n",
+ " theta : array_like\n",
+ " The parameters for the trained polynomial linear regression.\n",
+ "\n",
+ " p : int\n",
+ " The polynomial order.\n",
+ " \"\"\"\n",
+ " # We plot a range slightly bigger than the min and max values to get\n",
+ " # an idea of how the fit will vary outside the range of the data points\n",
+ " x = np.arange(min_x - 15, max_x + 25, 0.05).reshape(-1, 1)\n",
+ "\n",
+ " # Map the X values\n",
+ " X_poly = polyFeatures(x, p)\n",
+ " X_poly -= mu\n",
+ " X_poly /= sigma\n",
+ "\n",
+ " # Add ones\n",
+ " X_poly = np.concatenate([np.ones((x.shape[0], 1)), X_poly], axis=1)\n",
+ "\n",
+ " # Plot\n",
+ " plt.plot(x, np.dot(X_poly, theta), '--', lw=2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Regularized Linear Regression
\n",
+ "In the first half of this exercize, we will implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. We begin by visualizing the dataset which is split into a training set (X,y), a cross validation set (Xval, yval), and a test set (Xtest, ytest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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OsrT4d2PLO3V3AP4qIp4BNucSlZmZ5SZLi///ArdLup7kUYqvAc6StAPwwxxjMzOzHIzZ4o+Ii4BXAt9Op1dFRH9E/C4iPlRtO0lfkfS4pDtHLfuEpF9Iuj2d3liPP8LMJp9SqcTSxYuZXSwyraOD2cUiSxcvplQqNTu0tpd1OGcHyVO3fg3sLek1Gbb5KnB4heXLI2L/dLo64/HNrIUMDAwwr7ub6f39rB4eZnMEq4eHmd7fz7zubgYGBpodYlvLMpzzbOAfgbuAP6WLA7ih1nYRcYOkudsYn5m1mFKpxDELF7Jq0yYOHrW8CzirXOaIcpkFCxeydmiIrq6uZoXZ1rK0+N8KvCQi3hQRR6TTgm045omShtKuoBdUW0lSn6RBSYMbNvgRv2at4rxzzuGEcnmLpD/awcDx5TLnL1/eyLBsFEVE7RWkAeAdE3nkYtrivyoi9kvnZwNPkPxi+CSwa0S8d6z99PT0xODg4HgPb2ZNMLtYZPXwMLXa8iVgfrHIo0891aiw2pKkdRHRs/XyLKN6NpGM6rmWUcM3I2LcFTsj4rFRAV0IXDXefZjZ5PbExo3sOcY6c9L1rDmyJP5V6bTNJO0aEY+ks28jeY6vmU0hM2fM4MExWvwPpetZc2Spx3/xRHYs6evAIcBMSQ8DHwcOkbQ/SVfPA8D7JrJvM5u8jjr6aC7q7+escrnqOv2FAkctWtTAqGy0qn38kq6MiHdKuoMkUW8hIrrzDm6E+/jNWkepVGJed/dzRvWMWAMs6Oz0qJ4GmEgf/8npv2/OJyQzm4q6urpYsXIlCxYu5PhymePLZeaQdO/0Fwr0FwqsWLnSSb+Jqg7nHNUX/zpgu4h4cPTUmPDMrBX19vaydmiIzX19zC8Wmd7Rwfxikc19fawdGqK3t7fZIba1LMM5/xl4FbAnsA64EbgxIm7PP7yEu3rMzMavWldPllo9H4uIw4D9gJuAD5F8AZiZWQvKUrLhfwPzgRnAbcAHSVr9ZmbWgrKM43878DTwPeC/gLUR8YdcozIzs9xk6eo5kOQC749Jn7ol6aa8AzMzs3xk6erZD3g18FqgB/g57uoxM2tZWbp6ziYpwfwF4JaIqH47npmZTXpZSja8SdJ2wIuBl0i6x8nfzKx1ZenqeS2wgqS2joA9JB0bETUfxGJmZpNTlq6ec4E3RMQ9AJJeDHwdOCjPwMzMLB9ZnsBVGEn6ABHxP0Ahv5DMzCxPWVr8g5IuAi5J59+N79w1M2tZWRL/B4AlwEkkffw3AF/MMygzM8tPllE9m0n6+c/NPxwzM8tb1cRf7QEsIxr5IBYzM6ufWi3+dwC/b1QgZmbWGLUS/2URcaCkSyLCD8c0M5siaiX+7SQdC7xS0tu3fjMivllrx5K+QvLYxscjYr902c7AFcBckhvC3hkRv5lY6GZmNhG1xvG/H5gH7AQcsdWU5Tm8XwUO32rZGcC1EbEPcG06b2ZmDVS1xR8RNwE3SRqMiIvGu+OIuEHS3K0WvwU4JH19MXA9cPp4921mZhOXpR7/uJN+DbNHHuKe/rtLtRUl9UkalDS4YcOGOoZgZtbespRsaIqIuCAieiKiZ9asWc0Ox8xsyqiZ+JXYo47He0zSrum+dwUer+O+zcwsg5qJPyIC+HYdj7cKODZ9fSzwnTru28zMMsjS1bNW0svHu2NJXwfWkDy85WFJxwGfAV4v6V6S5/d+Zrz7NTOzbZOlSNuhwPslPQD8jqRQW4xVsiEijqzy1uvGFaGZmdVVlsTfm3sUZmbWMFmGcz4I7AEclr7elGU7MzObnMZM4JI+TnKT1bJ0UQH4Wp5BmVn9lEolli5ezOxikWkdHcwuFlm6eDGlUqnZoVmTZGm5vw1YQNK/T0T8Etgxz6DMrD4GBgaY193N9P5+Vg8PszmC1cPDTO/vZ153NwMDA80O0ZogS+L/YzqsMwAk7ZBvSGZWD6VSiWMWLmTVpk2cVS7TRXJRrws4q1xm1aZNHLNwoVv+bShL4r9S0r8BO0k6Afgh0J9vWGa2rc475xxOKJc5uMr7BwPHl8ucv3x5I8OySUBJY36MlaTXA28gGcr5nxFxTd6BjdbT0xODg4ONPKRZy5tdLLJ6eJiuGuuUgPnFIo8+9VSjwrIGkrQuInq2Xj7mcE5JZ0fE6cA1FZaZ2ST1xMaN7DnGOnPS9ay9ZOnqeX2FZR7bbzbJzZwxgwfHWOehdD1rL1UTv6QPpA9cf4mkoVHT/cBQ40I0s4k46uijuahQqLlOf6HAUYv8ZNV2U7WPX9JfAC8APs2WT8oajohfNyC2P3Mfv9n4lUol5nV3s2rTpooXeNcACzo7WTs0RFdXrSsB1qqq9fFXbfFHxFMR8UBEHJnesft7kiGdMyTNyTFWM6uDrq4uVqxcyYLOTpYVCpSAMskF3WWFAgs6O1mxcqWTfhvKcufuEWk1zfuB/yJ5SLrv+jBrAb29vawdGmJzXx/zi0Wmd3Qwv1hkc18fa4eG6O315bp2NOZwTkk/AQ4DfhgRB0g6FDgyIvoaESC4q8fMbCLG3dUzSjkifgV0SOqIiOuA/eseoZmZNUSWssxPSpoB3ABcKulx4Ol8wzIzs7xkafG/heTC7lLg+yTXho7IMygzM8tP1Ra/pFOA/wZui4hn0sUXNyQqMzPLTa2unt2BzwMvlTQErCb5IljT6HH8ZmZWP1UTf0R8EEDSdkAP8ErgvcCFkp6MiH0netD0+b3DwDPA05WuOpuZWT6yXNydDhSBv0inXwJ31OHYh0bEE3XYj5mZjUOtPv4LgJeRtMxvJunqOTciftOg2MzMLAe1RvXMAZ4PPAr8AngYeLJOxw3gB5LWSap4I5ikPkmDkgY3bNhQp8OamVnNO3cliaTV/8p02g/4NckF3o9P+KDSX0XELyXtQlLn/58i4oZq6/vOXTOz8ZvQnbuRuBO4mqQ+z3+TPLLz5G0JJn1gOxHxOPAt4BXbsr96KJVKLF28mNnFItM6OphdLLJ08WI/j9Qazp9Fy1utevwnSbpc0s9J7tp9M3AP8HZg54keUNIOknYceU3ySMc7J7q/ehgYGGBedzfT+/tZPTzM5ghWDw8zvb+fed3dDAy4Jp01hj+L1gi16vGfSzp2PyIeqdsBpReRtPIhubh8WUR8qtY2eXb1uGa5TRb+LFq9TaQe/6kRsbKeST/d730R8bfp9LKxkn7ezjvnHE4olyv+jwZwMHB8ucz5y5c3MixrQ/4sWqOMWZZ5MsizxT+7WGT18DC12k8lYH6xyKNPPZVLDGbgz6LV37aUZW4547k49sTGjew5xv7mpOuZ5cmfRWuUKZf4x3txbOaMGTw4xj4fStdrFI/qaE+T8bNoU9OUSvylUoljFi5k1aZNnFUu00Vy9bgLOKtcZtWmTRyzcOEWCfSoo4/mokKh5n77CwWOWrQo19hHeFRH+5psn0WbwiJi0k8HHXRQZHHKBz4QywqFCKg6nVEoxNIlS/68zfr162NmZ2esrrL+aoiZnZ2xfv36TDFsi8kUizWe//tbvQGDUSGnTqkW/2Vf+xrHlcs11zm+XOaySy7583xXVxcrVq5kQWcnywoFSkCZ5CLaskKBBZ2drFi5siHD5zyqY0uN6PKaTN1qk+mzaFNcpW+DyTZlbfF3SFGu0doPiD9CTOvoeM6269evj6VLlsTsYjGmdXTE7GIxli5Z0tDW1S477hjrx4h/PcTsYrFhMTXL1VdfHTM7O2NZoRDrIcrp376sUIiZnZ1x9dVXt8QxJmIyfBZtaqBKi39KDeds9eFw0zo62BxRs1Z2GZje0cHTzzxTY63W1ogbmXyzlLWDthjO2eoXxzyqI9GILi93q1lbq/QzYLJNWbt6Wv3i2EQuTk9FjejycreatQPa4eJuq18cO/G007iwUGBNlffXkPxiWbJ0aeZ9TqaLl1k14kYm3yxl7WxKJX6A3t5e1g4Nsbmvj/nFItM7OphfLLK5r4+1Q0P09vY2O8Sq6v3F1ar3BDSiy8vdatbWKv0MmGxT1q6eqaIeozpaudurEV1e7lazdkCVrp6mJ/UsU7sl/npo5cTWiC+tVv5iNMuqWuKfcl09lpjIzWyTRSOu1bT69SCzbeHEP0W1+sXLRlyraeXrQWbbYkrdwGXPavWb2cxs27XFDVz2rFa/mc3M8tOUxC/pcEn3SFov6YxmxDDV5XFPgJlNDQ1P/JKmAecDvcC+wJGS9m10HFOdL16aWTXNaPG/AlgfyUPX/whcDrylCXFMeb54aWaVNPzirqSFwOERcXw6vwj4u4g4cav1+oA+gDlz5hz04INj3WdpZmajTaaLu6qw7DnfPhFxQUT0RETPrFmzGhCWmVl7aEbifxjYY9T87sAvmxCHmVlbakbivwXYR9JekrYD3gWsakIcZmZtqSk3cEl6I/A5YBrwlYj41Bjrb4AxiylOJjOBJ5odxCTlc1Odz011PjfV1To3e0bEc/rKW+LO3VYjabDSBRXzuanF56Y6n5vqJnJufOeumVmbceI3M2szTvz5uKDZAUxiPjfV+dxU53NT3bjPjfv4zczajFv8ZmZtxonfzKzNOPHnQNIHJYWkmem8JH0hLUM9JOnAZsfYaJI+K+ln6d//LUk7jXpvWXpu7pH0D82Ms1lcqvxZkvaQdJ2kuyXdJenkdPnOkq6RdG/67wuaHWuzSJom6TZJV6Xze0m6OT03V6Q3x1blxF9nkvYAXg88NGpxL7BPOvUBX2pCaM12DbBfRHQD/wMsA0hLcr8LeBlwOPDFtHR323Cp8ud4GjgtIv4amAcsSc/HGcC1EbEPcG06365OBu4eNX82sDw9N78Bjqu1sRN//S0HPsyWhefeAqxIH3y/FthJ0q5Nia5JIuIHEfF0OruWpEYTJOfm8ojYHBH3A+tJSne3E5cqHyUiHomIW9PXwyQJbjeSc3JxutrFwFubE2FzSdodeBPQn84LOAxYma4y5rlx4q8jSQuAX0TET7Z6azfg56PmH06Xtav3AgPpa58bn4OqJM0FDgBuBmZHxCOQfDkAuzQvsqb6HEnj8k/p/F8CT45qWI35+XlefrFNTZJ+CLywwlsfBT4CvKHSZhWWTblxtLXOTUR8J13noyQ/5S8d2azC+lPu3IzB56ACSTOAbwCnRMRvk4Zte5P0ZuDxiFgn6ZCRxRVWrfn5ceIfp4j4+0rLJf0NsBfwk/QDujtwq6RX0CalqKudmxGSjgXeDLwunr2BpC3OzRh8DrYiqUCS9C+NiG+mix+TtGtEPJJ2lT7evAibZj6wIC10uT1QJPkFsJOk56Wt/jE/P+7qqZOIuCMidomIuRExl+R/5gMj4lGSstPHpKN75gFPjfxkbReSDgdOBxZExKZRb60C3iXp+ZL2IrkA/uNmxNhELlU+StpnfRFwd0ScO+qtVcCx6etjge80OrZmi4hlEbF7mmPeBfwoIt4NXAcsTFcb89y4xd8YVwNvJLlwuQl4T3PDaYrzgOcD16S/iNZGxPsj4i5JVwI/JekCWhIRzzQxzoaLiKclnQj8J8+WKr+ryWE103xgEXCHpNvTZR8BPgNcKek4klFz72hSfJPR6cDlkv4FuI3ki7Mql2wwM2sz7uoxM2szTvxmZm3Gid/MrM048ZuZtRknfjOzNuPEb3Uh6YWSLpdUkvRTSVdLerGkQ0YqCDabpH+WVPMmszodZydJi+uwn+sl1fUB47X2KWmlpBfV2HY7STdI8jDwFufEb9ssveHmW8D1EdEVEfuSjLue3dzIthQRH4uIHzbgUDsB40r86c19Tfv/UdLLgGkRcV+1ddICctcC/9iwwCwXTvxWD4cC5Yj48siCiLg9Im5MZ2ekrcmfSbo0/aJA0sck3SLpTkkXjFp+vaSzJf1Y0v9IenW6vFPSlWlN/yvS+uM96XtvkLRG0q2S/iOt87IFSV+VtDB9/YCkM9P175D00grrXy2pO319m6SPpa8/Kel4STMkXTtqHyMVNT8DdEm6XdJn020+lP6tQ5LOTJfNVVJz/ovArWxZtmHrWJ7z90nqTW9+G1nnEEnfzXo+tvJu0rs9Je2ppK77TEkdkm6UNFKD6tvputbCnPitHvYD1tV4/wDgFJJa8y8iuTMT4LyIeHlE7AdMJ6njM+J5ERZvVdcAAANkSURBVPGKdLuPp8sWA79Ja/p/EjgIQMkDb/438PcRcSAwCJyaIe4n0vW/BHywwvs3AK+WVCS5q3gk7lcBNwJ/AN6W7uNQ4Jz0y+sMoBQR+0fEh9KkuQ9J+eX9gYMkvSbd10tISnYfEBEPVgqyxt93DTBP0g7pqv8IXDHB8zGf9L9hGsfZwJeB04CfRsQP0vXuBF4+xr5sknNfnTXCjyPiYYD0Fvy5wE3AoZI+DHQCOwN3Ad9NtxkpzLUuXR+ShPt5gIi4U9JQunweyZfKf6c/GrYD1mSIa/Qx3l7h/RuBk4D7ge8Br5fUCcyNiHuUFBI7K03ifyIphVupe+sN6XRbOj+D5IvgIeDB9BkNtVT8+9JSD98HjpC0kqRG+4eB11Zaf4xj7ApsGJmJiH5J7wDeT/JlNbL8GUl/lLRjWivfWpATv9XDXTxbIKqSzaNePwM8T9L2wBeBnoj4uaRPkFQb3HqbZ3j2c1qtLq+AayLiyHHGXekYo90C9AD3kbSuZwIn8Oyvm3cDs4CDIqIs6YGt/obR8X06Iv5ti4VJrfnfZYiz1t93BbAE+DVwS0QMp786xns+fj869vQLbuRhOTOA0Un++SS/dqxFuavH6uFHwPMlnTCyQNLLJb22xjYjSeaJtP+51hfHiJuAd6b73xf4m3T5WmC+pL3T9zolvXicf8NzpBczf54ecy3JL4APpv8C/AVJbfSypEOBPdPlw8COo3b1n8B7R/rZJe0maTwPEan1910PHEjyhXRFhvWruRvYe9T82STPTPgYcOHIQkl/CWyIiPI44rdJxonftllaW/9tJF0hJUl3AZ+gRk3wiHiSJKHcQXLB8JYMh/oiMCvt4jkdGCIpcb0B+F/A19P31gLPuVg7QTcCj6WlpG8kaQWPJP5LgR5JgySt/58BRMSvSLpZ7pT02bR//DJgjaQ7SB6RtyMZ1fr70kqmV5E8r/eqsdav4XvAIQDpF/bLgbMj4lLgj5JGKsoeSlJt1lqYq3Nay1DyUPJCRPxBUhfJ0MIXpy1z2waSppPUdJ9fqyy2pG8CyyLinoYFZ3XnPn5rJZ3AdelFVQEfcNKvj4j4vaSPk1ygfqjSOkoeEvNtJ/3W5xa/mVmbcR+/mVmbceI3M2szTvxmZm3Gid/MrM048ZuZtZn/D9cM/apxPVORAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex5data1.mat, where all variables will be store in a dictionary\n",
+ "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n",
+ "\n",
+ "# Extract train, test, validation data from dictionary\n",
+ "# and also convert y's form 2-D matrix (MATLAB format) to a numpy vector\n",
+ "X, y = data['X'], data['y'][:, 0]\n",
+ "Xtest, ytest = data['Xtest'], data['ytest'][:, 0]\n",
+ "Xval, yval = data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# m = Number of examples\n",
+ "m = y.size\n",
+ "\n",
+ "# Plot training data\n",
+ "plt.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we implement a regularized linear regression cost function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n",
+ " \"\"\"\n",
+ " Compute cost and gradient for regularized linear regression \n",
+ " with multiple variables. Computes the cost of using theta as\n",
+ " the parameter for linear regression to fit the data points in X and y. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset. Matrix with shape (m x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " theta : array_like\n",
+ " The parameters for linear regression. A vector of shape (n+1,).\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " The value of the cost function gradient w.r.t theta. \n",
+ " A vector of shape (n+1, ).\n",
+ " \"\"\"\n",
+ " # Initialize some useful values\n",
+ " m = y.size # number of training examples\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ "\n",
+ " h = X.dot(theta)\n",
+ " J = h-y\n",
+ " J = np.square(J)\n",
+ " J = np.sum(J)\n",
+ " J = J / (2*m)\n",
+ " tempTheta = theta[0]\n",
+ " theta[0] = 0\n",
+ " J += (lambda_/(2*m))*np.sum(np.sum(np.square(theta)))\n",
+ " theta[0] = tempTheta\n",
+ " \n",
+ " grad = (1/m)*X.transpose().dot(h-y)\n",
+ " grad[1:] += (lambda_/m)*theta[1:]\n",
+ " \n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at theta = [1, 1]:\t 303.993192 \n",
+ "Gradient at theta = [1, 1]: [-15.303016, 598.250744] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Test case for cost function\n",
+ "\n",
+ "theta = np.array([1, 1])\n",
+ "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
+ "\n",
+ "print('Cost at theta = [1, 1]:\\t %f ' % J)\n",
+ "print('Gradient at theta = [1, 1]: [{:.6f}, {:.6f}] '.format(*grad))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next we run train our linear regression model using this cost function and graph the resulting line of best fit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# add a columns of ones for the y-intercept\n",
+ "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "theta = trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n",
+ "\n",
+ "# Plot fit over the data\n",
+ "plt.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)')\n",
+ "plt.plot(X, np.dot(X_aug, theta), '--', lw=2);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Bias-Variance
\n",
+ "An important concept in machine learning is the bias-variance tradeoff. High bias models are not complex enough for the data and tend to underfit, while high variance models over fit the training data.\n",
+ "\n",
+ "In this portion of the exercise we attempt to diagnose bias-variance problems by plotting training and test errors on a learning curve. \n",
+ "\n",
+ "We begin by creating a function to return a vector of errors for the training and cross validation set, then plotting it on a graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def learningCurve(X, y, Xval, yval, lambda_=0):\n",
+ " \"\"\"\n",
+ " Generates the train and cross validation set errors needed to plot a learning curve\n",
+ " returns the train and cross validation set errors for a learning curve. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The training dataset. Matrix with shape (m x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each training datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " Xval : array_like\n",
+ " The validation dataset. Matrix with shape (m_val x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " yval : array_like\n",
+ " The functions values at each validation datapoint. A vector of\n",
+ " shape (m_val, ).\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " error_train : array_like\n",
+ " A vector of shape m. error_train[i] contains the training error for\n",
+ " i examples.\n",
+ " error_val : array_like\n",
+ " A vecotr of shape m. error_val[i] contains the validation error for\n",
+ " i training examples.\n",
+ " \"\"\"\n",
+ " # Number of training examples\n",
+ " m = y.size\n",
+ "\n",
+ " # You need to return these values correctly\n",
+ " error_train = np.zeros(m)\n",
+ " error_val = np.zeros(m)\n",
+ "\n",
+ " # ====================== YOUR CODE HERE ======================\n",
+ " \n",
+ " for i in range(1, m+1):\n",
+ " X_train = X[:i, :]\n",
+ " y_train = y[:i]\n",
+ " Theta = trainLinearReg(linearRegCostFunction, X_train, y_train, lambda_=0.0, maxiter=200)\n",
+ " error_train[i-1] = linearRegCostFunction(X_train,y_train,Theta,0)[0];\n",
+ " error_val[i-1] = linearRegCostFunction(Xval,yval,Theta,0)[0];\n",
+ " \n",
+ " # =============================================================\n",
+ " return error_train, error_val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "# Training Examples\tTrain Error\tCross Validation Error\n",
+ " \t1\t\t0.000000\t205.121096\n",
+ " \t2\t\t0.000000\t110.302641\n",
+ " \t3\t\t3.286595\t45.010231\n",
+ " \t4\t\t2.842678\t48.368911\n",
+ " \t5\t\t13.154049\t35.865165\n",
+ " \t6\t\t19.443963\t33.829962\n",
+ " \t7\t\t20.098522\t31.970986\n",
+ " \t8\t\t18.172859\t30.862446\n",
+ " \t9\t\t22.609405\t31.135998\n",
+ " \t10\t\t23.261462\t28.936207\n",
+ " \t11\t\t24.317250\t29.551432\n",
+ " \t12\t\t22.373906\t29.433818\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n",
+ "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n",
+ "\n",
+ "plt.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n",
+ "plt.title('Learning curve for linear regression')\n",
+ "plt.legend(['Train', 'Cross Validation'])\n",
+ "plt.xlabel('Number of training examples')\n",
+ "plt.ylabel('Error')\n",
+ "plt.axis([0, 13, 0, 150])\n",
+ "\n",
+ "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+ "for i in range(m):\n",
+ " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the resulting figure, we can see that both the taining and cross validation errors are high when the number of training examples is increase (specifically the training error increases to math cross validation). This reflects a problem of high bias in our model. That is to say, our model is too simple and unable to fit our data set well. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
3 Polynomial Regression
\n",
+ "The problem with our model was that it was too simple for the data and resulted in underfitting (high bias). In this portion of the exercise, we will address this problem by adding more features to produce a more complex fit to the data. We begin by creating a function to map the original training set into its higher powers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def polyFeatures(X, p):\n",
+ " \"\"\"\n",
+ " Maps X (1D vector) into the p-th power.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " A data vector of size m, where m is the number of examples.\n",
+ " \n",
+ " p : int\n",
+ " The polynomial power to map the features. \n",
+ " \n",
+ " Returns \n",
+ " -------\n",
+ " X_poly : array_like\n",
+ " A matrix of shape (m x p) where p is the polynomial \n",
+ " power and m is the number of examples. That is:\n",
+ " \n",
+ " X_poly[i, :] = [X[i], X[i]**2, X[i]**3 ... X[i]**p]\n",
+ " \"\"\"\n",
+ " X_poly = np.zeros((X.shape[0], p))\n",
+ " X_poly[:,0] = X[:,0]\n",
+ " for i in range(1,p):\n",
+ " X_poly[:,i] = np.power(X.transpose(), i+1)\n",
+ "\n",
+ " return X_poly"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now apply this function to our training set, test set, and cross validation set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p = 8\n",
+ "\n",
+ "# Map X onto Polynomial Features and Normalize\n",
+ "X_poly = polyFeatures(X, p)\n",
+ "X_poly, mu, sigma = featureNormalize(X_poly)\n",
+ "X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1)\n",
+ "\n",
+ "# Map X_poly_test and normalize (using mu and sigma)\n",
+ "X_poly_test = polyFeatures(Xtest, p)\n",
+ "X_poly_test -= mu\n",
+ "X_poly_test /= sigma\n",
+ "X_poly_test = np.concatenate([np.ones((ytest.size, 1)), X_poly_test], axis=1)\n",
+ "\n",
+ "# Map X_poly_val and normalize (using mu and sigma)\n",
+ "X_poly_val = polyFeatures(Xval, p)\n",
+ "X_poly_val -= mu\n",
+ "X_poly_val /= sigma\n",
+ "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the ability to map polynomial features, we can train our model via linear regression and plot to see how it fits our data. We will also plot a learning curve for lambda = 0 to see if we still have a bias/variance problem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Polynomial Regression (lambda = 0.000000)\n",
+ "\n",
+ "# Training Examples\tTrain Error\tCross Validation Error\n",
+ " \t1\t\t0.000000\t160.721900\n",
+ " \t2\t\t0.000000\t160.121511\n",
+ " \t3\t\t0.000000\t59.071634\n",
+ " \t4\t\t0.000000\t77.997728\n",
+ " \t5\t\t0.000000\t6.448961\n",
+ " \t6\t\t0.000000\t10.831639\n",
+ " \t7\t\t0.000000\t27.916727\n",
+ " \t8\t\t0.000064\t21.128258\n",
+ " \t9\t\t0.000147\t30.474290\n",
+ " \t10\t\t0.021425\t50.335502\n",
+ " \t11\t\t0.032329\t55.153697\n",
+ " \t12\t\t0.036300\t37.781163\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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nvli2maSm8fz5nFSvw6nXqk0Oqnp5iGWbcE7sB01EzgE2qeo8ETmpfHaww1UTw0RgIkBGRoY1NzDG1GjKt6sB+ONZg+iQ1NTjaOq3Glsrichc4HngJVXdFsFjHwucJyJnA02BVjgJp42IxLtXD92A9RE8pjGmEXv+msN5c946Lju8u9eh1Hvh1BlcBnQB5ojIKyJypkSgBkdV71DVbqrayz3G56p6JfAFcLG72gjg3doeyxhjABLj47jiyB42uF4YakwOqrpCVe8CBgAvAc8BP4nIfSISjdqcccBYEVmBUwcxOQrHMMY0IKpKTk4OM2fOJCcnh8Bq0vxdRdz19hK27yn2MMLYE1ZrIxEZCjwO/A14E+eX/U7g80gEoarTVfUc9/lKVT1CVfup6iWqWhSJYxhjGh5VZdKkSQxJT2fw4MGccMIJDB48mCHp6UyaNAm/38+dby9h6qyf+NM7S70ON6aEU+cwD9iO8wv+9oCT9SwROTaawRljTHVUldGjR5Odnc1wn48JQB9gJTBx2TKysrJ4Y9FmvmsxlJaJ8Yw7a5DHEceWcIbPuERVVwZboKq/inA8xhgTlsmTJ5Odnc0dwIN+f6Wmjll+P6M6D+CTpqkI8OhFQ+me3NyjSGNTqE5wV4mIr7rE4HaSOy56oRljTHCqypPjxzPc5+NB9m8Dv71pEjnn347ENaHJyq84e0gnL8KMaaGuHFKABW6x0jxgM06T037AicAW4PaoR2iMMVXk5uaSk5fHBPZPDGXi45Zzb2N96w50Wf8d37z5GHl5V5OWluZFqDGr2isHVf07MAx4GWgPnOpOrwOuVtWLVHV5nURpjDEBtm7dCjh1DMH0y19L2z07uOmdR8FfSn5+ft0F10CErHNQ1TJgmvswxph6ITnZaUUfrMw7Tv38+fNJjJ71Bm/v3g5ASkpKHUbXMHg+ZLcxxhyotLQ00gcNYqJIxfg6izr1J79Zq4p12u/ezkSfj8FpaaSm2jhKB8qSgzEmpqgqkydPZtv27cxT5S7gxzadGXHpfZz/m/FsSEpBgTuB+X4/t4wda8NyHwS7E5wxJmYE9m0YJkIf4K8t2vLKr+/H36wV3dcv451d25jk8zHf7ycrK4uRI0d6HXZMCqcTXBvgN0CvwPVV9abohWWMMfur1LdBlZ2JLTj90r+wqU0nitZ/zwfv/ZX31U/XTp2ZdP/9jBw50q4aDlI4Vw4fAt8CSwB/dMMxxpjgKvVt8Pspik/gdxfdzaYOvemTv4YH37iXkuJCxojga9PGEkMthZMcmqrq2KhHYowxIQT2bfCLjxvOG8ec7ul0KtjClFfvpmvhTgBuVmVMXh55eXnWt6EWwqmQniIiWSLSWUSSyx9Rj8wYYwIE9m3wqZ/0jT/QpnAnU179M10LNlesV973wfo21E44Vw7FOKOx3sW+u7Ip1fc/McaYiAvs23AacOvXL3H1gg9ot2dHpfXK+z5Y34baCefKYSzQT1V7qWpv92GJwRhTp9LS0hhwxm94pnWHil+pVRODgvVtiJBwkkMOsCfagRhjTCivz1tL0WGXsvnyhxnXJHG/m8tb34bICqdYqQxYKCJfABU33rGmrMaYujItdyO3v7kYgMHxG/lbSRGf+XyM8vv33cPB+jZEVDjJ4R33YYwxdW7Wynyuf2k+foWbTunHraefzXOHduCJxx9nTF5exXrpAwcy6bbbrAlrhEjgvVZjVUZGhs6dO9frMIwxEZa7fie/nvANBUWlXHFkDx68YHDFiV9VycvLIz8/n5SUFFJTUy0pHCARmaeqGcGWhdNDuj/wMJCGcz8HAKxS2hgTTdt2F/Ob52ZTUFTK2UM6cf/5gyud/EXE+jFEUTgV0s8DzwClwMnAv4Ap0QzKGGPaNG/C747vzbH9Unji14cS57OrgrpUY7GSe9kxXESWqOoQd95MVT2+TiIMgxUrGdNwlZb5iY+zAaSjIVSxUjjv+F4R8QHLReQGEbkQ6BCBoJqKyGwRWSQiOSJynzu/t4jMEpHlIvKqiCTU9ljGmNiwt6SMP76xiJ/y97Wet8TgjXDe9VuA5sBNwHDgamBEBI5dBJyiqocAhwJnichRwKPAE6raH9gGZEbgWMaYeq7Mr9zyykJem7uWa6fOoyE0lollNVZIq+oc9+ku4JpIHVidT36XO9nEfShwCnCFO/9F4F6cOg9jTAOlqvz53aV8lPMzSU3jeeySQ6zlkceqTQ4i8h/YrxNiBVU9r7YHF5E4YB7QD/gn8AOwXVVL3VXWAl2r2XYUMAqgR48etQ3FGOOhpz5bwUuzfiIx3sfkEYeT2rlVzRuZqApVrPQY8DjwI1AIZLuPXcDSSBxcVctU9VCgG3AEEGwwlKAJSlUnqmqGqma0b98+EuEYYzzw5ry1PPHp9/gE/u/ywziitw36XB9Ue+Wgql8CiMj9qnpCwKL/iMiMSAahqttFZDpwFNBGROLdq4duwPpIHssYU3+s2rKb299yhsW459x0zkjv5HFEplw4FdLtRaSiw5uI9AZq/VNdRNq7tyBFRJrhjMKbB3wBXOyuNgJ4t7bHMsbUT73ateDP56SRdXxvRhzTy+twTIBwxla6FZguIuXDpPfCLeuvpc7Ai269gw94TVXfF5Fc4BUReQBYAEyOwLGMMfXUb47u5XUIJohwWit95A6hMcid9Z2qFoXaJhyquhg4LMj8lTj1D8aYBqiwuIw/vLGIW04bQL8OLb0Ox1QjnCsH3GSwKMqxGGMaOL9fufXVhXyU8zOr8nfznxuOsyar9ZR1PTTG1JknP1te0ZfhyV8faomhHrPkYIypEx8s3sBTny3HJ/DPK4bRr0OS1yGZEGpMDuK4SkTudqd7iIjVCRhjwrZ03Q5ue30hAHeencoJA6xvUn0XzpXD08DRwOXudAFOb2ZjjKnR7qJSRk+Zx94SPxcP70bmcb29DsmEIZzkcKSqXg/sBVDVbYCNlGqMCUuLxHhuPrU/R/VJ5sELB1s9Q4wIp7VSidsXQcHpvAb4oxqVMaZBufTw7lw8vBs+u2FPzAjnyuEp4G2gg4g8CHwFPBTVqIwxMe+DxRtYvrGgYtoSQ2wJpxPcVBGZB5wKCHCBquZFPTJjTMzKWb+DW19bSJwIn9x6At2Tm3sdkjlAYXWCA5YDO8vXF5EeqvpT1KIyxsSsgr0lXD91PsWlfi4/orslhhhVY3IQkRuBe4CNQBnO1YMCQ6MbmjEm1qgqt7+5hFX5e0jt3Ip7zk33OiRzkMK5crgZGKiq+dEOxhgTG1SV3Nxctm7dSnJyMmlpaYgI//pmNR8s2UDLxHj+ecVhNG0S53Wo5iCFkxzWADuiHYgxpv5TVSZPnsyT48eTk7ev6jE9NZVLrxvHlA1O57ZHLhpCn/Y2qF4sC3Wb0LHu05U4Q3Z/AFSMxqqq46McmzGmHlFVRo8eTXZ2NsN9PiYAfXBOEBOXLePRf2TT8fw/cPXRfTlnaBePozW1FerKoXzgk5/cRwL7Or9Ve29pY0zDNHnyZLKzs7kDeNDvJ7Bhapbfz53Lvuaxyavp1u9eYLAnMZrIEdXQ53kRuURVX69pnpcyMjJ07ty5XodhTIOlqgxJT6fpsmXMqZIYdiY0p1XxHhTI8PkoHjSIxUuXWk/oGCAi81Q1I9iycDrB3RHmPGNMA5Wbm0tOXh6jqiSGeV0Gcex1z/PG4FMQYJTfz9LcXPLyrCtUrAtV5/AL4Gygq4g8FbCoFVAa7cCMMfXH1q1bAaeOoWJes1bccP44ChJbkNehD/B5xfL8fGvcGOtC1TmsB+YC5wHzAuYX4NxX2hjTSCQnJwNO5TOAH2HsL8eyoVV7Dlv3HeOmv1BpeUpKSp3HaCKr2uSgqouARSLykqqW1GFMxph6Ji0tjfTUVCYuW0aW38+zR17E9L4ZtCncyT/efZQEfykKTPT5GDxoEKmpqV6HbGqpxjoHSwzGGBHhlrFjmef3k9UtncdPuBqAJ94fT9eCzShwJzDf7+eWsWOtMroBCHdsJWNMI5eZmcms2bP5kMNp4ovj6G9ep3TlXCbgXDHM9/vJyspi5MiRXodqIqDaKwcRmeL+vTkaBxaR7iLyhYjkiUhO+XFEJFlEponIcvdv22gc3xhzYESEiRMmcMOhCcSvns0rM6dwBjAGKBo4kEmTJjFhwgS7amggqu3nICK5wC+A94CToFILNlR1a60OLNIZ6Kyq80UkCafS+wLgt8BWVX1ERG4H2qrquFD7sn4OxtQtVSUvL4/8/HxSUlJITU21pBCDQvVzCFWs9CzwEU7rtXlUTg5K5VZtB0xVNwAb3OcFIpIHdAXOx0lGAC8C04GQycEYE13/W7GFddsLuSSjO+BcRaSlpXkclYmmUK2VngKeEpFnVPXaaAYhIr2Aw4BZQEc3caCqG0SkQzSPbYwJbdPOvdz0ygK27ComqWkTzhrcyeuQTB0I505w14rIIcDx7qwZqro4UgGISEvgTeAWVd0Z7qWpiIwCRgH06NEjUuEYYwKUlvm58WUnMRzTN4XT0zp6HZKpIzU2ZRWRm4CpQAf3MdW9AVCtiUgTnMQwVVXfcmdvdOsjyuslNgXbVlUnqmqGqma0b98+EuEYY6p48tPlzPpxK+2TEnnyskOJs/tANxrhjK30O+BIVb1bVe8GjgKyantgcS4RJgN5VYb/fg8Y4T4fAbxb22MZYw7cF8s28Y8vVuATeOqyw+iQ1NTrkEwdCqefg+DcHrRc+a1Ca+tY4GpgiYgsdOfdCTwCvCYimThDhV8SgWMZYw7A+u2FjH3V+bcce/oAju5rw2E0NuEkh+eBWSLytjt9Ac4v/lpR1a+oPsmcWtv9G2MOXmFJGSktExnSrQ3XndTP63CMB2q8nwOAiAwDjsM5mc9Q1QXRDuxAWD8HYyJvd1EpJWV+2jRPqHllE5MOtp9DBVWdD8yPaFTGmHpnw45COrduBkCLRBtdpzELp0LaGNMIrNm6hzOfmMHvX19EUWlZzRuYBs2SgzGGotIyrn9pPjv3lrJtdzFNfHZqaOzC6efwaDjzjDGx64H381i8dgfd2jbj8UsPwWf9GRq9cH4enB5k3i8iHYgxJvpUlZycHGbOnElOTg6qyrsL1zHl29UkxPl4+sphVgFtgND3kL4WuA7oIyKBw2UkAV9HOzBjTOSoKpMnT+bJ8ePJycurmJ96+ImUnHob4OPuc9MY2q2Nd0GaeiVUc4SXgP8CDwO3B8wvqO1w3caYuqOqjB49muzsbIb7fEzAGVJ5JfBY12MoUR+di9ZwxRFWIGD2CTUq6w5gh4hUHS67pYi0VNWfohuaMSYSJk+eTHZ2NncAD/r9lXqeXvnBeC7eup6Pv3mV5wc3ITMz06swTT1TYyc4EVmCc/8GAZoCvYFlqpoe/fDCY53gjAlOVRmSnk7TZcuYUyUxVKwDZPh8FA8axOKlS+2mPY1IqE5wNVZIq+oQVR3q/u0PHAF8FekgjTGRl5ubS05eHqMCEsOiTv0Zd9aNFMYnAs6vvlF+P0tzc8kLqI8wjdsBd4F0b+t5eDSCMcZE1tatTvVg+W0bN7Vow+hf3cXPSe3ovmMjN3zzWqXl+fn5dR+kqZdqTA4iMjZg0gcMAzZHLSJjTMQkJycDTuVzsS+e68+/g5+T2pGxNodRs96qWG+l+zclxUZfNY5wrhySAp6XAh/g3KDHGFPPpaWlkZ6aysRly/jp1CzmdE+nU8EWnn7nYRL8pYBT5zDR52PwoEGkpqZ6G7CpN8K5Teh9ACKS5EzqrqhHZYyJCBHhlrFjufX/XmfLsF+SUFrMs28/RIfd2wEnMdwJzPf7mTR2rFVGmwrhFCsNBqYAye70FmCEqi6NcmzGmAg44qyLabe8Awo0mfYMszZ8z2acoqSJPh/z/X6ysrIYOXKkx5Ga+iScYqWJwFhV/QJARE5y5x0TxbiMMREyoGMS5x/WnXU/LievZC1jApalDxzIpNtuY+TIkXbVYCoJp5/DIlU9pKZ5XrJ+DsaEpqr4FXwCeXl55Ofnk5KSQmpqqiWFRqy2N/tZKSJ/xilaArgK+DFSwRljIq/Mrzz75Q/89phetEiMR0SIc3NAWlqat8GZmBDOqKwjgfbAW+6jHXBNNIMyxtTOAx/k8rePlzFqyoOqX0AAAB/3SURBVFzCuRWwMVWF01ppG3BTHcRijImAf32ziue/XkWTOOGmU/pbsZE5KHa7J2MakM/yNnLvezkAPHrRUI7sY53azMHxNDmIyHMisklElgbMSxaRaSKy3P3b1ssYjYkVc1Zt5bqp8/Er3HRKP341rJvXIZkY5vWVwwvAWVXm3Q585g7y9xmV7yVhjAlidf5uRr4wh6JSP5cf0Z1bTx/gdUgmxoXTCe6pILN3AHNV9d3aHFxVZ4hIryqzzwdOcp+/CEwHqt5TwhgToGubZpw9uDM7Ckt44IIhVs9gai2cpqxNgUHA6+70RUAOkCkiJ6vqLRGOqaOqbgBQ1Q0i0iHC+zemwYmP8/HIRUMoKVPifJYYTO2FU6zUDzhFVf9PVf8POA1IBS4EzohmcKGIyCgRmSsiczdvtkFiTeOzuaCIW19dyI7CEsAZRykh3uuSYtNQhHPl0BVogVOUhPu8i6qWiUhRFGLaKCKd3auGzsCmYCup6kScYTzIyMiwhtymQVJVcnNz2bp1K8nJyaSlpSEibC4o4orsb1m+aRciMP7SQ70O1TQw4SSHvwILRWQ6zk2jTgAeEpEWwKdRiOk9YATwiPu3VvUaxsQiVWXy5Mk8OX48OQF3Z0tPTSXrpt/zQWFflm/axYCOLbnrbBtm20ReOJ3gJovIhzi3BxXgTlVd7y7+Q20OLiIv41Q+txORtcA9OEnhNRHJBH4CLqnNMYyJNarK6NGjyc7OZrjPxwScO7WtBJ5Zu5lH5+wlof0u+nVoydTfHUVKy0SPIzYNUbi3CfXh3P0tHugnIv1UdUZtD66ql1ez6NTa7tuYWDV58mSys7O5A3gw4N7Pq9p05l+/vp+ENp0o3vIT5/ZuSvukE70M1TRg4TRlfRT4NU4LJb87W4FaJwdjTGWqypPjxzPc56uUGADeGHIqa9t0Yuj671nz1n1M+qYrN436rTVbNVERzpXDBcBAVY1G5bMxJkBubi45eXlMAKqe8sfOnErLokKuWvghU4sLGZO7g7y8PBtl1URFOO3eVgJNoh2IMQa2bt0KOHUMAG+nncSmFm0A8KGMmf0mLYsLK5bn5+fXfZCmUQjnymEPTmulz4CKqwdVtZFajYmw5ORkAFaIj1kn/ZbsI37Foeu/441//5F49Vest9L9m5JiA+uZ6AgnObznPowxUZaWlkba0OE8PuAcSvoeTnxZKZcv+rhSYlCcez8PHjSI1FRrxmqiI5ymrC/WRSDGGFiwZjtx595NSWkcCYU7efHthzl6zZKK5QrcCcz3+5k0dqxVRpuoqTY5iMhrqnqpiCzB+U5WoqpDoxqZMY3Mi/9bxf3v51Lqj6N16VZynr+NG3bnM4p9/Rwm+nzM9/vJyspi5MiRHkdsGrJQVw43u3/PqYtAjGns9paUUepXMo/rzR/PPIt/pxbzxOOPMyawh/TAgUy67TZGjhxpVw0mqqSm+8uKyEhgpqour5uQDlxGRobOnTvX6zDqRHVj7Zj6r+pnN2hQKj9tK6R3uxYAlPmVWSvzOaZfu0rb5OXlkZ+fT0pKCqmpqfZ5m4gRkXmqmhFsWTgV0r2Aq0SkJzAPmImTLBZGLkRTk1Bj7dwydiyZmZl20qingn12CR370vW8sSS06870cafRqXUz4nxSKTGAM9Kq9WMwXqjxyqFiRZFmQBbwe6CrqsZFM7AD0dCvHKqOtTPK769UBj3PLYOeMGGCJYh6pupnd2ViSxaccDUzDjkTxEfprq0c5c/l9X8+ZJ+dqXO1unIQkT8BxwItgQU4yWFmRCM0QPVFRtWNtQOQ5fdzJ/BIdjZHHnkkmZmZHkVvgin/7MY2bUnHwy8kO+M89iQ0I76slN/Oe4ftX7/M48WFPDe8n312pl4Jp85hPlAKfAB8CXyrqnvrILawxfqVQ6gio5tvvZW/jx9Pwup1/Ln3cBZ0GcTydt3Z3jSJB6Y9zfB136HAIV0HUTT0WF56+q8M7d6GJnEHftMXq8+ILFVlSHo6TZct45AL7uKz/kcCcMqK2dw5/Tn65a9FgQyfj+JBg1i8dKm936ZO1erKQVWHiUgScBxwOpAtIhtV9bgIx9kohRqeeeKyZVx3+320PupSWp1zArfGVR7FJL9Za8AZgye1/9HMGnohFz37Dc0T4hjesy3H9mvHaakd6du+RciTTqzXZ0QiqUU6MS5dt4PVP66sGCdp+KzX2dskkdtmTmHY+mUV6wkwyu9nTG6ujZNk6pVwipUGA8cDJwIZwBqsWCliQhUZXZLQnOEjnsSf0Az1l3H06kWc8ON8Bm1eRftd2+i5fUPFusPWfcdnSz5lwFFnsH53GTOXb2Hm8i088t/vOL5/O6ZkHhn0+DUlp6ysLGbPnl0v6zMikdQimRg3FezlvYXreXP+OvI27GRYR+ffqw8wfN13TH31T0G3s3GSTL2kqiEfOMVJ44BjgCY1re/FY/jw4RqL/H6/pqem6nCfT/2gGuRxx7GXa/JZN+qjrTsGXV7+eNbpqKg5OTm6cWehvrtwnd76ygI95L6P9d73llYcc+OOQh376kL9cPF6LdhbotnZ2QroHbBfDH7Q2939Tpo0ycN3an9+v1+zsrIU0OE+n04AnQY6wZ0GNCsrS/1+f1T38VP+bp3w5Qq95Nn/ae/b39ee45zHIfd9rLe8MENx9xfuZ2dMXQLmanXn/uoWVFoJEoDB7qPeJYhYTQ5Lly7d7+SxPbGFfp/SvWK6DLQr6GFBTt6BJ/FhPp8OTkvb70RWUlqmOwqLK6ZfmrW64gTW/84Ptc9vH9O0Yb/UdUkpB7zvcn6/X5cuXaozZszQpUuXhjyZRkokktqB7sPv9+vKzbt07bY9+/Yx44eK97PfnR9o1otz9L9LNujektKwkn84768x0VKr5IBTnLQapzJ6BvAjcEJN29XlI1aTw4wZzi/Lae6JYk98ol541d906E0v68JO/StOILe6J6lI/Lr/KX+3PjN9hV709Nfaa9y+X7o9x72v51/9WNCTWHW/bP1+v2ZnZ2t6aqrirgNoemqqZmdnR+1kF4mTbk372Niijc7umqpph56l/S69Qy959n865J6PtOe49/WhD3Mr9rNqyy69+eX5+t7CdZWScLlYvTIzjUOo5BBOJ7jxwBmqugxARAYALwPDD7gMy1RSPjxz+fDLd58+hvldU+mycxPtd2+rWG+A+/dh4OMg/RwOZKyd7snNGXNiX8ac2Jf3P/2Sy269n9P7Hcl3vYfRrKSoos6jVHxcdNXf6Je/hviNK0nc+ANrft5CeX2pqnd1FaFuiFMuWEXv3pIyft6xl+2FJSzIXc6quM5cmjGIR5q1Zmvz1jz48T9J8JcCMObCu5jfdd+Ip7N/dO6z0K5lAk18+1qC9UxpwZOXHVZtrJmZmcyePZuHs7Nr/dkZU5fCSQ5NyhMDgKp+LyJ2858ISEtLIz01lYnLltFu0Am8PvR0mpbs5bnX76NLwRbA+VmZ7fORPnAgt4wdy5Pjx0dsrJ3enduxe+ln/GLpZ7wc14T85m0qli3t1I9FXQayqMtAGAKdgNGfFNDuf9Pontyc4bq8oiL9qpQebG+WRLs9O0gv2s3FJcU8XFbMY9mTQva98PuV4jI/RSV+ikrLSGrahGYJTt/K9dsLWZW/m6ISP3uKy9hTXEphSRl7istY9aNTEV9ekXvHmdezvlV7Cps0ZU+Tpuxpkkhhk6bsaNKU1vPeq6jo/WZlPtc8P6fi+O1+OZbPgc/d6Rv/9wo9dmwEYPDPP1AmcTTdto6PNv3IQ+Nu4JLTjqZ9UuIBvc8iwoQJEzjyyCNtnCQTU8JJDnNFZDIwxZ2+EmcYDVNLIsItY8cy+uY/MO70MQDc9+kEBm1ZDVB5eObbbiMzM5PMzMyIjbUTmJyyykroWrC5YlnqppW8OeX35HTsw4Md+1LSM40m7XqyZVcxW3YVs+zzFyruc/yHw8/njSGn77f/nsBD365k5EhFRNhdVMqhf/nEKVPBGUso0P9dfhjnHtIFgP8sWs/D//0uaNzN4p3XW37F9W2PofyY3DXour7EFhU3xGndrAk9kpvTqlk8CVrMjE8+5JTCnZy4Zwcpe3aQVLSnYru/fPosABOA14CTB91Ph1ZNa3hHgxMRMjMzGTlypI2TZGJGOMnhWuB64Cacq/UZwNPRDKoxyczMZHJOKRsSW9Jk5Vy2LZ7GNKovdojkWDvlySkrK4u7gAfZV0yTWFbKsPXf8cb67/gemDRpEtdccxYbC/by5bxcLn/saya4TW97b13PEWuWkt+8NbsSmrM3PoG98QkUNUlk966CSu33S8oqJ4TEeB8J8T4S4+PwBZwou7VtzlF9kkmMj6N5QhzNEpy/zRPiaZEQR/ZbblLz+7lv2rOU+uJoVlJE85K9NC/ZS9OSvZxXVkzL3j0qbogzrEdbZvzxZMApFhvy7I2sXLaMV6s0IS4X6Zvq2DhJJqZUVxkRS49YrZBWVV2xqUB7jntf+97+H00dftx+FbuTJk2KaiuWwOacw3w+fRb0E7cSelg1zTmrVqRX9/gYFF+8zpgxo+JYe0tKtaikTItLy2r1urxorWRMQ8PBtFYClgCLq3tUt12kHsBZwDJgBXB7qHVjOTmoqi5Zu13fWbBW/X6/5uTk6IwZMzQnJ6fOmjb6/X6dNGlS0FZHwZJTsCa4dd1+/2CSWjT2YUwsO9jkMAin2Djoo7rtIvEA4oAfcOocE4BFQFp168d6cqgvwk1O9aX9/oEmtWjtw5hYFSo5VDvwnojMV2dcpSmqevVBlVkdJBE5GrhXVc90p+8AUNWHg60fqwPv/bB5F33bt/Q6jIMyadIksrKynGE/qNyktLwi/RF3vWiPNqpa+xviRGIfxsSagx14L0FERgDHiMivqi5U1bciFWAQXXHGcCq3Fqg0OJCIjAJGAfTo0SOKoUTH9xsLOOOJGRzfvx3/GnlEzJ2I6lP7/UhU9FplsTGVhUoOY3CarbYBzq2yTIFoJofqGo/sm1CdCEwE58ohirFExQv/WwVAr5TQI6bWV9Z+35iGrdrkoKpfAV+JyFxVnVyHMYFzpdA9YLobsL6OY4iaHYUlvD1/HQAjjunpcTQHz9rvG9NwhXM/h7pODABzgP4i0htYB1wGXOFBHFHxweINFJaUcUzfFPp1SPI6nFqzIhljGp5wOsHVOVUtFZEbgI9xWi49p6o5HocVMW8vWAvAr4Z18zgSY4wJLmRyEKdsoJuqrgm1XjSo6ofAh3V93Ghbs3UPc1Zto2kTH2cN7uR1OMYYE1TIGw277WDfqaNYGoXvfi6gRUIcZ6Z3omVivbxwM8aYsIqVvhWRw1V1Ts2rmpqcntaRuX86nYK9JV6HYowx1QonOZwMjBGRVcBunGamqqpDoxlYQ9bMHUjOGGPqq3CSwy+iHkUjsXbbHtq1TKRpE0sMxpj6LWSdA4Cqrsbpc3CK+3xPONuZ/d322iKG3T+t4q5ixhhTX9V45SAi9wAZwEDgeaAJ8G/g2OiG1rBs213M3NXbEGBgp9jv22CMadjCuQK4EDgPp74BVV0P2NntAM1Yvpkyv3Jkn2RaN7O7rBpj6rdwkkOx26RVAUSkRXRDapi+XuHcE/qE/u09jsQYY2oWTnJ4TUQmAG1EJAv4FJgU3bAaFlXl6xXOTe6P7dfO42iMMaZm4Yyt9JiInA7sxKl3uFtVp0U9sgZkdf4e1m0vpG3zJqR1buV1OMYYU6NwKqQfVdVxwLQg80wY5q7eBsDRfVPw+Wy0UmNM/RdOP4fTgaqJ4BdB5plqXDSsKxk921JS5vc6FGOMCUu1yUFErgWuA/qIyOKARUnA19EOrCEREXq1s3p8Y0zsCHXl8BLwX+Bh4PaA+QWqar24jDGmAau2tZKq7lDVVap6udszuhCnOWtLEYm9mzZ75O0Fa/nF32fy8uyfvA7FGGPCVmNTVhE5V0SWAz8CXwKrcK4oTBhm/7iNvA07bRRWY0xMCaefwwPAUcD3qtobOBWrcwjbfLel0vCebT2OxBhjwhdOcihR1XzAJyI+Vf0CODTKcTUIOwpL+H5TAQlxPtK7tPY6HGOMCVs4TVm3i0hLYAYwVUQ2AaXRDathWLhmO6owuGsrG6bbGBNTwrlyOB+nMvpW4CPgB+DcaAbVUCz8aTsAh/WwIiVjTGwJ1c/hFpy6hQWqWubOfrFOomoglqzbAcDQblakZIyJLaGKlboBfwcGuZ3g/oeTLL6xfg7hufyI7vRt34JhduVgjIkxofo5/F5VjwE6AXcCW4GRwFIRya3NQUXkEhHJERG/iGRUWXaHiKwQkWUicmZtjuO1U1M7csfZqXRPbu51KMYYc0DCqZBuBrQCWruP9cCSWh53KfArYELgTBFJAy4D0oEuwKciMiCgWMsYY0wdCFXnMBHnJF0AzMIpVhqvqttqe1BVzXOPUXXR+cArqloE/CgiK4AjgG9qe8y6Nn3ZJjbs2Mvx/dvRra1dORhjYkuo1ko9gETgZ2AdsBbYHuV4ugJrAqbXuvP2IyKjRGSuiMzdvHlzlMM6cK/OWcMdby1h1kqrnjHGxJ5qrxxU9SxxftqnA8cAtwGDRWQrTqX0PaF2LCKf4tRXVHWXqr5b3WbBQqkmvonARICMjIyg63hp6XqnpdLgrtZSyRgTe0LWObj3jl4qItuBHe7jHJyinpDJQVVPO4h41gLdA6a74dRxxJQdhSWs2VpIYryPvu1tqG5jTOyptlhJRG4SkVdEZA1O7+hzgGU4FcnJUYrnPeAyEUkUkd5Af2B2lI4VNcs3FgDQv2NL4uPC6WdojDH1S6grh17AG8CtqrohkgcVkQuB/wPaAx+IyEJVPVNVc0TkNSAXZ4iO62OxpdL3G3cBMKBDkseRGGPMwQlV5zA2WgdV1beBt6tZ9iDwYLSOXRe+d68cBnSy5GCMiU1W5hElSYnxDOxoycEYE5vEqXOObRkZGTp37lyvw6hEVfErxPmCNcAyxhjvicg8Vc0ItiycHtLmIIgIcZYXjDExyoqVImxvSRmlZX6vwzDGmFqx5BBhr85ZQ9o9HzN+2vdeh2KMMQfNkkOEfb+xgOJSP62aWomdMSZ2WXKIsOWb3D4O1lLJGBPDLDlE2I9bdgPQx4bNMMbEMEsOEVSwt4TNBUUkxPvo0rqZ1+EYY8xBs+QQQavz9wDQO6UFPuvfYIyJYZYcImilW6TUq53d3McYE9usSU0EHd6rLX+/7FCSWyR4HYoxxtSKJYcI6ty6GecfGvTGdcYYE1OsWMkYY8x+LDlEiKry8H/zmPLNKhs+wxgT86xYKUK27i5mwpcrSUqM56qjenodjjHG1IpdOURIeee33u1bIGLNWI0xsc2SQ4RUJId21jPaGBP7LDlEyE9bnQ5wPZOtj4MxJvZZcoiQNW5y6GbJwRjTAFhyiJA12woB6N7WkoMxJvZZcoiQts2b0K5lAt3a2oB7xpjY50lTVhH5G3AuUAz8AFyjqtvdZXcAmUAZcJOqfuxFjAdq0ojDvQ7BGGMixqsrh2nAYFUdCnwP3AEgImnAZUA6cBbwtIjEeRSjMcY0Wp4kB1X9RFVL3clvgW7u8/OBV1S1SFV/BFYAR3gR44EoKi2jzK9eh2GMMRFTH3pIjwRedZ93xUkW5da68/YjIqOAUe7kLhFZFqX42gFborTvuhDr8UPsv4ZYjx9i/zXEevwQnddQ7XAOUUsOIvIp0CnIortU9V13nbuAUmBq+WZB1g/6k1xVJwITIxBqSCIyV1Uzon2caIn1+CH2X0Osxw+x/xpiPX6o+9cQteSgqqeFWi4iI4BzgFNVtTwBrAW6B6zWDVgfnQiNMcZUx5M6BxE5CxgHnKeqewIWvQdcJiKJItIb6A/M9iJGY4xpzLyqc/gHkAhMcwep+1ZVx6hqjoi8BuTiFDddr6plHsVYLupFV1EW6/FD7L+GWI8fYv81xHr8UMevQfaV6BhjjDEO6yFtjDFmP5YcjDHG7MeSQzVE5EYRWSYiOSLy14D5d4jICnfZmV7GGA4R+b2IqIi0c6dFRJ5yX8NiERnmdYzBiMjfROQ7N8a3RaRNwLKY+QxE5Cw3zhUicrvX8dRERLqLyBcikud+92925yeLyDQRWe7+bet1rKGISJyILBCR993p3iIyy43/VRFJ8DrGUESkjYi84f4P5InI0XX9GVhyCEJETsbprT1UVdOBx9z5MTW8h4h0B04HfgqY/QucVmD9cToRPuNBaOGI+SFW3Lj+ifOepwGXu/HXZ6XAbaqaChwFXO/GfDvwmar2Bz5zp+uzm4G8gOlHgSfc+LfhjN9Wn/0d+EhVBwGH4LyWOv0MLDkEdy3wiKoWAajqJnd+rA3v8QTwRyp3JDwf+Jc6vgXaiEhnT6ILoYEMsXIEsEJVV6pqMfAKTvz1lqpuUNX57vMCnJNSV5y4X3RXexG4wJsIayYi3YBfApPcaQFOAd5wV6nv8bcCTgAmA6hqsTswaZ1+BpYcghsAHO9ehn4pIuVDrnYF1gSsV+3wHl4TkfOAdaq6qMqimHkNAUYC/3Wfx1L8sRTrfkSkF3AYMAvoqKobwEkgQAfvIqvRkzg/ivzudAqwPeDHRn3/HPoAm4Hn3aKxSSLSgjr+DOrD2EqeCDW8B8770hbnsvpw4DUR6cMBDO9RF2p4DXcCZwTbLMg8T15DtIdYqQdiKdZKRKQl8CZwi6rudPsj1Xsicg6wSVXnichJ5bODrFqfP4d4YBhwo6rOEpG/40ExXqNNDqGG9xCRa4G33GE9ZouIH2fQq3o1vEd1r0FEhgC9gUXuP3U3YL6IHEE9eg2NYIiVWIq1gog0wUkMU1X1LXf2RhHprKob3GLITdXvwVPHAueJyNlAU6AVzpVEGxGJd68e6vvnsBZYq6qz3Ok3cJJDnX4GVqwU3Ds4ZZSIyAAgAWc0xJgY3kNVl6hqB1Xtpaq9cL5sw1T1Z5zX8Bu31dJRwI7yS9X6pIEMsTIH6O+2lEnAqUh/z+OYQnLL5ycDeao6PmDRe8AI9/kI4N26ji0cqnqHqnZzv/eXAZ+r6pXAF8DF7mr1Nn4A9/90jYgMdGedijNqRJ1+Bo32yqEGzwHPichSnLvVjXB/udbH4T0O1IfA2TgVuXuAa7wNp1qxNMRKUKpaKiI3AB8DccBzqprjcVg1ORa4GlgiIgvdeXcCj+AUr2bitH67xKP4DtY44BUReQBYgFvZW4/dCEx1f1SsxPk/9VGHn4ENn2GMMWY/VqxkjDFmP5YcjDHG7MeSgzHGmP1YcjDGGLMfSw7GGGP2Y8nB1BkR6SQir4jIDyKSKyIfisgAETmpfPRMr4nIX0QkZOe8CB2njYhcF4H9TBeRiN50PtQ+3ZFC+4TYNkFEZoiINZOPcZYcTJ1wO1e9DUxX1b6qmobTfr6jt5FVpqp3q+qndXCoNsABJQe346Jn/7Mikg7EqerK6tZxBxj8DPh1nQVmosKSg6krJwMlqvps+QxVXaiqM93JlgHj1091kwkicreIzBGRpSIyMWD+dBF5VERmi8j3InK8O7+5iLwmzn0gXnUHT8xwl50hIt+IyHwRed0dP6gSEXlBRC52n68Skfvc9ZeIyKAg638oIkPd5wtE5G73+f0i8jsRaSkinwXso3xU1keAviKyUET+5m7zB/e1LhaR+9x5vcQZz/9pYD6Vh+OoGst+r09EfuF2Gixf5yQR+U+470cVV+L2yhWRnuLcV6CdiPhEZKaIlI/l9Y67rolhlhxMXRkMzAux/DDgFpz7HvTB6akL8A9VPVxVBwPNcMZaKhevqke4293jzrsO2ObeB+J+YDiAODc7+hNwmqoOA+YCY8OIe4u7/jPA74Msn4Ezgm8rnB7b5XEfB8wE9gIXuvs4GXjcTXC3Az+o6qGq+gf3xNofZ5jvQ4HhInKCu6+BOMOsH6aqq4MFGeL1TQOOEmdUT3B+0b96kO/HsbifoRvHo8CzwG1Arqp+4q63FGfAShPDrFzQ1BezVXUtgDtsQy/gK+BkEfkj0BxIBnKA/7jblA8KN89dH5yT8t8BVHWpiCx25x+Fk3i+di8+EoBvwogr8Bi/CrJ8JnAT8CPwAXC6iDQHeqnqMnEGsXvIPdH7cYaKDlaUdob7WOBOt8RJFj8Bq917b4QS9PW5Q3h8BJwrIm/g3Ofgj8CJwdav4RidcYaSBkBVJ4nIJcAYnIRWPr9MRIpFJMm9J4SJQZYcTF3JYd/AZ8EUBTwvA+JFpCnwNJChqmtE5F6ckTarblPGvu9ydWNLCzBNVS8/wLiDHSPQHCADZ/ybaTij92ax7yrpSqA9MFxVS0RkVZXXEBjfw6o6odJM554Ku8OIM9TrexW4HtgKzFHVAvfq5UDfj8LA2N0kWH4TppZAYCJIxLlqMjHKipVMXfkcSBSRrPIZInK4iJwYYpvyE9EWtzw8VHIp9xVwqbv/NGCIO/9b4FgR6ecuay7OiLu14lbArnGP+S3OlcTv3b8ArXHuL1Aizu1ne7rzC4CkgF19DIwsL/cXka4iciA3cwn1+qbj3B8gCydR1LR+dfKAfgHTj+LcZ+NuILt8poikAJtVteQA4jf1jCUHUyfcUW0vxCl2+UFEcoB7CTGuvntrxGxgCU4l55wwDvU00N4tThoHLMYZlnwz8FvgZXfZt8B+FcwHaSaw0R1afCbOr+ny5DAVyBCRuThXEd8BqGo+TpHOUhH5m1te/xLwjYgswRnDP4kwhXp97qi17+Pcy/r9mtYP4QPgJAA3qR8OPKqqU4FiESkf4fdknNF/TQyzUVlNgyIicUATVd0rIn1xmlUOcH/hm1oQkWY490U4NtQw6SLyFnCHqi6rs+BMxFmdg2lomgNfuBXBAlxriSEyVP+/vTu0AQCEoShIJ2clZmIJJAjkZwGSO13/0lR0r6rq7R7V52um7v+BIQz/szkAENwcAAjiAEAQBwCCOAAQxAGAcABL+4QcyTMW8wAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lambda_ = 0\n",
+ "theta = trainLinearReg(linearRegCostFunction, X_poly, y,\n",
+ " lambda_=lambda_, maxiter=55)\n",
+ "\n",
+ "# Plot training data and fit\n",
+ "plt.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+ "\n",
+ "plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
+ "\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)')\n",
+ "plt.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+ "plt.ylim([-20, 50])\n",
+ "\n",
+ "plt.figure()\n",
+ "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n",
+ "plt.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
+ "\n",
+ "plt.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
+ "plt.xlabel('Number of training examples')\n",
+ "plt.ylabel('Error')\n",
+ "plt.axis([0, 13, 0, 100])\n",
+ "plt.legend(['Train', 'Cross Validation'])\n",
+ "\n",
+ "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n",
+ "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+ "for i in range(m):\n",
+ " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the resulting figures, we can see that our curve fits the data extremely well. In fact, it fits it too well. Along the samples it follows perfectly, however it fails to follow the trend along the extremes. We can also see this in the learning curve, as while the training error is extremely low, the cross validation error (the error we would realistically expect to see) is still high. This imply we now have an issue of high-variance, or overfitting. To address this, we can add a regularization term. In order to choose an effective lambda, we automate the process by testing a sequence of lambdas and choosing the one with the least error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def validationCurve(X, y, Xval, yval):\n",
+ " \"\"\"\n",
+ " Generate the train and validation errors needed to plot a validation\n",
+ " curve that we can use to select lambda_.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The training dataset. Matrix with shape (m x n) where m is the \n",
+ " total number of training examples, and n is the number of features \n",
+ " including any polynomial features.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each training datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " Xval : array_like\n",
+ " The validation dataset. Matrix with shape (m_val x n) where m is the \n",
+ " total number of validation examples, and n is the number of features \n",
+ " including any polynomial features.\n",
+ " \n",
+ " yval : array_like\n",
+ " The functions values at each validation datapoint. A vector of\n",
+ " shape (m_val, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " lambda_vec : list\n",
+ " The values of the regularization parameters which were used in \n",
+ " cross validation.\n",
+ " \n",
+ " error_train : list\n",
+ " The training error computed at each value for the regularization\n",
+ " parameter.\n",
+ " \n",
+ " error_val : list\n",
+ " The validation error computed at each value for the regularization\n",
+ " parameter.\n",
+ " \"\"\"\n",
+ " # Selected values of lambda\n",
+ " lambda_vec = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n",
+ "\n",
+ " error_train = np.zeros(len(lambda_vec))\n",
+ " error_val = np.zeros(len(lambda_vec))\n",
+ "\n",
+ " for i in range(len(lambda_vec)):\n",
+ " lambda_ = lambda_vec[i]\n",
+ " Theta = trainLinearReg(linearRegCostFunction, X, y, lambda_, maxiter=200)\n",
+ " error_train[i] = linearRegCostFunction(X,y,Theta,0)[0]\n",
+ " error_val[i] = linearRegCostFunction(Xval,yval,Theta,0)[0]\n",
+ "\n",
+ " return lambda_vec, error_train, error_val"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now plot a cross validation curve of error vs lambda which allows us to select which lambda paremeter to use."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "lambda\t\tTrain Error\tValidation Error\n",
+ " 0.000000\t0.036300\t37.781163\n",
+ " 0.001000\t0.112707\t9.842030\n",
+ " 0.003000\t0.170997\t16.309292\n",
+ " 0.010000\t0.221517\t16.944779\n",
+ " 0.030000\t0.281841\t12.830156\n",
+ " 0.100000\t0.459318\t7.586964\n",
+ " 0.300000\t0.921783\t4.636755\n",
+ " 1.000000\t2.076199\t4.260602\n",
+ " 3.000000\t4.901376\t3.822923\n",
+ " 10.000000\t16.092273\t9.945554\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lambda_ = 3\n",
+ "theta = trainLinearReg(linearRegCostFunction, X_poly, y,\n",
+ " lambda_=lambda_, maxiter=55)\n",
+ "\n",
+ "# Plot training data and fit\n",
+ "plt.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+ "\n",
+ "plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
+ "\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)')\n",
+ "plt.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+ "plt.ylim([-20, 50])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 5:\n",
+ " Regularized Linear Regression and Bias vs. Variance
\n",
+ " \n",
+ "
Introduction
\n",
+ "In this exercise, we will implement regularized linear regression and use it to study models with different bias-variance properties. To start, we will import necessary modules, implement some useful functions from previous exercises, and load our data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def trainLinearReg(linearRegCostFunction, X, y, lambda_=0.0, maxiter=200):\n",
+ " \"\"\"\n",
+ " Trains linear regression using scipy's optimize.minimize.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset with shape (m x n+1). The bias term is assumed to be concatenated.\n",
+ "\n",
+ " y : array_like\n",
+ " Function values at each datapoint. A vector of shape (m,).\n",
+ "\n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ "\n",
+ " maxiter : int, optional\n",
+ " Maximum number of iteration for the optimization algorithm.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " theta : array_like\n",
+ " The parameters for linear regression. This is a vector of shape (n+1,).\n",
+ " \"\"\"\n",
+ " # Initialize Theta\n",
+ " initial_theta = np.zeros(X.shape[1])\n",
+ "\n",
+ " # Create \"short hand\" for the cost function to be minimized\n",
+ " costFunction = lambda t: linearRegCostFunction(X, y, t, lambda_)\n",
+ "\n",
+ " # Now, costFunction is a function that takes in only one argument\n",
+ " options = {'maxiter': maxiter}\n",
+ "\n",
+ " # Minimize using scipy\n",
+ " res = optimize.minimize(costFunction, initial_theta, jac=True, method='TNC', options=options)\n",
+ " return res.x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def featureNormalize(X):\n",
+ " \"\"\"\n",
+ " Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+ " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+ " working with learning algorithms.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+ " and n is the number of dimensions for each example.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " X_norm : array_like\n",
+ " The normalized input dataset.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of size n corresponding to the mean for each dimension across all examples.\n",
+ "\n",
+ " sigma : array_like\n",
+ " A vector of size n corresponding to the standard deviations for each dimension across\n",
+ " all examples.\n",
+ " \"\"\"\n",
+ " mu = np.mean(X, axis=0)\n",
+ " X_norm = X - mu\n",
+ "\n",
+ " sigma = np.std(X_norm, axis=0, ddof=1)\n",
+ " X_norm /= sigma\n",
+ " return X_norm, mu, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotFit(polyFeatures, min_x, max_x, mu, sigma, theta, p):\n",
+ " \"\"\"\n",
+ " Plots a learned polynomial regression fit over an existing figure.\n",
+ " Also works with linear regression.\n",
+ " Plots the learned polynomial fit with power p and feature normalization (mu, sigma).\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " polyFeatures : func\n",
+ " A function which generators polynomial features from a single feature.\n",
+ "\n",
+ " min_x : float\n",
+ " The minimum value for the feature.\n",
+ "\n",
+ " max_x : float\n",
+ " The maximum value for the feature.\n",
+ "\n",
+ " mu : float\n",
+ " The mean feature value over the training dataset.\n",
+ "\n",
+ " sigma : float\n",
+ " The feature standard deviation of the training dataset.\n",
+ "\n",
+ " theta : array_like\n",
+ " The parameters for the trained polynomial linear regression.\n",
+ "\n",
+ " p : int\n",
+ " The polynomial order.\n",
+ " \"\"\"\n",
+ " # We plot a range slightly bigger than the min and max values to get\n",
+ " # an idea of how the fit will vary outside the range of the data points\n",
+ " x = np.arange(min_x - 15, max_x + 25, 0.05).reshape(-1, 1)\n",
+ "\n",
+ " # Map the X values\n",
+ " X_poly = polyFeatures(x, p)\n",
+ " X_poly -= mu\n",
+ " X_poly /= sigma\n",
+ "\n",
+ " # Add ones\n",
+ " X_poly = np.concatenate([np.ones((x.shape[0], 1)), X_poly], axis=1)\n",
+ "\n",
+ " # Plot\n",
+ " plt.plot(x, np.dot(X_poly, theta), '--', lw=2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Regularized Linear Regression
\n",
+ "In the first half of this exercize, we will implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. We begin by visualizing the dataset which is split into a training set (X,y), a cross validation set (Xval, yval), and a test set (Xtest, ytest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex5data1.mat, where all variables will be store in a dictionary\n",
+ "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n",
+ "\n",
+ "# Extract train, test, validation data from dictionary\n",
+ "# and also convert y's form 2-D matrix (MATLAB format) to a numpy vector\n",
+ "X, y = data['X'], data['y'][:, 0]\n",
+ "Xtest, ytest = data['Xtest'], data['ytest'][:, 0]\n",
+ "Xval, yval = data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# m = Number of examples\n",
+ "m = y.size\n",
+ "\n",
+ "# Plot training data\n",
+ "plt.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we implement a regularized linear regression cost function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n",
+ " \"\"\"\n",
+ " Compute cost and gradient for regularized linear regression \n",
+ " with multiple variables. Computes the cost of using theta as\n",
+ " the parameter for linear regression to fit the data points in X and y. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset. Matrix with shape (m x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " theta : array_like\n",
+ " The parameters for linear regression. A vector of shape (n+1,).\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The computed cost function. \n",
+ " \n",
+ " grad : array_like\n",
+ " The value of the cost function gradient w.r.t theta. \n",
+ " A vector of shape (n+1, ).\n",
+ " \"\"\"\n",
+ " # Initialize some useful values\n",
+ " m = y.size # number of training examples\n",
+ " J = 0\n",
+ " grad = np.zeros(theta.shape)\n",
+ "\n",
+ " h = X.dot(theta)\n",
+ " J = h-y\n",
+ " J = np.square(J)\n",
+ " J = np.sum(J)\n",
+ " J = J / (2*m)\n",
+ " tempTheta = theta[0]\n",
+ " theta[0] = 0\n",
+ " J += (lambda_/(2*m))*np.sum(np.sum(np.square(theta)))\n",
+ " theta[0] = tempTheta\n",
+ " \n",
+ " grad = (1/m)*X.transpose().dot(h-y)\n",
+ " grad[1:] += (lambda_/m)*theta[1:]\n",
+ " \n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cost at theta = [1, 1]:\t 303.993192 \n",
+ "Gradient at theta = [1, 1]: [-15.303016, 598.250744] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Test case for cost function\n",
+ "\n",
+ "theta = np.array([1, 1])\n",
+ "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
+ "\n",
+ "print('Cost at theta = [1, 1]:\\t %f ' % J)\n",
+ "print('Gradient at theta = [1, 1]: [{:.6f}, {:.6f}] '.format(*grad))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next we run train our linear regression model using this cost function and graph the resulting line of best fit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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z+H7X3pC6DyacaidtYyzaqJ6HgYdF5DFVvbauLyxO074QKKsxr8+rwBhgivvvK3V9bWNM06SqPPzuKh5859OQuhV3nWHDMuPEy6iea0XkOOAkt2iuqoZOah1qGHAlsFxEPnLLJuEk/BdFJBtnDiCb/sGYZm7H7r38c/F6Xl76Ja1a7J8UuEv71sz99Uhr4ceZl4VYbsLpa6/sqnlGRKar6v+L9jxVfR8iroo2qk5RGmOapNVfb+PUB/5TrSxrWC9O7X8IY37ck1YHeJkZ3jQ2L1+zvwSGqOr3ACIyFVgARE38xpjktbb8e06+f05I+SO/GMhZGYfZSVufeUn8grPkYqXK5ReNMaaaVZu2MXraf8LW/fcPZ9PCEn5C8JL4nwAWisjL7uMLcE7aGmMMAF9/t5MT7n0nbN2qe8/igBbWpZNIvJzcnSYic4DhOC39q1V1aawDM8Ykvv9+vY1RD4S28AMCn91rLfxE5elUuqouAZbEOBZjTBPx/mffcEXhwpDyk/t25W9Xn4DXCzWNP2wMlTHGsw9WfcMvCkIT/i+G9ODeCzIs4TcRlviNMbWa99nXXFlYFLbu8/vOtoTfxHgZxz9VVW+vrcwY0/xEGqXTpmWAT+4+y4eITGPw0uI/jdBpk88KU2aMaSZeXLSOX/8r9AL9Lu1bU3zHaB8iMo0p2rTM1wLXAb1FpOonoAMwP9aBGWPi7x/F67jtn6EJ/45zUvnlSbXOy2iaiGgt/meBN4H7cFbJqvSd1ymZjTFNw6zSjeQ8VRxSftngHtx30QAfIjKxFG12zq3AVhGp2aXTXkTaq+oXsQ3NGBNrbyzfwHXPhI7UPrhtS5beeboPEZl48NLHPwNn/n0B2gC9gJVAegzjMsbE0MLV5fx8+och5SP6duWprME+RGTiycuVu9V+54nIQGBczCIyxsTMjc8t5bWPvwopP2dANx65fKAPERk/1Hkcv6ouEZETYhGMMSY27nqthCfmrwkp/+sVgzgz47CY7FNVKS0tpaKigpSUFNLS0my8f4LwMo4/t8rDADAQ+DpmERljGs3kl5fzzMLQ03FXDO3BPRfE5qStqlJYWMhD06ZRUla2rzw9NZXxublkZ2fbF4DPvLT4O1S5vwenz/9fsQnHGNMYZizbwPXPhp60zTmpF5PPSYvZflWVcePGkZ+fz6BAgDycxblXA9NXriQnJ4eioiLy8vIs+fvISx//XQAi0sF5qNtiHpUxpl4emb2K+2euDCm/54IMrhh6VMz3X1hYSH5+PhOBe4PBagt35ASDztqr+fkMGTKE7OzsmMdjwhNVjb6BSAbwNJDiFn0DjFHVFTGObZ/MzEwtLg4dY2yMcWTe8w7fbNsZUv7Qz3/EBccfEZcYVJUB6em0WbmSRTWS/r5tgMxAgF39+7NsxQpr9ceYiCxW1cya5V66eqYDuao6232hkW7Zjxs1QmNMnV346HyWfrElpPzl637M8T0OjmsspaWllJSVkUfkJfoEGBsMck1pKWVlZaSlxa7byUTmJfG3q0z6AKo6R0TaxTAmY0wtznhwLis3fhdSPn50H8aP7utDRFBR4VzQX9vEDpX15eXlMY3HROYl8a8Wkd/gdPcAXAF8HruQjDGRFL7/OXe/XhpSPu2S47hoYHcfItovJcXpDV5dy3aV9Z07d45pPCYyL4k/C7gLeMl9PBe4OmYRGWNCnPPwPEq++jakPP+qTE5LO9SHiEKlpaWRnprqjN6J0sc/PRAgo39/UlNT4x2icXkZ1bMZuCkOsRhjaug5YUbY8rdvHkHfQzuErfOLiDA+N5ecnBwmA/dSva9fgUnAkmCQgtxcO7HrI1uBy5gEo6qk3TmTH3bvDambOX4E/Q5LrIRfVXZ2NkVFRdyXn8/MQICxweD+cfyBAEuCQXJycsjKyvI71KRmid+YBKGq9Jr4Rti6ROrSiUZEyMvLY8iQITz4wANcU/XK3X79KLjlFrKysqy177Nax/EnAhvHb5qzYFDpPSl8wn/puh8zMM7DMhuLqlJWVkZ5eTmdO3cmNTXVEn6c1Xscv4g8HKZ4K1Csqq80RnDGJKPde4P0mfxm2Lon/u8ETul/SJwjalwiYuP0E5SXrp42QH/gH+7jnwIlQLaInKKq42MVnDHNUbSEn+h9+KZ58JL4jwFOVdU9ACLyGPA2ziLsy2MYmzHNyg+79pJ651th62bcNJz0ww+Kc0QmWXlJ/EcA7XC6d3DvH66qe0UkdHIQY0w1O3bvpf9vwif89245md5d28c5IpPsvCT+PwIficgcnGG5I4A/uNM2vBPD2Ixp0r7dsZtjf/d22LoPJpzK4Z0OjHNExji8XMBVKCJvAINxEv8kVa1cu+22WAZnTFNUvm0ng+4J3yb6z20jOaqzTXVl/OV1HH8AZ9WtA4BjROQYVZ0bu7CMaXo2f7+L4++eFbZu8R2j6dy+dZwjMiY8L8M5pwI/xxnJE3SLFWfOHmOS3hfl2xlx/+ywdQsmnkq3g6xLxyQWLy3+C4B+qmonco2p4n9bdzD0vnfD1q246wzat7YL401i8jQtM9ASsMRvDFD61bec/fC8sHWf3H0mbVq2iHNExtSNl8S/HWdUz7tUSf6qajN2mqSy5pvvGfmnOWHryn5/Jge2soRvmgYvif9V92ZMUlq/eTvDp4bvw//0nrNodUAgzhEZ0zBehnM+WZ8XFpHHgXOBTaqa4ZalAC8APYE1wCXufP/GJJziNRVc/NcFYess4ZumLGLiF5EXVfUSEVmOM4qnGlU9tpbX/hvwF+CpKmUTgHdVdYqITHAf317nqI2JoY/WbeGCR+aHrfvvH86mRcBmmDRNW7QW/6/cf8+tzwur6lwR6Vmj+HxgpHv/SWAOlvhNgnh6wRp+80pJ2LrVfzibgCV800xETPyqusG9OwqYp6qfNcL+Dq18XVXdICIR550VkbHAWIAePXo0wq6NCW/+qm+4vGBh2LrP7zvb5pA3zY6Xk7s9gStE5ChgMTAP54vgo1gGpqrTgengLMQSy32Z5BSthW8J3zRnXk7u3gkgIgcCOTjz8zwE1Gfs2kYR6ea29rsBm+rxGsY0yIxlG7j+2SVh69ZMOSfO0RgTf16mbLgDGAa0B5YCt+K0+uvjVWAMMMX911bwMnHzXNEXTHwpdAmJ43t04uXrhvkQkTH+8NLVcxGwB5gB/Af4UFV31PYkEXkO50RuFxFZD/wWJ+G/KCLZwBfAz+oZtzGeXffMYt5Y/r+wddbCN8nIS1fPQBHpAAzHWXUrX0Q2qurwWp53WYSqUXUP05i6e2rBGu4M04c/4IiDeO3GqB9fY5o1L109GcBJwMlAJrCO+nf1GBNz454uZmbJxrB1ydrCV1VKS0upqKggJSWFtLQ0O3mdxLx09UzFmYL5YWCRqu6ObUjG1M+f3/mMB9/5NKT8lH5deeLqwT5E5D9VpbCwkIemTaOkrGxfeXpqKuNzc8nOzrYvgCTkpavnHBFpBfQF+onISkv+JpGMfaqYt0tDW/gZR3Tk9RtP8iGixKCqjBs3jvz8fAYFAuQBvXGm252+ciU5OTkUFRWRl5dnyT/JeOnqORln2oU1OEsvHikiY2wFLuO3659dwoxlG0LKR6ceSsGYTB8iSiyFhYXk5+czEbg3GKRqas8JBpkETMnPZ8iQIWRnZ/sUpfGDqEa/NkpEFgO/UNWV7uO+wHOqOigO8QHOBVzFxcXx2p1JcJNfXs4zC78IKb/0hCOZ8tPappBKDqrKgPR02qxcyaIaSX/fNkBmIMCu/v1ZtmKFtfqbIRFZrKohrSAvffwtK5M+gKp+KiItGzU6YzwYePcsKr7fFVLesc0BLPvdGT5ElLhKS0spKSsjD8ImfdzyscEg15SWUlZWRlpaWhwjNH7ykviLRaQQeNp9fDnO1A3GxMXI+2ezpnx7SPn40X0YP7qvDxElvoqKCsDp04+msr68vDym8ZjE4iXxXwtcD9yE00iYCzway6CMAeg5YUbY8tPTDmX6VdaHH01KSgrgnMiNprK+c+fOMY3HJJZa+/gTgfXxJ5deE2cQ7mP5u5+k8X/DesU/oCbI+vgN1KOPP9ICLJU8LMRiTJ30njiDYJhP3JVDj+LuCzLiH1ATJiKMz80lJyeHycC9VO/rV2ASsCQYpCA315J+konW1fMz4Id4BWKSV6QuHUv4DZOdnU1RURH35eczMxBgbDC4fxx/IMCSYJCcnByysrL8DtXEWbTE/6w7T8/Tqnpl3CIySSNSwr/7ggyuHHpUnKNpfkSEvLw8hgwZwoMPPMA1Va/c7dePgltuISsry1r7SShiH7+IrADuB+7EmYO/GlV9Kbah7Wd9/M2HqtJr4hth6+44J5VfnlTbOBRTH6pKWVkZ5eXldO7cmdTUVEv4SaA+4/ivwRm62Qn4SY06BeKW+E3TFy3h5105iDPSD4tzRMlFRGycvtkn2pq77wPvi0ixqhbGMSbTjASDSu9J4RO+jdIxxh9eJmmzpG/qLFrC/3v2EIb36RLniIwxlbxcwGWMZ7v2BOl7x5th616/cTgZRxwU54iMMTVFTfzinP3prqrr4hSPaaJ27tlLvzveClv3yvXDOO7ITnGOyBgTSdTEr6oqIv8G4jYTp59slaK627F7L/1/Ez7hv/mrk0jt1jHOETUP9lk0sRTwsM2HInJCzCPxkapSUFDAgPR0MjIyGDFiBBkZGQxIT6egoICmMK1FvG3buYeeE2aETfrP5QxlzZRzLOnXg30WTTx4mY+/FOiHsxDL9zhXfms8p2yI5Tj+mqsU1by6cbF7daOtUuTY+sNujrvr7bB1c287hR6d28Y5oubDPoumsTVkPv6zYhBPwrBVirxZV7Gdk/44O2zd7FtH0qtLuzhH1PzYZ9HEi6fZOUVkONBHVZ8Qka5Ae1X9PObRuWLV4rcZDGu36dsdDP7Du2HrlvzmNFLatYpzRM2TfRZNLNS7xS8ivwUycbp7ngBaAn8HhjV2kI3Jy8kxW6UosjXffM/IP80JW1d8x2i6tG8d34CaOfssmnjy0tVzIXA8sARAVb8SkQ4xjaoBVJXCwkIemjaNkqqTUqWmMj43l+zs7H1fAIm8SpFfozrWb97O8Knhu3SKJo3ikI5tYh5DMkrkz6Jpfrwk/l3usE4FEJGE7cyteXIsD/afHFu5kpycHIqKivadHEvEVYrq8sXVmFZt+o7R0+aGrSu56wzatbZr/WIpET+LpvnyMqrnVqAPcBpwH5AFPKeqD8c+PIfXPv6CggJycnKck2OEX3hiirtddnZ2wvWr+jGq471PNpL1t/DHtuz3Z3JgqxaNsp/6iscvn0QYM59on0XTPETq40dVa73hJP37gT8Bp3l5TmPeBg0apLUJBoOanpqqgwIBDYJqmFsQdGAgoBlpaRoMBlVVNT8/XwGd6NbX3H6C8/emBQUFtcbQUPGMZekXm/Wo218Pe9u1Z28jvJuGCQaDmp+fr+mpqYr7vgFNT03V/Pz8ff9/ib6Pukikz6JpHoBiDZfTwxVW2wCmeimL5c1L4l+xYoUCmhch6Vfe/ur+8ZSUlKiq88efk5OjuF8KfwV9291uYCCggObk5MQ8CdT3i6uu3ivbmNAJX7X6/8mgQEDzQGe5/7eDGun/JB77aEhMfn4WTfPRkMS/JEzZstqe15g3L4l/7ty5ivvHGy3xv+0m/rlz5+57bjAY1IKCgrAtv4KCgrj8odX3i8urJWsrIib8PXsTK5HEo+WbqK3rRPgsmuYjUuKPtgLXtcB1OOdH/1ulqgMwX1WvqKV7qdF46eMvKSkhIyODPGBslO3ycFaYKSkpCRkOp+rfKkXz5s1jxIgRzAJGR9luFnA6MHfuXE466aRaX/ed0o388qnwx+7z+85OuH5i1dj3dcdjHw3l52fRNB/1Gcf/LPAmzgndCVXKv1PVikaOr8HS0tJIT011Ru9E+WOeHgiQ0b8/qampIfV+rlLU2KM6Zn+yiav/tihsXbtXb+Pm3Nw6Rhgf8RjP3hTGzNuKWSaWIk7SpqpbVXWNql6mqmuBH3ByZ3sR6RG3CD0SEcbn5rI4GGQyTqBVVY7qWRIMMj43N+FaT/u+uAKBkNgr7fviSksL+8UFsHhtBT0nzAib9AumnsukqedyoBH5JloAAA9qSURBVDu0ddy4cUT6xeeXeIxntzHzJtl5uXL3J8A04HBgE3AUUAakxza0usvOzqaoqIj78vOZGWY45BJ3OGRWVpbfoYao/OLKyclhMpGHoy4JBikI88X17MIvmPTy8rCv/fnUc5vMvC/xGM9uY+ZN0gvX8V/1BnwMdAaWuo9PAabX9rzGvHk5uVupKZ8cq8+ojpeXrA97wvboK+6N+QihWIjH6KZ4jaAyxm80YFRPse7/Agi494tqe15j3uqS+CsFg0EtKSnRuXPnaklJSZP54/X6xXX/W59EHKUT6xFCsZbMo3qMaUyREr+X6/C3iEh7YC7wjIhsAvY09JdGrDXVk2MiQnZ2NllZWWFHdby1YgPX/H1JyPOO7X4Qr94wHHBGCEHT7cOOR5ddU+4WNKahvCT+84EdwM3A5cBBwO9jGZQJ/eKav+obLi9YGLJdl/atKb6j+gDQpt6HLSLk5eUxZMgQHnzgAa6pOmdRv34U3HILWVlZDTpBH499GJOooo3jHw/Mx+nb97WFH8sVuBLdg7M+5c/vfhZSfkiH1hRNDj/iX5vAOHWvNA7j2eOxD2P8UJ9x/N2BPwP9RWQZ8AHOF8ECbeA4fhE5033tFkCBqk5pyOs1RzNL/se4pxeHlOee1pebRvWJ+tyGjhBKJPHosmuq3YLG1JeX2Tlb4SzE8mPgRPe2RVXr9ZciIi2AT3EmflsPLAIuU9XSSM9Jphb/na+s4KkFa0PKB/dK4cVxJ3p+HdX9M30OjNKHbeu3GtN8NWTN3QOBjjh9+wcBXwHhB4x7MxhYpaqr3cCexzmPEDHxJ4PH5vyXqW99ElJ+9bCe/PYndb9kwvqwjTGRROvjn45zkdZ3wELgQ+BDVd3coB2KXAycqaq/dB9fCQxR1RtqbDcWd9qdHj16DFq7NrQV3Bw89M6nPPROaB/+fRcN4LLBjXOBtPVhG5Oc6tPi7wG0Bj4DvsTpltnSGLGEKQv59lHV6cB0cLp6GmG/CeXlpeu5+YWPQ8qnXXIcFw3s3qj7sj5sY0xVERO/qp4pTrMwHad//xYgQ0QqcE7w/rae+1wPHFnlcXec7qOk8P/e/YwHZn0aUv737CEM79PFh4iMMckmah+/e+XXChHZAmx1b+fi9NPXN/EvAvqISC+cXxKXAr+o52s1Gec/Mp+P14X+YHrpuh8zsMfBPkRkjElWERO/iNyE09IfBuzGHcoJPE4DTu6q6h4RuQGYiTOc83FVLanv6yW68c8v5d8fhf6gef3G4WQccZAPERljkl20Fn9P4J/Azaq6oTF3qqpvAG805msmmnFPFzOzZGNI+Z8v/RHn/+gIHyIyxhhHtD7+xFypI8E9MnsV989cGVL+wtihDOmdWFMjGGOSk5dx/KYWqsoleQtYtCZ0pOsr1w/juCM7+RCVMcaEZ4m/AVSV0x6cy6pN20Lq5v36FI5MaetDVMYYE50l/npQVS7+6wIWrw1t4VvCN8YkOkv8dRAMKpcXLGTB6tD561+9YRjHdrcuHWNM4rPE70EwqEx4aRkvFq8PqfvPbSM5qnM7H6Iyxpj6scQfxa49Qfre8WbYuvdvP4XuB1uXjjGm6bHEH0akhN8jpS2v3jCMTm1b+RCVMcY0Dkv8VezaE2Tc08XMXvl1SF3R5FEc0qGND1EZY0zjssTv+vfSLxn/wkch5YvvGE3n9q19iMgYY2IjqRP/nr1BduwJ0r71AcxYvn9WiqO7tuOla4dxUNuWPkZnjDGxkZSJ/4dde7micCGL124ma1gv7vxJGrmn9WVU/0M4/0dHcGCrFn6HaIwxMZNUif/7nXu4dPqHLP9y676yD1eXszeopHbrSGq3jj5GZ4wx8ZEUif+7Hbu5+LEFrNz4XbXyUf0PYfpVmbQI2DKExpjk0ewT/+K1Ffz0sQXVys7KOIyHLzueli0CPkVljDH+afaJ/6jO7TiwZQt+2L2X8447nGmXHMcBlvCNMUms2Sf+Lu1b88oNwzima3sC1qVjjDHNP/ED9D20g98hGGNMwrA+D2OMSTKW+I0xJslY4jfGmCRjid8YY5KMJX5jjEkylviNMSbJWOI3xpgkY4nfGGOSjKiq3zHUSkS+Btb6HUcddAG+8TuIBGXHJjI7NpHZsYks2rE5SlW71ixsEom/qRGRYlXN9DuORGTHJjI7NpHZsYmsPsfGunqMMSbJWOI3xpgkY4k/Nqb7HUACs2MTmR2byOzYRFbnY2N9/MYYk2SsxW+MMUnGEr8xxiQZS/yNTERuFREVkS7uYxGRh0VklYgsE5GBfscYbyJyv4h84r7/l0WkU5W6ie6xWSkiZ/gZp59E5Ez3GKwSkQl+x+MnETlSRGaLSJmIlIjIr9zyFBGZJSKfuf8e7HesfhGRFiKyVERedx/3EpGF7rF5QURaRXu+Jf5GJCJHAqcBX1QpPgvo497GAo/5EJrfZgEZqnos8CkwEUBE0oBLgXTgTOBREWnhW5Q+cd/zIziflTTgMvfYJKs9wC2qmgoMBa53j8cE4F1V7QO86z5OVr8Cyqo8ngo86B6bzUB2tCdb4m9cDwK/BqqeMT8feEodHwKdRKSbL9H5RFXfVtU97sMPge7u/fOB51V1p6p+DqwCBvsRo88GA6tUdbWq7gKexzk2SUlVN6jqEvf+dzgJ7gicY/Kku9mTwAX+ROgvEekOnAMUuI8FOBX4p7tJrcfGEn8jEZHzgC9V9eMaVUcA66o8Xu+WJass4E33vh0bhx2HCESkJ3A8sBA4VFU3gPPlABziX2S+egingRl0H3cGtlRpXNX6+UmKxdYbi4i8AxwWpmoyMAk4PdzTwpQ1uzG00Y6Nqr7ibjMZ52f8M5VPC7N9szs2HthxCENE2gP/Asar6rdOwza5ici5wCZVXSwiIyuLw2wa9fNjib8OVHV0uHIRGQD0Aj52P5zdgSUiMhjn2/fIKpt3B76KcahxF+nYVBKRMcC5wCjdf/FIUhwbD+w41CAiLXGS/jOq+pJbvFFEuqnqBre7dJN/EfpmGHCeiJwNtAE64vwC6CQiB7it/lo/P9bV0whUdbmqHqKqPVW1J84f8kBV/R/wKnCVO7pnKLC18udqshCRM4HbgfNUdXuVqleBS0WktYj0wjkBXuRHjD5bBPRxR2a0wjnh/arPMfnG7bMuBMpUdVqVqleBMe79McAr8Y7Nb6o6UVW7u3nmUuA9Vb0cmA1c7G5W67GxFn/svQGcjXPicjtwtb/h+OIvQGtglvuL6ENVvUZVS0TkRaAUpwvoelXd62OcvlDVPSJyAzATaAE8rqolPoflp2HAlcByEfnILZsETAFeFJFsnJFzP/MpvkR0O/C8iNwDLMX54ozIpmwwxpgkY109xhiTZCzxG2NMkrHEb4wxScYSvzHGJBlL/MYYk2Qs8ZsGE5HDROR5EfmviJSKyBsi0ldERlbOHug3Efm9iES9yKyR9tNJRK5rhNcZLyJtG/D8NZUzxNbhOSIi74lIxyjbdBWRt+obl0kMlvhNg7gX27wMzFHVo1U1DWfM9aH+Rladqt6pqu/EYVedgDolfjfh1vxbHA/UO/HXc/9nAx+r6reRtlXVr4ENIjIsHrGZ2LDEbxrqFGC3qv61skBVP1LVee7D9iLyT3c+/mfcLwpE5E4RWSQiK0RkepXyOSIyVUSKRORTETnJLW8rIi+KM6f/C+7c45lu3ekiskBElojIP9w5XqoRkb+JyMXu/TUicpe7/XIR6R9m+zdE5Fj3/lIRudO9f7eI/FJE2ovIu1Veo3I2zSnA0SLykYjc7z7nNve9LhORu9yynuLMN/8osIQqUzaIyE3A4cBsEZntlj0mIsXizE9f+RqjROTlKs87TUQqpzeo+l5y3eO8QkTGR9n/5bhXfIrICW68bUSknbvfDPcl/+1ua5oqVbWb3ep9A27CmQc8XN1IYCvO3CEBYAEw3K1LqbLd08BP3PtzgAfc+2cD77j3bwXy3PsZOFf6ZgJdgLlAO7fuduDOMLH8DbjYvb8GuNG9fx1QEGb7CcD1OHOhLAJmuuWzgX44V713dMu64FyZLUBPYEWV1zkdZzFscY/B68AId7sgMDTCsVsDdKnyOMX9t4V7jI51X/MToKtb92yV47jGjWsQsBxoB7QHSnBmuwzZP7AW6FDl8T3An3DWCphYpfwIYLnfnz271f9mLX4Ta0Wqul5Vg8BHOAkH4BS31b4cZy7x9CrPqWy1Lq6y/XCceepR1RXAMrd8KM7iJfPdy/vHAEd5iCvcPqqah5OghwMzcH65tAV6qupKnKT7BxFZBryDkwzDdW+d7t6W4rSs++PMSQSwVp01Gry4RESWuK+TDqSpk4WfBq4QZ1WzE9k/5XWl4cDLqvq9qm5z3/dJEfafos7895V+j7OwUCbwxyrlm3B+kZgmyubqMQ1Vwv7JocLZWeX+XuAAEWkDPApkquo6EfkdzkyDNZ+zl/2f0Uhz8gowS1Uvq2Pc4fZR1SKchLcaZwWxLkAOzhcFOF0dXYFBqrpbRNbUeA9V47tPVfOqFTrzzH/vJVBxJrC7FThBVTeLyN+q7OsJ4DVgB/AP3T8ne9X9R1Jz/3tEJOB+SQOk4PxKaOnur3L7NsAPXmI3icla/Kah3gNai0hOZYHbP3xylOdUJq1v3P74aF8cld4HLnFfPw0Y4JZ/CAwTkWPcurYi0reO7yGEOithrXP3+SHOL4Bb3X8BDsKZF323iJzC/l8Z3wEdqrzUTCCr8ryDiBwhIl4WEKn6Oh1xku5WETkUZ4nGyji/wpmC9w6c7qya5gIXuMelHXBhlfdQ00qgd5XH04Hf4KyfMLVKeV9ghYf3YBKUtfhNg6iqisiFwEPiLBK+A6d/eTwRVgFS1S0iko/T97wGp3Vdm0eBJ92ulaU4XT1bVfVrEfk/4DkRae1uewfO2r4NNQ9n/YDtIjIP51xFZdJ8BnhNRIpxurA+cd9buYjMF5EVwJuqepuIpAIL3PPX24ArcH5pRDMdeFNENqjqKSKyFOfX1Wpgfo1tn8Hp5y+t+SKqusT9hVA53XWBqi51f3HUNAPnvMwqEbkK2KOqz4qzJvAHInKqqr6Hc0J/Ri3xmwRms3OaJsFNPi1VdYeIHI2z2HZft2We1ETkL8BSVY06Fa+H1+mGsz70abVsNxc4X1U3N2R/xj/W4jdNRVuc4Y0tcfqtr7WkDyKyGKcb6JaGvpY6K1vli0hHjTCWX0S6AtMs6Tdt1uI3xpgkYyd3jTEmyVjiN8aYJGOJ3xhjkowlfmOMSTKW+I0xJsn8f4/eQmcx90tvAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# add a columns of ones for the y-intercept\n",
+ "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "theta = trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n",
+ "\n",
+ "# Plot fit over the data\n",
+ "plt.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n",
+ "plt.xlabel('Change in water taylor(x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)')\n",
+ "plt.plot(X, np.dot(X_aug, theta), '--', lw=2);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Bias-Variance
\n",
+ "An important concept in machine learning is the bias-variance tradeoff. High bias models are not complex enough for the data and tend to underfit, while high variance models over fit the training data.\n",
+ "\n",
+ "In this portion of the exercise we attempt to diagnose bias-variance problems by plotting training and test errors on a learning curve. \n",
+ "\n",
+ "We begin by creating a function to return a vector of errors for the training and cross validation set, then plotting it on a graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def learningCurve(X, y, Xval, yval, lambda_=0):\n",
+ " \"\"\"\n",
+ " Generates the train and cross validation set errors needed to plot a learning curve\n",
+ " returns the train and cross validation set errors for a learning curve. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The training dataset. Matrix with shape (m x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each training datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " Xval : array_like\n",
+ " The validation dataset. Matrix with shape (m_val x n + 1) where m is the \n",
+ " total number of examples, and n is the number of features \n",
+ " before adding the bias term.\n",
+ " \n",
+ " yval : array_like\n",
+ " The functions values at each validation datapoint. A vector of\n",
+ " shape (m_val, ).\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " error_train : array_like\n",
+ " A vector of shape m. error_train[i] contains the training error for\n",
+ " i examples.\n",
+ " error_val : array_like\n",
+ " A vecotr of shape m. error_val[i] contains the validation error for\n",
+ " i training examples.\n",
+ " \"\"\"\n",
+ " # Number of training examples\n",
+ " m = y.size\n",
+ "\n",
+ " # You need to return these values correctly\n",
+ " error_train = np.zeros(m)\n",
+ " error_val = np.zeros(m)\n",
+ "\n",
+ " # ====================== YOUR CODE HERE ======================\n",
+ " \n",
+ " for i in range(1, m+1):\n",
+ " X_train = X[:i, :]\n",
+ " y_train = y[:i]\n",
+ " Theta = trainLinearReg(linearRegCostFunction, X_train, y_train, lambda_=0.0, maxiter=200)\n",
+ " error_train[i-1] = linearRegCostFunction(X_train,y_train,Theta,0)[0];\n",
+ " error_val[i-1] = linearRegCostFunction(Xval,yval,Theta,0)[0];\n",
+ " \n",
+ " # =============================================================\n",
+ " return error_train, error_val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "# Training Examples\tTrain Error\tCross Validation Error\n",
+ " \t1\t\t0.000000\t205.121096\n",
+ " \t2\t\t0.000000\t110.302641\n",
+ " \t3\t\t3.286595\t45.010231\n",
+ " \t4\t\t2.842678\t48.368911\n",
+ " \t5\t\t13.154049\t35.865165\n",
+ " \t6\t\t19.443963\t33.829962\n",
+ " \t7\t\t20.098522\t31.970986\n",
+ " \t8\t\t18.172859\t30.862446\n",
+ " \t9\t\t22.609405\t31.135998\n",
+ " \t10\t\t23.261462\t28.936207\n",
+ " \t11\t\t24.317250\t29.551432\n",
+ " \t12\t\t22.373906\t29.433818\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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jx/PHP/6RtLQ0nnjiCZ566inA9dHw7rvvIiLcd999/O53v+O2225rMc7LL7+cP/7xj5x88sn87Gc/C03v168fc+bMIRAIsHr1aqZOncqiRYuYNWsWt956Ky+++CIA8+bNC61z4403Mn78eJ599lneeOMNLr300lBHRCtXrmTu3LlUVVUxbNgwrrnmGtLT01uMqz3sSiHWQvUKS/2Nw5gEdPTRRzNo0CAAFixYwDe+8Q169uxJdnY23/zmN5k/fz6jR4/mtdde4/rrr2f+/Pn06tWL3NxcAoEAV111FX//+9/Jyso6aNsFBQWMHDmS119/nSVLlpCenh6qCygrK+OMM85g9OjR3HLLLXzyySctxlhRUcHu3bs5+eSTAbjkkktC8+rq6vjud7/L6NGjueiiiyI2993cggULQts49dRT2blzJxUVLrGec845ZGZmkp+fT79+/SJewXSUXSnEWsFoWPqE1SuY+NfKL/poGTlyJE8//XSL88Obz26p3bYvfelLLF68mJdffpkbbriBr33ta/zyl79k4cKFvP766zz++OP86U9/4o033jho3WARUv/+/UNFRwA/+MEP+OlPf8p5553HvHnzmDlzZosxqirNugYIueOOO+jfvz8fffQRjY2NBAKBFrfT2nEGt5+ZmRma1lVNg9uVQqzZHUjGtOjUU0+lpqaGe++9NzTt/fff58033zxo2ZNOOolnn32Wffv2sXfvXp555hlOPPFENm/eTFZWFhdffDHXXXcdH3zwAXv27KGiooKzzz6bO++8M1T80twFF1zAyy+/zBNPPMGUKVNC0ysqKigqKgLgoYceavUY8vLy6NWrFwsWLADgkUceabKdwsJCUlJS+Otf/xqqMM/JyaGqqiri9k466aTQNubNm0d+fj65ubmtxnAo7Eoh1vo361shJdXfeIyJIyLCM888w49//GNmzZpFIBCgpKSEO++8k02bNjVZdsKECUybNi10d9BVV13F+PHjeeWVV/jZz35GSkoK6enp3HXXXVRVVTF58mSqq6tRVe64446I+8/Ly+PYY49l69atoWIqcJW6F110EUVFRRx77LGsW7eu1eN48MEHueKKK8jKyuKMM84ITf/e977HBRdcwFNPPcUpp5wSuvIZM2YMaWlpjB07lmnTpjF+/Pgm+7788ssZM2YMWVlZbSalQxXVprOjLaGazg53+wio3ATXLrK+FUxcsaazk0/cNJ1tWmGVzcaYOGVJwQ9Wr2CMiVOWFPwQSgrL/I3DmAgSuUjZNNWZ79KSgh/sSsHEqUAgwM6dOy0xJAFVZefOne267TWc3X3kh7wSyMiBPVtgzzbI7ud3RMYAUFxcTFlZGdu3b/c7FNMFAoEAxcXFHVrHkoIfUlJc43gb3nFXC0ee5ndExgCQnp7e5FZM0/1Y8ZFfrAjJGBOHopYUROQBEdkmIsvCpt0iIitFZKmIPCMieWHzbhCRNSKySkTOiLzVJGJJwRgTh6J5pTAbOLPZtDnAKFUdA3wK3AAgIiOAKcBIb53/FZHkftTXkoIxJg5FLSmo6lvArmbTXlXVYItN7wLBGpDJwOOqWqOq64A1QPt6tkhUwb4Vdq6G2n1+R2OMMYC/dQpXAP/wxouAjWHzyrxpBxGR6SKySEQWJfQdEsG+FbTR9a1gjDFxwJekICL/CdQDweYDI7UzG/FGaVW9R1VLVbW0b9++0QoxNqy5C2NMnIl5UhCRy4Bzge/ogSdkyoCBYYsVA5tjHVvMWb2CMSbOxDQpiMiZwPXAeaoaXpD+PDBFRDJFZBAwFFgYy9h8YUnBGBNnovbwmog8BkwC8kWkDLgRd7dRJjDH6znoXVW9WlU/EZEngeW4YqXvq2pDtGKLG9a3gjEmzkQtKajq1AiT729l+V8Dv45WPHGpZx/ILXJ9K+xaB/lH+h2RMaabsyea/WaVzcaYOGJJwW9Wr2CMiSOWFPxmScEYE0csKfit/yj3aknBGBMHLCn47bBBkJF9oG8FY4zxkSUFv6Wk2NWCMSZuWFKIB1avYIyJE5YU4kEwKZR/5G8cxphuz5JCPCj+snv9/F9gHaYbY3xkSSEe9BsO2QWwZytsW+53NMaYbsySQjwQgSGnuvHP3vA3FmNMt2ZJIV4MOcW9WlIwxvjIkkK8GDzJvX7+NtRV+xmJMaYbs6QQL7L7uaa066thwzt+R2OM6aYsKcQTK0IyxvjMkkI8CVY2r53rbxzGmG7LkkI8Ofw4SAu4J5utHSRjjA8sKcST9AAc8RU3vnaer6EYY7onSwrxJvS8ghUhGWNiz5JCvBkcVtlsTV4YY2IsaklBRB4QkW0isixsWm8RmSMiq73Xw7zpIiJ/EJE1IrJURCZEK664138k9Ozn+lfYtsLvaIwx3Uw0rxRmA2c2mzYDeF1VhwKve+8BzgKGesN04K4oxhXfwpu8sLuQjDExFrWkoKpvAbuaTZ4MPOSNPwScHzb9YXXeBfJEpDBascU9e17BGOOTWNcp9FfVcgDvtZ83vQjYGLZcmTftICIyXUQWicii7du3RzVY3wye5F7X/8uavDDGxFS8VDRLhGkRa1lV9R5VLVXV0r59+0Y5LJ/kFLguOuv3w8b3/I7GGNONxDopbA0WC3mvwSe0yoCBYcsVA5tjHFt8GTzJvVoRkjEmhmKdFJ4HLvPGLwOeC5t+qXcX0rFARbCYqduy/hWMMT5Ii9aGReQxYBKQLyJlwI3ALOBJEbkS2ABc5C3+MnA2sAbYB1werbgSxhFfgdRM2LIU9u6Anvl+R2SM6QailhRUdWoLs06LsKwC349WLAkpvQcccZxr7mLtPBh9od8RGWO6gXipaDaRWBGSMSbGLCnEs/B2kKzJC2NMDFhSiGf9RkLPvlC1Gbav8jsaY0w3YEkhnqWkNG0gzxhjosySQryzdpCMMTFkSSHeDZ7kXtcvgPoaPyMxxnQDlhTiXW4h9BsBdfusyQtjTNRZUkgE1hubMSZGLCkkAqtsNsbEiCWFRHDEVyA1A8o/gr07/Y7GGJPELCkkgowsOPw4QGHdPL+jMcYkMUsKicJ6YzPGxIAlhUQRqmyeZ01eGGOixpJCoug/GrLyobIMdqz2OxpjTJKypJAoUlKsNzZjTNRZUkgk1uSFMSbKLCkkkmBl87r5UF/rbyzGmKRkSSGR5A6AvkdB3V4oW+h3NMaYJGRJIdFYkxfGmCiypJBorItOY0wUtZkURCRVRG7pyp2KyE9E5BMRWSYij4lIQEQGich7IrJaRJ4QkYyu3GfSCDZ5sflD2LfL72iMMUmmzaSgqg3ARBGRrtihiBQBPwRKVXUUkApMAX4L3KGqQ4EvgCu7Yn9JJ6MnDDwG1+TFm35HY4xJMu0tPvoQeE5ELhGRbwaHQ9hvGtBDRNKALKAcOBV42pv/EHD+IWw/uVkRkjEmStqbFHoDO3En7q97w7md2aGqbgJuBTbgkkEFsBjYrar13mJlQFGk9UVkuogsEpFF27dv70wIiS/UDtJca/LCGNOl0tqzkKpe3lU7FJHDgMnAIGA38BRwVqTdthDLPcA9AKWlpd3zjFgwFnr0hoqNsPMzyD/S74iMMUmiXVcKIlIsIs+IyDYR2Soi/ycixZ3c5+nAOlXdrqp1wN+BrwB5XnESQDGwuZPbT34pKdZqqjEmKtpbfPQg8DwwAFes84I3rTM2AMeKSJZXeX0asByYC1zoLXMZ8Fwnt989WG9sxpgoaG9S6KuqD6pqvTfMBvp2Zoeq+h6uQvkD4GMvhnuA64GfisgaoA9wf2e2320ErxTWz4eGOn9jMcYkjXbVKQA7RORi4DHv/VRcxXOnqOqNwI3NJq8Fju7sNrudXsWQPwx2rIKy993zC8YYc4jae6VwBfAtYAvujqELvWnGT1avYIzpYu16ohm4QFXPU9W+qtpPVc9X1c9jEJ9pjbWDZIzpYu19onlyDGIxHXXE8ZCSDps/sCYvjDFdor3FR/8SkT+JyIkiMiE4RDUy07bMbNfkhTbCurf8jsYYkwTaW9EcrMW8KWya4p5wNn4acgp8vsD1xjbSWgYxxhyaNpOCiKQAd6nqkzGIx3TUkFPhjf+BNW+4Ji+6pt1CY0w31Z46hUbg2hjEYjqjcCz0OAwqNsCutX5HY4xJcO2tU5gjIteJyEAR6R0cohqZaZ+UVBg8yY3branGmEPUkecUvg+8hWvRdDGwKFpBmQ6yW1ONMV2kva2kDop2IOYQDG7W5EVqur/xGGMSVqtXCiLy87Dxi5rNuzlaQZkOyhsIfYZCTSVsWux3NMaYBNZW8dGUsPEbms07s4tjMYfCmrwwxnSBtpKCtDAe6b3xk9UrGGO6QFtJQVsYj/Te+KnkBEhJg02LYP9uv6MxxiSotpLCWBGpFJEqYIw3Hnw/OgbxmfbKzIHio63JC2PMIWk1KahqqqrmqmqOqqZ548H3dotLvAkWIa21IiRjTOe09zkFkwhC9QpW2WyM6RxLCslkwDgI5MEX663JC2NMp1hSSCYpqTD4ZDdudyEZYzrBkkKysSIkY8wh8CUpiEieiDwtIitFZIWIHOc1sjdHRFZ7r4f5EVvCCzZ5se4taKj3NxZjTMLx60rh98A/VfUoYCywApgBvK6qQ4HXvfemow47AnoPcU1ebP7A72iMMQkm5klBRHKBk4D7AVS1VlV34/qBfshb7CHAuhHrLCtCMsZ0kh9XCoOB7cCDIvKhiNwnIj2B/qpaDuC99ou0sohMF5FFIrJo+/btsYs6kVg7SMaYTvIjKaQBE3BdfI4H9tKBoiJVvUdVS1W1tG/fvtGKMbGVnAiSCmWLoLrC72iMMQnEj6RQBpSp6nve+6dxSWKriBQCeK/bfIgtOQRyYeDRoA2wbr7f0RhjEkjMk4KqbgE2isgwb9JpwHLgeeAyb9plwHOxji2pDLYiJGNMx7Wr57Uo+AHwiIhkAGuBy3EJ6kkRuRLYAFzUyvqmLUNOhXk3WztIxpgO8SUpqOoSoDTCrNNiHUvSGjAeAr1ccxe71kFv61HVGNM2e6I5WaWmwaCT3LhdLRhj2smSQjKz3tiMMR1kSSGZBZPCujetyQtjTLtYUkhmh5XAYYPcswqbP/Q7GmNMArCkkOysNzZjTAdYUkh21g6SMaYDLCkku0FekxcbF0J1pd/RGGPinCWFZBfoBcWlrsmL9Qv8jsYYE+csKXQHVoRkjGknSwrdQbAdpFhUNtdVuzudypdGf1/GmC7nV9tHJpaKJkJmLuxcA1987npn6wr7dsHWZS4BbPnYDTtWQaP3TMSYKXD271wRljEmIVhS6A6CTV6sfNFdLUyc1rH1VaFiozvphyeAig0HLyspkP8l2L0Rlj4On78N3/wLHPGVLjkUY0x0WVLoLoac4pLCZ20khYY62PFpWALwkkD17oOXTesB/UdCwWgoHAMFY6DfCMjIgh2r4f+ugvIlMPscOOEncPIMSMuI2iEaYw6dJYXuIvQQ2zxobICUVKipgq2fND35b1sBDTUHr5/Vx530C0ZD4Vj32udIt51I8ofCVa/BvFmw4HaYfxuseR2+eS/0/VLUDtMYc2gsKXQXvQe7Zi++WA+PXORed60F9OBlDxvkTvoFY7wrgNGQUwgiHdtnajqc9l9w5OnwzHR31fCXk+CMX0HplR3fnjEm6kQ1wkkhQZSWluqiRYv8DiNxvPhTWHT/gfcp6dBveNOTf/+R0akYrq6Af1wPHz3m3g/9Gkz+M2T36/p9GWNaJSKLVTVSnzaWFLqVPdtg8UPQq8glgPxhsS/jX/Z3ePEnro4iKx8m/wmGnRXbGIzp5iwpmPhSsQmevcY16Q0w8XI449eQ0dPfuIzpJlpLCvbwmom9XkVwybNwxs2QmgGLH4S7T4RNi/2OzJhuz5KC8UdKChz3ffjuXHcb667P4L6vwpu3WIdAxvjIt6QgIqki8qGIvOi9HyQi74nIahF5QkTshvbuoGCUSwzHft812jf3VzD7bNi1zu/IjOmW/LxS+BGwIuz9b4E7VHUo8AVwpS9RmdhLD8CZN7sipZxC2Pge3H0CfPiIe5raGBMzviQFESkGzgHu894LcCrwtLfIQ8D5fsRmfDTkFLjmbRgxGWr3wHPfgycvdW0sGWNiwq8rhTuBnwON3lYBmKQAABbkSURBVPs+wG5VDRYmlwFFkVYUkekiskhEFm3fvj36kZrYyuoNFz0E598NGTmw4nm46yvW7LcxMRLzpCAi5wLbVDX8VpNIj7ZGLDdQ1XtUtVRVS/v27RuVGI3PRGDcVLhmAQw8FqrK4a/fgH/e4JrmNsZEjR9XCscD54nIeuBxXLHRnUCeiASb3SgGNvsQm4knh5XAtJfg1F9AShq8+79wzyTXRpMxJipinhRU9QZVLVbVEmAK8IaqfgeYC1zoLXYZ8FysYzNxKDUNTvoZXPmqa4Bv+wq491R4+4/Q2Nj2+saYDomn5xSuB34qImtwdQz3t7G86U6KJsK/vwWlV0BDLbz6C3j4PNi63IqUjOlC1syFSTyr/gHPXQv7dhyY1rMv5BZBr2I35Ba5J6d7DXTjOQUtN/NtTDfTWjMX1nS2STzDzoLvvQOv/CdseAcqN8Pe7W4oXxJ5HUmF3AFhyaIYcoubjmf1tua8TbdnScEkpux+cMG9bryxAfZshYoyN1RuOnh873bXpWjFRtjYwjbTergkEemKI7fIPVgX6GWJwyQ1Swom8aUErwIGwMCjIy9TVw1Vm71ksQkqvddQ4tgENRWwc40bWpKe5YqicgZAbqFLFLkDmk7LLrBuR03CsqRguof0gOt9rvfglpeprjyQICo2Nh2vKofKcqjb63qs27W29f1l5XtJY8CB15wCL4F4iaTHYXbVYeKOJQVjggK5bug3PPJ8VdevdVW5q8cIvW5pOm3PVlcJvm9H689UpGY2TRQ5hS5RBHq1PGT0tERiosqSgjHtJXIgcfQd1vJyDfWuDqNqs7u6aJI8wqbVVMLuz93Q7hhSW08agbyOJ5XGRtdCbWN92NDW+3Ysk5EDeQPdHWAZWZ37zE3MWVIwpqulprkio9zCFlrw8tTsaZoo9mxxfVm3NtTtg/273NAZkurqRcKTgMbgIcCefSHvcJcg8g5vOvQaCJnZ0Y/BtIslBWP8kpkNmUdC/pHtX6e+1l1hVFe4fq5bTCCVLSSVvVBbdfB2U9LChtTW30tq28ukpLj97d544O6vvdtb7l0vq0/khBFMGoHczn3GXaGxEeqr3RWWpLiB4LgkXXGeJQVjEklaBqTlQ8/8zq3fUAd1+5ueyIMnt2hpbHBXRLs3uEr73Z+78d0bD0zbt9MNLT1nEshrOWGkpLq7y+r3H/xaX+OOt766hdeayOvVVbtl6qvdE/RtCSaLJgkjJSyRSCvzwtcTuOB+GPjlLvwCOsaSgjHdSWq6G2IpJdV7SLAIOO7g+Y2NsHeblygiDBUb3VXRlt2wZWlsYw9KzXQnbG0MG5RQY87BaV2hPUkoiiwpGGP8lZLiPedREPk5E1VX9BQpWVSUuWXSMt3Dh+mBCK/e0Oq8HhFevW2mZbZ8JaV6IDk0TxihRKHNpkWaF7ZeTkGUPuj2saRgjIlvIu4J9ux+UByxuR7/NKlTSI62teKplVRjjDE+s6RgjDEmxJKCMcaYEKtTMMZ0e6pKdV0jldV1VFXXUVldT1V1vRvfX8/+ugbyszMo7NWDwl4B+uVmkpmWHHUIzVlSMMYkvJr6Bir3u5N4lXdCD57g3Xg9lfvrQif6qup6qmrqmqxT39ixDsfyszMo6BWgINclioJegbDXHhTkBuiRkXiJw5KCMSYqVJWa+kaq6xqormtkf12DN97A/roGappMazo/8rTG0Lrh76tq6qmtP/RnBDLSUsgNpJEbSCcnkEZO6DWNQHoqO/fUUl6xny0V1WytqmHHnlp27Kll2abKFreZl5VOQW4wWTRNHsFp2ZnxdRqOr2iMMQlJVdmwax/vrt3Ju2t38d7anZRXVhOr3n7TU4WcQDq5zU7mwfHcZq85gXRyezRdtiPFQQ2Nyo49NZRXVLOlYr/3Wn3gtdIlj9376ti9r46VWyI0LeLJyUxzVxxeorjihEEcVeBfsx6WFIwxHaaqfL4zmARcIthSWX3QchlpKfRITyWQnkIgPZUe6alkpqcSSEuhR0YqgbRU95qeQmZwPC2VHhlu+UBaKoGMsOXTD8zPTHPv3Qk9BYlhG0SpKUL/3AD9cwMwMC/iMo2Nyq59tWHJolnyqKymvGI/VTX1VG3bw+ptewC4qHRgzI4jkpgnBREZCDwMFACNwD2q+nsR6Q08AZQA64FvqeoXsY7PGHOw9iSBw7LSOXZwn9BwZL9sUlOSq7G4jkhJEfKzM8nPzmRUUa+Iy6gqFfvrmiSLI/v622KsH1cK9cB/qOoHIpIDLBaROcA04HVVnSUiM4AZwPU+xGdMl2to1Cbl6cHy8PCy84bGRvKzM0O/QDPS/LtjvD1JoHfPDI4Z1DuUBIb2yyalGyeBzhAR8rIyyMvKYHihjy3Bhol5UlDVcqDcG68SkRW4VucnA5O8xR4C5mFJwfisqrqOtz7dwc69NeyvjVwh2tJJPrxCtbah4xWh+dmZFPZyCaLJ3S25gVAZdFZG1/wLqyrrmySBnWytrGmyjCWB7sHXOgURKQHGA+8B/b2EgaqWi0i/FtaZDkwHOPzww2MTqOlWausbmbdqG88t2cxrK7ZS0wV3tgAE0oPl62Fl62HTUkTYsaeGLRXVbKuqZseeGnbsqeHjTRUtbjM3kOZuf/QSRtME4m6LzO2RdlB5e3uTwLGDDySBI/taEugOfEsKIpIN/B/wY1WtbG8lkareA9wDUFpaGqN7G0yya2xUFq7fxXNLNvHyx1uo2F8Xmnd0SW++VJAdVikaHFKaVJQGwk74gWYn/I5WhNY3NLIj7BbILZVN724Jvq+srqeyuopVW1u+u6VHemqThFHXqCxcZ0nAROZLUhCRdFxCeERV/+5N3ioihd5VQiGwzY/YTPehqqwor+K5JZt4/qPNlFccKDM/qiCH88cX8fWxAyjK6xHz2NJSU0JFRC1RVXbtrW0xYQQTyt7aBtbu2MvaHXubrN88CQztlx3TO3hMfPLj7iMB7gdWqOrtYbOeBy4DZnmvz8U6NtM9bNy1j+c/2syzH24K3QYIUJTXg8njBjB5XBHDCnJ8jLB9RIQ+2Zn0yc5k5IDId7eAqxcJvw2yvkEpLTnMkoCJyI8rheOBS4CPRSTY997/wyWDJ0XkSmADcJEPsZkktWtvLS8t3cyzSzaz+PMDdzoflpXOOWMKmTyuiImHH5aUxSXuAa10hvaP/0Rn/OfH3UcLgJb+806LZSwmue2rrWfO8q08++Em5q/eEWrbpkd6Kl8d0Z/zxw/gxKF9SU+1xoKNCbInmk1SqWtoZMHqHTy7ZBOvfrKV/XUNgHsCddKwvpw/roivjuhPzzhrb8aYeGH/GSbhqSqLP/+C55Zs5qWPy9m190DH5xMOz+P88UWcM7qQPtmZPkZpTGKwpGAS1rode3l68UaeW7KZsi/2h6Yf2S+b88cN4LyxRRzeJ8vHCI1JPJYUTMLZva+WO+Z8yt/e20CDV09QkBvgvHEDmDxuACMKc+2uGmM6yZKCSRj1DY08tnADt835lN376kgRuGBCMRdOLOboQb27deNrxnQVSwomIbz92Q5uemF5qF364wb34cbzRvja7rwxyciSgolrG3ft49cvreCfn2wBoPiwHvzinOGcMbLAioiMiQJLCiYu7aut5655n/GXt9ZSW99Ij/RUvn/KEK46cTCB9MTr99aYRGFJwcQVVeX5jzbzm5dXhtrvP3/cAGacNbzVdoCMMV3DkoKJGx+XVTDzhU9CzVCMLurFzPNGMPGI3j5HZkz3YUnB+G57VQ23vrKKJxdvRBXyszP4+RlHceHE4qRsi8iYeGZJwfimtr6Rh95ezx9eX01VTT3pqcLlxw/i2lOPJDeQ7nd4xnRLlhSML+au3Mb/vLg81Mb/qUf14xfnDGewz52WG9PdWVIwMfXZ9j38z4vLmbdqOwCD83vyX18fwSnDIva+aoyJMUsKJiYqq+v4w2urmf32euoblZzMNH50+lAuPa6EjDRrutqYeGFJwURVQ6Py9OKN3PLKKnbsqUUEpnx5INedMYx8a7XUmLhjScFEzaL1u5j5wics21QJQOkRhzHzvJGMKmq560hjjL8sKSSx3ftqWV5eyYryKlaUV7J8cyWf79xLigjpaSlkpKaQniakp7rxjOC01JTQ/Iw0iTAtfDmJuO5ry7fy/EebASjsFWDGWUdx3tgB1jSFMXHOkkISaGxUPt+1jxXllaGT/4rySjZXVLe8Uk3048pMS+HfTxrM1ZOGkJVhf2rGJAL7T00w+2rrWbmlqkkCWLWlir21DQctG0hPYVhBLiMKcxhRmMvwwlyO7JeNiFDX0EhtfSN1DW6oqW+krkFD02rD5h+YphGmhW2nXt37hkZ6Z2Uw/aTBDOxtndwYk0jiLimIyJnA74FU4D5VneVzSL5QVbZW1rC8vIIV5VWuGGhzJet27kX14OX752YyvDA3dPIfXpjLoPye1seAMaZD4iopiEgq8Gfgq0AZ8L6IPK+qy/2Ip6FRWb65Mib7qm9sZO32ve4KYIu7AvhiX91By6WlCEf2z26WAHKs/2FjTJeIq6QAHA2sUdW1ACLyODAZ8CUpVNc18PU/LfBj1wD06pHO8MIcRhT2YnhhDsMLcxnaP5vMNGs62hgTHfGWFIqAjWHvy4BjwhcQkenAdO9tjYgsi1FssZAP7AifsNSnQLrAQceS4JLpeJLpWCC5jidWx3JESzPiLSlEKgBvUoKuqvcA9wCIyCJVLY1FYLGQTMeTTMcCyXU8yXQskFzHEw/HEm/tC5QBA8PeFwObfYrFGGO6nXhLCu8DQ0VkkIhkAFOA532OyRhjuo24Kj5S1XoRuRZ4BXdL6gOq+kkrq9wTm8hiJpmOJ5mOBZLreJLpWCC5jsf3YxGNdNO7McaYbineio+MMcb4yJKCMcaYkIRNCiJypoisEpE1IjLD73g6S0QGishcEVkhIp+IyI/8jqkriEiqiHwoIi/6HcuhEJE8EXlaRFZ639Fxfsd0KETkJ97f2TIReUxEAn7H1BEi8oCIbAt/PklEeovIHBFZ7b0e5meM7dXCsdzi/a0tFZFnRCQv1nElZFIIaw7jLGAEMFVERvgbVafVA/+hqsOBY4HvJ/CxhPsRsMLvILrA74F/qupRwFgS+JhEpAj4IVCqqqNwN3NM8TeqDpsNnNls2gzgdVUdCrzuvU8Eszn4WOYAo1R1DPApcEOsg0rIpEBYcxiqWgsEm8NIOKparqofeONVuJNOkb9RHRoRKQbOAe7zO5ZDISK5wEnA/QCqWququ/2N6pClAT1EJA3IIsGeA1LVt4BdzSZPBh7yxh8Czo9pUJ0U6VhU9VVVrffevot7ViumEjUpRGoOI6FPpAAiUgKMB97zN5JDdifwc6DR70AO0WBgO/CgVxR2n4j09DuozlLVTcCtwAagHKhQ1Vf9japL9FfVcnA/soB+PsfTVa4A/hHrnSZqUmizOYxEIyLZwP8BP1bV2DTNGgUici6wTVUX+x1LF0gDJgB3qep4YC+JUzRxEK+sfTIwCBgA9BSRi/2NykQiIv+JK1p+JNb7TtSkkFTNYYhIOi4hPKKqf/c7nkN0PHCeiKzHFeudKiJ/8zekTisDylQ1eOX2NC5JJKrTgXWqul1V64C/A1/xOaausFVECgG8120+x3NIROQy4FzgO+rDg2SJmhSSpjkMcZ0W3w+sUNXb/Y7nUKnqDaparKoluO/lDVVNyF+jqroF2Cgiw7xJp+FTM+5dZANwrIhkeX93p5HAFedhngcu88YvA57zMZZD4nUydj1wnqru8yOGhEwKXkVMsDmMFcCTbTSHEc+OBy7B/aJe4g1n+x2UCfkB8IiILAXGATf7HE+neVc8TwMfAB/j/v99b1ahI0TkMeAdYJiIlInIlcAs4KsishrXQVdC9NbYwrH8CcgB5njngrtjHpc1c2GMMSYoIa8UjDHGRIclBWOMMSGWFIwxxoRYUjDGGBNiScEYY0yIJQXTKSKiInJb2PvrRGRmF217tohc2BXbamM/F3ktn85tNr1ERL7dyW2+3Y5l7kuSRg9DRGSP3zGYrmFJwXRWDfBNEcn3O5BwXgu67XUl8D1VPaXZ9BIgYlLwGpJrkaq2+YSwql6lqon8EJxJYpYUTGfV4x58+knzGc1/6Qd/RYrIJBF5U0SeFJFPRWSWiHxHRBaKyMciMiRsM6eLyHxvuXO99VO99ubf99qb//ew7c4VkUdxD2U1j2eqt/1lIvJbb9ovgROAu0XklmarzAJO9B4e+omITBORp0TkBeBVEckWkddF5ANvu5PD9hV+rPPkQF8Mj3hPEeNNLw0uLyK/FpGPRORdEenvTR/ivX9fRG5q6Ze4iFzsfX5LROQv3md0hLi+BfJFJMX7HL/mLf+siCwW16fC9PC4ReS33rzXRORoL861InKet8w0EXlORP4pri+TG1uI6Wdh39F/e9N6ishL3nEuE5F/i7SuiQOqaoMNHR6APUAusB7oBVwHzPTmzQYuDF/We50E7AYKgUxgE/Df3rwfAXeGrf9P3I+Wobg2iALAdOAX3jKZwCJc426TcI3VDYoQ5wBc8w59cQ3cvQGc782bh+tboPk6k4AXw95P82Lo7b1PA3K98XxgDQceBA0/1gpcu1wpuCdXT2i+X1xDjl/3xn8XdnwvAlO98auD220W53DgBSDde/+/wKXe+FW4p5d/BvwlbJ3gMfQAlgF9wuI4yxt/BngVSMf1IbEk7HMoB/qErV/a7Li/hvuxIN5xv4hrfvwC4N6wOHr5/TdsQ+TBrhRMp6lrzfVhXMct7fW+uj4kaoDPcCcfcL/wS8KWe1JVG1V1NbAWOAp3wrlURJbgmhfvg0saAAtVdV2E/X0ZmKeuEbhgq5MndSDeoDmqGmz7XoCbvaYvXsM1294/wjoLVbVMVRuBJc2OL6gWd+IEWBy2zHHAU974oy3EdBowEXjf+0xOwzX3jareh2su4Wpcwg76oYh8hGurfyAHPr9aXCIG9128qa7RvObfyxxV3amq+3EN6p3QLKavecOHuOY0jvL28THu6u+3InKiqla0cEzGZ62WjxrTDnfi/vkfDJtWj1c06RWZZITNqwkbbwx730jTv8fm7a8o7mT8A1V9JXyGiEzCXSlEEqmZ9c4I3/53cFceE1W1TlyLsJG6tQw/1gYi/7/VqffTuZVlWiLAQ6p6UO9cIpLFgQ5asoEq73M6HThOVfeJyLywuMPjCH0vqtrYrB4l0vfSPKbfqOpfIsQ0ETgb+I2IvKqqN7XvME0s2ZWCOSTer+cncZW2Qetxv2DBtd+f3olNX+SVhw/B/fpdhWsA8RpxTY0jIl+Stju9eQ842StfTwWmAm+2sU4V7ld2S3rh+oyoE5FTgCPacTwd9S6uyAVa7jLzdeBCEekHob6Kg7H8FndV9Evg3rC4v/ASwlG47l876qvefnrgejj7V7P5rwBXiOsfBBEpEpF+IjIA2Keqf8N19JPITZAnNbtSMF3hNlyrtUH3As+JyELciaulX/GtWYU7efcHrlbVahG5D1eU8YF3BbKdNrpeVNVyEbkBmIv7FfuyqrbVtPJSoN4rZpkNfNFs/iPACyKyCFcstLIjB9ZOPwb+JiL/AbyEq59oQlWXi8gvcJXfKUAdro/vElyx2fGq2iAiF4jI5bhiqKu9Yq9VuMTTUQuAvwJHAo+q6qJmMb0qIsOBd7x69T3Axd7yt4hIoxfnNZ3Yt4kBayXVmDjkFf/sV1UVkSm4Smdf+yEXkWm4iuVr21rWJC67UjAmPk0E/uRdEe3G9ddrTNTZlYIxxpgQq2g2xhgTYknBGGNMiCUFY4wxIZYUjDHGhFhSMMYYE/L/ATzi1AKPw9gQAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+ "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n",
+ "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n",
+ "\n",
+ "plt.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n",
+ "plt.title('Learning curve for linear regression')\n",
+ "plt.legend(['Train', 'Cross Validation'])\n",
+ "plt.xlabel('Number of training examples')\n",
+ "plt.ylabel('Error')\n",
+ "plt.axis([0, 13, 0, 150])\n",
+ "\n",
+ "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+ "for i in range(m):\n",
+ " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the resulting figure, we can see that both the taining and cross validation errors are high when the number of training examples is increase (specifically the training error increases to math cross validation). This reflects a problem of high bias in our model. That is to say, our model is too simple and unable to fit our data set well. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
3 Polynomial Regression
\n",
+ "The problem with our model was that it was too simple for the data and resulted in underfitting (high bias). In this portion of the exercise, we will address this problem by adding more features to produce a more complex fit to the data. We begin by creating a function to map the original training set into its higher powers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def polyFeatures(X, p):\n",
+ " \"\"\"\n",
+ " Maps X (1D vector) into the p-th power.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " A data vector of size m, where m is the number of examples.\n",
+ " \n",
+ " p : int\n",
+ " The polynomial power to map the features. \n",
+ " \n",
+ " Returns \n",
+ " -------\n",
+ " X_poly : array_like\n",
+ " A matrix of shape (m x p) where p is the polynomial \n",
+ " power and m is the number of examples. That is:\n",
+ " \n",
+ " X_poly[i, :] = [X[i], X[i]**2, X[i]**3 ... X[i]**p]\n",
+ " \"\"\"\n",
+ " X_poly = np.zeros((X.shape[0], p))\n",
+ " X_poly[:,0] = X[:,0]\n",
+ " for i in range(1,p):\n",
+ " X_poly[:,i] = np.power(X.transpose(), i+1)\n",
+ "\n",
+ " return X_poly"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now apply this function to our training set, test set, and cross validation set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p = 8\n",
+ "\n",
+ "# Map X onto Polynomial Features and Normalize\n",
+ "X_poly = polyFeatures(X, p)\n",
+ "X_poly, mu, sigma = featureNormalize(X_poly)\n",
+ "X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1)\n",
+ "\n",
+ "# Map X_poly_test and normalize (using mu and sigma)\n",
+ "X_poly_test = polyFeatures(Xtest, p)\n",
+ "X_poly_test -= mu\n",
+ "X_poly_test /= sigma\n",
+ "X_poly_test = np.concatenate([np.ones((ytest.size, 1)), X_poly_test], axis=1)\n",
+ "\n",
+ "# Map X_poly_val and normalize (using mu and sigma)\n",
+ "X_poly_val = polyFeatures(Xval, p)\n",
+ "X_poly_val -= mu\n",
+ "X_poly_val /= sigma\n",
+ "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the ability to map polynomial features, we can train our model via linear regression and plot to see how it fits our data. We will also plot a learning curve for lambda = 0 to see if we still have a bias/variance problem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Polynomial Regression (lambda = 0.000000)\n",
+ "\n",
+ "# Training Examples\tTrain Error\tCross Validation Error\n",
+ " \t1\t\t0.000000\t160.721900\n",
+ " \t2\t\t0.000000\t160.121511\n",
+ " \t3\t\t0.000000\t59.071634\n",
+ " \t4\t\t0.000000\t77.997728\n",
+ " \t5\t\t0.000000\t6.448961\n",
+ " \t6\t\t0.000000\t10.831639\n",
+ " \t7\t\t0.000000\t27.916727\n",
+ " \t8\t\t0.000064\t21.128258\n",
+ " \t9\t\t0.000147\t30.474290\n",
+ " \t10\t\t0.021425\t50.335502\n",
+ " \t11\t\t0.032329\t55.153697\n",
+ " \t12\t\t0.036300\t37.781163\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "lambda_ = 0\n",
+ "theta = trainLinearReg(linearRegCostFunction, X_poly, y,\n",
+ " lambda_=lambda_, maxiter=55)\n",
+ "\n",
+ "# Plot training data and fit\n",
+ "plt.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+ "\n",
+ "plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
+ "\n",
+ "plt.xlabel('Change in water level (x)')\n",
+ "plt.ylabel('Water flowing out of the dam (y)')\n",
+ "plt.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+ "plt.ylim([-20, 50])\n",
+ "\n",
+ "plt.figure()\n",
+ "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n",
+ "plt.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
+ "\n",
+ "plt.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
+ "plt.xlabel('Number of training examples')\n",
+ "plt.ylabel('Error')\n",
+ "plt.axis([0, 13, 0, 100])\n",
+ "plt.legend(['Train', 'Cross Validation'])\n",
+ "\n",
+ "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n",
+ "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+ "for i in range(m):\n",
+ " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looking at the resulting figures, we can see that our curve fits the data extremely well. In fact, it fits it too well. Along the samples it follows perfectly, however it fails to follow the trend along the extremes. We can also see this in the learning curve, as while the training error is extremely low, the cross validation error (the error we would realistically expect to see) is still high. This imply we now have an issue of high-variance, or overfitting. To address this, we can add a regularization term. In order to choose an effective lambda, we automate the process by testing a sequence of lambdas and choosing the one with the least error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def validationCurve(X, y, Xval, yval):\n",
+ " \"\"\"\n",
+ " Generate the train and validation errors needed to plot a validation\n",
+ " curve that we can use to select lambda_.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The training dataset. Matrix with shape (m x n) where m is the \n",
+ " total number of training examples, and n is the number of features \n",
+ " including any polynomial features.\n",
+ " \n",
+ " y : array_like\n",
+ " The functions values at each training datapoint. A vector of\n",
+ " shape (m, ).\n",
+ " \n",
+ " Xval : array_like\n",
+ " The validation dataset. Matrix with shape (m_val x n) where m is the \n",
+ " total number of validation examples, and n is the number of features \n",
+ " including any polynomial features.\n",
+ " \n",
+ " yval : array_like\n",
+ " The functions values at each validation datapoint. A vector of\n",
+ " shape (m_val, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " lambda_vec : list\n",
+ " The values of the regularization parameters which were used in \n",
+ " cross validation.\n",
+ " \n",
+ " error_train : list\n",
+ " The training error computed at each value for the regularization\n",
+ " parameter.\n",
+ " \n",
+ " error_val : list\n",
+ " The validation error computed at each value for the regularization\n",
+ " parameter.\n",
+ " \"\"\"\n",
+ " # Selected values of lambda\n",
+ " lambda_vec = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n",
+ "\n",
+ " error_train = np.zeros(len(lambda_vec))\n",
+ " error_val = np.zeros(len(lambda_vec))\n",
+ "\n",
+ " for i in range(len(lambda_vec)):\n",
+ " lambda_ = lambda_vec[i]\n",
+ " Theta = trainLinearReg(linearRegCostFunction, X, y, lambda_, maxiter=200)\n",
+ " error_train[i] = linearRegCostFunction(X,y,Theta,0)[0]\n",
+ " error_val[i] = linearRegCostFunction(Xval,yval,Theta,0)[0]\n",
+ "\n",
+ " return lambda_vec, error_train, error_val"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now plot a cross validation curve of error vs lambda which allows us to select which lambda paremeter to use."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'pyplot' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mlambda_vec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalidationCurve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_poly\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_poly_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlambda_vec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'-o'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlambda_vec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'-o'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Train'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Cross Validation'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'lambda'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'pyplot' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n",
+ "\n",
+ "pyplot.plot(lambda_vec, error_train, '-o', lambda_vec, error_val, '-o', lw=2)\n",
+ "pyplot.legend(['Train', 'Cross Validation'])\n",
+ "pyplot.xlabel('lambda')\n",
+ "pyplot.ylabel('Error')\n",
+ "\n",
+ "print('lambda\\t\\tTrain Error\\tValidation Error')\n",
+ "for i in range(len(lambda_vec)):\n",
+ " print(' %f\\t%f\\t%f' % (lambda_vec[i], error_train[i], error_val[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With this, we can see the optimal lambda would be around 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(-20, 50)"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "In this exercise, we will use support vector machines (SVMs) to build a spam classifier. To start we will import necessary libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import regular expressions to process emails\n",
+ "import re\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline\n",
+ "\n",
+ "from os.path import join"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Support Vector Machines
\n",
+ "In the first half of this exercise, we will be using SVMs with various 2d datasets. Experimenting with these datasets can reveal how SVMs work and show us how to use a gaussian kernel with SVMs.\n",
+ "\n",
+ "
1.1 Example Dataset 1
\n",
+ "We begin with a 2D example dataset which can be seperated by a linear boundary. We will plot the data into a figure where positive samples are represented with an x and negative samples with an o. Notice there is an outlier positive sample. We will see how this affects our SVM decision boundary. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotData(X, y, grid=False):\n",
+ " \"\"\"\n",
+ " Plots the data points X and y into a new figure. Uses `+` for positive examples, and `o` for\n",
+ " negative examples. `X` is assumed to be a Mx2 matrix\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : numpy ndarray\n",
+ " X is assumed to be a Mx2 matrix.\n",
+ "\n",
+ " y : numpy ndarray\n",
+ " The data labels.\n",
+ "\n",
+ " grid : bool (Optional)\n",
+ " Specify whether or not to show the grid in the plot. It is False by default.\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This was slightly modified such that it expects y=1 or y=0.\n",
+ " \"\"\"\n",
+ " # Find Indices of Positive and Negative Examples\n",
+ " pos = y == 1\n",
+ " neg = y == 0\n",
+ "\n",
+ " # Plot Examples\n",
+ " plt.plot(X[pos, 0], X[pos, 1], 'X', mew=1, ms=10, mec='k')\n",
+ " plt.plot(X[neg, 0], X[neg, 1], 'o', mew=1, mfc='y', ms=10, mec='k')\n",
+ " plt.grid(grid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex6data1\n",
+ "# You will have X, y as keys in the dict data\n",
+ "data = loadmat(os.path.join('Data', 'ex6data1.mat'))\n",
+ "X, y = data['X'], data['y'][:, 0]\n",
+ "\n",
+ "# Plot training data\n",
+ "plotData(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this part of the exercise, we will try using different values of the C paremeter with SVMs. Informally, the C parameter is a positive value that controls the penalty for misclassified training samples. A large C tells the SVM to classify all samples correctly. C plays a role similar to 1/(lambda) where lambda is the regularization parameter used in logistic regression.\n",
+ "
\n",
+ "
\n",
+ "
SVM Decision boundary for example dataset 1
\n",
+ "
\n",
+ "
\n",
+ "
C=1
\n",
+ "
C=100
\n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see the impacts more directly, you can vary C in the following code and run the SVM training again. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def svmTrain(X, Y, C, kernelFunction, tol=1e-3, max_passes=5, args=()):\n",
+ " \"\"\"\n",
+ " Trains an SVM classifier using a simplified version of the SMO algorithm.\n",
+ "\n",
+ " Parameters\n",
+ " ---------\n",
+ " X : numpy ndarray\n",
+ " (m x n) Matrix of training examples. Each row is a training example, and the\n",
+ " jth column holds the jth feature.\n",
+ "\n",
+ " Y : numpy ndarray\n",
+ " (m, ) A vector (1-D numpy array) containing 1 for positive examples and 0 for negative examples.\n",
+ "\n",
+ " C : float\n",
+ " The standard SVM regularization parameter.\n",
+ "\n",
+ " kernelFunction : func\n",
+ " A function handle which computes the kernel. The function should accept two vectors as\n",
+ " inputs, and returns a scalar as output.\n",
+ "\n",
+ " tol : float, optional\n",
+ " Tolerance value used for determining equality of floating point numbers.\n",
+ "\n",
+ " max_passes : int, optional\n",
+ " Controls the number of iterations over the dataset (without changes to alpha)\n",
+ " before the algorithm quits.\n",
+ "\n",
+ " args : tuple\n",
+ " Extra arguments required for the kernel function, such as the sigma parameter for a\n",
+ " Gaussian kernel.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " model :\n",
+ " The trained SVM model.\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is a simplified version of the SMO algorithm for training SVMs. In practice, if\n",
+ " you want to train an SVM classifier, we recommend using an optimized package such as:\n",
+ "\n",
+ " - LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)\n",
+ " - SVMLight (http://svmlight.joachims.org/)\n",
+ " - scikit-learn (http://scikit-learn.org/stable/modules/svm.html) which contains python wrappers\n",
+ " for the LIBSVM library.\n",
+ " \"\"\"\n",
+ " # make sure data is signed int\n",
+ " Y = Y.astype(int)\n",
+ " # Dataset size parameters\n",
+ " m, n = X.shape\n",
+ "\n",
+ " passes = 0\n",
+ " E = np.zeros(m)\n",
+ " alphas = np.zeros(m)\n",
+ " b = 0\n",
+ "\n",
+ " # Map 0 to -1\n",
+ " Y[Y == 0] = -1\n",
+ "\n",
+ " # Pre-compute the Kernel Matrix since our dataset is small\n",
+ " # (in practice, optimized SVM packages that handle large datasets\n",
+ " # gracefully will **not** do this)\n",
+ "\n",
+ " # We have implemented the optimized vectorized version of the Kernels here so\n",
+ " # that the SVM training will run faster\n",
+ " if kernelFunction.__name__ == 'linearKernel':\n",
+ " # Vectorized computation for the linear kernel\n",
+ " # This is equivalent to computing the kernel on every pair of examples\n",
+ " K = np.dot(X, X.T)\n",
+ " elif kernelFunction.__name__ == 'gaussianKernel':\n",
+ " # vectorized RBF Kernel\n",
+ " # This is equivalent to computing the kernel on every pair of examples\n",
+ " X2 = np.sum(X**2, axis=1)\n",
+ " K = X2 + X2[:, None] - 2 * np.dot(X, X.T)\n",
+ "\n",
+ " if len(args) > 0:\n",
+ " K /= 2*args[0]**2\n",
+ "\n",
+ " K = np.exp(-K)\n",
+ " else:\n",
+ " K = np.zeros((m, m))\n",
+ " for i in range(m):\n",
+ " for j in range(i, m):\n",
+ " K[i, j] = kernelFunction(X[i, :], X[j, :])\n",
+ " K[j, i] = K[i, j]\n",
+ "\n",
+ " while passes < max_passes:\n",
+ " num_changed_alphas = 0\n",
+ " for i in range(m):\n",
+ " E[i] = b + np.sum(alphas * Y * K[:, i]) - Y[i]\n",
+ "\n",
+ " if (Y[i]*E[i] < -tol and alphas[i] < C) or (Y[i]*E[i] > tol and alphas[i] > 0):\n",
+ " # select the alpha_j randomly\n",
+ " j = np.random.choice(list(range(i)) + list(range(i+1, m)), size=1)[0]\n",
+ "\n",
+ " E[j] = b + np.sum(alphas * Y * K[:, j]) - Y[j]\n",
+ "\n",
+ " alpha_i_old = alphas[i]\n",
+ " alpha_j_old = alphas[j]\n",
+ "\n",
+ " if Y[i] == Y[j]:\n",
+ " L = max(0, alphas[j] + alphas[i] - C)\n",
+ " H = min(C, alphas[j] + alphas[i])\n",
+ " else:\n",
+ " L = max(0, alphas[j] - alphas[i])\n",
+ " H = min(C, C + alphas[j] - alphas[i])\n",
+ "\n",
+ " if L == H:\n",
+ " continue\n",
+ "\n",
+ " eta = 2 * K[i, j] - K[i, i] - K[j, j]\n",
+ "\n",
+ " # objective function positive definite, there will be a minimum along the direction\n",
+ " # of linear equality constrain, and eta will be greater than zero\n",
+ " # we are actually computing -eta here (so we skip of eta >= 0)\n",
+ " if eta >= 0:\n",
+ " continue\n",
+ "\n",
+ " alphas[j] -= Y[j] * (E[i] - E[j])/eta\n",
+ " alphas[j] = max(L, min(H, alphas[j]))\n",
+ "\n",
+ " if abs(alphas[j] - alpha_j_old) < tol:\n",
+ " alphas[j] = alpha_j_old\n",
+ " continue\n",
+ " alphas[i] += Y[i]*Y[j]*(alpha_j_old - alphas[j])\n",
+ "\n",
+ " b1 = b - E[i] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n",
+ " - Y[j] * (alphas[j] - alpha_j_old) * K[i, j]\n",
+ "\n",
+ " b2 = b - E[j] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n",
+ " - Y[j] * (alphas[j] - alpha_j_old) * K[j, j]\n",
+ "\n",
+ " if 0 < alphas[i] < C:\n",
+ " b = b1\n",
+ " elif 0 < alphas[j] < C:\n",
+ " b = b2\n",
+ " else:\n",
+ " b = (b1 + b2)/2\n",
+ "\n",
+ " num_changed_alphas += 1\n",
+ " if num_changed_alphas == 0:\n",
+ " passes += 1\n",
+ " else:\n",
+ " passes = 0\n",
+ "\n",
+ " idx = alphas > 0\n",
+ " model = {'X': X[idx, :],\n",
+ " 'y': Y[idx],\n",
+ " 'kernelFunction': kernelFunction,\n",
+ " 'b': b,\n",
+ " 'args': args,\n",
+ " 'alphas': alphas[idx],\n",
+ " 'w': np.dot(alphas * Y, X)}\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def linearKernel(x1, x2):\n",
+ " \"\"\"\n",
+ " Returns a linear kernel between x1 and x2.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " x1 : numpy ndarray\n",
+ " A 1-D vector.\n",
+ "\n",
+ " x2 : numpy ndarray\n",
+ " A 1-D vector of same size as x1.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " : float\n",
+ " The scalar amplitude.\n",
+ " \"\"\"\n",
+ " return np.dot(x1, x2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualizeBoundaryLinear(X, y, model):\n",
+ " \"\"\"\n",
+ " Plots a linear decision boundary learned by the SVM.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " (m x 2) The training data with two features (to plot in a 2-D plane).\n",
+ "\n",
+ " y : array_like\n",
+ " (m, ) The data labels.\n",
+ "\n",
+ " model : dict\n",
+ " Dictionary of model variables learned by SVM.\n",
+ " \"\"\"\n",
+ " w, b = model['w'], model['b']\n",
+ " xp = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n",
+ " yp = -(w[0] * xp + b)/w[1]\n",
+ "\n",
+ " plotData(X, y)\n",
+ " plt.plot(xp, yp, '-b')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# You should try to change the C value below and see how the decision\n",
+ "# boundary varies (e.g., try C = 1000)\n",
+ "C = 1\n",
+ "\n",
+ "model = svmTrain(X, y, C, linearKernel, 1e-3, 20)\n",
+ "visualizeBoundaryLinear(X, y, model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1.2 SVM with Gaussian Kernals
\n",
+ "In this part of the exercise, we will be using SVMs to do non-linear classification. To find non-linear decision boundaries with the SVM, we need to first implement a Gaussian kernel. A Gaussian kernal can be throught of as a similarity function that measures the \"distance\" between pairs of examples. The kernel is also parameterized by a bandwidth parameter sigma which determines how fast the similarity metric decreases as examples are further apart. We now create a function to compute the Gaussian kernel between to examples. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gaussianKernel(x1, x2, sigma):\n",
+ " \"\"\"\n",
+ " Computes the radial basis function\n",
+ " Returns a radial basis function kernel between x1 and x2.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " x1 : numpy ndarray\n",
+ " A vector of size (n, ), representing the first datapoint.\n",
+ " \n",
+ " x2 : numpy ndarray\n",
+ " A vector of size (n, ), representing the second datapoint.\n",
+ " \n",
+ " sigma : float\n",
+ " The bandwidth parameter for the Gaussian kernel.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " sim : float\n",
+ " The computed RBF between the two provided data points.\n",
+ " \"\"\"\n",
+ " sim = 0\n",
+ " temp = np.square(x1-x2)\n",
+ " temp = np.sum(temp)\n",
+ " temp = temp * (-1)\n",
+ " temp = temp / (2 * (sigma**2))\n",
+ " sim = np.exp(temp)\n",
+ "\n",
+ " return sim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now load and plot dataset 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex6data2\n",
+ "# You will have X, y as keys in the dict data\n",
+ "data = loadmat(os.path.join('Data', 'ex6data2.mat'))\n",
+ "X, y = data['X'], data['y'][:, 0]\n",
+ "\n",
+ "# Plot training data\n",
+ "plotData(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From this figure we can observe that there is no linear decision boundary which could seperate the positive and negative examples for this dataset. However, by using the Gaussian kernal with the SVM, we will be able to learn a non-linear decision boundary that can perform reasonably well."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualizeBoundary(X, y, model):\n",
+ " \"\"\"\n",
+ " Plots a non-linear decision boundary learned by the SVM and overlays the data on it.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " (m x 2) The training data with two features (to plot in a 2-D plane).\n",
+ "\n",
+ " y : array_like\n",
+ " (m, ) The data labels.\n",
+ "\n",
+ " model : dict\n",
+ " Dictionary of model variables learned by SVM.\n",
+ " \"\"\"\n",
+ " plotData(X, y)\n",
+ "\n",
+ " # make classification predictions over a grid of values\n",
+ " x1plot = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n",
+ " x2plot = np.linspace(min(X[:, 1]), max(X[:, 1]), 100)\n",
+ " X1, X2 = np.meshgrid(x1plot, x2plot)\n",
+ "\n",
+ " vals = np.zeros(X1.shape)\n",
+ " for i in range(X1.shape[1]):\n",
+ " this_X = np.stack((X1[:, i], X2[:, i]), axis=1)\n",
+ " vals[:, i] = svmPredict(model, this_X)\n",
+ "\n",
+ " plt.contour(X1, X2, vals, colors='y', linewidths=2)\n",
+ " plt.pcolormesh(X1, X2, vals, cmap='YlGnBu', alpha=0.25, edgecolors='None', lw=0)\n",
+ " plt.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# SVM Parameters\n",
+ "C = 1\n",
+ "sigma = 0.1\n",
+ "\n",
+ "model= svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+ "visualizeBoundary(X, y, model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now gain more practical skills on how to use an SVM with a Gaussian kernel. We begin by loading and displaying the third dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex6data3\n",
+ "# You will have X, y, Xval, yval as keys in the dict data\n",
+ "data = loadmat(os.path.join('Data', 'ex6data3.mat'))\n",
+ "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Plot training data\n",
+ "plotData(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this dataset we have the variables X, y, Xval, yval. Our task is to use the cross validation set Xval, yval to determine the best C and sigma parameters to use for our decision boundary."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def dataset3Params(X, y, Xval, yval):\n",
+ " \"\"\"\n",
+ " Returns your choice of C and sigma for Part 3 of the exercise \n",
+ " where you select the optimal (C, sigma) learning parameters to use for SVM\n",
+ " with RBF kernel.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " (m x n) matrix of training data where m is number of training examples, and \n",
+ " n is the number of features.\n",
+ " \n",
+ " y : array_like\n",
+ " (m, ) vector of labels for ther training data.\n",
+ " \n",
+ " Xval : array_like\n",
+ " (mv x n) matrix of validation data where mv is the number of validation examples\n",
+ " and n is the number of features\n",
+ " \n",
+ " yval : array_like\n",
+ " (mv, ) vector of labels for the validation data.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " C, sigma : float, float\n",
+ " The best performing values for the regularization parameter C and \n",
+ " RBF parameter sigma.\n",
+ " \"\"\"\n",
+ " C = 1\n",
+ " sigma = 0.3\n",
+ "\n",
+ " sampleVec = np.array([.01, .03, .1, .3, 1, 3, 10, 30])\n",
+ " model= svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+ " predictions = svmPredict(model, Xval)\n",
+ " error = np.mean(predictions != yval)\n",
+ " for i in range(8):\n",
+ " for j in range(8):\n",
+ " tempC = sampleVec[i]\n",
+ " tempSigma = sampleVec[j]\n",
+ " tempModel = svmTrain(X, y, tempC, gaussianKernel, args=(tempSigma,))\n",
+ " tempPredictions = svmPredict(tempModel, Xval)\n",
+ " tempError = np.mean(tempPredictions != yval)\n",
+ " if tempError < error:\n",
+ " error = tempError\n",
+ " C = tempC\n",
+ " sigma = tempSigma\n",
+ "\n",
+ " return C, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.0 0.1\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "Many email services today provide spam filters which are able to classify emails into spam and non-spam email with high accuracy. In this part of the exercise, we will use SVMs to build our own spam filter.\n",
+ "\n",
+ "We will be training a classifier to classify whether a given email, x, is spam (y=1) or non-spam (y=0). In particular we need to convert each email into an n-dimensional feature vector.\n",
+ "\n",
+ "
2.1 Preprocessing Emails
\n",
+ "Before starting on a machine learning task, it is usually insightful to take a look at examples from the dataset. the following figure shows a sample email which contains a URL, email addess, numbers, and dollar amounts. While many emails would contain similar types of entitiers, the specific entities will be different in almost every email. Therefore, one method often employed in processing emails is to \"normalize\" these values, so that all URLs are treated the same, all numbers the same, etc. For examplem we could replace each URL with the unique string \"httppadr\" to indicate a URL was present. \n",
+ "\n",
+ "This has the effect of letting the spam classifier make a classification decision based on whether any URL was present as opposed to a specific URL. in processEmail we have implemented the following steps:\n",
+ "- **Lower-casing**: The entire email is converted into lower case, so that captialization is ignored (e.g., IndIcaTE is treated the same as Indicate).\n",
+ "\n",
+ "- **Stripping HTML**: All HTML tags are removed from the emails. Many emails often come with HTML formatting; we remove all the HTML tags, so that only the content remains.\n",
+ "\n",
+ "- **Normalizing URLs**: All URLs are replaced with the text “httpaddr”.\n",
+ "\n",
+ "- **Normalizing Email Addresses**: All email addresses are replaced with the text “emailaddr”.\n",
+ "\n",
+ "- **Normalizing Numbers**: All numbers are replaced with the text “number”.\n",
+ "\n",
+ "- **Normalizing Dollars**: All dollar signs ($) are replaced with the text “dollar”.\n",
+ "\n",
+ "- **Word Stemming**: Words are reduced to their stemmed form. For example, “discount”, “discounts”, “discounted” and “discounting” are all replaced with “discount”. Sometimes, the Stemmer actually strips off additional characters from the end, so “include”, “includes”, “included”, and “including” are all replaced with “includ”.\n",
+ "\n",
+ "- **Removal of non-words**: Non-words and punctuation have been removed. All white spaces (tabs, newlines, spaces) have all been trimmed to a single space character.\n",
+ "\n",
+ "\n",
+ "\n",
+ "The result of these preprocessing steps is shown in the figure below. \n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After preprocessing the emails, we have a list of words for each email. The next step is to choose which words we would like to use in our classifier and which we would want to leave out.\n",
+ "\n",
+ "For this exercise, we have chosen only the most frequently occuring words as our set of words considered (the vocabulary list). Since words that occur rarely in the training set are only in a few emails, they might cause the\n",
+ "model to overfit our training set. The complete vocabulary list is in the file `vocab.txt` (inside the `Data` directory for this exercise) and also shown in the figure below.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Our vocabulary list was selected by choosing all words which occur at least a 100 times in the spam corpus,\n",
+ "resulting in a list of 1899 words. In practice, a vocabulary list with about 10,000 to 50,000 words is often used.\n",
+ "Given the vocabulary list, we can now map each word in the preprocessed emails into a list of word indices that contains the index of the word in the vocabulary dictionary. The figure below shows the mapping for the sample email. Specifically, in the sample email, the word “anyone” was first normalized to “anyon” and then mapped onto the index 86 in the vocabulary list.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Our task now is to complete the code in the function `processEmail` to perform this mapping. In the code, we have a string `word` which is a single word from the processed email. We need to look up the word in the vocabulary list `vocabList`. If the word exists in the list, we will add the index of the word into the `word_indices` variable. If the word does not exist, and is therefore not in the vocabulary, we will skip the word.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getVocabList():\n",
+ " \"\"\"\n",
+ " Reads the fixed vocabulary list in vocab.txt and returns a cell array of the words\n",
+ " % vocabList = GETVOCABLIST() reads the fixed vocabulary list in vocab.txt\n",
+ " % and returns a cell array of the words in vocabList.\n",
+ "\n",
+ " :return:\n",
+ " \"\"\"\n",
+ " vocabList = np.genfromtxt(join('Data', 'vocab.txt'), dtype=object)\n",
+ " return list(vocabList[:, 1].astype(str))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PorterStemmer:\n",
+ " \"\"\"\n",
+ " Porter Stemming Algorithm\n",
+ "\n",
+ " This is the Porter stemming algorithm, ported to Python from the\n",
+ " version coded up in ANSI C by the author. It may be be regarded\n",
+ " as canonical, in that it follows the algorithm presented in\n",
+ "\n",
+ " Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,\n",
+ " no. 3, pp 130-137,\n",
+ "\n",
+ " only differing from it at the points maked --DEPARTURE-- below.\n",
+ "\n",
+ " See also http://www.tartarus.org/~martin/PorterStemmer\n",
+ "\n",
+ " The algorithm as described in the paper could be exactly replicated\n",
+ " by adjusting the points of DEPARTURE, but this is barely necessary,\n",
+ " because (a) the points of DEPARTURE are definitely improvements, and\n",
+ " (b) no encoding of the Porter stemmer I have seen is anything like\n",
+ " as exact as this version, even with the points of DEPARTURE!\n",
+ "\n",
+ " Vivake Gupta (v@nano.com)\n",
+ "\n",
+ " Release 1: January 2001\n",
+ "\n",
+ " Further adjustments by Santiago Bruno (bananabruno@gmail.com)\n",
+ " to allow word input not restricted to one word per line, leading\n",
+ " to:\n",
+ "\n",
+ " release 2: July 2008\n",
+ " \"\"\"\n",
+ " def __init__(self):\n",
+ " \"\"\"\n",
+ " The main part of the stemming algorithm starts here.\n",
+ " b is a buffer holding a word to be stemmed. The letters are in b[k0],\n",
+ " b[k0+1] ... ending at b[k]. In fact k0 = 0 in this demo program. k is\n",
+ " readjusted downwards as the stemming progresses. Zero termination is\n",
+ " not in fact used in the algorithm.\n",
+ "\n",
+ " Note that only lower case sequences are stemmed. Forcing to lower case\n",
+ " should be done before stem(...) is called.\n",
+ " \"\"\"\n",
+ " self.b = \"\" # buffer for word to be stemmed\n",
+ " self.k = 0\n",
+ " self.k0 = 0\n",
+ " self.j = 0 # j is a general offset into the string\n",
+ "\n",
+ " def cons(self, i):\n",
+ " \"\"\"cons(i) is TRUE <=> b[i] is a consonant.\"\"\"\n",
+ " if self.b[i] in 'aeiou':\n",
+ " return 0\n",
+ " if self.b[i] == 'y':\n",
+ " if i == self.k0:\n",
+ " return 1\n",
+ " else:\n",
+ " return not self.cons(i - 1)\n",
+ " return 1\n",
+ "\n",
+ " def m(self):\n",
+ " \"\"\"\n",
+ " m() measures the number of consonant sequences between k0 and j.\n",
+ " if c is a consonant sequence and v a vowel sequence, and <..>\n",
+ " indicates arbitrary presence,\n",
+ "\n",
+ " gives 0\n",
+ " vc gives 1\n",
+ " vcvc gives 2\n",
+ " vcvcvc gives 3\n",
+ " ....\n",
+ " \"\"\"\n",
+ " n = 0\n",
+ " i = self.k0\n",
+ " while 1:\n",
+ " if i > self.j:\n",
+ " return n\n",
+ " if not self.cons(i):\n",
+ " break\n",
+ " i = i + 1\n",
+ " i = i + 1\n",
+ " while 1:\n",
+ " while 1:\n",
+ " if i > self.j:\n",
+ " return n\n",
+ " if self.cons(i):\n",
+ " break\n",
+ " i = i + 1\n",
+ " i = i + 1\n",
+ " n = n + 1\n",
+ " while 1:\n",
+ " if i > self.j:\n",
+ " return n\n",
+ " if not self.cons(i):\n",
+ " break\n",
+ " i = i + 1\n",
+ " i = i + 1\n",
+ "\n",
+ " def vowelinstem(self):\n",
+ " \"\"\"vowelinstem() is TRUE <=> k0,...j contains a vowel\"\"\"\n",
+ " for i in range(self.k0, self.j + 1):\n",
+ " if not self.cons(i):\n",
+ " return 1\n",
+ " return 0\n",
+ "\n",
+ " def doublec(self, j):\n",
+ " \"\"\" doublec(j) is TRUE <=> j,(j-1) contain a double consonant. \"\"\"\n",
+ " if j < (self.k0 + 1):\n",
+ " return 0\n",
+ " if self.b[j] != self.b[j-1]:\n",
+ " return 0\n",
+ " return self.cons(j)\n",
+ "\n",
+ " def cvc(self, i):\n",
+ " \"\"\"\n",
+ " cvc(i) is TRUE <=> i-2,i-1,i has the form consonant - vowel - consonant\n",
+ " and also if the second c is not w,x or y. this is used when trying to\n",
+ " restore an e at the end of a short e.g.\n",
+ "\n",
+ " cav(e), lov(e), hop(e), crim(e), but\n",
+ " snow, box, tray.\n",
+ " \"\"\"\n",
+ " if i < (self.k0 + 2) or not self.cons(i) or self.cons(i-1) or not self.cons(i-2):\n",
+ " return 0\n",
+ " ch = self.b[i]\n",
+ " if ch in 'wxy':\n",
+ " return 0\n",
+ " return 1\n",
+ "\n",
+ " def ends(self, s):\n",
+ " \"\"\"ends(s) is TRUE <=> k0,...k ends with the string s.\"\"\"\n",
+ " length = len(s)\n",
+ " if s[length - 1] != self.b[self.k]: # tiny speed-up\n",
+ " return 0\n",
+ " if length > (self.k - self.k0 + 1):\n",
+ " return 0\n",
+ " if self.b[self.k-length+1:self.k+1] != s:\n",
+ " return 0\n",
+ " self.j = self.k - length\n",
+ " return 1\n",
+ "\n",
+ " def setto(self, s):\n",
+ " \"\"\"setto(s) sets (j+1),...k to the characters in the string s, readjusting k.\"\"\"\n",
+ " length = len(s)\n",
+ " self.b = self.b[:self.j+1] + s + self.b[self.j+length+1:]\n",
+ " self.k = self.j + length\n",
+ "\n",
+ " def r(self, s):\n",
+ " \"\"\"r(s) is used further down.\"\"\"\n",
+ " if self.m() > 0:\n",
+ " self.setto(s)\n",
+ "\n",
+ " def step1ab(self):\n",
+ " \"\"\"step1ab() gets rid of plurals and -ed or -ing. e.g.\n",
+ "\n",
+ " caresses -> caress\n",
+ " ponies -> poni\n",
+ " ties -> ti\n",
+ " caress -> caress\n",
+ " cats -> cat\n",
+ "\n",
+ " feed -> feed\n",
+ " agreed -> agree\n",
+ " disabled -> disable\n",
+ "\n",
+ " matting -> mat\n",
+ " mating -> mate\n",
+ " meeting -> meet\n",
+ " milling -> mill\n",
+ " messing -> mess\n",
+ "\n",
+ " meetings -> meet\n",
+ " \"\"\"\n",
+ " if self.b[self.k] == 's':\n",
+ " if self.ends(\"sses\"):\n",
+ " self.k = self.k - 2\n",
+ " elif self.ends(\"ies\"):\n",
+ " self.setto(\"i\")\n",
+ " elif self.b[self.k - 1] != 's':\n",
+ " self.k = self.k - 1\n",
+ " if self.ends(\"eed\"):\n",
+ " if self.m() > 0:\n",
+ " self.k = self.k - 1\n",
+ " elif (self.ends(\"ed\") or self.ends(\"ing\")) and self.vowelinstem():\n",
+ " self.k = self.j\n",
+ " if self.ends(\"at\"):\n",
+ " self.setto(\"ate\")\n",
+ " elif self.ends(\"bl\"):\n",
+ " self.setto(\"ble\")\n",
+ " elif self.ends(\"iz\"):\n",
+ " self.setto(\"ize\")\n",
+ " elif self.doublec(self.k):\n",
+ " self.k = self.k - 1\n",
+ " ch = self.b[self.k]\n",
+ " if ch in 'lsz':\n",
+ " self.k += 1\n",
+ " elif self.m() == 1 and self.cvc(self.k):\n",
+ " self.setto(\"e\")\n",
+ "\n",
+ " def step1c(self):\n",
+ " \"\"\"step1c() turns terminal y to i when there is another vowel in the stem.\"\"\"\n",
+ " if self.ends(\"y\") and self.vowelinstem():\n",
+ " self.b = self.b[:self.k] + 'i' + self.b[self.k+1:]\n",
+ "\n",
+ " def step2(self):\n",
+ " \"\"\"step2() maps double suffices to single ones.\n",
+ " so -ization ( = -ize plus -ation) maps to -ize etc. note that the\n",
+ " string before the suffix must give m() > 0.\n",
+ " \"\"\"\n",
+ " if self.b[self.k - 1] == 'a':\n",
+ " if self.ends(\"ational\"): self.r(\"ate\")\n",
+ " elif self.ends(\"tional\"): self.r(\"tion\")\n",
+ " elif self.b[self.k - 1] == 'c':\n",
+ " if self.ends(\"enci\"): self.r(\"ence\")\n",
+ " elif self.ends(\"anci\"): self.r(\"ance\")\n",
+ " elif self.b[self.k - 1] == 'e':\n",
+ " if self.ends(\"izer\"): self.r(\"ize\")\n",
+ " elif self.b[self.k - 1] == 'l':\n",
+ " if self.ends(\"bli\"): self.r(\"ble\") # --DEPARTURE--\n",
+ " # To match the published algorithm, replace this phrase with\n",
+ " # if self.ends(\"abli\"): self.r(\"able\")\n",
+ " elif self.ends(\"alli\"): self.r(\"al\")\n",
+ " elif self.ends(\"entli\"): self.r(\"ent\")\n",
+ " elif self.ends(\"eli\"): self.r(\"e\")\n",
+ " elif self.ends(\"ousli\"): self.r(\"ous\")\n",
+ " elif self.b[self.k - 1] == 'o':\n",
+ " if self.ends(\"ization\"): self.r(\"ize\")\n",
+ " elif self.ends(\"ation\"): self.r(\"ate\")\n",
+ " elif self.ends(\"ator\"): self.r(\"ate\")\n",
+ " elif self.b[self.k - 1] == 's':\n",
+ " if self.ends(\"alism\"): self.r(\"al\")\n",
+ " elif self.ends(\"iveness\"): self.r(\"ive\")\n",
+ " elif self.ends(\"fulness\"): self.r(\"ful\")\n",
+ " elif self.ends(\"ousness\"): self.r(\"ous\")\n",
+ " elif self.b[self.k - 1] == 't':\n",
+ " if self.ends(\"aliti\"): self.r(\"al\")\n",
+ " elif self.ends(\"iviti\"): self.r(\"ive\")\n",
+ " elif self.ends(\"biliti\"): self.r(\"ble\")\n",
+ " elif self.b[self.k - 1] == 'g': # --DEPARTURE--\n",
+ " if self.ends(\"logi\"): self.r(\"log\")\n",
+ " # To match the published algorithm, delete this phrase\n",
+ "\n",
+ " def step3(self):\n",
+ " \"\"\"step3() dels with -ic-, -full, -ness etc. similar strategy to step2.\"\"\"\n",
+ " if self.b[self.k] == 'e':\n",
+ " if self.ends(\"icate\"): self.r(\"ic\")\n",
+ " elif self.ends(\"ative\"): self.r(\"\")\n",
+ " elif self.ends(\"alize\"): self.r(\"al\")\n",
+ " elif self.b[self.k] == 'i':\n",
+ " if self.ends(\"iciti\"): self.r(\"ic\")\n",
+ " elif self.b[self.k] == 'l':\n",
+ " if self.ends(\"ical\"): self.r(\"ic\")\n",
+ " elif self.ends(\"ful\"): self.r(\"\")\n",
+ " elif self.b[self.k] == 's':\n",
+ " if self.ends(\"ness\"): self.r(\"\")\n",
+ "\n",
+ " def step4(self):\n",
+ " \"\"\"step4() takes off -ant, -ence etc., in context vcvc.\"\"\"\n",
+ " if self.b[self.k - 1] == 'a':\n",
+ " if self.ends(\"al\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'c':\n",
+ " if self.ends(\"ance\"): pass\n",
+ " elif self.ends(\"ence\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'e':\n",
+ " if self.ends(\"er\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'i':\n",
+ " if self.ends(\"ic\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'l':\n",
+ " if self.ends(\"able\"): pass\n",
+ " elif self.ends(\"ible\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'n':\n",
+ " if self.ends(\"ant\"): pass\n",
+ " elif self.ends(\"ement\"): pass\n",
+ " elif self.ends(\"ment\"): pass\n",
+ " elif self.ends(\"ent\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'o':\n",
+ " if self.ends(\"ion\") and (self.b[self.j] == 's' or self.b[self.j] == 't'): pass\n",
+ " elif self.ends(\"ou\"): pass\n",
+ " # takes care of -ous\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 's':\n",
+ " if self.ends(\"ism\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 't':\n",
+ " if self.ends(\"ate\"): pass\n",
+ " elif self.ends(\"iti\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'u':\n",
+ " if self.ends(\"ous\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'v':\n",
+ " if self.ends(\"ive\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'z':\n",
+ " if self.ends(\"ize\"): pass\n",
+ " else: return\n",
+ " else:\n",
+ " return\n",
+ " if self.m() > 1:\n",
+ " self.k = self.j\n",
+ "\n",
+ " def step5(self):\n",
+ " \"\"\"step5() removes a final -e if m() > 1, and changes -ll to -l if\n",
+ " m() > 1.\n",
+ " \"\"\"\n",
+ " self.j = self.k\n",
+ " if self.b[self.k] == 'e':\n",
+ " a = self.m()\n",
+ " if a > 1 or (a == 1 and not self.cvc(self.k-1)):\n",
+ " self.k = self.k - 1\n",
+ " if self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1:\n",
+ " self.k = self.k -1\n",
+ "\n",
+ " def stem(self, p, i=0, j=None):\n",
+ " \"\"\"In stem(p,i,j), p is a char pointer, and the string to be stemmed\n",
+ " is from p[i] to p[j] inclusive. Typically i is zero and j is the\n",
+ " offset to the last character of a string, (p[j+1] == '\\0'). The\n",
+ " stemmer adjusts the characters p[i] ... p[j] and returns the new\n",
+ " end-point of the string, k. Stemming never increases word length, so\n",
+ " i <= k <= j. To turn the stemmer into a module, declare 'stem' as\n",
+ " extern, and delete the remainder of this file.\n",
+ " \"\"\"\n",
+ " # copy the parameters into statics\n",
+ " self.b = p\n",
+ " self.k = j or len(p) - 1\n",
+ " self.k0 = i\n",
+ " if self.k <= self.k0 + 1:\n",
+ " return self.b # --DEPARTURE--\n",
+ "\n",
+ " # With this line, strings of length 1 or 2 don't go through the\n",
+ " # stemming process, although no mention is made of this in the\n",
+ " # published algorithm. Remove the line to match the published\n",
+ " # algorithm.\n",
+ "\n",
+ " self.step1ab()\n",
+ " self.step1c()\n",
+ " self.step2()\n",
+ " self.step3()\n",
+ " self.step4()\n",
+ " self.step5()\n",
+ " return self.b[self.k0:self.k+1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def processEmail(email_contents, verbose=True):\n",
+ " \"\"\"\n",
+ " Preprocesses the body of an email and returns a list of indices \n",
+ " of the words contained in the email. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " email_contents : str\n",
+ " A string containing one email. \n",
+ " \n",
+ " verbose : bool\n",
+ " If True, print the resulting email after processing.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " word_indices : list\n",
+ " A list of integers containing the index of each word in the \n",
+ " email which is also present in the vocabulary.\n",
+ " \"\"\"\n",
+ " # Load Vocabulary\n",
+ " vocabList = getVocabList()\n",
+ "\n",
+ " # Init return value\n",
+ " word_indices = []\n",
+ "\n",
+ " # ========================== Preprocess Email ===========================\n",
+ " # Find the Headers ( \\n\\n and remove )\n",
+ " # Uncomment the following lines if you are working with raw emails with the\n",
+ " # full headers\n",
+ " # hdrstart = email_contents.find(chr(10) + chr(10))\n",
+ " # email_contents = email_contents[hdrstart:]\n",
+ "\n",
+ " # Lower case\n",
+ " email_contents = email_contents.lower()\n",
+ " \n",
+ " # Strip all HTML\n",
+ " # Looks for any expression that starts with < and ends with > and replace\n",
+ " # and does not have any < or > in the tag it with a space\n",
+ " email_contents =re.compile('<[^<>]+>').sub(' ', email_contents)\n",
+ "\n",
+ " # Handle Numbers\n",
+ " # Look for one or more characters between 0-9\n",
+ " email_contents = re.compile('[0-9]+').sub(' number ', email_contents)\n",
+ "\n",
+ " # Handle URLS\n",
+ " # Look for strings starting with http:// or https://\n",
+ " email_contents = re.compile('(http|https)://[^\\s]*').sub(' httpaddr ', email_contents)\n",
+ "\n",
+ " # Handle Email Addresses\n",
+ " # Look for strings with @ in the middle\n",
+ " email_contents = re.compile('[^\\s]+@[^\\s]+').sub(' emailaddr ', email_contents)\n",
+ " \n",
+ " # Handle $ sign\n",
+ " email_contents = re.compile('[$]+').sub(' dollar ', email_contents)\n",
+ " \n",
+ " # get rid of any punctuation\n",
+ " email_contents = re.split('[ @$/#.-:&*+=\\[\\]?!(){},''\">_<;%\\n\\r]', email_contents)\n",
+ "\n",
+ " # remove any empty word string\n",
+ " email_contents = [word for word in email_contents if len(word) > 0]\n",
+ " \n",
+ " # Stem the email contents word by word\n",
+ " stemmer = PorterStemmer()\n",
+ " processed_email = []\n",
+ " for word in email_contents:\n",
+ " # Remove any remaining non alphanumeric characters in word\n",
+ " word = re.compile('[^a-zA-Z0-9]').sub('', word).strip()\n",
+ " word = stemmer.stem(word)\n",
+ " processed_email.append(word)\n",
+ "\n",
+ " if len(word) < 1:\n",
+ " continue\n",
+ "\n",
+ " # Look up the word in the dictionary and add to word_indices if found\n",
+ " # ====================== YOUR CODE HERE ======================\n",
+ "\n",
+ " for i in range(len(vocabList)):\n",
+ " if word == vocabList[i]:\n",
+ " word_indices.append(i)\n",
+ "\n",
+ " # =============================================================\n",
+ "\n",
+ " if verbose:\n",
+ " print('----------------')\n",
+ " print('Processed email:')\n",
+ " print('----------------')\n",
+ " print(' '.join(processed_email))\n",
+ " return word_indices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "----------------\n",
+ "Processed email:\n",
+ "----------------\n",
+ "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+ "-------------\n",
+ "Word Indices:\n",
+ "-------------\n",
+ "[85, 915, 793, 1076, 882, 369, 1698, 789, 1821, 1830, 882, 430, 1170, 793, 1001, 1894, 591, 1675, 237, 161, 88, 687, 944, 1662, 1119, 1061, 1698, 374, 1161, 476, 1119, 1892, 1509, 798, 1181, 1236, 511, 1119, 809, 1894, 1439, 1546, 180, 1698, 1757, 1895, 687, 1675, 991, 960, 1476, 70, 529, 1698, 530]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# To use an SVM to classify emails into Spam v.s. Non-Spam, you first need\n",
+ "# to convert each email into a vector of features.\n",
+ "\n",
+ "# Extract Features\n",
+ "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
+ " file_contents = fid.read()\n",
+ "\n",
+ "word_indices = processEmail(file_contents)\n",
+ "\n",
+ "#Print Stats\n",
+ "print('-------------')\n",
+ "print('Word Indices:')\n",
+ "print('-------------')\n",
+ "print(word_indices)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.2 Extracting Features from Emails
\n",
+ "We will now implement the feature extraction which converts each email into an n-dimensional vector. We will use n = # words in vocab list. Specificall the i-th feature in {0,1} for an email corresponds to whether the i-th word in the dictionary occurs in the email. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def emailFeatures(word_indices):\n",
+ " \"\"\"\n",
+ " Takes in a word_indices vector and produces a feature vector from the word indices. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " word_indices : list\n",
+ " A list of word indices from the vocabulary list.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " x : list \n",
+ " The computed feature vector.\n",
+ " \"\"\"\n",
+ " # Total number of words in the dictionary\n",
+ " n = 1899\n",
+ " \n",
+ " x = np.zeros(n)\n",
+ "\n",
+ " for i in range(n):\n",
+ " if np.any(word_indices == i):\n",
+ " x[i] = 1\n",
+ " \n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "----------------\n",
+ "Processed email:\n",
+ "----------------\n",
+ "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+ "\n",
+ "Length of feature vector: 1899\n",
+ "Number of non-zero entries: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract Features\n",
+ "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
+ " file_contents = fid.read()\n",
+ "\n",
+ "word_indices = processEmail(file_contents)\n",
+ "features = emailFeatures(word_indices)\n",
+ "\n",
+ "# Print Stats\n",
+ "print('\\nLength of feature vector: %d' % len(features))\n",
+ "print('Number of non-zero entries: %d' % sum(features > 0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.3 Training SVM for Spam Classification
\n",
+ "Now that we have functions setup to extract features, we will load a preprocessed training dataset that will be used to train an SVM classifier. spamTrain.mat contains 4000 training samples of spam and non-spam email, while spamTest.mat contains 1000 test samples. Each original email was processed using the processEmail and emailFeatures functions then converted into a 1,899-dimensional vector. After loading the dataset, we will train an SVM to classify between spam and non-spam and test its accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Linear SVM (Spam Classification)\n",
+ "This may take 1 to 2 minutes ...\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load the Spam Email dataset\n",
+ "# You will have X, y in your environment\n",
+ "data = loadmat(os.path.join('Data', 'spamTrain.mat'))\n",
+ "X, y= data['X'].astype(float), data['y'][:, 0]\n",
+ "\n",
+ "print('Training Linear SVM (Spam Classification)')\n",
+ "print('This may take 1 to 2 minutes ...\\n')\n",
+ "\n",
+ "C = 0.1\n",
+ "model = svmTrain(X, y, C, linearKernel)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Accuracy: 99.83\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute the training accuracy\n",
+ "p = svmPredict(model, X)\n",
+ "\n",
+ "print('Training Accuracy: %.2f' % (np.mean(p == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Evaluating the trained Linear SVM on a test set ...\n",
+ "Test Accuracy: 98.80\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load the test dataset\n",
+ "# You will have Xtest, ytest in your environment\n",
+ "data = loadmat(os.path.join('Data', 'spamTest.mat'))\n",
+ "Xtest, ytest = data['Xtest'].astype(float), data['ytest'][:, 0]\n",
+ "\n",
+ "print('Evaluating the trained Linear SVM on a test set ...')\n",
+ "p = svmPredict(model, Xtest)\n",
+ "\n",
+ "print('Test Accuracy: %.2f' % (np.mean(p == ytest) * 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.4 Top Predictors for Spam
\n",
+ "To better understand how the spam classifier works, we can inspect the parameters to see which words the classifier thinks are most productive of spam. We will now find the parameters with the largest positive values in the classifier and display the corresponding words."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top predictors of spam:\n",
+ "word weight \n",
+ "---- ------\n",
+ "our 0.50\n",
+ "click 0.47\n",
+ "remov 0.42\n",
+ "guarante 0.39\n",
+ "visit 0.37\n",
+ "basenumb 0.35\n",
+ "dollar 0.32\n",
+ "will 0.27\n",
+ "price 0.27\n",
+ "pleas 0.26\n",
+ "most 0.26\n",
+ "nbsp 0.25\n",
+ "lo 0.25\n",
+ "hour 0.24\n",
+ "ga 0.24\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Sort the weights and obtin the vocabulary list\n",
+ "# NOTE some words have the same weights, \n",
+ "# so their order might be different than in the text above\n",
+ "idx = np.argsort(model['w'])\n",
+ "top_idx = idx[-15:][::-1]\n",
+ "vocabList = getVocabList()\n",
+ "\n",
+ "print('Top predictors of spam:')\n",
+ "print('%-15s %-15s' % ('word', 'weight'))\n",
+ "print('----' + ' '*12 + '------')\n",
+ "for word, w in zip(np.array(vocabList)[top_idx], model['w'][top_idx]):\n",
+ " print('%-15s %0.2f' % (word, w))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex6/Data/emailSample1.txt b/ex6/Data/emailSample1.txt
new file mode 100644
index 0000000..eac52a3
--- /dev/null
+++ b/ex6/Data/emailSample1.txt
@@ -0,0 +1,10 @@
+> Anyone knows how much it costs to host a web portal ?
+>
+Well, it depends on how many visitors you're expecting.
+This can be anywhere from less than 10 bucks a month to a couple of $100.
+You should checkout http://www.rackspace.com/ or perhaps Amazon EC2
+if youre running something big..
+
+To unsubscribe yourself from this mailing list, send an email to:
+groupname-unsubscribe@egroups.com
+
diff --git a/ex6/Data/emailSample2.txt b/ex6/Data/emailSample2.txt
new file mode 100644
index 0000000..e47acda
--- /dev/null
+++ b/ex6/Data/emailSample2.txt
@@ -0,0 +1,34 @@
+Folks,
+
+my first time posting - have a bit of Unix experience, but am new to Linux.
+
+
+Just got a new PC at home - Dell box with Windows XP. Added a second hard disk
+for Linux. Partitioned the disk and have installed Suse 7.2 from CD, which went
+fine except it didn't pick up my monitor.
+
+I have a Dell branded E151FPp 15" LCD flat panel monitor and a nVidia GeForce4
+Ti4200 video card, both of which are probably too new to feature in Suse's default
+set. I downloaded a driver from the nVidia website and installed it using RPM.
+Then I ran Sax2 (as was recommended in some postings I found on the net), but
+it still doesn't feature my video card in the available list. What next?
+
+Another problem. I have a Dell branded keyboard and if I hit Caps-Lock twice,
+the whole machine crashes (in Linux, not Windows) - even the on/off switch is
+inactive, leaving me to reach for the power cable instead.
+
+If anyone can help me in any way with these probs., I'd be really grateful -
+I've searched the 'net but have run out of ideas.
+
+Or should I be going for a different version of Linux such as RedHat? Opinions
+welcome.
+
+Thanks a lot,
+Peter
+
+--
+Irish Linux Users' Group: ilug@linux.ie
+http://www.linux.ie/mailman/listinfo/ilug for (un)subscription information.
+List maintainer: listmaster@linux.ie
+
+
diff --git a/ex6/Data/ex6data1.mat b/ex6/Data/ex6data1.mat
new file mode 100644
index 0000000..ae0d2aa
Binary files /dev/null and b/ex6/Data/ex6data1.mat differ
diff --git a/ex6/Data/ex6data2.mat b/ex6/Data/ex6data2.mat
new file mode 100644
index 0000000..c6ad661
Binary files /dev/null and b/ex6/Data/ex6data2.mat differ
diff --git a/ex6/Data/ex6data3.mat b/ex6/Data/ex6data3.mat
new file mode 100644
index 0000000..a0441ac
Binary files /dev/null and b/ex6/Data/ex6data3.mat differ
diff --git a/ex6/Data/spamSample1.txt b/ex6/Data/spamSample1.txt
new file mode 100644
index 0000000..bab0ca2
--- /dev/null
+++ b/ex6/Data/spamSample1.txt
@@ -0,0 +1,42 @@
+Do You Want To Make $1000 Or More Per Week?
+
+
+
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+will personally demonstrate to you a system that will
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+
+
+Call our 24 hour pre-recorded number to get the
+details.
+
+
+
+000-456-789
+
+
+
+I need people who want to make serious money. Make
+the call and get the facts.
+
+Invest 2 minutes in yourself now!
+
+
+
+000-456-789
+
+
+
+Looking forward to your call and I will introduce you
+to people like yourself who
+are currently making $10,000 plus per week!
+
+
+
+000-456-789
+
+
+
+3484lJGv6-241lEaN9080lRmS6-271WxHo7524qiyT5-438rjUv5615hQcf0-662eiDB9057dMtVl72
+
diff --git a/ex6/Data/spamSample2.txt b/ex6/Data/spamSample2.txt
new file mode 100644
index 0000000..f8e8fce
--- /dev/null
+++ b/ex6/Data/spamSample2.txt
@@ -0,0 +1,8 @@
+Best Buy Viagra Generic Online
+
+Viagra 100mg x 60 Pills $125, Free Pills & Reorder Discount, Top Selling 100% Quality & Satisfaction guaranteed!
+
+We accept VISA, Master & E-Check Payments, 90000+ Satisfied Customers!
+http://medphysitcstech.ru
+
+
diff --git a/ex6/Data/spamTest.mat b/ex6/Data/spamTest.mat
new file mode 100644
index 0000000..b7bf953
Binary files /dev/null and b/ex6/Data/spamTest.mat differ
diff --git a/ex6/Data/spamTrain.mat b/ex6/Data/spamTrain.mat
new file mode 100644
index 0000000..1b9c81f
Binary files /dev/null and b/ex6/Data/spamTrain.mat differ
diff --git a/ex6/Data/vocab.txt b/ex6/Data/vocab.txt
new file mode 100644
index 0000000..27f64a3
--- /dev/null
+++ b/ex6/Data/vocab.txt
@@ -0,0 +1,1899 @@
+1 aa
+2 ab
+3 abil
+4 abl
+5 about
+6 abov
+7 absolut
+8 abus
+9 ac
+10 accept
+11 access
+12 accord
+13 account
+14 achiev
+15 acquir
+16 across
+17 act
+18 action
+19 activ
+20 actual
+21 ad
+22 adam
+23 add
+24 addit
+25 address
+26 administr
+27 adult
+28 advanc
+29 advantag
+30 advertis
+31 advic
+32 advis
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+34 af
+35 affect
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+38 africa
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+63 alwai
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+65 amaz
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+67 american
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+71 an
+72 analysi
+73 analyst
+74 and
+75 ani
+76 anim
+77 announc
+78 annual
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+82 anti
+83 anumb
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+89 anywher
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+91 ap
+92 apolog
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+125 atol
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 6: Support Vector Machines
\n",
+ "
Introduction
\n",
+ "In this exercise, we will use support vector machines (SVMs) to build a spam classifier. To start we will import necessary libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import regular expressions to process emails\n",
+ "import re\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline\n",
+ "\n",
+ "from os.path import join"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Support Vector Machines
\n",
+ "In the first half of this exercise, we will be using SVMs with various 2d datasets. Experimenting with these datasets can reveal how SVMs work and show us how to use a gaussian kernel with SVMs.\n",
+ "\n",
+ "
1.1 Example Dataset 1
\n",
+ "We begin with a 2D example dataset which can be seperated by a linear boundary. We will plot the data into a figure where positive samples are represented with an x and negative samples with an o. Notice there is an outlier positive sample. We will see how this affects our SVM decision boundary. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotData(X, y, grid=False):\n",
+ " \"\"\"\n",
+ " Plots the data points X and y into a new figure. Uses `+` for positive examples, and `o` for\n",
+ " negative examples. `X` is assumed to be a Mx2 matrix\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : numpy ndarray\n",
+ " X is assumed to be a Mx2 matrix.\n",
+ "\n",
+ " y : numpy ndarray\n",
+ " The data labels.\n",
+ "\n",
+ " grid : bool (Optional)\n",
+ " Specify whether or not to show the grid in the plot. It is False by default.\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This was slightly modified such that it expects y=1 or y=0.\n",
+ " \"\"\"\n",
+ " # Find Indices of Positive and Negative Examples\n",
+ " pos = y == 1\n",
+ " neg = y == 0\n",
+ "\n",
+ " # Plot Examples\n",
+ " plt.plot(X[pos, 0], X[pos, 1], 'X', mew=1, ms=10, mec='k')\n",
+ " plt.plot(X[neg, 0], X[neg, 1], 'o', mew=1, mfc='y', ms=10, mec='k')\n",
+ " plt.grid(grid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex6data1\n",
+ "# You will have X, y as keys in the dict data\n",
+ "data = loadmat(os.path.join('Data', 'ex6data1.mat'))\n",
+ "X, y = data['X'], data['y'][:, 0]\n",
+ "\n",
+ "# Plot training data\n",
+ "plotData(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this part of the exercise, we will try using different values of the C paremeter with SVMs. Informally, the C parameter is a positive value that controls the penalty for misclassified training samples. A large C tells the SVM to classify all samples correctly. C plays a role similar to 1/(lambda) where lambda is the regularization parameter used in logistic regression.\n",
+ "
\n",
+ "
\n",
+ "
SVM Decision boundary for example dataset 1
\n",
+ "
\n",
+ "
\n",
+ "
C=1
\n",
+ "
C=100
\n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see the impacts more directly, you can vary C in the following code and run the SVM training again. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def svmTrain(X, Y, C, kernelFunction, tol=1e-3, max_passes=5, args=()):\n",
+ " \"\"\"\n",
+ " Trains an SVM classifier using a simplified version of the SMO algorithm.\n",
+ "\n",
+ " Parameters\n",
+ " ---------\n",
+ " X : numpy ndarray\n",
+ " (m x n) Matrix of training examples. Each row is a training example, and the\n",
+ " jth column holds the jth feature.\n",
+ "\n",
+ " Y : numpy ndarray\n",
+ " (m, ) A vector (1-D numpy array) containing 1 for positive examples and 0 for negative examples.\n",
+ "\n",
+ " C : float\n",
+ " The standard SVM regularization parameter.\n",
+ "\n",
+ " kernelFunction : func\n",
+ " A function handle which computes the kernel. The function should accept two vectors as\n",
+ " inputs, and returns a scalar as output.\n",
+ "\n",
+ " tol : float, optional\n",
+ " Tolerance value used for determining equality of floating point numbers.\n",
+ "\n",
+ " max_passes : int, optional\n",
+ " Controls the number of iterations over the dataset (without changes to alpha)\n",
+ " before the algorithm quits.\n",
+ "\n",
+ " args : tuple\n",
+ " Extra arguments required for the kernel function, such as the sigma parameter for a\n",
+ " Gaussian kernel.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " model :\n",
+ " The trained SVM model.\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " This is a simplified version of the SMO algorithm for training SVMs. In practice, if\n",
+ " you want to train an SVM classifier, we recommend using an optimized package such as:\n",
+ "\n",
+ " - LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)\n",
+ " - SVMLight (http://svmlight.joachims.org/)\n",
+ " - scikit-learn (http://scikit-learn.org/stable/modules/svm.html) which contains python wrappers\n",
+ " for the LIBSVM library.\n",
+ " \"\"\"\n",
+ " # make sure data is signed int\n",
+ " Y = Y.astype(int)\n",
+ " # Dataset size parameters\n",
+ " m, n = X.shape\n",
+ "\n",
+ " passes = 0\n",
+ " E = np.zeros(m)\n",
+ " alphas = np.zeros(m)\n",
+ " b = 0\n",
+ "\n",
+ " # Map 0 to -1\n",
+ " Y[Y == 0] = -1\n",
+ "\n",
+ " # Pre-compute the Kernel Matrix since our dataset is small\n",
+ " # (in practice, optimized SVM packages that handle large datasets\n",
+ " # gracefully will **not** do this)\n",
+ "\n",
+ " # We have implemented the optimized vectorized version of the Kernels here so\n",
+ " # that the SVM training will run faster\n",
+ " if kernelFunction.__name__ == 'linearKernel':\n",
+ " # Vectorized computation for the linear kernel\n",
+ " # This is equivalent to computing the kernel on every pair of examples\n",
+ " K = np.dot(X, X.T)\n",
+ " elif kernelFunction.__name__ == 'gaussianKernel':\n",
+ " # vectorized RBF Kernel\n",
+ " # This is equivalent to computing the kernel on every pair of examples\n",
+ " X2 = np.sum(X**2, axis=1)\n",
+ " K = X2 + X2[:, None] - 2 * np.dot(X, X.T)\n",
+ "\n",
+ " if len(args) > 0:\n",
+ " K /= 2*args[0]**2\n",
+ "\n",
+ " K = np.exp(-K)\n",
+ " else:\n",
+ " K = np.zeros((m, m))\n",
+ " for i in range(m):\n",
+ " for j in range(i, m):\n",
+ " K[i, j] = kernelFunction(X[i, :], X[j, :])\n",
+ " K[j, i] = K[i, j]\n",
+ "\n",
+ " while passes < max_passes:\n",
+ " num_changed_alphas = 0\n",
+ " for i in range(m):\n",
+ " E[i] = b + np.sum(alphas * Y * K[:, i]) - Y[i]\n",
+ "\n",
+ " if (Y[i]*E[i] < -tol and alphas[i] < C) or (Y[i]*E[i] > tol and alphas[i] > 0):\n",
+ " # select the alpha_j randomly\n",
+ " j = np.random.choice(list(range(i)) + list(range(i+1, m)), size=1)[0]\n",
+ "\n",
+ " E[j] = b + np.sum(alphas * Y * K[:, j]) - Y[j]\n",
+ "\n",
+ " alpha_i_old = alphas[i]\n",
+ " alpha_j_old = alphas[j]\n",
+ "\n",
+ " if Y[i] == Y[j]:\n",
+ " L = max(0, alphas[j] + alphas[i] - C)\n",
+ " H = min(C, alphas[j] + alphas[i])\n",
+ " else:\n",
+ " L = max(0, alphas[j] - alphas[i])\n",
+ " H = min(C, C + alphas[j] - alphas[i])\n",
+ "\n",
+ " if L == H:\n",
+ " continue\n",
+ "\n",
+ " eta = 2 * K[i, j] - K[i, i] - K[j, j]\n",
+ "\n",
+ " # objective function positive definite, there will be a minimum along the direction\n",
+ " # of linear equality constrain, and eta will be greater than zero\n",
+ " # we are actually computing -eta here (so we skip of eta >= 0)\n",
+ " if eta >= 0:\n",
+ " continue\n",
+ "\n",
+ " alphas[j] -= Y[j] * (E[i] - E[j])/eta\n",
+ " alphas[j] = max(L, min(H, alphas[j]))\n",
+ "\n",
+ " if abs(alphas[j] - alpha_j_old) < tol:\n",
+ " alphas[j] = alpha_j_old\n",
+ " continue\n",
+ " alphas[i] += Y[i]*Y[j]*(alpha_j_old - alphas[j])\n",
+ "\n",
+ " b1 = b - E[i] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n",
+ " - Y[j] * (alphas[j] - alpha_j_old) * K[i, j]\n",
+ "\n",
+ " b2 = b - E[j] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n",
+ " - Y[j] * (alphas[j] - alpha_j_old) * K[j, j]\n",
+ "\n",
+ " if 0 < alphas[i] < C:\n",
+ " b = b1\n",
+ " elif 0 < alphas[j] < C:\n",
+ " b = b2\n",
+ " else:\n",
+ " b = (b1 + b2)/2\n",
+ "\n",
+ " num_changed_alphas += 1\n",
+ " if num_changed_alphas == 0:\n",
+ " passes += 1\n",
+ " else:\n",
+ " passes = 0\n",
+ "\n",
+ " idx = alphas > 0\n",
+ " model = {'X': X[idx, :],\n",
+ " 'y': Y[idx],\n",
+ " 'kernelFunction': kernelFunction,\n",
+ " 'b': b,\n",
+ " 'args': args,\n",
+ " 'alphas': alphas[idx],\n",
+ " 'w': np.dot(alphas * Y, X)}\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def linearKernel(x1, x2):\n",
+ " \"\"\"\n",
+ " Returns a linear kernel between x1 and x2.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " x1 : numpy ndarray\n",
+ " A 1-D vector.\n",
+ "\n",
+ " x2 : numpy ndarray\n",
+ " A 1-D vector of same size as x1.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " : float\n",
+ " The scalar amplitude.\n",
+ " \"\"\"\n",
+ " return np.dot(x1, x2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualizeBoundaryLinear(X, y, model):\n",
+ " \"\"\"\n",
+ " Plots a linear decision boundary learned by the SVM.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " (m x 2) The training data with two features (to plot in a 2-D plane).\n",
+ "\n",
+ " y : array_like\n",
+ " (m, ) The data labels.\n",
+ "\n",
+ " model : dict\n",
+ " Dictionary of model variables learned by SVM.\n",
+ " \"\"\"\n",
+ " w, b = model['w'], model['b']\n",
+ " xp = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n",
+ " yp = -(w[0] * xp + b)/w[1]\n",
+ "\n",
+ " plotData(X, y)\n",
+ " plt.plot(xp, yp, '-b')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# You should try to change the C value below and see how the decision\n",
+ "# boundary varies (e.g., try C = 1000)\n",
+ "C = 1\n",
+ "\n",
+ "model = svmTrain(X, y, C, linearKernel, 1e-3, 20)\n",
+ "visualizeBoundaryLinear(X, y, model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1.2 SVM with Gaussian Kernals
\n",
+ "In this part of the exercise, we will be using SVMs to do non-linear classification. To find non-linear decision boundaries with the SVM, we need to first implement a Gaussian kernel. A Gaussian kernal can be throught of as a similarity function that measures the \"distance\" between pairs of examples. The kernel is also parameterized by a bandwidth parameter sigma which determines how fast the similarity metric decreases as examples are further apart. We now create a function to compute the Gaussian kernel between to examples. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gaussianKernel(x1, x2, sigma):\n",
+ " \"\"\"\n",
+ " Computes the radial basis function\n",
+ " Returns a radial basis function kernel between x1 and x2.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " x1 : numpy ndarray\n",
+ " A vector of size (n, ), representing the first datapoint.\n",
+ " \n",
+ " x2 : numpy ndarray\n",
+ " A vector of size (n, ), representing the second datapoint.\n",
+ " \n",
+ " sigma : float\n",
+ " The bandwidth parameter for the Gaussian kernel.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " sim : float\n",
+ " The computed RBF between the two provided data points.\n",
+ " \"\"\"\n",
+ " sim = 0\n",
+ " temp = np.square(x1-x2)\n",
+ " temp = np.sum(temp)\n",
+ " temp = temp * (-1)\n",
+ " temp = temp / (2 * (sigma**2))\n",
+ " sim = np.exp(temp)\n",
+ "\n",
+ " return sim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now load and plot dataset 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex6data2\n",
+ "# You will have X, y as keys in the dict data\n",
+ "data = loadmat(os.path.join('Data', 'ex6data2.mat'))\n",
+ "X, y = data['X'], data['y'][:, 0]\n",
+ "\n",
+ "# Plot training data\n",
+ "plotData(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From this figure we can observe that there is no linear decision boundary which could seperate the positive and negative examples for this dataset. However, by using the Gaussian kernal with the SVM, we will be able to learn a non-linear decision boundary that can perform reasonably well."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualizeBoundary(X, y, model):\n",
+ " \"\"\"\n",
+ " Plots a non-linear decision boundary learned by the SVM and overlays the data on it.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " (m x 2) The training data with two features (to plot in a 2-D plane).\n",
+ "\n",
+ " y : array_like\n",
+ " (m, ) The data labels.\n",
+ "\n",
+ " model : dict\n",
+ " Dictionary of model variables learned by SVM.\n",
+ " \"\"\"\n",
+ " plotData(X, y)\n",
+ "\n",
+ " # make classification predictions over a grid of values\n",
+ " x1plot = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n",
+ " x2plot = np.linspace(min(X[:, 1]), max(X[:, 1]), 100)\n",
+ " X1, X2 = np.meshgrid(x1plot, x2plot)\n",
+ "\n",
+ " vals = np.zeros(X1.shape)\n",
+ " for i in range(X1.shape[1]):\n",
+ " this_X = np.stack((X1[:, i], X2[:, i]), axis=1)\n",
+ " vals[:, i] = svmPredict(model, this_X)\n",
+ "\n",
+ " plt.contour(X1, X2, vals, colors='y', linewidths=2)\n",
+ " plt.pcolormesh(X1, X2, vals, cmap='YlGnBu', alpha=0.25, edgecolors='None', lw=0)\n",
+ " plt.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# SVM Parameters\n",
+ "C = 1\n",
+ "sigma = 0.1\n",
+ "\n",
+ "model= svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+ "visualizeBoundary(X, y, model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now gain more practical skills on how to use an SVM with a Gaussian kernel. We begin by loading and displaying the third dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load from ex6data3\n",
+ "# You will have X, y, Xval, yval as keys in the dict data\n",
+ "data = loadmat(os.path.join('Data', 'ex6data3.mat'))\n",
+ "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Plot training data\n",
+ "plotData(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this dataset we have the variables X, y, Xval, yval. Our task is to use the cross validation set Xval, yval to determine the best C and sigma parameters to use for our decision boundary."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def dataset3Params(X, y, Xval, yval):\n",
+ " \"\"\"\n",
+ " Returns your choice of C and sigma for Part 3 of the exercise \n",
+ " where you select the optimal (C, sigma) learning parameters to use for SVM\n",
+ " with RBF kernel.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " (m x n) matrix of training data where m is number of training examples, and \n",
+ " n is the number of features.\n",
+ " \n",
+ " y : array_like\n",
+ " (m, ) vector of labels for ther training data.\n",
+ " \n",
+ " Xval : array_like\n",
+ " (mv x n) matrix of validation data where mv is the number of validation examples\n",
+ " and n is the number of features\n",
+ " \n",
+ " yval : array_like\n",
+ " (mv, ) vector of labels for the validation data.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " C, sigma : float, float\n",
+ " The best performing values for the regularization parameter C and \n",
+ " RBF parameter sigma.\n",
+ " \"\"\"\n",
+ " C = 1\n",
+ " sigma = 0.3\n",
+ "\n",
+ " sampleVec = np.array([.01, .03, .1, .3, 1, 3, 10, 30])\n",
+ " model= svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+ " predictions = svmPredict(model, Xval)\n",
+ " error = np.mean(predictions != yval)\n",
+ " for i in range(8):\n",
+ " for j in range(8):\n",
+ " tempC = sampleVec[i]\n",
+ " tempSigma = sampleVec[j]\n",
+ " tempModel = svmTrain(X, y, tempC, gaussianKernel, args=(tempSigma,))\n",
+ " tempPredictions = svmPredict(tempModel, Xval)\n",
+ " tempError = np.mean(tempPredictions != yval)\n",
+ " if tempError < error:\n",
+ " error = tempError\n",
+ " C = tempC\n",
+ " sigma = tempSigma\n",
+ "\n",
+ " return C, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.0 0.1\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Try different SVM Parameters here\n",
+ "C, sigma = dataset3Params(X, y, Xval, yval)\n",
+ "\n",
+ "# Train the SVM\n",
+ "# model = utils.svmTrain(X, y, C, lambda x1, x2: gaussianKernel(x1, x2, sigma))\n",
+ "model = svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+ "visualizeBoundary(X, y, model)\n",
+ "print(C, sigma)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Spam Classification
\n",
+ "Many email services today provide spam filters which are able to classify emails into spam and non-spam email with high accuracy. In this part of the exercise, we will use SVMs to build our own spam filter.\n",
+ "\n",
+ "We will be training a classifier to classify whether a given email, x, is spam (y=1) or non-spam (y=0). In particular we need to convert each email into an n-dimensional feature vector.\n",
+ "\n",
+ "
2.1 Preprocessing Emails
\n",
+ "Before starting on a machine learning task, it is usually insightful to take a look at examples from the dataset. the following figure shows a sample email which contains a URL, email addess, numbers, and dollar amounts. While many emails would contain similar types of entitiers, the specific entities will be different in almost every email. Therefore, one method often employed in processing emails is to \"normalize\" these values, so that all URLs are treated the same, all numbers the same, etc. For examplem we could replace each URL with the unique string \"httppadr\" to indicate a URL was present. \n",
+ "\n",
+ "This has the effect of letting the spam classifier make a classification decision based on whether any URL was present as opposed to a specific URL. in processEmail we have implemented the following steps:\n",
+ "- **Lower-casing**: The entire email is converted into lower case, so that captialization is ignored (e.g., IndIcaTE is treated the same as Indicate).\n",
+ "\n",
+ "- **Stripping HTML**: All HTML tags are removed from the emails. Many emails often come with HTML formatting; we remove all the HTML tags, so that only the content remains.\n",
+ "\n",
+ "- **Normalizing URLs**: All URLs are replaced with the text “httpaddr”.\n",
+ "\n",
+ "- **Normalizing Email Addresses**: All email addresses are replaced with the text “emailaddr”.\n",
+ "\n",
+ "- **Normalizing Numbers**: All numbers are replaced with the text “number”.\n",
+ "\n",
+ "- **Normalizing Dollars**: All dollar signs ($) are replaced with the text “dollar”.\n",
+ "\n",
+ "- **Word Stemming**: Words are reduced to their stemmed form. For example, “discount”, “discounts”, “discounted” and “discounting” are all replaced with “discount”. Sometimes, the Stemmer actually strips off additional characters from the end, so “include”, “includes”, “included”, and “including” are all replaced with “includ”.\n",
+ "\n",
+ "- **Removal of non-words**: Non-words and punctuation have been removed. All white spaces (tabs, newlines, spaces) have all been trimmed to a single space character.\n",
+ "\n",
+ "\n",
+ "\n",
+ "The result of these preprocessing steps is shown in the figure below. \n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After preprocessing the emails, we have a list of words for each email. The next step is to choose which words we would like to use in our classifier and which we would want to leave out.\n",
+ "\n",
+ "For this exercise, we have chosen only the most frequently occuring words as our set of words considered (the vocabulary list). Since words that occur rarely in the training set are only in a few emails, they might cause the\n",
+ "model to overfit our training set. The complete vocabulary list is in the file `vocab.txt` (inside the `Data` directory for this exercise) and also shown in the figure below.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Our vocabulary list was selected by choosing all words which occur at least a 100 times in the spam corpus,\n",
+ "resulting in a list of 1899 words. In practice, a vocabulary list with about 10,000 to 50,000 words is often used.\n",
+ "Given the vocabulary list, we can now map each word in the preprocessed emails into a list of word indices that contains the index of the word in the vocabulary dictionary. The figure below shows the mapping for the sample email. Specifically, in the sample email, the word “anyone” was first normalized to “anyon” and then mapped onto the index 86 in the vocabulary list.\n",
+ "\n",
+ "\n",
+ "\n",
+ "Our task now is to complete the code in the function `processEmail` to perform this mapping. In the code, we have a string `word` which is a single word from the processed email. We need to look up the word in the vocabulary list `vocabList`. If the word exists in the list, we will add the index of the word into the `word_indices` variable. If the word does not exist, and is therefore not in the vocabulary, we will skip the word.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getVocabList():\n",
+ " \"\"\"\n",
+ " Reads the fixed vocabulary list in vocab.txt and returns a cell array of the words\n",
+ " % vocabList = GETVOCABLIST() reads the fixed vocabulary list in vocab.txt\n",
+ " % and returns a cell array of the words in vocabList.\n",
+ "\n",
+ " :return:\n",
+ " \"\"\"\n",
+ " vocabList = np.genfromtxt(join('Data', 'vocab.txt'), dtype=object)\n",
+ " return list(vocabList[:, 1].astype(str))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PorterStemmer:\n",
+ " \"\"\"\n",
+ " Porter Stemming Algorithm\n",
+ "\n",
+ " This is the Porter stemming algorithm, ported to Python from the\n",
+ " version coded up in ANSI C by the author. It may be be regarded\n",
+ " as canonical, in that it follows the algorithm presented in\n",
+ "\n",
+ " Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,\n",
+ " no. 3, pp 130-137,\n",
+ "\n",
+ " only differing from it at the points maked --DEPARTURE-- below.\n",
+ "\n",
+ " See also http://www.tartarus.org/~martin/PorterStemmer\n",
+ "\n",
+ " The algorithm as described in the paper could be exactly replicated\n",
+ " by adjusting the points of DEPARTURE, but this is barely necessary,\n",
+ " because (a) the points of DEPARTURE are definitely improvements, and\n",
+ " (b) no encoding of the Porter stemmer I have seen is anything like\n",
+ " as exact as this version, even with the points of DEPARTURE!\n",
+ "\n",
+ " Vivake Gupta (v@nano.com)\n",
+ "\n",
+ " Release 1: January 2001\n",
+ "\n",
+ " Further adjustments by Santiago Bruno (bananabruno@gmail.com)\n",
+ " to allow word input not restricted to one word per line, leading\n",
+ " to:\n",
+ "\n",
+ " release 2: July 2008\n",
+ " \"\"\"\n",
+ " def __init__(self):\n",
+ " \"\"\"\n",
+ " The main part of the stemming algorithm starts here.\n",
+ " b is a buffer holding a word to be stemmed. The letters are in b[k0],\n",
+ " b[k0+1] ... ending at b[k]. In fact k0 = 0 in this demo program. k is\n",
+ " readjusted downwards as the stemming progresses. Zero termination is\n",
+ " not in fact used in the algorithm.\n",
+ "\n",
+ " Note that only lower case sequences are stemmed. Forcing to lower case\n",
+ " should be done before stem(...) is called.\n",
+ " \"\"\"\n",
+ " self.b = \"\" # buffer for word to be stemmed\n",
+ " self.k = 0\n",
+ " self.k0 = 0\n",
+ " self.j = 0 # j is a general offset into the string\n",
+ "\n",
+ " def cons(self, i):\n",
+ " \"\"\"cons(i) is TRUE <=> b[i] is a consonant.\"\"\"\n",
+ " if self.b[i] in 'aeiou':\n",
+ " return 0\n",
+ " if self.b[i] == 'y':\n",
+ " if i == self.k0:\n",
+ " return 1\n",
+ " else:\n",
+ " return not self.cons(i - 1)\n",
+ " return 1\n",
+ "\n",
+ " def m(self):\n",
+ " \"\"\"\n",
+ " m() measures the number of consonant sequences between k0 and j.\n",
+ " if c is a consonant sequence and v a vowel sequence, and <..>\n",
+ " indicates arbitrary presence,\n",
+ "\n",
+ " gives 0\n",
+ " vc gives 1\n",
+ " vcvc gives 2\n",
+ " vcvcvc gives 3\n",
+ " ....\n",
+ " \"\"\"\n",
+ " n = 0\n",
+ " i = self.k0\n",
+ " while 1:\n",
+ " if i > self.j:\n",
+ " return n\n",
+ " if not self.cons(i):\n",
+ " break\n",
+ " i = i + 1\n",
+ " i = i + 1\n",
+ " while 1:\n",
+ " while 1:\n",
+ " if i > self.j:\n",
+ " return n\n",
+ " if self.cons(i):\n",
+ " break\n",
+ " i = i + 1\n",
+ " i = i + 1\n",
+ " n = n + 1\n",
+ " while 1:\n",
+ " if i > self.j:\n",
+ " return n\n",
+ " if not self.cons(i):\n",
+ " break\n",
+ " i = i + 1\n",
+ " i = i + 1\n",
+ "\n",
+ " def vowelinstem(self):\n",
+ " \"\"\"vowelinstem() is TRUE <=> k0,...j contains a vowel\"\"\"\n",
+ " for i in range(self.k0, self.j + 1):\n",
+ " if not self.cons(i):\n",
+ " return 1\n",
+ " return 0\n",
+ "\n",
+ " def doublec(self, j):\n",
+ " \"\"\" doublec(j) is TRUE <=> j,(j-1) contain a double consonant. \"\"\"\n",
+ " if j < (self.k0 + 1):\n",
+ " return 0\n",
+ " if self.b[j] != self.b[j-1]:\n",
+ " return 0\n",
+ " return self.cons(j)\n",
+ "\n",
+ " def cvc(self, i):\n",
+ " \"\"\"\n",
+ " cvc(i) is TRUE <=> i-2,i-1,i has the form consonant - vowel - consonant\n",
+ " and also if the second c is not w,x or y. this is used when trying to\n",
+ " restore an e at the end of a short e.g.\n",
+ "\n",
+ " cav(e), lov(e), hop(e), crim(e), but\n",
+ " snow, box, tray.\n",
+ " \"\"\"\n",
+ " if i < (self.k0 + 2) or not self.cons(i) or self.cons(i-1) or not self.cons(i-2):\n",
+ " return 0\n",
+ " ch = self.b[i]\n",
+ " if ch in 'wxy':\n",
+ " return 0\n",
+ " return 1\n",
+ "\n",
+ " def ends(self, s):\n",
+ " \"\"\"ends(s) is TRUE <=> k0,...k ends with the string s.\"\"\"\n",
+ " length = len(s)\n",
+ " if s[length - 1] != self.b[self.k]: # tiny speed-up\n",
+ " return 0\n",
+ " if length > (self.k - self.k0 + 1):\n",
+ " return 0\n",
+ " if self.b[self.k-length+1:self.k+1] != s:\n",
+ " return 0\n",
+ " self.j = self.k - length\n",
+ " return 1\n",
+ "\n",
+ " def setto(self, s):\n",
+ " \"\"\"setto(s) sets (j+1),...k to the characters in the string s, readjusting k.\"\"\"\n",
+ " length = len(s)\n",
+ " self.b = self.b[:self.j+1] + s + self.b[self.j+length+1:]\n",
+ " self.k = self.j + length\n",
+ "\n",
+ " def r(self, s):\n",
+ " \"\"\"r(s) is used further down.\"\"\"\n",
+ " if self.m() > 0:\n",
+ " self.setto(s)\n",
+ "\n",
+ " def step1ab(self):\n",
+ " \"\"\"step1ab() gets rid of plurals and -ed or -ing. e.g.\n",
+ "\n",
+ " caresses -> caress\n",
+ " ponies -> poni\n",
+ " ties -> ti\n",
+ " caress -> caress\n",
+ " cats -> cat\n",
+ "\n",
+ " feed -> feed\n",
+ " agreed -> agree\n",
+ " disabled -> disable\n",
+ "\n",
+ " matting -> mat\n",
+ " mating -> mate\n",
+ " meeting -> meet\n",
+ " milling -> mill\n",
+ " messing -> mess\n",
+ "\n",
+ " meetings -> meet\n",
+ " \"\"\"\n",
+ " if self.b[self.k] == 's':\n",
+ " if self.ends(\"sses\"):\n",
+ " self.k = self.k - 2\n",
+ " elif self.ends(\"ies\"):\n",
+ " self.setto(\"i\")\n",
+ " elif self.b[self.k - 1] != 's':\n",
+ " self.k = self.k - 1\n",
+ " if self.ends(\"eed\"):\n",
+ " if self.m() > 0:\n",
+ " self.k = self.k - 1\n",
+ " elif (self.ends(\"ed\") or self.ends(\"ing\")) and self.vowelinstem():\n",
+ " self.k = self.j\n",
+ " if self.ends(\"at\"):\n",
+ " self.setto(\"ate\")\n",
+ " elif self.ends(\"bl\"):\n",
+ " self.setto(\"ble\")\n",
+ " elif self.ends(\"iz\"):\n",
+ " self.setto(\"ize\")\n",
+ " elif self.doublec(self.k):\n",
+ " self.k = self.k - 1\n",
+ " ch = self.b[self.k]\n",
+ " if ch in 'lsz':\n",
+ " self.k += 1\n",
+ " elif self.m() == 1 and self.cvc(self.k):\n",
+ " self.setto(\"e\")\n",
+ "\n",
+ " def step1c(self):\n",
+ " \"\"\"step1c() turns terminal y to i when there is another vowel in the stem.\"\"\"\n",
+ " if self.ends(\"y\") and self.vowelinstem():\n",
+ " self.b = self.b[:self.k] + 'i' + self.b[self.k+1:]\n",
+ "\n",
+ " def step2(self):\n",
+ " \"\"\"step2() maps double suffices to single ones.\n",
+ " so -ization ( = -ize plus -ation) maps to -ize etc. note that the\n",
+ " string before the suffix must give m() > 0.\n",
+ " \"\"\"\n",
+ " if self.b[self.k - 1] == 'a':\n",
+ " if self.ends(\"ational\"): self.r(\"ate\")\n",
+ " elif self.ends(\"tional\"): self.r(\"tion\")\n",
+ " elif self.b[self.k - 1] == 'c':\n",
+ " if self.ends(\"enci\"): self.r(\"ence\")\n",
+ " elif self.ends(\"anci\"): self.r(\"ance\")\n",
+ " elif self.b[self.k - 1] == 'e':\n",
+ " if self.ends(\"izer\"): self.r(\"ize\")\n",
+ " elif self.b[self.k - 1] == 'l':\n",
+ " if self.ends(\"bli\"): self.r(\"ble\") # --DEPARTURE--\n",
+ " # To match the published algorithm, replace this phrase with\n",
+ " # if self.ends(\"abli\"): self.r(\"able\")\n",
+ " elif self.ends(\"alli\"): self.r(\"al\")\n",
+ " elif self.ends(\"entli\"): self.r(\"ent\")\n",
+ " elif self.ends(\"eli\"): self.r(\"e\")\n",
+ " elif self.ends(\"ousli\"): self.r(\"ous\")\n",
+ " elif self.b[self.k - 1] == 'o':\n",
+ " if self.ends(\"ization\"): self.r(\"ize\")\n",
+ " elif self.ends(\"ation\"): self.r(\"ate\")\n",
+ " elif self.ends(\"ator\"): self.r(\"ate\")\n",
+ " elif self.b[self.k - 1] == 's':\n",
+ " if self.ends(\"alism\"): self.r(\"al\")\n",
+ " elif self.ends(\"iveness\"): self.r(\"ive\")\n",
+ " elif self.ends(\"fulness\"): self.r(\"ful\")\n",
+ " elif self.ends(\"ousness\"): self.r(\"ous\")\n",
+ " elif self.b[self.k - 1] == 't':\n",
+ " if self.ends(\"aliti\"): self.r(\"al\")\n",
+ " elif self.ends(\"iviti\"): self.r(\"ive\")\n",
+ " elif self.ends(\"biliti\"): self.r(\"ble\")\n",
+ " elif self.b[self.k - 1] == 'g': # --DEPARTURE--\n",
+ " if self.ends(\"logi\"): self.r(\"log\")\n",
+ " # To match the published algorithm, delete this phrase\n",
+ "\n",
+ " def step3(self):\n",
+ " \"\"\"step3() dels with -ic-, -full, -ness etc. similar strategy to step2.\"\"\"\n",
+ " if self.b[self.k] == 'e':\n",
+ " if self.ends(\"icate\"): self.r(\"ic\")\n",
+ " elif self.ends(\"ative\"): self.r(\"\")\n",
+ " elif self.ends(\"alize\"): self.r(\"al\")\n",
+ " elif self.b[self.k] == 'i':\n",
+ " if self.ends(\"iciti\"): self.r(\"ic\")\n",
+ " elif self.b[self.k] == 'l':\n",
+ " if self.ends(\"ical\"): self.r(\"ic\")\n",
+ " elif self.ends(\"ful\"): self.r(\"\")\n",
+ " elif self.b[self.k] == 's':\n",
+ " if self.ends(\"ness\"): self.r(\"\")\n",
+ "\n",
+ " def step4(self):\n",
+ " \"\"\"step4() takes off -ant, -ence etc., in context vcvc.\"\"\"\n",
+ " if self.b[self.k - 1] == 'a':\n",
+ " if self.ends(\"al\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'c':\n",
+ " if self.ends(\"ance\"): pass\n",
+ " elif self.ends(\"ence\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'e':\n",
+ " if self.ends(\"er\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'i':\n",
+ " if self.ends(\"ic\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'l':\n",
+ " if self.ends(\"able\"): pass\n",
+ " elif self.ends(\"ible\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'n':\n",
+ " if self.ends(\"ant\"): pass\n",
+ " elif self.ends(\"ement\"): pass\n",
+ " elif self.ends(\"ment\"): pass\n",
+ " elif self.ends(\"ent\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'o':\n",
+ " if self.ends(\"ion\") and (self.b[self.j] == 's' or self.b[self.j] == 't'): pass\n",
+ " elif self.ends(\"ou\"): pass\n",
+ " # takes care of -ous\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 's':\n",
+ " if self.ends(\"ism\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 't':\n",
+ " if self.ends(\"ate\"): pass\n",
+ " elif self.ends(\"iti\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'u':\n",
+ " if self.ends(\"ous\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'v':\n",
+ " if self.ends(\"ive\"): pass\n",
+ " else: return\n",
+ " elif self.b[self.k - 1] == 'z':\n",
+ " if self.ends(\"ize\"): pass\n",
+ " else: return\n",
+ " else:\n",
+ " return\n",
+ " if self.m() > 1:\n",
+ " self.k = self.j\n",
+ "\n",
+ " def step5(self):\n",
+ " \"\"\"step5() removes a final -e if m() > 1, and changes -ll to -l if\n",
+ " m() > 1.\n",
+ " \"\"\"\n",
+ " self.j = self.k\n",
+ " if self.b[self.k] == 'e':\n",
+ " a = self.m()\n",
+ " if a > 1 or (a == 1 and not self.cvc(self.k-1)):\n",
+ " self.k = self.k - 1\n",
+ " if self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1:\n",
+ " self.k = self.k -1\n",
+ "\n",
+ " def stem(self, p, i=0, j=None):\n",
+ " \"\"\"In stem(p,i,j), p is a char pointer, and the string to be stemmed\n",
+ " is from p[i] to p[j] inclusive. Typically i is zero and j is the\n",
+ " offset to the last character of a string, (p[j+1] == '\\0'). The\n",
+ " stemmer adjusts the characters p[i] ... p[j] and returns the new\n",
+ " end-point of the string, k. Stemming never increases word length, so\n",
+ " i <= k <= j. To turn the stemmer into a module, declare 'stem' as\n",
+ " extern, and delete the remainder of this file.\n",
+ " \"\"\"\n",
+ " # copy the parameters into statics\n",
+ " self.b = p\n",
+ " self.k = j or len(p) - 1\n",
+ " self.k0 = i\n",
+ " if self.k <= self.k0 + 1:\n",
+ " return self.b # --DEPARTURE--\n",
+ "\n",
+ " # With this line, strings of length 1 or 2 don't go through the\n",
+ " # stemming process, although no mention is made of this in the\n",
+ " # published algorithm. Remove the line to match the published\n",
+ " # algorithm.\n",
+ "\n",
+ " self.step1ab()\n",
+ " self.step1c()\n",
+ " self.step2()\n",
+ " self.step3()\n",
+ " self.step4()\n",
+ " self.step5()\n",
+ " return self.b[self.k0:self.k+1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def processEmail(email_contents, verbose=True):\n",
+ " \"\"\"\n",
+ " Preprocesses the body of an email and returns a list of indices \n",
+ " of the words contained in the email. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " email_contents : str\n",
+ " A string containing one email. \n",
+ " \n",
+ " verbose : bool\n",
+ " If True, print the resulting email after processing.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " word_indices : list\n",
+ " A list of integers containing the index of each word in the \n",
+ " email which is also present in the vocabulary.\n",
+ " \"\"\"\n",
+ " # Load Vocabulary\n",
+ " vocabList = getVocabList()\n",
+ "\n",
+ " # Init return value\n",
+ " word_indices = []\n",
+ "\n",
+ " # ========================== Preprocess Email ===========================\n",
+ " # Find the Headers ( \\n\\n and remove )\n",
+ " # Uncomment the following lines if you are working with raw emails with the\n",
+ " # full headers\n",
+ " # hdrstart = email_contents.find(chr(10) + chr(10))\n",
+ " # email_contents = email_contents[hdrstart:]\n",
+ "\n",
+ " # Lower case\n",
+ " email_contents = email_contents.lower()\n",
+ " \n",
+ " # Strip all HTML\n",
+ " # Looks for any expression that starts with < and ends with > and replace\n",
+ " # and does not have any < or > in the tag it with a space\n",
+ " email_contents =re.compile('<[^<>]+>').sub(' ', email_contents)\n",
+ "\n",
+ " # Handle Numbers\n",
+ " # Look for one or more characters between 0-9\n",
+ " email_contents = re.compile('[0-9]+').sub(' number ', email_contents)\n",
+ "\n",
+ " # Handle URLS\n",
+ " # Look for strings starting with http:// or https://\n",
+ " email_contents = re.compile('(http|https)://[^\\s]*').sub(' httpaddr ', email_contents)\n",
+ "\n",
+ " # Handle Email Addresses\n",
+ " # Look for strings with @ in the middle\n",
+ " email_contents = re.compile('[^\\s]+@[^\\s]+').sub(' emailaddr ', email_contents)\n",
+ " \n",
+ " # Handle $ sign\n",
+ " email_contents = re.compile('[$]+').sub(' dollar ', email_contents)\n",
+ " \n",
+ " # get rid of any punctuation\n",
+ " email_contents = re.split('[ @$/#.-:&*+=\\[\\]?!(){},''\">_<;%\\n\\r]', email_contents)\n",
+ "\n",
+ " # remove any empty word string\n",
+ " email_contents = [word for word in email_contents if len(word) > 0]\n",
+ " \n",
+ " # Stem the email contents word by word\n",
+ " stemmer = PorterStemmer()\n",
+ " processed_email = []\n",
+ " for word in email_contents:\n",
+ " # Remove any remaining non alphanumeric characters in word\n",
+ " word = re.compile('[^a-zA-Z0-9]').sub('', word).strip()\n",
+ " word = stemmer.stem(word)\n",
+ " processed_email.append(word)\n",
+ "\n",
+ " if len(word) < 1:\n",
+ " continue\n",
+ "\n",
+ " # Look up the word in the dictionary and add to word_indices if found\n",
+ " # ====================== YOUR CODE HERE ======================\n",
+ "\n",
+ " for i in range(len(vocabList)):\n",
+ " if word == vocabList[i]:\n",
+ " word_indices.append(i)\n",
+ "\n",
+ " # =============================================================\n",
+ "\n",
+ " if verbose:\n",
+ " print('----------------')\n",
+ " print('Processed email:')\n",
+ " print('----------------')\n",
+ " print(' '.join(processed_email))\n",
+ " return word_indices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "----------------\n",
+ "Processed email:\n",
+ "----------------\n",
+ "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+ "-------------\n",
+ "Word Indices:\n",
+ "-------------\n",
+ "[85, 915, 793, 1076, 882, 369, 1698, 789, 1821, 1830, 882, 430, 1170, 793, 1001, 1894, 591, 1675, 237, 161, 88, 687, 944, 1662, 1119, 1061, 1698, 374, 1161, 476, 1119, 1892, 1509, 798, 1181, 1236, 511, 1119, 809, 1894, 1439, 1546, 180, 1698, 1757, 1895, 687, 1675, 991, 960, 1476, 70, 529, 1698, 530]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# To use an SVM to classify emails into Spam v.s. Non-Spam, you first need\n",
+ "# to convert each email into a vector of features.\n",
+ "\n",
+ "# Extract Features\n",
+ "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
+ " file_contents = fid.read()\n",
+ "\n",
+ "word_indices = processEmail(file_contents)\n",
+ "\n",
+ "#Print Stats\n",
+ "print('-------------')\n",
+ "print('Word Indices:')\n",
+ "print('-------------')\n",
+ "print(word_indices)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.2 Extracting Features from Emails
\n",
+ "We will now implement the feature extraction which converts each email into an n-dimensional vector. We will use n = # words in vocab list. Specificall the i-th feature in {0,1} for an email corresponds to whether the i-th word in the dictionary occurs in the email. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def emailFeatures(word_indices):\n",
+ " \"\"\"\n",
+ " Takes in a word_indices vector and produces a feature vector from the word indices. \n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " word_indices : list\n",
+ " A list of word indices from the vocabulary list.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " x : list \n",
+ " The computed feature vector.\n",
+ " \"\"\"\n",
+ " # Total number of words in the dictionary\n",
+ " n = 1899\n",
+ " \n",
+ " x = np.zeros(n)\n",
+ "\n",
+ " for i in range(n):\n",
+ " if np.any(word_indices == i):\n",
+ " x[i] = 1\n",
+ " \n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "----------------\n",
+ "Processed email:\n",
+ "----------------\n",
+ "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+ "\n",
+ "Length of feature vector: 1899\n",
+ "Number of non-zero entries: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract Features\n",
+ "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
+ " file_contents = fid.read()\n",
+ "\n",
+ "word_indices = processEmail(file_contents)\n",
+ "features = emailFeatures(word_indices)\n",
+ "\n",
+ "# Print Stats\n",
+ "print('\\nLength of feature vector: %d' % len(features))\n",
+ "print('Number of non-zero entries: %d' % sum(features > 0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.3 Training SVM for Spam Classification
\n",
+ "Now that we have functions setup to extract features, we will load a preprocessed training dataset that will be used to train an SVM classifier. spamTrain.mat contains 4000 training samples of spam and non-spam email, while spamTest.mat contains 1000 test samples. Each original email was processed using the processEmail and emailFeatures functions then converted into a 1,899-dimensional vector. After loading the dataset, we will train an SVM to classify between spam and non-spam and test its accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Linear SVM (Spam Classification)\n",
+ "This may take 1 to 2 minutes ...\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load the Spam Email dataset\n",
+ "# You will have X, y in your environment\n",
+ "data = loadmat(os.path.join('Data', 'spamTrain.mat'))\n",
+ "X, y= data['X'].astype(float), data['y'][:, 0]\n",
+ "\n",
+ "print('Training Linear SVM (Spam Classification)')\n",
+ "print('This may take 1 to 2 minutes ...\\n')\n",
+ "\n",
+ "C = 0.1\n",
+ "model = svmTrain(X, y, C, linearKernel)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training Accuracy: 99.83\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute the training accuracy\n",
+ "p = svmPredict(model, X)\n",
+ "\n",
+ "print('Training Accuracy: %.2f' % (np.mean(p == y) * 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Evaluating the trained Linear SVM on a test set ...\n",
+ "Test Accuracy: 98.80\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load the test dataset\n",
+ "# You will have Xtest, ytest in your environment\n",
+ "data = loadmat(os.path.join('Data', 'spamTest.mat'))\n",
+ "Xtest, ytest = data['Xtest'].astype(float), data['ytest'][:, 0]\n",
+ "\n",
+ "print('Evaluating the trained Linear SVM on a test set ...')\n",
+ "p = svmPredict(model, Xtest)\n",
+ "\n",
+ "print('Test Accuracy: %.2f' % (np.mean(p == ytest) * 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2.4 Top Predictors for Spam
\n",
+ "To better understand how the spam classifier works, we can inspect the parameters to see which words the classifier thinks are most productive of spam. We will now find the parameters with the largest positive values in the classifier and display the corresponding words."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top predictors of spam:\n",
+ "word weight \n",
+ "---- ------\n",
+ "our 0.50\n",
+ "click 0.47\n",
+ "remov 0.42\n",
+ "guarante 0.39\n",
+ "visit 0.37\n",
+ "basenumb 0.35\n",
+ "dollar 0.32\n",
+ "will 0.27\n",
+ "price 0.27\n",
+ "pleas 0.26\n",
+ "most 0.26\n",
+ "nbsp 0.25\n",
+ "lo 0.25\n",
+ "hour 0.24\n",
+ "ga 0.24\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Sort the weights and obtin the vocabulary list\n",
+ "# NOTE some words have the same weights, \n",
+ "# so their order might be different than in the text above\n",
+ "idx = np.argsort(model['w'])\n",
+ "top_idx = idx[-15:][::-1]\n",
+ "vocabList = getVocabList()\n",
+ "\n",
+ "print('Top predictors of spam:')\n",
+ "print('%-15s %-15s' % ('word', 'weight'))\n",
+ "print('----' + ' '*12 + '------')\n",
+ "for word, w in zip(np.array(vocabList)[top_idx], model['w'][top_idx]):\n",
+ " print('%-15s %0.2f' % (word, w))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex7/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/ex7/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..57e9ed4
--- /dev/null
+++ b/ex7/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,5471 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 7: K-means Clustering and Principal Component Analysis
\n",
+ "
Introduction
\n",
+ "In this exercise, we will implement the K-means clustering algorithm and apply it to compress an image. In the second part, we will use principle component analasys to find a low-dimensional representation of face images. To begein we import necessary libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import regular expressions to process emails\n",
+ "import re\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "import matplotlib as mpl\n",
+ "\n",
+ "from IPython.display import HTML, display, clear_output\n",
+ "\n",
+ "try:\n",
+ " pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n",
+ "except ValueError:\n",
+ " pyplot.rcParams[\"animation.html\"] = \"html5\"\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from matplotlib.animation import FuncAnimation\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 K-means Clustering
\n",
+ "In this exercise, we will implement the K-means algorithm and use it for image compression. We begin with an example 2D dataset that will help us gain an intuition of how the K-means algorithm works. After the, we will use K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common.\n",
+ "\n",
+ "The algorithm works by, with an initial set of centroids, assigning each data point to its closest centroid. This will be accomplishes in the findClosestCentroids function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def findClosestCentroids(X, centroids):\n",
+ " \"\"\"\n",
+ " Computes the centroid memberships for every example.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of size (m, n) where each row is a single example. \n",
+ " That is, we have m examples each of n dimensions.\n",
+ " \n",
+ " centroids : array_like\n",
+ " The k-means centroids of size (K, n). K is the number\n",
+ " of clusters, and n is the the data dimension.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " idx : array_like\n",
+ " A vector of size (m, ) which holds the centroids assignment for each\n",
+ " example (row) in the dataset X.\n",
+ " \"\"\"\n",
+ " K = centroids.shape[0]\n",
+ " m = X.shape[0]\n",
+ " idx = np.zeros(X.shape[0], dtype=int)\n",
+ " \n",
+ " for i in range(m):\n",
+ " tempX = X[i,:]\n",
+ " tempSums = np.zeros(K)\n",
+ " for j in range(K):\n",
+ " tempCentroid = centroids[j,:]\n",
+ " tempDiff = tempX - tempCentroid\n",
+ " tempDiff = np.square(tempDiff)\n",
+ " tempSums[j] = np.sum(tempDiff)\n",
+ " idx[i] = np.argmin(tempSums)\n",
+ "\n",
+ " return idx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell tests our code (we should see the closest centroids appear as [0 2 1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Closest centroids for the first 3 examples:\n",
+ "[0 2 1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load an example dataset that we will be using\n",
+ "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Select an initial set of centroids\n",
+ "K = 3 # 3 Centroids\n",
+ "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+ "\n",
+ "# Find the closest centroids for the examples using the initial_centroids\n",
+ "idx = findClosestCentroids(X, initial_centroids)\n",
+ "\n",
+ "print('Closest centroids for the first 3 examples:')\n",
+ "print(idx[:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The next step in the algorithm computes, for each centroid, the average of the points assigned to it. This will be accomplished in the computeCentroids function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def computeCentroids(X, idx, K):\n",
+ " \"\"\"\n",
+ " Returns the new centroids by computing the means of the data points\n",
+ " assigned to each centroid.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The datset where each row is a single data point. That is, it \n",
+ " is a matrix of size (m, n) where there are m datapoints each\n",
+ " having n dimensions. \n",
+ " \n",
+ " idx : array_like \n",
+ " A vector (size m) of centroid assignments (i.e. each entry in range [0 ... K-1])\n",
+ " for each example.\n",
+ " \n",
+ " K : int\n",
+ " Number of clusters\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " A matrix of size (K, n) where each row is the mean of the data \n",
+ " points assigned to it.\n",
+ " \"\"\"\n",
+ " # Useful variables\n",
+ " m, n = X.shape\n",
+ " centroids = np.zeros((K, n))\n",
+ " \n",
+ " for i in range(K):\n",
+ " # Find examples which fall into cluster k\n",
+ " sel = np.argwhere(idx==i)\n",
+ " centroids[i,:] = np.mean(X[sel,:], axis=0)\n",
+ "\n",
+ " return centroids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will test this function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Centroids computed after initial finding of closest centroids:\n",
+ "[[2.42830111 3.15792418]\n",
+ " [5.81350331 2.63365645]\n",
+ " [7.11938687 3.6166844 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute means based on the closest centroids found in the previous part.\n",
+ "centroids = computeCentroids(X, idx, K)\n",
+ "\n",
+ "print('Centroids computed after initial finding of closest centroids:')\n",
+ "print(centroids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have all the pieces necessary to run the K-means algorithm, as all we do is repeat the last two steps for a set number of iterations. As we do so, the means will converge to the centers of any clusters in our dataset. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotProgresskMeans(i, X, centroid_history, idx_history):\n",
+ " \"\"\"\n",
+ " A helper function that displays the progress of k-Means as it is running. It is intended for use\n",
+ " only with 2D data. It plots data points with colors assigned to each centroid. With the\n",
+ " previous centroids, it also plots a line between the previous locations and current locations\n",
+ " of the centroids.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " i : int\n",
+ " Current iteration number of k-means. Used for matplotlib animation function.\n",
+ "\n",
+ " X : array_like\n",
+ " The dataset, which is a matrix (m x n). Note since the plot only supports 2D data, n should\n",
+ " be equal to 2.\n",
+ "\n",
+ " centroid_history : list\n",
+ " A list of computed centroids for all iteration.\n",
+ "\n",
+ " idx_history : list\n",
+ " A list of computed assigned indices for all iterations.\n",
+ " \"\"\"\n",
+ " K = centroid_history[0].shape[0]\n",
+ " pyplot.gcf().clf()\n",
+ " cmap = pyplot.cm.rainbow\n",
+ " norm = mpl.colors.Normalize(vmin=0, vmax=2)\n",
+ "\n",
+ " for k in range(K):\n",
+ " current = np.stack([c[k, :] for c in centroid_history[:i+1]], axis=0)\n",
+ " pyplot.plot(current[:, 0], current[:, 1],\n",
+ " '-Xk',\n",
+ " mec='k',\n",
+ " lw=2,\n",
+ " ms=10,\n",
+ " mfc=cmap(norm(k)),\n",
+ " mew=2)\n",
+ "\n",
+ " pyplot.scatter(X[:, 0], X[:, 1],\n",
+ " c=idx_history[i],\n",
+ " cmap=cmap,\n",
+ " marker='o',\n",
+ " s=8**2,\n",
+ " linewidths=1,)\n",
+ " pyplot.grid(False)\n",
+ " pyplot.title('Iteration number %d' % (i+1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def runkMeans(X, centroids, findClosestCentroids, computeCentroids,\n",
+ " max_iters=10, plot_progress=False):\n",
+ " \"\"\"\n",
+ " Runs the K-means algorithm.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The data set of size (m, n). Each row of X is a single example of n dimensions. The\n",
+ " data set is a total of m examples.\n",
+ "\n",
+ " centroids : array_like\n",
+ " Initial centroid location for each clusters. This is a matrix of size (K, n). K is the total\n",
+ " number of clusters and n is the dimensions of each data point.\n",
+ "\n",
+ " findClosestCentroids : func\n",
+ " A function (implemented by student) reference which computes the cluster assignment for\n",
+ " each example.\n",
+ "\n",
+ " computeCentroids : func\n",
+ " A function(implemented by student) reference which computes the centroid of each cluster.\n",
+ "\n",
+ " max_iters : int, optional\n",
+ " Specifies the total number of interactions of K-Means to execute.\n",
+ "\n",
+ " plot_progress : bool, optional\n",
+ " A flag that indicates if the function should also plot its progress as the learning happens.\n",
+ " This is set to false by default.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " A (K x n) matrix of the computed (updated) centroids.\n",
+ " idx : array_like\n",
+ " A vector of size (m,) for cluster assignment for each example in the dataset. Each entry\n",
+ " in idx is within the range [0 ... K-1].\n",
+ "\n",
+ " anim : FuncAnimation, optional\n",
+ " A matplotlib animation object which can be used to embed a video within the jupyter\n",
+ " notebook. This is only returned if `plot_progress` is `True`.\n",
+ " \"\"\"\n",
+ " K = centroids.shape[0]\n",
+ " idx = None\n",
+ " idx_history = []\n",
+ " centroid_history = []\n",
+ "\n",
+ " for i in range(max_iters):\n",
+ " idx = findClosestCentroids(X, centroids)\n",
+ "\n",
+ " if plot_progress:\n",
+ " idx_history.append(idx)\n",
+ " centroid_history.append(centroids)\n",
+ "\n",
+ " centroids = computeCentroids(X, idx, K)\n",
+ "\n",
+ " if plot_progress:\n",
+ " fig = pyplot.figure()\n",
+ " anim = FuncAnimation(fig, plotProgresskMeans,\n",
+ " frames=max_iters,\n",
+ " interval=500,\n",
+ " repeat_delay=2,\n",
+ " fargs=(X, centroid_history, idx_history))\n",
+ " return centroids, idx, anim\n",
+ "\n",
+ " return centroids, idx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will run K-means on our dataset and show each step along the way to give an intuition for how the algorithm works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
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+ "\n",
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+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load an example dataset\n",
+ "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+ "\n",
+ "# Settings for running K-Means\n",
+ "K = 3\n",
+ "max_iters = 10\n",
+ "\n",
+ "# For consistency, here we set centroids to specific values\n",
+ "# but in practice you want to generate them automatically, such as by\n",
+ "# settings them to be random examples (as can be seen in\n",
+ "# kMeansInitCentroids).\n",
+ "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+ "\n",
+ "\n",
+ "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n",
+ "# the progress of K-Means\n",
+ "centroids, idx, anim = runkMeans(X, initial_centroids,\n",
+ " findClosestCentroids, computeCentroids, max_iters, True)\n",
+ "anim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The initial assignments of centroids for the previous dataset were predetermined. However, in practice a good strategy for initializing the centroids is to select random examples from the training set. We will now create a function to do just that. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def kMeansInitCentroids(X, K):\n",
+ " \"\"\"\n",
+ " This function initializes K centroids that are to be used in K-means on the dataset x.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like \n",
+ " The dataset of size (m x n).\n",
+ " \n",
+ " K : int\n",
+ " The number of clusters.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " Centroids of the clusters. This is a matrix of size (K x n).\n",
+ " \"\"\"\n",
+ " m, n = X.shape\n",
+ " centroids = np.zeros((K, n))\n",
+ "\n",
+ " # Initialize the centroids to be random examples\n",
+ "\n",
+ " # Randomly reorder the indices of examples\n",
+ " randidx = np.random.permutation(X.shape[0])\n",
+ " # Take the first K examples as centroids\n",
+ " centroids = X[randidx[:K], :]\n",
+ "\n",
+ " return centroids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this exercise, you will apply K-means to image compression. We will use the image below as an example (property of Frank Wouters with permission to this class).\n",
+ "\n",
+ "\n",
+ "In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often referred to as the RGB encoding. Our image contains thousands of colors, and in this part of the exercise, we will reduce the number of colors to 16 colors.\n",
+ "\n",
+ "By making this reduction, it is possible to represent (compress) the photo in an efficient way. Specifically, we only need to store the RGB values of the 16 selected colors, and for each pixel in the image we now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n",
+ "\n",
+ "In this exercise, we will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image. Concretely, we will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3-dimensional RGB space. Once we have computed the cluster centroids on the image, we will then use the 16 colors to replace the pixels in the original image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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8e/1l0oHwzGdr8jethgwDeWm6ZcBwKNdVVBVhJN/RTl+FYWzIcxmv4XhE4ETQ9yK9vnCB9c1c223hIhHOkYkwiXynRy1pq5OsMBpIH8t8g8rJ4KkMJ2QDp7P4ypUQSF8inUg5V0Al69LcEqEachRQlnJeU8nAlC6nV8gYFBS1QA+GIky7/T42keUsHdMvZEJnqzHWyfJ8R+7zOx87TK8tY5AklrOXXpU+Bpn2Nag1+KrK8a+E1T4FIVQqvY2IXNmOJfTy1m8PI3LV7DEQqKyrvOB1TI4hqIWwwdRi0VsmbOWIAllpncXeUobIHbHruwxXvs9vffjQnaXGzMB2guNGsVNbswTUVnz+zNVcH28/djUV//R+s7bvhmkhbc9/ck9CeyueO5XPFOTPnZoIx2sV9NGZZ2eOoRfc8CxXPI5feQG4WlDD1cL6qwG3SmB/BnjYGHM/wuf6vQj71Ww4sDYiwpKqqdNQkhXywYpi+fgGNZ8HlOmqaG2ADduMchFGsa4L4xZGL68IFnFqyjEtEQqFi2pBELoYq4Kns7RCeyyCuBzrhKCCdigPnEscaSbnKhBtjlYLStWg2aBVykd7sxQtc1BCpJOHqBtSWWnXZQG5k+PaiVoOijblSB60DVcwrlQbV+04KkqCUIWpc6SlvhSVhUqE0MiKkEySqNZ6R2aOKpNrMB2ZPKy/McSoEDetDplOhrISokqEc1cFdxkdwCbSbuTeoMpke9oRwd12LWwp45ljKFNpS+csdCNDqy1CODcxpV5PEiRk3uTtIm1/nkLNXlHYq6+xGF4AIG4dor8kL+1oMKSrgj4oUtAJTLctk4e5xTne+og8E+fXhsRPVXpd0rHBOGBpTq0bVcj6qpK7qRWlqqraHeBwRDqeJZaqlrgTU74x3h1iMKip3rtxjCXQYypqjwyBm0wEvEk+CAwTpdvczgTMvb3LdwluppDeDTeqWXvh/PZj+2tNOVh5pNaS/bpprXl632mciF+84hgPf+xugtsL4a0CeHr9tKC+evsT9XjsZI3w8Jr1RGBPzm3PTzw1C32ZqK8PBvXyQr/PuTcuXNFe0kruOC17L7glAts5Vxpj/ixCoxcC/1jpLxs0aHAXoXmXGzS4c3CrNGycc7+G8MfuCmthNLYE1tX+1aqCqK3mR9Uoi6ysA4RM0oZSdZBqCKqdZQMxlUbtBSLVqstySBXqgaEYv9smoVBN2OYZ+Vg0Rusi2kYDZrqyPQ5D8kI0ryIf4YwGSAzfkGOIsC3RWoMoqQOm4uqiNpPTm5O4gshBUCk1sdnEeP+omswzl9JLRDscbG5CLtp+roFThc2w6jsdZA6j5vPSpsSq/cVqc8+yS4SqFYchWPVXv7EqWmhiMgr17ZdFSpxIv4dFST8V60AcSVtVvE6kpvSiDCiVZjxU7XNs21Tq57dhhLHSblpKX4euoheoTzc+hKtk5jtgDqs3tReo794NSXI5rh1GpGo5iMqW3gMwkYxRyIBUrzdMIkx1WcZL6Zzb8wskHbnuRx8Ycs9RcRk9/5WB3iPLkeOHABil64w3Luo1qnWFiTnaGEOh1xsQEmlgXmkLHWODt21bZyi9PxqBcQGFV5vDaauxxdcT8CGHDurIuCAIahfD7cBe3uW7GbfaTD7d/izT926QY64+blqz3m5frznP0rqvB7O06J3WT2+35y9e0afHmT1G05ilaQO875n/BFD7shf6fQ4sTQoNelO5N4/fzdo13EKBvRdYZ8nyAUnSItKgMmNiTKSmj8oHZKXEbfFRl+Mcq0E9ptvCjsXH66PEs2yDysiHOnTQ0mjDMlefaujqoCBrelQtOa/NhxTqhyx90Fto6glBREIQaWCRfkSLdBOby4NhomWsRioTS/9bNqV8Q4KkBtZQesFjMpJE2s1VWBEuMFbz+qDI6LZFGCyo/3dz5DDI9jiKOSfzDDKXsH9O9i1DMXOHpSMdqTAJ2rhAH95UxnNIRaLCZlwNaeUiBNv9DpERITkYeQGVU3V0jIIWqfrGIx3DMolq4R/2D1FkakbWaNTe/DzjdZnouPl5ShVGudtPW8dgoOb5sijohXJ8yznSUidAkbSfpyNabZ1QxH1i34esYoScz2oQ4cLCQWKdtDxy7wrf/o1iSr+w+qJeV5slCSMgCWNOqSB1+hxSWhJ1PJfW1eZtQ4BV4e0zFHAaLAYEoSNQ53VVZzs4otCvm0T7V87ixXqiz05hyzpzQIL9mwqltxrTQuNGg8d2E/je77yd4H5p9TgPL+1Uwn5vmCXUPYKVJ+vt12oa3w69+z/A8NR2xbZuPt57Qr4dz67KS/zG6iWeWNq8Yt1WJBqXczcK74aatEGDBg0aNLgLcEdo2IExdOIYl0RUlQaSRY6w0hw8NU8GUUCmptKyKCf5zszj1Axd5JLOY+0Q1ERcuALaYiINvdZSheQ+jcjmOA0Qws1hYjlfoibc8XCTSk3puDaRE02xpXnYtlXSsqIdumKdStdnhbSZbRa16bg0hrTQ4zsxoZrSq0z6lZUXaOm+w8rSjTQaO5LrSjoheSkass1HLHZkNplHB0lRrTOTfkdVVUc4D+26z4hDg8kZZm1cVzTppGyR6oyzsoZxKOs1jozIdWmpxSGPgjq1bTyUY0aEGM3njPOUDdUuOzqGiQsZatRzOy3qFLHKDCi6fb12zQqILJVqlLktsE6vVz0JG6NN5tSMHhtDqSlilCmJ3tNSgxDLfEirJ8GBSX8/7377YwD89rNnAXj++TEP37sMwEtfdkTIuUJf8jYI6ugw6yoC//hU1STHvc4f99oyYINaQ/aBZpLvPzGvBxrpKOwDXovX4DMnLctxFebKdNKvOWwXPXwrUB574roDxbb2casZfGtE99uP7Z+pZd9M7frNxPDUr9K7/wP18ixs1d6nzegn4ms/17R5/Ak1jT/LPo4c92N8ii9vGUavXd+taDTsBg0aNGjQ4C7AHaFhOyAHOnlCoVOIiIgi9ZqJaHuWiHSkaUjVZL3JKwg0f1v91s5Yck0RI4iIvB9RU4Aq0wI7Cdhy6ic1JVRGNNVhKn7xKitpB6IVbw6GGA06i3S/pFOB+iatLQlK6WPiiTESi9Pc6aIMaUVyfGwjzFhTxMaq6fYX2Lgg2rRxGWsDZRzL5bp6nR4mF606DAIC1ci61WXGTqwImbL/ZGVKZSf+dqupVIGqiSZOyb2flQJn1Zc8hsD7wdE88SigyrUv6YCsDvqS/ShGVKHsWxYlC4fvl+2qiY9KSztWcpvNDBvLvungHFklGm7ROgjAvsixti5++hElod6nCFGxswISzeOmGxBZTRELJ7PnMvOa9pi4LVaKIGpz4IAEmL37UZmFb1x+nZVliYt4ufcGQVeusRvKvS2rnOFI7k1gDFHtr3agVhlv3XFTWjfO4LxaXJPruSlGPLDGE9yA8Rq9D2VgwoRWYQjCr+259a3Srp87ldepTrOwV0176/43oq3fLMzyh0+vs+c/yUurEttxMzR7r1k/vSbWrLctfumG2/R4/eSzV2jWfvm9Jz4mK57Z5NHH3iH7nj5V79dRIie4Mj/7bvNn3xECGxzGFUSkOG+DtRY71g+afvEKM4bA58lOBeLYASXebK5BZWWHUqOWQyzW+DbkkIqiNm+GJiRTs3teVZQbasoeicC2VVoHGFUmpzcvAiDVvOZxmmJa3qSeUWn0eVvZz+a7XQoNWhuS0NaI8LAYMy7UrK8C//LFIZe9SdvO0TJyXC+WdVnmGHlrbdgm8VR+zhEPRMgNU29SDzHqFghtVEekRxqqnJcFhZplo6SD0T6aOCDPZByNMqVVhKxpUnVSDckSuXbPMDfKHJpmTa+3n3IkY7Sm0fXD9Q3ydQkIy9KcxYNiBg9MRce7FjYlIt7OG9ZSGY+5XkhsZN+2ke1kFZ4oNbAQ68QtCrtkKvA8EWOVlVSZvKBRZ4kglk6euP+w9J827Tk5/3JnjgPzsu/aUI+xIZ4LZZyOa44vY6iZyjI8+1kwoYcNpuhu1fwfmAnFrLWuFsguoH4WbU0FOwlWC8KwjjhvcOOYFtIn4qsnA9NEILOE7XaTh1n7bo2KfkbdOjtNEraDF6pwbYJ1WghPHzurvavWrZ7m0ccm273wnYWTL3yOBx99x679uRY8UzzC49dx3BO/+5t16WP1uiPH7+ehqWC03XA3CO+v7Wl7gwYNGjRocJfgjtCwo9Cwb74DrqgJGIOywsbKRJZrqkwVUamJ1poKU/k0o6I2RVo8dSk4ZRQzYQKVMpXh6TMLAs0VNrYgUGatbHCxLoSR56KxBpGhiCS3b5QsUWpbqZpiq6gi6WoKFyVZKiZrE4iW1olaFIWYjjtmE6e0m4McMiPL1Uj76i6zrClLbwwLbC7aY7suamFJjLSVmwCrlKhp6ijU+pBo+lWVB2Q6optpUDPDKeMrpTUUqUz50zKjG3o2uKgu/pGnSuHJKmvqbuh2ugSBasjar/biCk4tA5fWE869KhrE2ddelvZHq3V6Vbu1zMVMzru83GVR+zMeS177qJqDgVzj3OIhCnUhpJUvtpIRaopXkOVULbmnWVHVGqzXfvO8oOWZ5ZwlSsQUfu/DMo+PO6/y6jlliBsbjh0St4I9r+luRZfxsKzPy5Si6+xUzrQu+VS/wBl8vZRJOZtJoZMgmJS5CYypixLUfzFEOrbOQdTMra8Ls/KCT8QvXpGP/PgO++7VFL81z/lE/CLUKVxTqVW7pHXNwrRWvZ22fa2a9G5ID72fp9cmv0++8DkAXnvlavP20Xsfq7cDV2nbT689RvvcRwCu0No9tlKjXgsD2lZMm8n9fXz99CmeEG8XH1295qbuaNwRAhsCnOuQY8ApJaQJMJ6yMhDBmVQ5hZrMx0zyWANjCN1UFS6gDCKcEdOzdROTYsuf0hqKwgtJQ9CWj2OviuviGf2WfLyL8ZDBuphU0m7MRqn53XquygV1wY+FfkQRSVTyfF8EbxUYxmOdUJQVoZXrMaEhUMGVKpnKvvl5yrH6b9sBRslKMl23noUE6tM1rsBoW4WtKLWChNO89Q4Vxmo+dR7VbgGrY1VFEU6JUbIyI/AVL7KAvKt91ziAlguJ1eqfxnP1RGFRyViKYZeXX3oJgNfPvsjG6pqOgRzU7/TpLsn9iIIWfRVAbjVjY/OcrF+UtsbtPsVFieJetj1iFW1pJL7uKD1L4SdLxuEKH2Fd1gKx0GenLHMqzWEPyowwkT70luXDsTja5LVL8sEzccTcggj/rlITVwNHqOH1oTFUZiJYC18IxBOrBK6ufgYGo6b+cCra2z+z1k5V1XATwhXvIjd2EmvgrL2ymlqDa8ZMDuszV/7cmUpz96jiaVP4dtSfHl4wXSG8bxKuRzBvhReoT6/tLKRBBPUs+ONmmcmfXnvspvq0d8Lq/LtY2vgMQE2mspNp/FpN4bfTdN5M2xs0aNCgQYO7AHeEhu0IKc0cZeGwypZVmKzWVqzzZm6LMWJmTgjItSRlQIu6ylLNRAWB5vUWRQZqWk60kpa1Qc2ElrTbBKqRRbQwmvAb+baiTUwqEYedwVeYa60AMA5E4ztfViRqGegWhsuam2xLXwXaMdyU5a4JaIVamCJMKTV86sCCXqMLa+1xOWlRqLn33EUJgCvKTaxGMKdlxXyoEVFVRpWr1umrhFnjA5nptHPGmhc+9pHjNgJ1MWAySs/wlqVkC5rLqBp+kG8SaF58FXSwXbn28UjM1WfOnOH0q0I36qg4clSOP7Askd+93hytnlxLEsb0+z7KPCBXillfeSxahfSQaCirDuas0r7OiX0rKdbJCtm3ZbqM9T63nKXU4MFIKVsdUV0NjHSdQPPp/b2fW15moSca/nyrZFO5cfOx138NTtnPrINKGelCY7G+Ykyt/IZTpTQd1lfemjZ3+xKmU8Wwg6l96srhgalr+zpn6/ztBjeO8tgToBr0tVSqmqVtX6mVy7M6HUg2K6hMcrBl/eevp+NTmA4kuxmadXro/QC88CUxXZ9cH1+hWc/SpvcSaObbn4VppjUJzFPrxDbVvrajKZ2sk+0vfOk0X+RdsmF1tlZ/PVry7QxKuyMEtnWWcT4isGA9AUkIRCrElEyFoEVVl7a0hNp9E3QJAiXX8CZLYzDqNy7KjKJUP6Dh8A0AACAASURBVKX3FZuIUClGo9AS6MfRRIahCtqRJ3EJEsr2Ae3XkOWOmKHH/qPb6pMhAmxjlJOlIkxeH4gA3UxDcq2zfN9iha/DVCau5qlu+YkKY0r1rbdCS1c/5IkfAmcIVUiPxxarvmaTg0092YhSeLoAowKo3TaUfrysJ1aJSI30NQxcLYBKl9cpXC1Nhxjags2BjHHSCgjnxF3w/AvCzV0MNklasv34ygOESis7r7WqD+w/SKQ+7G5voaYjTVpRbfbPR5LOdvbMKwwua4WuI4vkbZkg+Yj5KuwQFjoGFZhSxjaMepRKUzrW1LZ5G1F6QhYsrtIPtU5uoqTHyj65d4/dt85HtXZ2bjXmIQhwOqmqrBNeb6QCl49Un67K5e+nw07KdrrJulBDx0PCiRB2k+KZntjHU7KAuGwaYtJbg2DlyR3N2NOCeXrZ12Se5ZPdiulqWzeCm01XOgtfXJc+biesZwnpJ9/x0I5tfvJzX77i96x0r+uZNG1N8dqKRx87zusf+U0APr8qxx9Y2lfzi+dZfgWRylZBfCdW9mpM4g0aNGjQoMFdgDtCw5aY2JwwSogipdcMLJVSeFZelWGinVZVSKgaFxGEsWo+tSXT0I61mEUck2uAmatzXw1WNeyiTAiNzqTCNjG+2IhW6MIRdee1C3OkRjUuDRRbtKtsKgFKHrTqthbmfP3niI2BVhxzhgtrqhHui5lXPS0vxOSdh4ZAteKqNIw0yT9pqaadtBkrKUjsMsab/oKqOrCuqscoqDU3YxMSrV4WKllKkQ/YVIt6K0iEbhMowhinFLCjtkblJ5bqiMy+g6jHuZMy03/9tdcBOLrvAAsLGmy3dIB+S6LIFw+KZWJusU+gwW5h0mG+JybpuJ3U0eNuSTTdVqvN+EsadbqWYw/Ifdw4LwVUwtgxpxFwNgzBVwlzJbGeN880GK/IKTRwLgjA+qpX+qdMh+xfkT4uzJ/k8XukXxc3pa/rlybPWTtIMGoSz6sC4wlofB62MeDzpY2ZMRsO6ihwsaOrqd1IK/K/J1axNUlLGQRcEZ7e4KaijkbepdLUdvDaNo89soMpXHA9VbqmMZ1bPYsMZS/w2vSDj77jqgCzG9Wqt+67Vcu+VniNercqYNPwWvdzp57iyHEhcDqwevW4b6UpbfKwGzRo0KBBgwY3BXeEhm1wJGEBVDX7UxCYyXQi0bQcawg0ICuOUpyWgMSNMJqjazWv2ZgIq1SboYmJ2p6JTIuHBAVWA8XSrCD3WlpWEWmKVuSrZLYiKi336IqAjVQ1PlHi6DGmYyRdoFslrBWiMY411mkpSXHLcjGdfpdFTZ9a6IW41JeM1PQt28J6pjSGDIdyklAD7KowoapEQ+/GIblq+aUtCH0akaa+BVFJqoUzTFlg1P+a4s8FRrenY4czcq6smCfXALP5jgZ6dRaxWn7z8pk1LnzlKwAcWhAf9cGD97CwLDP2ODHMq7bcXzygYxjXppJuq0PcFk241+/Q7czpNcr2paVlTFv6+txzn8UhPuxV9fO3gy79lq8fHYBed2VLYqWADVQTTtOUdk9jEcoQNODO+sIvZUYyJ/2+/94l1i9KLvh7Tsh1/8ZvDAnU4hIE+USDdlOFZIzPl54KKnOB+LGlk3rMVL1s4yasaM7UQWi2NolM9sXYxod9k7F9ANm1Ydp3PcuPfaOa9CzsJdBsmpYTJMUJrtSWHxSWXk6+8LmZqVs3qlm/50l59z/xyTfq46Y17VtBXTqNx+9PsOfVCnFCxuvDz1xkoS/fnmmK0mncyQVC7giBDYbKxFQShguAM2FtGjZeWDqHVXN0GFsi/3WzBU7N555bO8JRKg1lHPep9FIz//F1AZESasR2TKjv7DBP2VS60NwHLnWgq+bzJCkp9KOf+qpZtkupH98uOe2BCO+1Qsz7YX+BJQ0Ui8I5Wi3lqQ6h9HzUGjmeuJRVJSuJ2hbtIqYl17cxyISzGkk5t3q9o7wgTqSNINJIZgLQaykyh9PAutT6fgcYzTG+WKTMa0S6TSuCA7LPeF3O9fypV9m4LIQwrspZWBCB/sC9b5G+RC06cyJ45/tzxCqE80LrcYcRkeZAD8YVpZLi9BbarCsxyQGtspP0Eg6viHl94YF7+PIZmRzkpbxg6aWzdbAdWEoV2JELsc6Tv2g+P0Vdcsy5CFdMggtBuM5NIPfjgXvu5aUvSpLuNz50BICPffIpKg1YtMbilPM7jAKcPp/eWF2ZoqYWDQPqiHFPoRsYgy+7VVrqsQ+tBF5Kf9Qd4mxtvjdW8rYbXB+2CuStudU7mV1PxC9eJXx3CpCaBK7dfIF9MzBNcLIdCQpsHwHuBe71CO7t2roWXEs+/FYcefAJnv30x4DJpOrDz1ysg852w7RpfFYA2u0ISmtM4g0aNGjQoMFdgDtCwzYGWmFIGWb4SgvWlZM6xGpaLo3F1SbvFlXgzaJjwlIDwdScnbsYnASKFXSwlVa4KuVvEMRUPu2mMARGU47CknkN8BroeasKNjRXuOtiuj3RmBZ72j/XqnOcY5fV5tb8kpiY1zYjKk1RW+ynbGxeBiBx+zn3mmiNh/a19PiCVuILSDhyNWObVP6O8hFFqbPNPMNqFTGHoSw0QK2thQtcC1/kaZiCqXyQky/okVN41s0oZqCWg147YCOT/p5dFRNxVmYM188DsH/fPo6uPADA/OJhPWdEGGtgnulRadpVrKlew7Sg0Morcf8Ya0MxS106Zdh/RMzql16TdUXHEGiue9EOWTwkN8JZmRm/MbrIONfxiqFOhy4hUwuNDgEmNoz1mYoCS6U52UHoUwJDqnysY9Dm3ntEGzjQEwvBg/f3eOErMgaGoKYic1hKzz2q5uwYQ+lN3oipexrOUVsAcA7nC30YMaHLatW0g8DX78IFjriZWl83Zmlns9KEZu1X8gRv17zeazFzT9K9RAv3+cU3G9Mmbx9YtdUMvhNee+VLNevXgaV9u2rWHnvRrD/xyTeuqa1Pfm57s/heGOdmYVIURH8vbV5VI3s77KZB347gtDtCYDtbUKRnoQwwbaX9DA2VnZBIgJRXrGofZJtcpVES9qba8nSjqxjkgXFuH5Xr+R2krSKl0OjkIGkRKhGHi2xNJen5m6MANMCaJCoxcU/Pq9SoNqUVa95u1SZYFsGTlhLVbNeHrKtZduNyyn1zImHOppfqUp2tSsyy1lS01TQdBI6kLwJmtK7lLk1JqVHLazamq2MTR2kdhV3lOqkJE9TqSmZSCl9eU+lIQxJSJZQpg2VoydgPMIyH6zoGMkbFqmXxgND7HT76EMuH7tWx0bEyHUrlYx+airlFcZDFPXkpR+M5Tp+VD547W3L0qFzj8vIyr3xR1l808sFpt46TK41pe97yloPiT+5oBHiSJIzHcmEdB4G6FcIqJXEa2e+LugUJc2019VdlTUfbVvN8EGYM1iTSPekEHDkm12gHcq73/64Vfuu3JNd8rXR1loHNJO8aJhS5xpg6T5tqKq7bU4ziaju5caauxIkB42dWOonEgFGBHpmwjvxvcHOwFwHgo8iDYkK24rHVPD4pWbn3fOnrzbPeTlB7v/XXLVxkdf3q7Z6u8+i9j12XoJ42ae+0fS9CfjvsRXDvRKzyvvd/E7/yzK8AUnJzOz+2xyx/9rVEk9+qiPNm3t6gQYMGDRrcBbgzNGwTU0QrmLAkjkXLM+UGQS65yV7TdkGLwKmdmov0jcxiStrYWDRBH1wWVEtEuWjNxm7QCkVDrSrVKMs2G1pvu7BgfP63qQiNnCP2DFcmqTX7oqqIci3g4Nm6kgBvv8yrjEg15Pm+1r0uR0Qjn289JB1KW4thRLuvgXEdPad1GKtFR/JNPJ1W15twTYs40qIjUVhfb+E6RHZirpVxG+EqGZf5sI1eLpWvEx4FuEhngHZA0RfzdhQm9KxEeb/2olgJ2tEGh1a+DoCDK/cRlDrXU9PxGobunBxzeOUEa5ty7U+dEvN/Vr3G4w/t0zHucO6CaNUvP/0J+ksy9qNUA+Q6Q6rL0tbCuw4yUBrTxUjDWquHSc8LnagtzlK29ukYvEKlz8eokmNWohZFpnXNw4i2WhlKVcFbnQin7HrFMKCjVcg21BLzzgfu4bvfJ4Fo/+9/eL3OMgjjgFBdDLneg8q5WkMOwqCm1nUaaOas5P8DGGuo9BxBZWpLiA+ms8YRePO7czWbWoPbB59jPU0rup3Jexar2bWY1KfzrLfbBhMzOEw07CPH75+pbft8a5jNYLYX7Xo7rXqnALLdNPEn3/EQw1PXFym+kzbtt03j8fsTvueEfFs++J/PXLV9J8zK226Czho0aNCgQYMGV+G6NWxjzHHgnwKHEHfdzznnftYYswz8EnAf8DLwh5xzu1QjNTjaFDaHsaqUYUwQie/SlKIpB+WISNOfbByDKttJYiGW44pQfclZxHgg2p0zlrgtWqvTms9VOWROE603TJuh8nC7KqPwWo5q3a0owaqPcSPPCDaldORiV7RjejG5BjuFJqajLFxRogFXrR6RanFh5Vjd8NpdQawc6E75rpMg5OVXJY2pv5zQ1fWrmtdThQGB5l6HJmQ40rKhPRjpOTL17+ZBQBV437qlQDVrHcMwiCg1F532Ap15CSRzQ8fqi9KHBfUJLx99mH0LouF2+/uJ+zJrv7yh5TXtIt1ItNOXX0948RWZ3R7X+tL7zXKtrXfmHSiT2uK+w5DI49EZy7W++OVzvP0dok3Y1xzJwXukD3OS6mVaI9YC8Ss7ytoSMt/pY3QMIlVZXVGQK7Nbf6FL5YP41CRSlAXj1ZGuazO3T66BSIITe50D/JH/+u0A/KfPnuXCOZ/DXk6CwlSrdpg61925AF+t1PgFa0C5zgOoy2+KD9xHWMqf0JjaN46B4BbPrW/u+3z7sVNu9XY52Lv5R6d92XsJJrvWnOyHl07z8WdKXY7q5QmuPahsdf5dteY8ncrlsRe/9Xba8/Wyl3lM52nfKnjNe1rb/jM/9K0AfPQrv1aneO3my94NWznJb5XmfSMm8RL4EefcU8aYOeCzxpiPAH8S+A3n3N8yxvxl4C8Df2nHllyFKdcJww420kCwaB6j1KAmV3NwOiDX3FeXHIKOBvvkI6yaz6NAc5iNwYi8JR9Z1jXkOw49yUWfdigBXwvOsjgvQmzoWqRDX7RBbsLAhZRDJShJ14lyb16Xm5JVIVrnAxcGLHXluLgnwnixmGOg0e3jUYpLVKC6MaNU9h37gBYsmwN5eHqLAakPbNKCHaMcCo2ID1tgNKc6zQ029NXFpK82S8l8JHPQqklSCjSy3BoyK5Hdtt2nSmX9GyfPUliJIF2Mxdzcmz9CpSZv2l/PSyel3TfWpG718Qf7nNLvybmXf4d9h3QytKFCpzVmPJBr/PKpl0n6Sv155g2WVEg+8MTvAuDEkf186QuflvFmlRdPyb155Jjc0Pn4DUq9B/ceeIB9i3INwYVNhmt6nwM/kWkTt2R7u72A0fsfKBFNujHEamWvxUOH6SRazUtf4PPnRyypyf73ffM9/D8flIlChSPxr49mK1RUlJWPGLd13rsPOjMRhL5Smpu4LnCWygeg+X3NJKjNWVdPCm4hbt77fAdgp8jw7YS5r2tdHntiR0H++P0JzzEJNpslvK+XOMWbul9ahSNqAb/W6O+t+83Kud6uhvUsXKtAPvnC52ZODmZNCH7hF/9VvX66/bctTvbp3f8BAO7lw7uS2uxU/GOnbQDf/95H+Tsf+q0d99kNOxUPuZ79dsN1T9udc2edc0/p8ibwPHAU+G7gn+hu/wT4nuvuXYMGDd4UNO9zgwZ3Pm5K0Jkx5j7gHcB/Blacc2dBPgLGmIPbHPNDwA8BHDm4QEEf7JiwEs0mHzhaPZl2BYkGYZk+ZMq2la3WmpENWkSIJphroFknNrhENLdW4Dio2vq4Eg0rZYFcc4Xn4zarvuZ2Ms9iS/a9ONCgto01Lq/Kuk5rnrEVTbRIpa8Pd0NK5+kxDUXuI4gq7UtAUYjmNgwtttLCFGmGUS29Na+1tS+8RuHji6puXaoTTa8yLsVkEtBVVBYTqBncZbRKud4I7bcxZLkGYTGu2b2iUtq6nA8o+8dk3zJh8OxJGYPRBssrRwHo9+T2lcnjnHpZ5nfnPvNJDhyRPhxeeRCAU1/4EkUggTLzh/ezsPwWbUv68vRTn+b8uminYatFXohV9aHjx9lYk7Y+8m//HQA9k9Na0JzuuYDRuoz9vFouopbl/q97NwALiaOViTWgbC8xtyjPRDdW60zkCLqiIQetiCzT3H11oRhTsO9eYaeKwwSnefqF0sMW1SbxojyH3/z4MX51WQJVVi+FDLwpfCpVK/AUo6aalMr0+da4moLUGbSGLIAlUG3baXBZUVqiYJL3VdZlXG49bvh9PjD/5nR0j9itkIRnKTtx5llOxJP1JTtrarPg072uNw/7evOsfSoXbF8iE67Ufqc15Gl4bXmnbR6/+eEPXnWeWab47dr1WvVW7OSmOPLgE1cFne1WcnMaf/CtY/75x+VxXh8MbppZfJYGfbNM5DcssI0xfeBfAz/snNvwH6Td4Jz7OeDnAJ549KirYjCuT+gpPuc3GA/E3OqUZMPMLRNolSeXDqk0d7oqN6liiVaOjJhPC+vIA1mOwgqnH79uW4RtXFpi5ZR1NiZSYpIxjkDNmgcSEaxngopMTZXZ2DLvebA76uu2a+DNn0WHgfqV55Y6erEBhZKaZFlKvy8Csxx0OHNZBNdoQ4TdoEzY15F+j/OckfJnB94vHoZsWEmqdHmLwkclZy1yNc2OVECMbYjTqPu2qUjxUfHi26+qFlks17L2+giG4lbodOfpr0jEeNz6egCe/fQrRIsyRo/c8xAjJS559aQI4aWuY1TIpGPznOXMy2LSznPxW+9bbvOWR8TGd+DAMl2NoM9cn/Vz8iHqb7T1/Ae458ijsrxwhPKgCMwolv2OHyvZv18mGu3xJcozQugShyHdnvrnYxHSVeDodmQMgjAm0LGNe+I/ay8cIb8sx4eLPUoj+24OlUwltmSp9PXhx07w3q+X633+C5d46rRMlmxturZ1nr/D1VHgxnONT0WGE0y80mEQUynPuvE8AYGZVJ4joK3v1Qa3FjfjfX7rw4fevNnFm4DdcoCnhXMd5Y0I6mvJrb7SV321UN4LIYrHLNpRYFfT9Sxhup3gvYKX/BoF/nY52dP+7Hv58Mx9ZmGrcL5WYQ3i+njfA5Jt8uzqvXzhxeev+VgPL4inTd67RY/fCFf5DUWyGGNi5OX+5865X9bV540xh3X7YeDCjZyjQYMGbw6a97lBgzsbNxIlboB/BDzvnPvpqU3/DvgTwN/Sv/92t7YcAaXtEoUJZeBrObdAo8SDVKII89UzmDmlwuysEHoa03KTshz6xgQ2BNWWChdjVBPdQLW1KCLUYhJhUNFXbTnPK7KhzpoC0WQPL3ZR6zuv5/fQUWL/npV+pXmALUTbGuUpoe6cajsmMsQ60s5ltDSnOisD5udFe1vXvOIyTMkKOX69GNHt6ng4ab+VzGPa0i9bVeRDjTJ3lrG6C6xGypt4Hqf56WFU0lHNe1NN9pkJGK6LJllevsSc0owePf4YpRUN9wu//TIAi/sNrQPKWhakHDmqplur9a7bhmROriXudnFqpbj0mjK59QMOqqm06BpKdTv02xlv+4ZvkbFXX8CltU2ef0FyXs899V/Yd6+4Ox66R45xR58gDESbz01Bq6OsaIzoaZGWqiXbY5sS6BhkZUG3q7nx/h5tnibwye7O4NRlMlZmueWVeS5fkjE4tHKEr3+XmPrzi8/ygmrm41RrZAexVPUAcBWRRozXPAI4wtBHqU8KfkiedR11JtsDMZQDYE2ted8q3Mz3+U7H4/cn11yh6/NnLvL2Y/pjKm3Xm8w/f+biFWxnXpveS23q956I6mOuR5veDrNqWu8WFLab1jyN3RjMZuVpTwedbdfOK3wbwBWa9l40573gfe//Jln4yG/y5Y5Y5fZiGr8ebflGzOM3YhL/PcAfA75gjPFcAj+GvNj/0hjzA8CrwB/crSEHVDbAhIZspKbrOCRuKZ1oXwRvzzlMJh/ScrROpfzf2DahEkqHWiIycJZCTZVxN4bK+4DlkPW0R6qEGr2FBZxWj1qI4PxIBaYKtrbJWBpJKpeNI4ZjGfBCua3jyFL5FLPK1ak7Rs3g3X6PSxohHdIlXRXBGtmLWI2AdypIgsEGG4EIkE4/Jq7EZO15q/stQ19pNS+PwemJx66Ejvj6ux0RcJkFO5LxGmYtUvWpZ3XwcheG0pd4X5ej98iH59yZDidP/aasX5YxPH7o27nnUeH3Pri8H6OkNZ1lOVe3F9Oe92QqEIvlmbFWUQsTx33LsrIXRaxqGt1GWjA6L66PBS1R+sADXY4+Kr6433rKMqfpcfd83Vvl77HDLCqtbNiyDFc1bSvp4HzFMnUhFKOIVBljOt0ucSjHubHcz+Ewpb8gkzgbwvnTLwAw0ipj3XSAGyvxipvjLffKvr8eWr7uMZmAfPIzmpbiJuVhCavJ5FHXVWYq0tsZnN/gzKQsp29gUqkTa1ztJ7+FuGnv83aYlWJzp2MWAco0gpUnd/RTiwAWH/T10I7CbJKU7TBtCp8W0t7H7PFN3/Z99fK1UIfOSsHargrXrDSt7QhTtrbznicPUJ1/Wn/tXUhv9WH7Z23arTGdDeDdHB8FHjoudMvXahq/FsE77de+GXSl1y2wnXOfYFLpdyu+5XrbbdCgwZuP5n1u0ODOxx1BTSo0FgFFZmi1lVq01WeciQm0yjR3NrAEgSf9yIitaEkFbapA82eVMCMyBWYspuO07BC1VRNMRNsaZGB6YgIOWwGBmtfjpOLofvluXdQgqNxlzB0Q7bHz8nOUiKm+N+frGRtKzbO2lLTUrO9UnU8HKS0NCOv2QsaaiztehyjXYhROLQALhmqgfRx0sInsuzIn49LptVgfSWRjMT5PrsU7MiDTa9gcSSBbEAeUGolc5TmFWiEKDZpb2xixcEBmlcPWkzz7glgExtHzvPs7JNjsyGH5O98/zEBrSQdxzNwBCfJr7VMLQcvROyzX2wor1t7QoC01F483Mr6gZXKWOxFtZb1Zmm9TZtLfZ3773wNQJj3KZTEp3rNyiAcelkj0R/aLttMhIfb5zCahFcm9H1UDurpsVbMnDCnUEtOOEnI1d/la1P2lw6AVvCgScj3OKsXp5gi6GjSWDUZ0VNO9//gRjuspPm0mbl1f+9q4uA5A86pyaCKsV7vNJADNQk1D6oNKHBanJvWogiq8u6lJpyN638xzbXder1lFZ56dRIcr9ejNhJi7rzaTz9K2t9Oer9VM/uzq3LZR2tMaNexNq4bZWvNuhCez8rinz7udhn4juF7T+ZHj99d571+4xsdgO3P4tDa9l+OuBQ01aYMGDRo0aHAX4M7QsB1QheQupVTmrzIb1sFCgfVBWptUSicalwlhLv7qqJUSOvEnW9XGy3iOIBQtMKhWCSpNsdLiDvv6jg3VZDdzS6xaWFiEdBLx+/aVjctWJeVI2lret0hrQ6JP0kyCFMIgYjASP2crNjWbVqmpXkWak3TkGvqtNtb7RMM2Tmk5q0w0umFWEcWynK1vEh4Qn+kh1S43h4YqF8tCWQZ1rWdIcKqtF7GypuV9UIuEsyNyLTFaOaEYDfcd43L2sFzLhZM88sR9cq7Df4peX87ridJcN2DlPgnkChccKwekj6p80g1zXn1druv8i6+Qr0pAVl993FEnZrgh68597nPEF0XDiMOKS4FqzgfEcjDqBqxojewn3/lOxqvS7pkviH8u6PTYd0QjgaqCttGgsyQjLWTfxHgWsoSespeFlARaGjXSOACbbdZa8ahIMZH0pb+oNbizlHhO+jJeq4jboiG/8y0P8PyXxee+1JfnYG1YEqgGXVlXG5h9XWxhotOa5M7VedgJzlfdrN3eRWUJNa+rnC7FeZfi9ZPP1sFEHntJ39kOs/yVWzXqnXzn5bEnOHHm6vWeqWw3H/aJ+MUrioF4bKdBe3+0b/fzZy5Oad7XF3Dmc6+Pbkl/v5Z0Ko9Z2u60Br1TqcxPfu7LtM99pP6dHnr/VfvM8ldv5w//1MkjAHzDg6/v2Oe9YDolz1PMTuPRx47Xtcw71xF8No3dNOjbFXR202CtZZRugomJrHwo024bM/K80MrDHUX1x68gIVRhVIxGBJEPOlPzZnaBHDVTlylt+yoADhEKLllgri3SKHMJTmtJpy7BKmnGWAPc2kHKXFdrSZfzVGPN29V62GO6lEp40TWORAOeKg12qoqKIhL7ab7mOP+GtDvXKoliuZ5WJROOjWGr5qBumTUWjDzIGxvSv82N8xSZRnabiQvAluGEwEO5xE0nJEzkWkrXpghEUEcH5YUK1he576AI//t/1+O02yK81/OKUIPg5g+JwB0tGlrHZWwTm3PmRfmgrfSksxde+SJvnBfT8OraRapSrnHwvASU5RdPTsz3UVxzmLskYuGoTA4WD8iEoG9Kji+IQH7m889SbIhgbM/JPVg6/laqRJbjsE8rkCpi+WvPEpYaSe550zt9wmTymJdOq8GpQLfRHLaQazh56jXWXpV722vL/SjzTcL4gC4PWUzkZV6Z77Ch9cEPHZZJ4PrJi3VNcgxYvTd1hHdla9M3ODSQntI5PEeK5w93xtW52YGNCeO7ux72K3zbTRHQW7Gbmf1TJ4/c9PNK5PhE4O6E7SK/r5e6dBpeUO8WzX0tmGXenmXSfurX/369PH1d04QtDx7a23m2YhLgdmNC+1MnjzA89dRV66fdIV54yzqZOD30jNyb68nL3opp4Ty9fL1m8cYk3qBBgwYNGtwFuCM0bOdKXLZGSQ+nlaTiwlJogYfMamBWKyFqawCaiSHSXFxaVGp+jjR1Kc7PYTQozRBg1UwdZjrDCwzWyCw5tCMiZL0rewwrEkWraQAAIABJREFUOS9WTCJr5ZhDc5rvHKe05kTLKsZp3f9Qc587SUlgRTOKQq8VWWLNrX790hq5ak5pZQgqTV0LVXtNKlI9fUXFG2dlFtuNVMsrRpSZzLOSsKCrOch5bmkpu5fTwhYuiBjlorpl7kE6c98g/c7lmEcfPchST8xPly4MGI9kfXdxge5hWV54q9rE11POfvZlua50g04gGugLJz8DwMbrT1NuiIad5ZsYTXlznrmrF3BgRUzL0eIh6Ejg3vBSgMlUm31NrCDmyAqrZ8X8fejwEsffITSki22xjpzPC1pqUbEVpKmYwYdrG7RUg20nYh/sFEWdxmarhFTpZD0z3WA4ZliIhn96bY50U673wZYGtVUBG8p6Nt+OKbXwSifJOLpP3CiPHZJxf+W1FpnOop0NpszYokoHkZmYvi14A7ghxGjNbF/wIw7juqKYw5Dbu8cmPs7cVexgt0K7vhbcrvN6PLx0mtenrONeK3146cY+vdMa7c2E16rb5z6CZmZuG/Q2nW52hCmLgZrHZ5nGYW/BZt487rGdxr11v50weTZnB6g9sSSprl/udG9qFa9pXK9Z/I4Q2GEYsDDfYjzMGFUaGZ6GdU5q5aOtiwFt5dS2NiLXr18+GlIMRTiTiuCLoiHtjnzw2p15Ml9WUaPJQ7fpiyyxwRKmlI9yP3ijJv0oNOK4SseM1AzdKgKCKNF+q1m2zOh3pP24lRCqKTRWB3A3HBNqJa3cxPgP+CjfoKP7+vKcQZTX5v0i69HptXQMpK28bHFJXQVxFFCqCbiEOqfbKmHMWlZwIRchObf8VlbmxFY1r2UqY5Nw8YKMYbz0MAf2ixCND3Q4qMGmw1U579rpV+H07wCw+sLv8JXXPiF9LIWqc6HX46j6lVeOHWFxSV73vva/3e/Rm5fzLywfYvGgROa25w7z6mvyEj73vJT0vDyAew/Kx2Dfyn0EiPCt1Ny8GMfiIwYuDzMuntXJ1toqcVv2WdB7tLGWU0YSf2Cifi1wNy/LV3TtkiPTiaELcjZHWkVsKO3P0SbVkq/7e22MkqyEoaGrLpV2V+7XfN8QKGfAhQtpbfJmukxm7dKhDgAIna0FdqnR727Kr42piMzdEyXeaZmreLt3K105je1ITXwU9ywf5I3AtxdN+bJ38l3v5tfeK/ZClvLsqjzLR+dvnNjEC+eTL3yOpY3PXLXdC+T3noiuIIeBK+/nkQefIFx5W/3b51HPEqLXIqx3MpvvJpi3mvHb5+Q9f/Sx3YlspnOyARb6fRaUvvrcGzdO8NeYxBs0aNCgQYOvEdwRGrZzhqKMaPU7VKlojBvjMd6ibAIxS8Q4RkM1kxcOhlo4Y/0VXFvMwL39MgNbWDxKr6OBTbZkVIg25DSgLApK2oFEmXdMyBilCM0sZSrr9y3LeReWu2QagW2jitAq9WdLZ0zFmKQlQxm1o9rcGqqGlEQBqdeWQogi+RHakJHmmlMqw1sUUmhAVVI4MjXxl1Zm1q9dOltrZqNhQapm1YIWqfZxYyzFPTaDfbSWTgBw+OA99Oc04ErpO4eDjKgnWnWyskL/PtW2OxXFRWkrysU8VL78OS4//WuyrnyJ48ekX/tWRBU/fngfBzWSvdttEykXa6w3MQgMgVK9xsMR43MS3FWt3cOReQl2u+99QhM4Kjd47nnR3E8/d4HWUWE4mzskJvFWt8VmJn09ORiwqa6Jfclh2lr8YzwWS8t6cAAX3CfHE3HwoPRrw0pfi+FrBEoXunnpZYz2e5BrMGBoQGlnx0VApysWgyK9zHgoz8H+vrhmjixvcFgLlfzahRcItGoadaBZWS8H4cSCZF21lRSN0FSUU0xowd1jEb8Cu1XImgV7/pMzNbrrqZp1J8BTj15Z5INt122F16phQjd6M7RrH9m9tDGJXvfR7cHKk1Na9IM7tvX6yWeZ1nu9tv2elck6rzXvxpR2LUFp09jKwLaVDnVilp9dDGUa/hn1Y3Fg9WJtHv/g3rp1y9Bo2A0aNGjQoMFdgDtDw8aQ2QRTtWh3lVXfrDMe+drCsurS6pDzZyVAaTAY0+uKFjV/cJmu+me786IxtrotIvUbV3YNl0tg0FBZrSK7Qaa8050kJ4lkfVG0GGo6mCtVS0xGpDq3qUgIddis+tOjMMNYX17T4SJf7MEHEIVsjOrIJ4z66QEGWtZzXieznbjFpQ1NYzO29qVe9vWbA4tVf/ewcqSppqaVJU61t1ZH/DXJwrtYOiQadhK2KHMthqL57cncPvK2+rMXItZUUz0wn9G6JD6b05/6/2TsX/hljqyI5eHYsTkO7BMNtd2SMWyZEaGmu7nUknnNX1nfwighacm4BkkbN1bLQetV3Fj8TvlIZsdR7whPPCLa9BurAz766Y8DsHFatIojJ55gqBaJ3HWxLfFxbwQH2NAgvA3Na99Yixifk2spNtc5fliej31tb204RNjW+zROaS9qUFkpfsVs4xK+NshoUNBuadnOMidLNe5BYx3u2z/HsSOaxz33EiOZnNeMd5iQQNP/QlwdkOeMwei900eHvAzrPGxb2Ukbdym2K7jhc1+n8ehjE+16L77vOwF1ec2pPOxZWvRecq+9lrc6/64bTuHyPt7Ln/m5LT5q6c+JWOn7ePGKYic7oTy2hU1uS43qcOVttSZcnX+69kN7rfg9Tx7Yk2Y9K93sevDcqfyKFC945IrtTyxt1mPUeebyzAC0veRs31Yu8ZsJ5wyVaxOZiEDldVzO8bJWsHr+y/L3zMsX6IYyMPff26dzUG58MHeMWD+knUSpSaMKq4UxjBXSDIChmqA7IeQqOEM3TxyLsFro/f/svXmMndd1J/i799ve/urVyipWcadIUZQoSpZkS4oTJ2kn6TaSdHccpGME3ZkAAQY9jQamgfTMP4MBZgazAI2ZwXQQIOjuoBEY8SSZLBi3J1EcL4mtxbItiaJoUdxEFrcq1vq2b793/jjnfu+rx1dVrCJpk+l3AKke3/vW+y3n/s75nd9JkFjs2ASTkVJAs0CJThRSI1XJUp9CFmCZ+vEwAWKWVzUluTKFY9N3HiJEHJ5vxi2EMTk8UTFkuhQ6pWVj1YXiDlZry9xEw/Gy8HyiiQkOAG7BgS7zzTX2Kn1XO4pykRxIohwIbphRrO4FAPjChTfLAiJeglGW2Fz52hu48j456rIg5vbTx2wcOEyhaztRCNeImb3e6vCxqqwjmqsFNJPkwCF9z/ZRLpPTqdRL8Ip0Dk5pHcUyi50wsU/7KxAlOu66bWP/HnqY/+BvqTHHUvk4JidM/bkLrUhidjXQWG9xU5HbtP3FD74DtInMNjUq4Y4dAgB0I75ecQVpRI6zXKljdPoAAKDE4jL+rTfhM3GlG2tUuTbf9hwkfP8UeeJ3aKaOApPejh+awvfOUMrGRLYtITJhFS2oPzbAzHAm0WkT9LIoLJ6Zvre+1I+DGSedJ1vNzB28J2JQv+2G1LaZDSKf7dTulQW+E6LZTuqtNxM2MTKlhlzW76TfvU4NTAaJwGxnz2LjeJlxzORh+5Y3zP0/eOfQPe8j76Tzfba3ct4bx4I+d658eeCyyexJoG9CmWfBH5m7u1+2cdbm870yyofSpEMb2tCGNrSh/R23RwJhCyngFC2kicRHHxKCOfPdD/HueUJUKROfpvdYOHiEQprHnpyFWySyT5xGsLl0JkkIHbc7EolBbApZCU6JFaNUosBVWfAjjYTDmgXtw7NZppRV06IAiBiZCxEiSWkmFnAzDFvbWSOHOE4Arp8te9yvW7uIOYTrFCRiJjR1VruIufQrTho8Gilg0GkkIRi5r3F8VUkNm0ljlizC5WYpSeEgdOMVAIBbo17WWjvwE0Ki5eoofEb+vqIwRnfcwvgo7//WdVz93p/Reue/glqD0OGxE9QcZHZsAmqRe1z7MdoRodo0oONOHQccqUfJssA9SyAZYUexD3+dSu98P4InaTxrjQq6gkhy5SYhYXekCW+EGSvlMkZcVi/zaZ8jbY1qg3awvBJj7RZd54VbLdh8HZc/IoUjp30GLz1P47G3UcZkjQg8pTLNjkW5hqUV2v+bb7+P2YO/SOOhqRyufmwUndYf0nEnbYRcY+8KBylfU9OXo1JyUSnS/p8/cgSXrn4AAGgyUVKIFIrLtyzYWftMIROYjIqp3HekMNxFCKEh9OM3t76Kz2aI5vyH83jtzEZ1r5ONHorZDboGHmzI3KB10+t6p3YvBLKd2P0qmeXD3+btYghweVLfmfgJyKkn+le/ZzsTb2yc0h+hyFdO76Q5xyD0fOn8O9l43Auxrn+Z8sHPbUDZ+VabvTapvXsxf1/eWaU03Xq7PXB/9ytpei/2SDjsOFS4cTHAN948j4vn+aXtLOPoMzRAhycpPDlZUaiP03ewGwBrjLuuguAcX2TqklMblqbfW90AnZgcR5Hz2p4DlG16wLrQkFyfGwgBi2UtFRfSBi0fkaLt2pYPyZ4p8um7SMdwUiNDGiHi0LDHofM0FWhzrllaDoTJY0oPBQ57qtB0wpIweYEgiJCEdAxhxLnVkoRkDepEuGgJCivJ6nOolffRtlgX3U8FkpDWa/srkNP0uxihfTbGXCy9TqFv/8LX4bQo1DQ1KTEz/SwAoF6g3FPoFxGaeuhqHUWW6BSSBWMKLtwSfa44Hhx+6Zk8rVApVMyTjk4Tmpn4gWXDSSnk3V6n1EchCRDfIZa4M90wmjIYLbIHu3UT6+t0jmuBiyQlh+0gQXuJwvrJCjnLY+Ma9iLJo87Pr6HLAjgT+8j52+UCOmu0gxN7JyH59XLtCsmhTu49hGOf/lVa/9u/B8X31FozBmcjsNai/dslgcMjNCFoRzVMjt4AADSbtE1t6Z5YuNSwmPqd6l7z61w7bDgwNdnWxl7aj7gZ4ZQ/eeuPNvRn/uwzG+uXjzb8LCy7E8vnwnfrsHcSSh+kK/4gpEW3st3kq18+fWTT2mozMdpsvMsHP7fLI6UQ9yAGv3GGeQd+L0KjmzlqYOPk5fV3LuLUCN1fg45/M4dulu1c+XLWqQ1Xot4kZnVw3/KJBkkRm5rsfBg8Hx5/mPb4TduHNrShDW1oQ/vP0B4JhL20tI5/9x/+X9i1BKdfoRng08dfwJ4pYjB3bhDqTsIuyg2mMCiBiMO9bVVBymQgl0OtFU+h4LAyVt1BFNHnNcPA9gS8GnfQcoOMndsKXJQcRn/MNPZVCAONXOkAXMutYvq921kD9/CA1IDgZhPdLi3XjTuIGcELJZFw/NMr2LC47rfk0LIilnBZFFCoFhyGqpWs33aPnb4cT0GPUF1mrbQX0qMwbqho/XYQIeTw98jcJCaOHKDfA0K6a9/8MqKzvwsAqFodTM1SJGNq6ik0KqRaVmwQwq5ViihVWeHN1vB4bB2HWeI24DAL3HHdrJ5ZczRBSxcGksZJhJhJX3EESG4U0l2imXV45zwsVntbvbWErkWh9HCVkHIychMjRQrVy3IBzS6HotqrCK6fo/MRtGx3uYvbXZoFv/DKCRzYRxEJr0aRGsupIUzo4t2cv4nbVym0Nz1BIfnz595FofoZAMCBl38JC2/+KYxJribwO1y3XymgyNK5bhqhbsLuvLzSFixzn0Bk8W+pdRYK71HLVFanDSFgqcdnbr242sX/9YeEiAyqPnZ8bkBN9twGhDuIRT4ICW+2zr2i7e1qwrdDz5v9bsLN/WbQ7XZEs3w/690QzF5/5yJW3qbnuYHB6YbtkPQg6c+8itlg+8KGf2VKZ9ydrV8etoe4D2XHnT8X83kzpP1Uncb/2PG57HzMceePdbOGJmb7/eHxe7V+stkP0x6ft8DQhja0oQ1taP8Z2yOBsEsVidOvFvHk8Sexd5ZmXWXPxsoNyiP6TSJAlSbrkFy+ZUHBSgn1drsS7YR7Z3NfbC1sJDaXenkBqox6U87/tkOFDpdq2SpEynMXqTV0TDlJxcjLdiwUOFeb+utIY1LsAhPCQl/CcrnuWCnYDNebTLgSoke+ElqhwEpmUhYQJ0xWY03xVtBBwuVoUhXRTgyRhdBcFGosJHQscuQIaiWKOAhZxrrPmustQqTa8TB3jPLW04ePYO0qFVbefuv3af07f4mZKp333gMvoTpBM/Gx2gxmGpSvnpgiElW9LmAmk0ImGQKWrsm9CmiuY5OOC5j2lTETxlSc9X9WaYCU8/R6fBKKa5CrM3Ssa/MTWL54AQCQxEW0u0QKsxxCypVKB2FEs2xHWwATAtdufQSba8ErXFut/ct4+hhRbg5OllATpA0suCbds0KMsSLexCdOo3iBUNDCNdpnzV7CG18lhbdf/o1fQTpJv4ult3FxgSIDksv/ZifrCLqEtv1uCJul7mxWUhNCQTNqTtJePtuSMuudbrTGIQQiXkACkOLxKeuqFW189pnxDQgIAM5tg2a2UkXL18zer/VvxyA+QzbbrLRpt3nre0HWQE/F7F6sP18N4K6ctUHWg1D1Zk00tkfT21u/0lm6sLFOO8sbs21FHjPnZqzRfBvHXiJFxBMHXcxs0X5zs/puM26/9c8/hW+hl8/u1wTYLRFyJyVeO7VHwmGXKyV88pOnUZAeAm7e0Wn6aC4Q8SiJ6QEuexPotsmBeY5EkePQe2tt1EFO9vYdcu7LoYMRJurYlgPXolMdr7JIhogQsJMXUQKX3t9wdYgOi5wUC+ShHKRIWJ6y1bqDMm9LMuPXdTW4QRZ8v4uoQ+eQcKCz6tpIIvOiVnBcFsxIEjTb9H2R6zZVqhAl3JtbCrS5ZtvlScCN9SLkFBM8nAYCrt+NOxZCkLPwuKZ7/4knUa6QQ7/69hu48fZ/AACMtujhOXRoHw4coV7So9PTqJapJnuqbmO6QdstN0y9tAvhmdtF94rMJTPCoKDBn3WUqd0IPn4ICSF4omELSJu7Wol1aG7Iorm/eXVkHzqztOztD99Hp0n7/bFP0mRu7tB+vPU63RtdUYayyGF3m1cgVih9Up6haz87uYJx9nW3z13EdZajlWaClkao86Rl7ugLODp71JwhAODaRxcgEpo4fuXP38SpF3+WTgeLuP4GTYDmanTxD86NYm2JJnNChGhUadLjFehc/KCDNGUiokavGiCVGUs8C44LBYsnMokWSJLHpx92oeBmL7tBIUfDxpVTL+8o5H2vy+7Esd9PzfW92HbM8XwY/F4sHy7ud9T9tex5R32vfaVNOBsY7LzN7ztx7NbUKVxlsZT8/bAdyzvvrE0Y/OgBO7t/zuFlnLur3/X3s+s/c/gkXn2ZjnOQ4/7ffvuNDcdgJlYmldAv9pN18RrASXuYzPC8DUPiQxva0IY2tKE9BvZIIGySfSLlqMinmZTyYyAm+nxljEKalnYR+jTLEdqDpQnZeAUbYw0KdZZLdEpXF0Lc7DA5zAG0xy0veYpSLsewuOQoCAqQ3NADcg3SJaKMaVYRhQo6oZmW5RShuHbaZdnNgqWxvs4zacdGymFgxXW6sU4Rc2/tIJFIGW23uilCvgSTrNC21NGmQyiEDDDiEeo9v8DtIiv7MWZzqKtwFMql3+PEhVui833q9PO0ATWKs39LM9rF9/8fTNg0NTxxghD6vqNPYWaaypvGR11McMi7WtdwOOJgO1xkLESG/gS0aegMcJmTgN1T61I+oLj2OCJEi6QLIUxMvQzBym9CCPTCG0xgq/oYT4js1pyYweIdGpDTR+i6ePUWyhUKfS9e7WDyAKHikhuhq+k6VRy6BlO2j+YijX2zKPHuZSKVXb5CCm77RxI8N0exu7WFt7Fnij4f/vRvAQDeK2pUhKn5/h5+8MGTNN7dSdRKhKArxQ7/LSIp0DWYrCnMcCObWplD4nERLSYqCimgtJG7VbDM3FkauVIFxWkUW1hQ1uM/t84j637bLOQ9KEy+3bI7sTPxExtqiO/XBkmTDrJ8Q4+8DSKb9Te0AIC/ee0Psu+eeqZHLtsNqs7bVqh6u+82Wx/INelAj+iVJ4oZBDyodnor+5O3KLJqygdPNlo4nzUy+UZWivfqL//P2fbz1vv3cczMzfN65todzt1TvchF/TLdi+vt9g8NWRu7b4cthLAAfBfADa3154QQBwF8CcAogO8D+DWt9Za0zHYnwRtvLcHxNPaNUTh3pNzB5AwJo0QOXeyllgXXJcc8PmZlYcQoBjQ7DrdIL9GDMx3cWaPddgOBJKSXX51WR8WVKLJubqXgQhkWrvBQMJHKiCYEBTdCzLXVKk2NtDRcfqGmQYggTXhbHtrcmttm9RCtUiTa9Lu2ELbpBR8FHUQ8gwh8clo32iEKRvK0NIVbd+gcbityzCPeNKwysedjN4HN90uifew7QjdqcIvyvwtXr6F5hcJlsyMtHNtPtdUHWWL04J5ZTPF4T9Q0nJJh0FsQnFM3eWekLhAZWc0IIqUd64T/piFEavL0MUxQWfO4iLgN8AQLbh2SnbRKQ8iEeAdpga63lQIVDifbbgGaJ0hWQC9Wu6EwQ4R2LC4s4+bHPLYSkB73Cufibcezs0nND67fxhtnTB9sCnOfEwqaFXSeExrF8sc0dt/67+jv9VHMHf4EAKDmWTh3ll5Ud5Y/xItHyLkfm2HOQRCAo9+oVwsYbdEEZKZKk6Il3UKo6MUUJyqb4FjCgmJHbQR4pHAhTO21siDlgxXl2MwexPO81lH4k7d8PFVf2hCiHeSojW3nbPvDkw9SkvRB2nbSpMZR31ml+8/U9vZbXiBkkCxnXud6UK56N84a2NwR72b9zZx3T6Dkudw6PQHTe9UKP//hPN59l8S1jJjJxXngF/n3ow074x2c+eLvAaD+2L0OXj0j9jl93uo+BTa/Zj8M5/0gpu3/EkBeZPV/BfC/a62PAlgF8BsPYB9DG9rQfjg2fJ6HNrRH1O4LYQshZgH8AwD/E4D/WlDh6E8C+FVe5D8C+O8B/M5W22l3I3z7+zeQ2hH2VGj2sm9W4sVnCUmOcx/nii2x0qYZ9c2Whz0NCrFGaYSuzySmJv11RYSaRSFtV3sIUlo25k4pMRRcDsFCRrBtrnNWHmKLkI3FMpTKknCZ9KOhsi5gRi7S8oBJBo/d2IfP0qQjLn1pSQk3MexfjVtMnEthoVGmeuDFJUZbgYIu0/neXujixhrto1Cn0E6jNoGEf2/7XVSYyHX0mdMAs8SvXiNC1uriWeyxie28b2oW+6cILc/Vaf9PHCyhXOC+11YCbTMpTHvIaFcm8h2sQscr/N06BJPwoEzIWyGJuFlKEgBcfy24flxoDWGK1d0lCK4Zl045I3ilKa+PMpRHDUpKWICXmOtI+7fTG9g3R1GXRO/B628Rak4Ches3KVR+kCMpaSVBElFoerW5jiAyx03j6ifAtXW6XoemrCzUL1rcrWupi3CE+gHb07O4xSSX/c/+GA48wTPqNfouiCVSk86ADYtr6A9O0bnum6sjukhRnevza706a2goTaMgmKynlYKWRvYsApKHHxJ/UM9zFAW4cfVD3ADwwXpPHewfvVTcdJ1zV6INCNHU7g6qs35UkfY3zyRZLfZm3bj6kXU/4WyQolf/b8DgZim7RdV529B1axeWlx4dhNb342zWKuRmrqtX79iPbEDYJtTdYMJXPnKxGfveRDFmVsez8LZB9TfnE4zu6S2bH+9jI3f3zO6REnvMdkM+Q8PF1y/Ti8aonz1su9+Q+P8B4LcAmITMGIA1bWKY1KBt73Yb0QAiaNipRjs2IUGBCr/ggzY9lPWawliDXt6tsID1JQp1Vp0UKYe8l7r0cpY2MF6l32teiDFNg9xhOrcjNUIu33JsDzGXgxUwhpSlR21mRYtOiCgkxySiEJFmcZYihZPrlTLixEwYJKo2LWszi1wpDcECI1pb6AQUphmZaKC1RC/9pYCcUXV6HB2edHy82kJQIAGTvaMHaF+FWdxqMuNcuth7kh7s1loKf41EQzrLdOONhwt44gA5thNPHsEJblk5OkmXq1KrQya0XwENkXWMioCAZTNDju+rADpgZxeLTAjGdETTcQTFoW0VdJFyeEgrul6W5UAU+TYp2PBs+mwVG5BVOka7SDd/d/kiopDC3yU1A8n5W9ulddzkNsYqdC7lp/agy8e6fPs2JsdonISkDmAhliBtus4HqjaWp8lpnOPWmI0CMDdNk7nRMY1inZxrd535B3EAP6LKA6tbzFL3ZdtF6finaIzepIdaCQdpaFjxLmolmnDOTdEYpWUbH96ibV3HWiZ9S/O+PoesE0DT/ZVqZPnsh2wP5HnOm3nhDmJC9zO8TW5zs1acD9I5mxexWljKyrhMvnOnEqSGCf7jz9g5VvjdzuTrl3vntBkzfDtHvZ1oyG5C2ps56d0w6G/ibr3wzbY/SFfc5LrvWrZurkOPGzAzdxATq3dfn5OZc/eza3kv1zEvWTrIBpV5mf3v1GHvtsXmrqftQojPAVjUWn8v//WARQc28hVC/KYQ4rtCiO+asq2hDW1oPxp7kM9zHMeDFhna0IZ2n3Y/CPsVAD8vhPj7AAoAaqAZ+ogQwuZZ+Sw20XvXWv8ugN8FgEq1rm0VoeRZOH2UUNaLpyRGWewkZJrXrfUCag6hoYJow2UUlcoqVIHWq/EcJE18RIx2okIBJY9QcVn0xCwEx3uDKITHyLsbNjNGuSGqxYmAanEf5NgHOHypuRuX53lZ6LgdtbMaX8HdwlJlobNGqLsbtOF4dKydFsAqoihLIjC11xSWm7TsclrHyBiFYroW/d5dWQAKxJp/+vnnEDUJwa7f+BDBIjXPGEkIaZ84sRfPPkG1y0/tn8QIh5PcEqFMGXehubc2oiQ7L1gWVMgIm1MBYayhOZSPwEea0DEq/l2mEonysu8URym0RWDN1oAV8ndJFyk3/LDEEoQgkpzmFIL2xrKOaJ2V72UEN4/Z2uheh1c9Qfu1W3j+FEUhFq4v4ObH7wMAllp0bTraxUydrt1IN8Bzh+n+OXWQQpKOXUV9hCIDs4c9RHyfnP+YzrWOWWnOAAAgAElEQVRQ2ofICMKEXbTW6Z47/lP/ALEhdtcISUtXQfp87WURBZYmLQQ03rdWF2FpOpeyZ6PjG2EdmTVJ0Xx/KkgoZlVKpLDkQy/oeGDPc61azZy6QZKDwuHbSYQ+bkZh8K1FUjYjLAEb0XUeVQ+qs/7pn34O/Zdip+j6fsPfg8y+fhaL/ch89uSWaP3mpbMb0LZB2fnQ+CAy39HGPPCMQb3jG7/HxkiJsdXaCxjN/dtEgH7tVz6ffZeP4Jje3v29svttp2Ipu+2JvWuErbX+b7XWs1rrAwB+BcDXtNZfAPB1AL/Ei/1TAH++230MbWhD++HY8Hke2tAefXsY0/Z/DeBLQoj/EcA7AP79diukaYJWcxlPPb8fn/k0IcoiLIQR5Vcdi3ISc6KDS2uEwsJOjD1FQkNzjQBwCPm0ODfatQVSoxgWRgiZ4GPbHK5TCWyufU6Vg5DLk5JYwjKTKUacfncNjuQ6bDuF3+T8LcNqu5aydBVQsYqwi9wUpEv7bwcpWMkTrufg43lqu1iv7kHMaLvVptxmnDp4+zrNvkqNaShNefiwReenLQsnX6JmFHZYweI8qW1Ft86ioanE4cUTJPF5+okpHN5L26+O2bBqJodMf/XKTQiuGRfKyRp1CCWBNOSLQyEAFaQwaZegm0K3aDyjkKVgkyTrH25LC7pAiEqwdCkcD8qh8xIAtB1k66sunZtqUYTAiq8gGaEyj7jThBR0/ZkLiM5KC8VxHg/EqHE++6VXjuCjS1TqcusCofZ3rtRgaxrb/fs0pmPmIrDUbFlGqHks6Sq7uHyNru3Fy4SEZvc2UN5L4/n1v/hrzBx7EQDQLY+is0iz8ymOeFh2G+Be5a2Oj4T7j4c+RVraPnDyCa6RL5zH985w/a9KMtKZMa11r6VmmkLIH1l7zR0/z0ma4M7qMp599pUMWW+Wl97MHqZMaR7tPTs7nuU3B7XR3IltRoIyuevN0PVmjT7yyBrY2EAlb6Z8aicI+0HkrTP0uc16O9lmuvBedj4vnz6SRRpMb/Bvnnl7Q5OVQfXugzgIhlvw1NwSmImDS+ff2ZHKXL99sD6Ovft7+3r/ox9ssXTPdpu/Bh6Qw9ZafwPAN/jzZQAv7mT9ckHjuWMRfvazT8FmxY4UKuvrbELbBb2EpybJgSyslbDKxKBrrRhTo+RgjHqmK0tYh2Eoxwg5vBjHtE7BBiQ7WZVESEMTkmzBYrLPnRVyFGHSxShrPPvtGAlMpykOk3fKiH1ydl5BIzHKJ4bxq1Osd8ixJbFGg7Wr/dBHscpiIqPkWP/Td1qQXEsObwYRs98tj7Z5+OhnEDZpjFbWPkL39rcBAJPyHH7iKPGBXjhGDnn2QA2FPRQAkpadheh1SI4RYQQdmxBsj6GcSgWpqjx2NAZSWgALn8RFGwE7vLhtNNoVZESTHpl0YHH/bjukcQkQQFg0BiLuZh3PpE2ENABQkuqVka5BXye2t3TrKJY47KWJ3tm+UUelRg+qGB2BLNK2pkcbOHWaXooL18mZFsoH8LVzNHbjV1fx3GFyqFNVJoKJEBdZ+vTmJRvz12m8SnXqBjZ94nl89at/CwBYX2rhyV/6BQDAx1du4ADXfAtOx8ThjSwFoEWMpMPCO23eVwBMjdEDPlZZg2dfovWiFDY7+pRJl0IIaHbilm1Dc2e6H4bd7/NcdQU+c8jFZ14qZkQeqlu4m8yznTDKdt28dmK99XtiKYPISDvRDO+XHu3vzJUnmuXNhGI//dl/suH7vNyo2dY//LnDve0PIGrthmg2c/jkQKfdr/MNYFNhmYcl62rO51OHgQ5P+IxAymrtBXzzTE8zfVCHNHP9LqzO4eY8XR8zls/93H+J3//SHwGga9A//ju1/GTr4jzd5duFxncbDgeG0qRDG9rQhja0oT0W9khIk1bKJbz60mlc/7iFDhOT4k4TCdcFg5ttFEUVDQ6xpnaINWZsjQuBiksoxuXGGgnaKIBmZwVbw1Jcf83xEFkCgpgRoeUiNfuwPUius47X6a+rEsQRobhIBZmspq0JWa0vB3Ac0zDEhcOh8joj6W7oI+SynBu3uyhwH+5qXaJSIXR57gd0/LfaKUbGCSX67bUe0jv0SQBAGAPtgNBl6ca7OCxolvvJJ0fwwgna1t5jRMJypuZg6qF1KgGD0iKarUIppMzoTaIIyqLzsSwJZXOnqCIdi9vYAyum8bC0QMQRi+46hZhX1hbQbXMKIwngCVO3zqFcESNpUw1qlHSgufzOCttwWc7TZkTpFksIOUqRJjGqE3QMzZiOL3Sfwsp1Cv+XwjaKMzSLLroJfvrTJB0adum77/7NR/jEqz8PAIjjFbz2zpu0rSXql111bRQ8uk9qxVHsmSEVOG8vkfXefv0d3GI0pMaO4RZL2I6srWD8FIXtJdd9RncsdNboBouDVcSKIiVdJlYnOoTByalWcPieC2QvHG5Kr1MSe6X9Jj01tsfBqrUqPvP3qKOSIQ4Vbv/VQIUpXOnVvr63RuHJUyMfbomm77fhx4OQIt2uqYex9XYbR+b23/X9IGR36fw7G8Lgn3+antPJAaj6QdggtH41pzhmypvyqHvQ2PeUywZbPu1g/t1vebSfPy4i1wHvrdG7+tL5dzD6wm9mv3+T+3+b2uusRhoAcCVD1ube+/0v/dHAUsO8JOrVXB/v/H3Y383rR2GPhMNudiW+9v0KtJVA2PRSl9pG6HCnKM6zUj2qYc4K2Owkj05bmOTIcpHD3K04AdhZTFQUBGcuFFN7u0GIkHOMtgd4XCft2Bqxov3VPa4D76bgCC9s4UCxdOgKM8eFTFFhZ9PutpCyrKfFOtxFR8J3mKHtCaSS8tJuYRqXbtA5fnSdHs56sQ6/S6FUr15BY4xuIjumMD3cGHLpDABgNPwL/MzT5MyePw6MHCRHbY/zC0IUoZnFrcIIYFayOX4dCUQBnXcCAc2dpCwZo8DtHoVl6uIdWDyRcIWdbcMdoTFw9+6FTmjc4lYTqU+OXLfpXGTcgXbpIulmGR11k69DG4U12odj+XwsRejCOP/uY2aCOor53AVt6rmfwfrHNB6rN85CC3oBF6cPocKyrZ/5e/TQ31m8ie++/QEA4PATx/Hyj5FwoWVRXrvVbMJmHoHQZayzROwP3iNH8/H1D7CwQudw6oVTwBrVpe8/3UC0Ti+hwNT+A+hEXJeuFLSgaytYVz3pdLM2mfvm9uHcBcp5+eFaVt+t2HlbykLETHnHkkiSgdVUj6QZaVIyw3YeB9bfuWvZSxv+Rb8X+iRN+22QM7evn93UsRjLO+rdtso0ZkKxecc9M3fwrlB4vVLZIJYyKGeaD4Ob7T4762/IET8MGyQdalpiAgBe/nUAGztdXeW/+/FaL52Bl7d02v1jbc7rDe7g9erLpzD/+hez3/OCKsb+1S/Ru+Pf/PHpDWInxnn/iwGiJ++tHd+QrwYoDG6ux6c/2wtn5/XLjWjPdkaTq9426hV6j2wWEje562FIfGhDG9rQhja0v+P2SCBsSImkWIZOUljMxtbwEJvGEYwSE2llfaHtAlDkmuxmXESnS7O4sQYhpyRJEKR8erFCs02zHsV1rmWdoBsz0lUaKTeLGNUKcYvD8hyWTUQJEatd6VQBjJKiDoWALccz/DI4QoN5Q/A5MuCIFCvMqm7HKcoebX9p2cc3zxJjPJWEDFMFFKvEOq4URuGU6Bh1geaKcu0aJha+AgD4uedcfOIYoebq/hnYoxT+QUqh8TTSCLj+O45spJpCtBE39NBKIOIpm21JVLkRSbEs4BT4hwqtY7luxi6H60JYhHALnB5QQiJlKde404JghK5YTS5cnodgidBxfx2tdRq727euYO084Sy1QvP3Ef8OMF7l8fIxwqhk4SIh5XTmCXiTVIfdDFOsXCdp0KniLUiL7ok99QMAgM/9wkv4k+7XAQAXPjqH8xfo3GdnCMGNje9Dk6VJr128jJvM4F/3KWTe7kQ4cogYquOjT6I2TesrH5iZoSiAWqMoRapt088DOtaocFqhUeImNI0iFM+up0fHUKnTOd5aXIQtmSDJj2QqkMnlpkrBkj880tmP2ih8eTdi2oltFSonJH5/CHtQSPzm/BX82Rm6rw3aArauvc6HwX/8GTsLFyezJweGrAeZQar9NkiqdCd9rI0NUh9LF3rHNnMYuHmpRx7cLkSendclg2S/gLmXvwAAdyHt/jH4V79Uwm//W7p2H+Rq1U2kpr+W3YS/DaoGgGeffWXL48uHxI2d/3A+i/qY0PjM3MEMwR8+djrb12aqZ/eDrI0NEfbQhja0oQ1taI+BPRoIGxoWAtiigFXuJYFoDa7LylkW5RXtogOPUUfZtmGzYlfR05CshmUUuOpVDxareEVpCqUJwQqLfm+1V5GCf9cSmslssaPRZH1sxe0gpd2Exe0NU6HR9WleVakT+l1f7yAqcP7XdqFZI9q0m0yEQsQlVY4sI2E1rNhtwSsQOrvDaY+RsoOQ0bpdqQEpkSjWLxG6nGu+jR87QSjy5P4QxXFC43Z9L1Dk2XmXZnLLqynugGqUQ7cIx8hc81X3tEKZgVvdSVFkHoDleRCWaTzBJWqJhPZZVzwtA6ae3S7wuNqw+Xo5jXEgNXXDtJ3y9Bx0JnSp0OBc734Z49bHVDN9/iukydH86FtwOz6f1wRsiw6y4HLEpTMPt0Bjb9WfwPIK1aKrqxfR0DST9hKq096/5zg+/6s/AwD4wz/+Nj46S7PfLveqXr56EbcvU96xG2qkUZsPm+6tfU8dxyd+jNbXIwJlhy7U5IiLoEvn2LxBM+7ZAuBxlKJcr8O26/yZrotbTpGUabwm9uzFRIO+v2Jfg8XNSBRHb4SMs/axFgBtPT457N3aZvXI92KUJyXk1d8eMV/OBVAu+108HDPI2rR7zCPtfP56cN66R8baDF1vhqb7LY+ud4Oqt7P+bc7xv+deBgDKff8lt7TcDnHna6+v4rMbNOUH5bP/+X9FpZX/5o+7GxThgI2oOm/5Ziv5+2xQ7+1MSzwXnclzKvKf31vrbedvcqnvonmZsj2o1puPhMOOYoVrCz5s+NkLT0oJWxoZUa7DFhIWM3od28q6Zu31VlDURAxaWaGBKZV9TNVowNtWDc2IXpSjrJAYtKooJxTmXm07KI/SUCws+AgtrulmFnrqW0iZZKXCFCUOTPgBO2lLwhL8u5ZImfhmmNRJkkIxAc62SvCqdNxhSyPk8LXiMHVqF1C0yckWExfqFhHM9vlEmDk9Czyzj5xRoT4Ou0xEM+HtRzekGuIVn7Z5QylEfIVrnoMyS6mWeUJSdhKUDRG/YAE8GQJkplIq+Fy06kKYKGAcQjvkMIXFxDvbAiKzMbcnOJ2P4fAYCceGtJlEZ1Ww9+hLAICxXyOG9pu/3YHiRibunqNoL5JDbdQoNOeIEG6DxEyqUyNINddoXkmRXqHgWGOWWOQeHOybpT7gv/APn8MfNKlu/e3XScK02+mgzOH/QEg4o/RCPHKUWOKzx46hXGWpWrUMt8CM9XYHI+xE49B0X6siCWnC4BTqsBxKJ8TMLLcQoR1w2qCbYmKKzqdQLma12lKYBiy20WCBQgo1UNb70TTTrWsnohSbOWvzspdTL2eksUG1wnKqR3xSC69nTnsQQe1+CWf3Y8aZGDYzsZh77ONBRLPtnPSDCn0/aPuZLxjHvfE6DnLCxl59eQLfAtXun7vy5eyamzOcOXwyJ6xyZwOZDKDQ96AUhLkXdzIp3K7u31Q19O/D1GM/DBuGxIc2tKENbWhDewzskUDYUAl0dxWy6AIJIy+vCMEh6ZDD0XYkAEkI2oeDvQ1CM2NeE4LLXoRFYUihJXREs9iSXELD5ppBLutKfQlwqZUQXTgphZavrbZQLJj9Enp0LIXEJrRcKZbRZhSvNIffywJlLuEKQgkzD9J8LlGcwuGSJssRKHCbyY8urWIloO1ajMA9y0GBz6EYXMVUfA0AcGiMQmxP7E1QclkKszSLxOHypdVxLHLDjrXE52GNMc6gd8Z20KjQ+ToeQ2Vp5aZsFnSG4nLzOCPlqVUWHYeKICyKBWmHr5ddBEp0jsIrQXAYW0uZrYMw5O8E4PKBeXVoJqs5TDIcfeIZLHyHZqnjhw4hXKCQ+eSTP0nrexLaJ9TtiRnsnaUQVdh6Bnfmua0nl22N2pdJpQ3AE7PH8KtfIDQfcbj5/feWoWK63tWpKdh7KHLgNHgsF68hWKdIS702ArtOx90ojkDdZjW2hAbGtzx0I0LVujAKhyGylhQWtYN11Pie00GIiVGKiIwUSljt0L2a8n0mlAXNXeyUVpDi0XhUd2I7QdmXzr+ToZ9TuRKd/vA2QCHtQSh7kOXR9sO2r1+OslC4sTzau3H1w746YZLWHBQKf9ioejN1tIeBzMsHP4cWI+zN6uXN8VhTpzKS27fwOZwx4XFe7uals1ml+KcOA+CmHUa9LE9WvB/Z0XuxUyMfZij79XcuZq1P361U7roPdtocZDN7JN4CWmmoMEE3SVB06UVZhEBLsSxmyA5UAC53YTo+4+HwGL3gPVtCevSiLbEjqIgIJu2now7qLOSx1OFa5DhBwA5isiZx6wbVGq4sddAY5Tw512b7QsASRj5Twy7w257f+rYlINhhh34b2qEXtFcgZ3bzzh2Uy/RyjgPg45ssotIMYIFe8C6LjXtqBF5MzuaoPotxToXsH+M8rrSBAt3Q685p3LpDn1f8JqRN6zUqtN+JoospdjCVmoAwzoLHXQgNbRLLSkBwn29EbcC0SPT5BaMFUdgBIPahjVPnDluQDsD5eHgVaJc/s344HAeZoLoWAPejRrcLtGkfgnPg05NjuMTH9dTkPtghOUZYhkmtIe0yH/cqanWSZJ05sA+rCxRevHWDtqmtVUxaFCa3tIMDe+jafOGfPAMA+PK0xvmPaV9dN4ESYTaeAKBsC3VOCzTDZdSXKPUiKg4mRrgKgPMHnVQhZHGZKLgNWaRrIyt03nEMJJpTPoUijp4kUYh3338fnTbvj2nmqQWwWi6EsiDk45nD3qofdr+dGlBLu5ltJS1KQh3kIC6szuEots6h7tQ2E03Jv6TzuWtjeWc9SFIzmT25I0d9r851J9KlD8ORf+rwTbxGekWbSqIOrAl/eQJ/yfLsedZ/PqSejQc77tffuXhXXjtv+d8OHzu9oSPYvd5//aFwwOTCKR/+2pn/4S6H/aBsGBIf2tCGNrShDe0xsEcEYSfw24vQbgmCe1/HUQzHNeFUQmPFQoITh4k19vxBFwWXG0gIF9olFnjMddyeXIeMGU07LgqMnArCMLQVNBOZXcdBe4XC60ordNZp2YRJWLFwMF5llA8LFn+vuE5bahupYqSZ2mh2KfRRrxN6DkMJS9N5Lccu3rlGNb7dyIHgTlGWx4x46WNfTOHgWiFBtUoIVfOVUrKO6zERrm5f9dD1SS3r2EwJB8YI0Y1NECx3yyXAYQRtaUAxajZEslRDcDhaqwQ64WXjBGDZVgQ9adFM7i3pZo1N0DLbVJmELCy3h6w9g7SdHhp3XIBJdlApdJNQUsrhd+l3UCtxl7CVBdTGqfmBjHmduAPNSFQ3ahCqzeddxf4naJZ7/j3a1tXzH0L51Oxk+kAMt0Zo/Ah34Pr8zx/BmxcpRbHsW7i+THW0i+tUjx101tFMCeGPJzHGuPGL1w3R4fGyTQ9srwXFBLjYBySnLhR3WkuUgsfXM4jbmKwQsW3P2B5ckUSSM127pFZwzXiGCYT7SDyq92SmW1d/OHiQffYZCgdvpWwGPBg50YdlmzX4MNYfBu83E95/751DAAjxnRr5MJPlNEYo9O6aaGO7aQLSb4PQ78zhk/e17fw2B9VWAxtD4nkzY/DVr5LWwrkr0QYimtlWL/JwZAPzO4+gjRmUvVkaZpDl67AHWV7adO/+4wOJZ4Y57vvdu1jk92qPxFtA6xRJvA7bUkhZ89vk7wDA5VzgSMXD7Di99NtdjVvr9LlQKkNyrtRJ6eXtOR04LC3qeQ7qXLY1MULbLUQKQUhhq47vo1Gi9TsJ0OWweSDpRQvZwDLnmoslwDNMXtu02dQQLOuJRCFNjAQdvdwdqWEzQ/vG/CpW2AlKq46Iy3g6fLrj4TVUG7Td0ClBcU6zGZNTWe9MYf1j2v9M5QI+eYTO4fCTe+CMcy6MO55R2JpzomkIzWVuIjYlW7IXH0e2KITW0GY2Y5x84mdhcqFj8LwKmjt0oRtCc4tSrRWEUc3m0LhyrEyDHW4xy1uncUDeDUDIpVjd1SVUuf1mMv8+5AGSJk1al2ncZAzB10hEE9B7yKE71T2Y2U9Osts5AAA48501nGFp0rC1irljlHsvOZSiOLDHgSPpuN5fHMH0HK23FpMzXVldwPItWn/i8ndQ5zmk00rhs3N1mOuAIIFREHWsWlZi2PFNS08HTVMSODaJG9eIn+B3fVic84+5vEvoOKsc0FLDUo8PS9zYZoxdYxQm74W0jSDFoBdjPmedd96DWin2h8kvrNL2TCvGC6tzA9sy7ta2Cn/eWV0GGnfnbc0xAQBW6VhOHe/leGcO95z1oHDxg3DOxjZrtXmvv29mppuX3f8dO9mBkwNsPF/zuXyQUgWdK1/utUHF2Q3scWCjyMu3Xr8zsGzLWOH2XwG3/woAcP527/t+gZT8d1tZfnLQX973oGwYEh/a0IY2tKEN7TGwRwJhCwC2FkDYgq8JkZWsFIEhPnEda2vdxnfOcE9nEUCyPGax2IXj0kzG1txMQ7pwWcyk7GlMVWhb1QJ3yrJjKJeIS1ErgORQZ7kgAJt7GvuEAv04wI0mkdKKfhHjVVqvyKS01AYibqyhISBZPtWxuLGGspBweN527EyQJUEKoeh4PNB5zVkpPEnn5dkKK5rQcnOdwuvF1MWJvXSurzxdw95j3EO61oBm4ls2DVMSmfpGoiGYLW1EYqDsHrksTiE4SsG9pejaaIMeW9AGVesEkFzQ7vLfNIGOOPIQNHsIOzbMcA3NHctSAODrFOs4C79HMA1DYtgF2m69aCNaovC05G3ZlgNw4xcZ3AGYpIfpp1Hgme3B/XTPdFqH8dabhKq/990zSEISWTnAqZGyPYopljG93eriDvf8lh51QdhXquPiAtVse8EdFHiMROxmRENw9ATSgeCwvZQeEmbtSxaRieMEKUc30mIXqy0isI24LupluqfCNe6brlMoPkYhJRIT/njMLC8Jacyg7htXPwSYjPbBWz6A8dznjWYYuABwnrvwAdiAlA0bPC/UMQhJH23MZ8SxmbmDDxRt99tnDg1mRZsmIaabFEDo+l7lSO/XdoKaH0Tf67zk6lb7zrPAgR7CNiHvm3Dxpx8S2r2wChy9/o0N6/cj9PuxzVB1gVF53gZ2otvCdssYHyLsoQ1taEMb2tAeA3skEDaEgPQkdKiI8ARAORqalbG4FTXWuxE6IatlWQIeI1kZNeGWuJ81aEYbaQ9Wh76zLI3SCiFJXgwjjobncotI7cFKuJSrEMBlcpQMuV82AkhuM9lp+6hzvrvIhCqReAg5N5nKFDb38TYkrlDZWGfeiUo9WCY/m0YosFpbiXP3s6MeTBvwWI5gOSS07/KxvjQr8dIzlKcZPTIHNIhEpb1RQPKKhjyWRlmjEu2WAc4bZx0qhJXlsIWW0KyABh1T9wkgkxPVOoEw10YnkIYEVaacMWoNiAarnq0tQ3Hvax3yNiMFzag9DdvQ3JNbIYCKuEEJH996UMTYCUIa5b1jCM+/y8vy/kUFcI0Cm4YdX+R9NSH2Uj/syhhFHk48U4LPx/DOd2z84AdEOCl6lAOckSW4Mz8LADg4uhcLN4kscv4CXe/CzXeBFW46EtpIGc3bqQVZJKQnQBGRUqUMbueOIAig+Jp6oPvJlSl8Ls9aWrkBFHqliFW+vst8n6nUhoJpJWtD4fEs69rOBpHRBpWAfbB+dw/l/PdP1ZeyXDCwERlthaBvzl/B0caP7jV4tDGPk5/8CQAblb+2Q4mb/X6/ue1BCD9f8b0btP3u9aUN57j4zS9tumw/Ajdo25zvDIBjx+m5yueYzXHdzK3z6sunstag+fzyIISct0Fciu16Yff3ezdRpEE57N0SzoBHxGELAdi2QKBkFga0IZCkRtiE/iZxgpRDx5bwECcsNhIC4M5IrpWTv7RYJEUrdGIOkbIza8UONDt/zxqDSMkxlqIElgm9BrR+pdCExVKatliGwzraRsAkiYCQgxXKsrIuYn5Ixx3GEms+HV+QpBAsUCKhkTK57ggz4sueQlPRvtaSOip8bT99hF7en3p6EiOHOIxWn4V2RngUXWSMJxMGl4BmYpSOQ0h2viJhZngskTViRpo5dJFE0Dy2CfdwjfwIskukMCtoIgvOFFmjvTwG1Gk8xMxhyHgP/b5K2t3pygJ0l9MZYRtpSE46Ctdg+paHAU2Q2qXDmC3TY2c1JmBZJM+aaDMRSiDNhCJGNtEQ8jY0s9MlS6c2Jvfh9AvkWGMUcebbtOyFD75Ho1Z6D2MOHbdXfwlFn66ZH4xlx1TliZsVJoiZhKfDCGs8kfR8viejAMKkQ+BC82RMJ3RMYRihyE46lR6aPBvSXgG+NvcPkwSFhsNOOhUJxGPur7cin/XbTmVNgc0dev63fFidm7Vloend2tnVKoCVu77vne9ghripwz75yZ/YkaM29iBJZ3knnHfO5rjyx3dzwDqb2W4kYPPbzcu05lnkP/3T9F354Ofw/f/vd3gJcqgbiGi5/+/GtnPSu7X7EVAZhsSHNrShDW1oQ3sM7JFA2AAgNZDEApo7aAUqhTR1qNwRKoEFkdLvloxhu4x2lEDKxLQuNw9RBYWCIKQqUiBheVPLo23GWsHS9HszVSi6JAeqpIDNyyIm9HinuwRHcLmY3UA9IRJThTs7BZEEyqxslCRwCtyne42QVzvoohUwylISIuVjVAolDo8frNKxpMU9WOP9FkMSSZgAACAASURBVCrAJyYJiT5/mCBBfd8xYJJLFexar/VWqnq5g8SEtlOC/wBEHGTNN0wBukgEYMLglgXB4XOUi9BM2NOMaleaEtqm8yrZAl5IoWGrSfUQ7voCxBKHA6qjQIUamAhuliEni0CL0LYKbiNu0dhF7Tb0KKHOxCIkVNq3H6U5CmnLNIZs0OdkkRXLvGqv3CyMoFjdTkYC0qimLVLzEG27aMwcoDF8wUOrSdfkyhs0+69+eBNelUhlRdgoBrQvj0Pj1XARcYvlcIMVuKzmJq0IPpe0+UxEk0phtMzhbztFyGkWW7AegFtBwqTKilOAxykVT9YxM0kRiRXOnQQI4WijTJdCS6ML+3jaIPJZ3voR+E4U0u7VBqHwBnaHsAlZb26b1V73d+aavAd0/SDR9CBLZk/2SrByCHeRP+eRrkHbm4XJ372+lJ2b+XsmfmIDSjfbuxeU3t8oJN/Z69UpoHPFlGCZNebxLHrh8ftB2Hk72pjfWIrXZ8eOz2Wduw4fO531OL+zend512MfElcqRdtvotvVEBwGDG0LHucIPaYnW5aX5VwjJIg5R5gigeC2nJYg5q12JTyujU5ECsXbsJndLGUMwfW3qeOhzC08y0UbHktKGmlt4Y0gVjTwkdyDM8v0MN5cI2c1WlhHtUgHULY9BMwIb3L9rR9p+MweVpaAYlFuCQv7eAJRGqV9LqOBGr+7jo7GeO4Q1QtP7KG6YNSmoFlrHHYRwuIwcSQAydKiZsIRBhAhO2dYGbMafN5ax0Bi6q0TCKN9XShDgJ0oy5H6YRXtmMbWbhdQ51rvWkA5IhW2gRVmcy9ch/DohWZxC1RZqkBzusIbO4IkpprquBugndI5cFoas4efgDO1j49Rw2bBFn2bHsEkXoUjjCSqBLKJmQthhF6aFL7XeBsm5D4xdRCnnz0AAFj4+BgA4OOrPiZmKKQpyx/CjcixCJ8eqtQXWF6k85ZOCrFCD17VA0osdyu5Hn81iDMWeKngQQpyyB0jN+oKKBb2kbqDqkfXtjxSh+Sc/bUbNIZhEEBx7bUQCo+ju+7P3w2S6zTW79A3MMnvw7Zz+Ku1F/BnH9PnfMh8UN6798Le3MlvdY4zcwfx7CxNGPNOcCf56gflvK/is9nn/XhtSyc6yIljQFcxY1mdNDvsEwfdDedgXOjiDvLhece9MTxO9eomfH1hdS4bY6DXKvPSefruqfrSjtIghsW/lbM2+z+Vu9Xee+E36cPlfztw+SFLfGhDG9rQhja0v8N2XwhbCDEC4N8BOAnCvv8FgPMA/m8ABwB8DOCXtdarW20nSTVWV0MkcQjJzChtAZFRwOKmFZZTgO0QsrJSCXRp2cTSSLWR2CQ05DgOOozC0jSCI/mzplm/SFNorsm2INFhdFlwXUhJyLmoOZxr66yLUrlahss9uVuSUOAtv4vZgLY74d5CwSVk7XA9teva6LZ41mdXkDK5y4LCkQbVV6dF7oHt2NhfJ0T49PQI9kxQeNyqmC5mEpBGkiyBZmKbjm0ApuaTkF2siwhBSDGKfKg1mtWVPYoQFKsaGKH9C8eFLjI6KFQAj/brMUlvYqID3KIa5pUwwZ07hGCnGIlOewEE95VWcQzB4W+7zYQs24M2qY1CEalL+5LVUQiWTxXMpq6ONbJObbAF5Az16nFu0Tb92xchQXKhjtAQ2hC1PGiWiBVcv444hl4hZriwXew/RLP+o09RROPc7XEsXqR7pja2gkJKt6rHXdBu3+kiTGnckjCCZBLfchCixtGNmmlYJrpIuhRFWHNjVB0mL7KUbTMU6LKSmVWooMv37Mi4i0adQvFz+w8AAFZbPiJOZygpoI2S3kO0B/U8x3GC23cWEYURXK9Xh2xQRT4kuBki3S6Efu+2NULPI/DNiGsGbZu/N+d7x9cfyh9Ud21Q2uefvru+fKdmkOq9IO2tkPurU8gY1Ffx2QyJAqQot50U7Gbh7LzynLF+5rk5rmT27jD8dtYvbWq2dew4SZee/3B+gxLaM/waOV+n98EH6+M7SoPk0Xi+Zn6QGZSfZ4s/++wrAIB33/32A1E9u1+E/X8C+Aut9XEApwD8AMB/A+CvtdZHAfw1/3toQxvao2/D53loQ3uEbdcIWwhRA/BpAP8MALTWEYBICPELAH6CF/uPAL4B4F9vtS2tFMLIhy0BZcp1FJCkprSIMni2m6DMPZddpwTFoCNVVoa8hSmL0XZW3aS0g8Akv7nsxpYWFLfXFMKFxbnaIE1g87KxIhaBiEKE3NSh1XZRciiHXK0TOkjKRVzo0ME0keAwaD3VJOTmWjbWWCxcpF1oJopNesDYONVRhw6Rz6bqFvaNUv53puagWKPPgkumUBoDuFEI9ZpmXbLVEM11Lovi5h6J48DnHtTdtTVMTBMa8Go0hrqkYRUZHqLXDEVEITRHIiyOJtQmRlDeQ1GA2WdOo71Oed/WRzQ7bp1/AyUmTMlyAalpBxmZmvA4I4olfguJ0YxPLUByv+kThHas0RmAS+NQqUEw0iwcohrrtTUL8R3ud10PYDP5T5cakKUa74+ul4htaOYU6JX34E48CwB47iS3KL16GOEt1vxevQmrxPrzgmbpjhJYCCia0PAqWFsnZFVyUixzGZqfsM68KKJe4vGUPaW+iuD7FxpFbhEaxBrrq3RezbVVhF0azwr/lSJEyjnyJE1hi4ebvXqgz7PWiEw5YJjrCcBoe1D+bqvc7/3YZnrmPQQ/uA48Xwpm8peDcp/5SMCgc9iopDae5YoNStwKBRsUPbjlZu+7vH72TmzDei//OoAe6k7w2n0rnCVb5LmBjQS23ewrT0DLN0pRC1uXYxnC4MlGa9PPg6z/+m+GuPN13o3m3ffMj6r5xyEAdwD8nhDiFIDvAfiXAKa01rcAQGt9SwgxeS8bExDQSmSlxNApNJOFLH5ZpWGMmFnk0pMoFEzY1IL2jEQmvRyDJMpOrpAUEVnkEF1h6l3RIy6lEkqYrlOAxaHZ2CVnF+sYMYuGJDpG4rOsJtfn1hrV7FjmVRkuE0j3Vmm5cjdBEtG2Wl2FiIleh+dqEA0KPZc4PFovWxhh5ZSxugM5TcQkPXWUxskZgWZiVdKO0V2k0HCgOxmhyeY+3OFqC9ESEbVmT7+IhBujtFdJRKB18RxGJ2nyUSwC8Jm0JsoQHpMsyrM03m4N0iVnaLt1FGq0XoMFEfSLL0GsfUxDeOG7iM7TAxjeos5kcZxm6QrpaCgmwzl7T8AJKAVRe+UXaPeWB3TIIWvbgajQvuw5GgNvxccCTw6c5lnUR8jhWusfARYx6KWZiIQdSL7mWq9Cr5ITrNo0Fgf2O7iyQi/0qHUHjsXVAJoac9xadJGEdO+0HQc2M+lTpVHm1Ibq8gTIASyXG79IBwlPWkyTM6li2B7dJ04SIu0aKlkAh/uDVxWdS8mJ0A74/hQChjj3EO2BPs+DzDjvfJjc2Hq7/dCcdr/lnWz+80Ci2/7jGRnNvKBvzl/ZEN40E5BffGZ04EvcSKYCH23rqAeFuo0s5xuXZrAfr9Gx5MLC6QLLduYFR/r6Tm8Wlu63nhP/woZj2krsZDObe/kLW/6eF0P5Kve7zofi7etnBzr9QcIqxk4c7EmXAvN3hejz17af6b9dV7W7jmP+yqZOezti24+CdGYDeA7A72itTwPoYAfhMiHEbwohviuE+K4R9xja0Ib2I7MH9jwr/Xjqng9taI+63Q/Cvg7gutb6Lf73H4Me8AUhxDTPxqcBLA5aWWv9uwB+FwBsx9ZCUFtGQzojQUYyixWjEqERm7abIoHgUixPWFBtRlks55jEISLzWccZyoq4TaYWEsKjMHZq+SgxOQsSSDXNfgohhaE1OrC4zlvrBAH35/YDDuuuxLDGCAl7ThXr3ARjlNt7SuHDYUJYJ0wwNUFIYm7PNCxWHasV6fjGrFHsZdKZvW8E2Ps0rdem2eDim99Ce41CtM3gGkYnaNnKiAeb1b00t7EcO/gM9jz1aTrvtauoNPgYj7xE59p9AsG5P6Uxat9C3CEELFpLcFlRrlB+ho5lz6ehG0RIEVYLRjbNNrXyThkoUHjfevEI3JdobErXSKXMf/sriD6mmXHcjQCue+8uXEL1J36GtlWi4xepBRFQWkE3Ra/BSIHGrXZkDgvzFLq7eW0E9hodd7kcQ69SX2mkRAhEuZi1AkWgoNMLdE2KdD321iWWOaIR+0VYZUJcphlLcyVEo07HtdzyMc4ldWv+Ika5eYzPSNm2y1iN6PN4oYiESwyXOlyPrRUqXH6opELMZDRbOvAc0webzrFqF7HOIXFK7Tx0hP3gnmfbHjgDH4Ss80Q0g1oHIe08or1fJD7RGBtIatuMSDZIKc2Y73exZ2LzoMOx43MZahyEFudf/yLOXemlDbZa9lOHAWuKUGseiRukOjd1CvOvfxEAsPjNL214ufeXUE3+eO/zvZSVTf/yRrLbzUtnNw1jDzr2zfpdm+9M+8wzV758F8rebJvmOPrNSIqqhR4B7ShnFF/LNY7J29nVaoawtwuNb9j/LpTyisXSrhH2rh221vq2EGJeCHFMa30ewE8BOMf//VMA/wv//fPttiU0IFMgUhrSomedgoDs8LQRpgAiw5BOFayIQpXacqG4/jXhcLMlAdNMCZaCYhGLlMPGcZJCSJNbBCJJb9JUezAIIbLoAZaphYgT4gnsrOuU5OVC5SPiXLFTKiHiqllhNMVRgORCcYkUx1kopDZZg+KXfY1rcucaRVQnONRafx7z77HoRuevAQDVYw2MW/TCcsY+A6vCAiOFOgQ7EEiafAirloWhk5EGurdJTCRp0zZLjX2wXvw12n7chMtM96h5BeEVesDWLpLD9W5+DdUxcvTu1CvQJRL6kCzrqROd5Z1FuQqUyLHJGcoZl//RaRQvk5hA963/BF/SQxFeegvTdb7mCXcGixQUi7+I4AbAzlOM0YTBrU1g+sgBAMCNUGHt6jwvew3FAuerIw6/q32QKYesW23AJn9jTdK42e46RkZZjtSPoBKuFResgtC1URznc01uI2bN78gXCPj6SpvuqYVugCm+PUN0gTL3Mg84vB9WYNc4pB4HUC1O9VijsCpGl57uo4O1AhbaNB5hImDBcA0ejj3I53kz2yok3p/X2ymjdquQ+k4Z59v18e43s+zM3ObyqACFpU0+ev+lL2bfzRzuLWNN/fo97dOaOpU5wbwzzIeh807dODbjAPMh7mT27JZdwgb1p57L7X/xm1+6ZzGUfN45b/luXLh+93qDJEs3+84ww98dsP87q8sZkz8fEr+zuoyz2HjN8058kG33e94+c8jF1y/3RFR+FDlsAPgXAL4ohHABXAbw66Aw+x8KIX4DwDUAn7/PfQxtaEP74djweR7a0B5huy+HrbV+F8AnBvz0UzveltBwbAmlDUJ2sk5RilnPSgCKEXSsBQoccvZsiQQGYRMySwUAZu9qREhZHlKkJryu4AamaYSNiFGSSBKA+2wnzObWVgQmn8PWadbsKlNdUwm6TOgqqCJKHJK2uKFHpFzYdUJmo+UUtTGe2bXqKNj8PaMDVSzhRkohts5r34FtfRsAMHniNACg8sTfh1ub4XEJIN1CNoaCSXSC67A1NMARC7tWR0VTPXPQJpJXqgVsd4TPsYiUw9ROaQR2jRCotZdIXOsffR/r52lGXZt/GwWPkLU7TZffHnsOssQUkM4CwHXvskxENe2UYVdoBls59ZNYfudNAEDx6EE4B07zdWBKhX8dMuDoiVBAmxuYFCgMLuszqB0iMtzKxatI9rxAx3h9EWmXpudewogCTdiKwuOpjKHBHdhcYmM7uo0RJt5FZy1oDlKkRrJVjCBcJ7RdtQuImbFealSw1CI0PM0NPTr+OhZTlpiNuhhhZbcG14S30wiscgrhREiYuxG0LBSZld+oMotcKRS540dTKnR/CHnhB/k899sgVA1sjroNWh6EtDdD31uF1HdrmzUiMfuoVyo5lNVD2PlOTwb9ffWr34daYOUrJmveT+/mnSikZeQsRtJ5hG1fPzsgZP4r2efNar7N9id//G7CW9761x8UHs8+Xzq7LVrfTo1tK5tojOHsABWBfJrEREzyqHsQkt4MXd8L8v6hh8QfpGkJaE8iTTQsxXKhSKGNw+UXuVCkRAkAluUgYeeb2hqeRU7KMG/TVEFzSNxKBSwWRjHiHK4l4VnkxFPlQnK5V1EKxJyXdZxeDlEYWc3Uhi857Mq65qlW8Dl32Wz6uNyi9S9dp311rRFggl7aU3BwJ2YhjWYCj5noVzlHPtrqYOQm5VmPzK3g6HP/mI5hhJxC3LmAcP0Mj4GAVSLnLh0bls1iJDZ3zXIbWaczjS5klZxnocCyoTIAYERYSCoVACQUUg73WuP0shqtjiPeSyHp7seXsX6Vul3ZP6AIacX9U7gV+r04+ZOwJw/QfhPKt4vARxyTk9RWBXZMKQK3tgoRsTIxM9O1W4VuEvsdUQfosj44S3UibMKZponE3uf34wYNB5prLyFeomtSVaQFXsICwJrcotKAZrnQeJVyW9KNUSrTvZGIEPDXeLc8gXNGYXRBRSlFsEoP2sSeKax26BhDdqapcoEO339aI2CxnbESi/04Baxy6kSoCNqkeuw2Qk5TrIOuYdmRqJbpfFd8AUs8fOGUB2VCCLieu6GkK//5QdrDLAcD6OVtmMU3sl+rmGj0ljUOYLXWc9gfvNWTwjQ618eOz93VSrPfmd2PA9+JTf74r/z/7L1brGXXdab3zTnXWnvvcz9Vdep+ZYksqkiJFCXrQkuW5JbklltpWU6cTqAkhuGOEcNGA+mX7ocgfshLPwQIkO6gAwNJ2jaMKLbRbSdCK5LckCXLsuXYlEhRFEtUsUhWsci6njqXfVtrzTnzMMece5/Dfepci66S9gAKZ9e+rNu+jDn+8Y//fwtMDkOyomsg88dHQuaxX75+GzFGJfph4ZNRiXuY3f5c9Qju2rfWvH6UMMtexOPzK3xtRCJP7ZT50QvOjba1WewUEh9Lk45jHOMYxzjG8QDEfVFhR9KZ0hm1zCPjHDpWFVJY6SyjyFW6zylZtZclRSYkJqm0+3RpCrvYofBFqHJaCV5XCbbNS0tX2No5Bq/C6rjshcuja48XgwdMRSZQppUS3ueeXMowY3usaIFYbYCbNWAy8YKuPe0oFzqhqXSonMt+qMZu+C5GRFieXZlh/lKYBz4s5iBnT9acPBOqiuMPv4O5ZiBE5a1ZlI6GFyJpqbokmEFrlIjD6ELMMpRP3tledahdqB591UH1hXAnx+cnDlKcCBW6OXCcxnzYbvd6mLO+/dpN/LXA0J5bfpmJawGyLuZF0GDiBEpm2W0Oi7dCBTyVt5m7+ddhXzOh6lY9j5d5ZHX7EkqLcUopBOXWz6BkFn7i7LvYvxK29eLr87TNBwGo2mKQoi/SVOEaZnYFmkKSE9KbKhx5P6yIpyf30V0Ky+xaRHMs0BdXt1zn9KpwvsZPMisEtVI+hy1jmGiIr/nsLBNx5joP592crFG3A9Tf6yqy+Flv1LTkOivpA/X78xzUgTh3Wa1S2kh1f3BifZU96vHhiDDhcNU8DI2/XXPaexnDcqff/3aXfyuV99lz4VyfmHuRyTOfAXYugLJZbARpp6p5qKqNTlfAGonP9ZD53URR1jtsDd+3/jYEuD5W2ebQEwm+f+HSM2mGfX2lvVcRK+jFUz/NwvxaEZ21xMO7V81bgcHj9nYjUTqusMcxjnGMYxzjeADivqiwvYfKQmY8RS4zsWU3zU4rmfk1yoWGN+C9Qcazqcoa15JGY0NGhLrQ74ZqRhuVpEfbNnoT13ipLhvWoKSMXyYnF8KRnhSyU5GhhbRWegtKxnmkeJ3MNLnYdt6pHKUJlW5TyGc5BldJT1grcOG48jJDSUXWL8JznVFMiJpW5Qp67XCMV/uhsnv21ddp/odwLPunv8c7zwVy2BMfOMeZR8MKfWImVJFZXofGfzgLfC294CxU/i4zaGHO+drjvfRXWRbpM1DRUMRMpPdB5Ycwj7w33J4Jq3c9dZDezaD6s7x4laXroeot3vijcExTR7HTQVq0N/XOZB4ycyDDroQedi4SpdTTqOVwrL7bBpnJpivGLf5rKJHwVCc+wNxjwSrw4OrXuVCJLehN+Zx0M/bpMM7W9EsoGXkjDxV2oY9hVOAMmEaH8lp4Xa8r/eWiQZaHCrtT9ilkVtB3uyjxXhe1UXJlOCiyscf2H2fhTCC7Tcgcd6ffpvFKqPZfvfIDKnk/yrbD1+HcujZ8NhYWjvLassz5FwrffXDESKI0adEoUhW93ghkVMS+3kbV9KhZ7O1UK9up0Lcz1hVjI4LaqLh4IRjSXAT49h8C8HtfgF/8gNj8nvnMPau4Ye1YGLzV5zq78vyafnGstmMMV+L18cfXWGrGfvZV3hqjet1XGZDizKEnUrU9eeZoMiWJlfZ3r3zrrn3sYW/v4fj6c6MRqs1sXGPVfPTEmTVEwhjR8GPYcnWUV/rj8ytrxgsfaNJZSCYVDpNkSFv5JJUkTi9JxaGJyGCWeXw9EEMp5buYixxk19Ugt2ufYUVwJYuktUrhbTj9Pg4v2tamyKlF0MLIbLcyCiVgRB9DQ+Dv6ADWcE0yL8xs1ycX7WpdROg8x8v8t1VNcll0uK5jOcKpcmI1E3QkodumoiFe0XUR7ptwmrYQ4G52b/Py34TE943nXuTssZCIP/TBkBgffc+j7D8USGl50cLX0S9bFjLMBhY2YLRBiTi7yxZwOs7DS4LzJcjcutEGP31Yjid88Ix7BabCvspD51i9chGAV18KX4Ts2uvM3PoaAJeu/TXHHhcy2/s+iq3E53rlB+G51TyqH85brV6FOiRnnLBC7HWcLLzwJeb0RwA4cPwkr/5t+OLcFPKWvZ2Td2XBZ3qYiXC8WSu2U0BHf/BshZ4s7EoRv6krx6S0U6pyBSfthpKSpsjZemk7LMwf4NjpsGg5ffo0x04HKdX9J8MXvV/3mH4xtA3cXxlevRzOt+9v0xNCnZeF4eVbN1npKzkWR+a3Tnq5X2I4SW+WrGG0m9dGca8SNbw1WW8kqDIq4g//VhP3+oiQOd/+Q575UkiCT33614GdQ+bbIbKtT9wxYoKOiXlNAr/yZ+nmMFS+VeZ2duX5tL9hePzDTy/w5RG6JOv9ttdva7MYFki523v7+PxKkh499+iJ1LoYjt6dH8lxfzWJqIya+w9s88H9Y9LZOMYxjnGMYxw/xnGfVNgKZTTOO3oyKtU0DTE9EBg5hVTbFpBqxNeeqheqRx0NPQArz1Xeo8SHWGtRH3M1lcx8Kw9KvKtNVVHJOJeqQ9WceUtf1BZb1GgZE4ojZirP6VoxCnElSAXsxJ1KlUAmRDNfk8ksr8kMdCJ6ECLDpsocW9ORUatGGfZZomJXAK+gQty4rOG5l8LK8/svh9nth7/2XX7242HF++SHP0RTVnhGrgG6FU4e8LoAFaBbbSpcZPrVojKmLF5IgM6WcQQdL2Nl+ZGKycUw382tNs194bkHToaq4Nqbih+8FvY/e+QMZ3/uH4XzOgC1OG9Vt8O+dN1Brca5+V5S5VQyN4+v4eYr4T7XJX4m5o69j4eeOg3AN/+fMHZWFieYF/WzmfL75GLYYqWS9r0+TkbqTH+VWohktUDiFk+/IcpzpWZKyGoahxHDl/lG+Jw0pmaYmg6r/tkDx5hcOARANh1U7CZaTR5rBuJeu32D5eVwLLc6UNcB9m/IKOMdOjSj/KvWeBONQh6suJu62UbR7Xa2XBVvBRrfzXz2KKOQ7VTaG8VWKvBIWPv+FwJk/syXbu662h6Ou3lrrzcPiRGr2vUQ+ac+/5vp9qjXbRbDMPowPB5duP6XfxUF9k4MuZ/tLIYh640qa4DFmZ9i3+GgETF55h3p8VHXvn3pxfR+Lcy/+JYqe6/83e+ThC15Q6skQ6q8SxKgTu4zGlLCdnVKIJW3GEmCLekJazJqSf5Ku5TkYoJDW0ycg9UkyFvrOmXPuE7o2SrB2LXxNCV5O+ln3um36Yslk9OaXKDSKLNqdD+5iNl8ilxY5pWtKCSp9wSrb5kWfRW21VAWI4mzI5KsTe+pRR4zR+GEVdxVDqXijG94/JlXerzyO38OwGev9fj43z8HwFwz/nh6lByL8xVID9u5FVkRDa6BUzkq9ru1JXlxRhvNXKOmZYHVvYVZDQkom5ZjWprg2PvDnPZTn/klmgdDf9f1ruNUgJJKK1+erEPuZGbbdzBiU6l1zNyTqEIOrL2Kf00YpL7kyMmwjzPnwjZfuWrpEGfzb0AvMK+NWGOSV9CX965yeFkUrFbhvGbzjL6IuLi8Ryaz/1NK0RQrzamZ0IqYnF/g+MHwpZ3fN5/ub+QyB648xVxgr585/36u3QrXpt3r8UYVWhtRK7+jSDKp3ltcds+1xO95bCd5bzXJ7oRxuxnjfLMf11EOX9uNUQn92KlH10Dq6+H1rzx3k+8vheTdvtRKyWwrkPeoee+N2OPrYzOY+VOf/827Jv/txHp4PMba/nG8/UP2KkYxvB+bvcm5ufg+veMtj8MgeX+Tz3BW4PH55f8vCa7sVaKOMYbExzGOcYxjHON4AOK+qbBRGo8jVtAem8hmWjBR53REyfHeY32sAn1U4ExVc2ZsMvGolU9s6aZAzJUjQKsE567oh91ymjqSrwSaNsak6lIbRRZnwauwzZXlLk4o61mWUcfKX5ZDha1SIZpVilrK1tr4JMeZiaRl1/RpChSvNNR5qMJMEatyg5bqT2UaRbwGFuR8I2Q/oRV9qbp/eOFHPPlTQZp0dn80L+njo/6rrwmOiqKKpiMJSs7bVXgflef6iV3u5BqqwpAJo7wx1cH1oqpZgJ+mu9Oc+lCwCJpYOIIVRKS8fYWVN8NKWQksbHwDbwTxMH2UyNFGOrZ2S3gh6SnbhFIgrmsvMSHz+I+eD2QR726QbTJm2AAAIABJREFUi6ua6xyiNuKnLhK2OYZavLlLaxFiP1X8HOmKQtopVjeZFg+ORgNOHXsIgNZE2OfM3GFm58KKu9AtnJAPK3GIK/KCsheucXNqhpOPhNffuXWDq7ffkGsf2zGWdkR6MvBuvLbeScxOTb2lCt9O1b7Zc4eNQrZrGnK32KgCj3Hhxcupwt4sRlW9u62Enzx+IMmXrq/wt0o22yjWwONCgovn+qd/+syutr0+hivr9d7WGxHNNoqn3xOq8N+78FOw+Nb3bzfz1zHGvwLjGMc4xjGOcTwAcd9U2F4FDWJjBspcpB62kMu8R0mJ7fGoaOiBhjhfXYWK0nqFiJqhlEbHqlleg1U4IahZ6/HSI+5ah437kN6pyjVKtMadyah6MjomClmlrXHiCFLbnFzMQ5wX+06jKZSUZlmNymUcLDdMxGpcKtbCFGQy4J17TSUVrpHRodpYSulxK6+S/rfyBiPz4RGFUBqmRDfcTc/z/PfCqNXkdDjXhWMnUXHWXSkQQlWmc1TUxxaFH6/6iVOglAPhAngxL9F6Dqo4511QzAXCVa8Xtn/iqfex8FCo8HU2Q1dWoLcufBPVC9swjTAqVk9MYopg9FGaVVwhFeqizIb3lkHsL1W2DyXKbSxdxxsxKMlCB+yIvkMpc9Rm8gjeBmKbq0NFa7tXkqFMbjN8S+ahpWqvnGdaFMu8UuxvZHINGxw9HNTcpiZDVT01O09L5rCtr+jL3HhPPptT2YHEj1hdXmKqGc7n2MkTXHgpCKL3RMs8s6JEB1ilcerBXFuP6lcPq58Nz2nf7TU7jeGqZrPKeSOjkfXEto2q7t1W18NV9WaktN7hT6Zq8xOfCPdt1Msevn87lfXwiNf6qrk+/vjI7Q6bigw/dydV95p+thzL+TPFGv/wncR632sYPWe9UXX9zW+F36YPP72Qbg/H2XPv2ZR0uNO4fxK2cyitI2kZrRRWfuiyaJXlwUXmt9IpM/kh8lVZR4GTOhGmtDf4CKvL9mtXo4XQ5XRNbiNUbqlNSIhFNPzQoE30QS5Z7EaCWTwsS1wd+AxyER0pxNEqzzR5JvKWZgJdxMSXkakobymJlwGU36trVC2CKsloRJNHo5OsjxehDUwD0wjbsALVl7bG9sOi4bvfucjVl14BYN9MSFpzB/bREKcp74uQ4QGlGgleN3Gh4ZfxShy0MNhEBFTpPq/CF6HbnuXWG+F1TXEAO3D2feTi3FWu3GT1RhCOyOYKCnMqHHcnEMJWl6+hp8J5FwceIusEQlbtA5zM4hxGxFT80gpKFi04B+JelgkxcGFWs9oT0/pujrEzcp3DNajqLnU/zvs7loV0louRSquYohKBlIYCU4ZzPDZ7ikP7A0N+RljgemIOK62XRqtFMS0/CMIiNw1DLouLst9l6ba4gE3Pc/hkWGC8vHxdjqtMfus2U5j6wTH/2E5sZgqyFzDi+hhOuBttf9RMeHzdXsHed4thEZaNfvwvyt/zZ0IyPLEF8tlOk/d6KdL1ZiCJGT5CsnS3EPma7ROSNjDSN3srERP1RlKim8HgwyzxRDr71g2+9Z0fpfvje3dj8S92dpAbxIO5bB/HOMYxjnGM4ycs7psKW3mF0ipVwNYO4G+bDKjT2DCh3A6P64xEgqprgRGdS2enlAOxmYzQssv7aJEmVU7HmTFKb8mEtFX7SEYyNOIoVqdPFaeLZISsWbSSfGVRtJgQqFNlIrWZO7Qca7cscX0xArFZguozOdc6tzSqWPn3IA8Vqs9kHI0GVtZZNY44pNxQNUfmw34fOhzgaGc1t24GyOb6jSXuLIZq5o3LAWjq9xbJG7Gyn8THShWf5rmUFziYCRQC9bsyyZhG5MIDOg+IwvLqMd68EsYZ3vl0gMGbswsJjq6Xf0hzStoNC0dxy7KtyNKzOd6H6jWbKcmbolomam9Kl7jbco07b2LEfhMdYGsAlYf9N8wcfTkHlVkyuZ6If3ld9rFVqKZ6NuPm1VA9tVqharaNJl1hDGbLNzkwF85x375pJqcDOlFMiK1p3sAKidCYjMzIzL+gM0rDzGzY/kOPvpPbbwZ7zvadZR46fRaAi68G9TNf9hnU1IZaP3jmH/drrB/rGlVNH144mG6/HRX13eJT7x4yEJFZ3+Gq+4VLYVb46Nlnt6Vqtp1qO8LQo0bJtlKpjzIL2UrlvZPqfHhGfL0k6ePzKyOlQ7dCMLvb7PuHn15YU2GfPRfek/g+/djNYXul8NaRSeJ0zuJc9GqO+uEOraJk5sC/2StD7Qc+2AC1tThJvN7UZAJVRhjb4VOPG+1w0ckKjxLms+2FZNQ3FgrpD5sMHdnp0vd2xuLkuPpVn7IrAiDx5IxPGui1VkzID7jJDEaSoBKWudUZWvrCNteYKAcqcqkuK7HCxs68SpB1rhVWINTGXLjv4eNHmWqFeeeq0+HmtQDBHlsIP0xmaP5c4ZIrGuR4H6HKyNpXiSXufCf1s+N7g7Mg8GaO4fQT7wfg0COPy+t7yYs8m5hATR4LL88m8YJYZ0og77pJbzUkzNkzDfxqmKk2U+GHs/ZvgOjEZ3YBXwUo3S/fCcL0gJoOf/PGFA0TnlvZDNWIffB43LelJw+9XpM77ZCEXR6eVykw8qHZb2oOzAVI/cChoxQTIXm7uBg0UEjrw6uBcE5kz2unUPKhm963n7NPvBuA73zjm0ybCdluOO8by13quKBUA47ET1qM0lzeqazjcGIehsI32952fmzvRXLvHf4kEJy9PidwcEzSMAQR7zC2knDjPjZaEIzyy45xdd3zIrwdk/heQObbibsxw+8Ww73rURFZ4sOJO+5rlNf2TmIMiY9jHOMYxzjG8QDEfVNhKxzeezxC6HLgpapQccjax3pP/kpllOuMSqqRKppCOFBSBVpClTL0SpxzeKmUjTGJ6OWtSipePdlm4TVllInMDK4KFVstHta+X2G8iPYrjRbHsUyq5lwVZBEq9QXEylyrdA6R2V3YnE4c2vYKLypdyrZkm4ZoU+axZMJ012jevBmOa/kvXwHg1QN3OPdIWPE/9ugxPnI+rADn9gcFrtbkkUTYcr5C+UjMK4nldpoLTk0HADPE1pdDrZbp3wmEqcl9R5k9+1PhmaJI5mqPK0MlrIsWKjspl0vjxanMzwVpU3+j4vrFACXNnnyK1qGwLZbDeZnD06DDate9dh23GPsoHtOXWfLpcN6qMUVWh8qq7HepRZo0Or151U3kxHZvnnomVLio0M6obZuWEP5mihZzRwIyMDd3JKEeEfLGDD5nde1QnfCZyKSqr8sKIzPhulUwLx7nh84+xGsvhSrjzKlAPrv42pt0K2mdYJN3+4MWm5HKNoq7uRl1u52RVfFwBb2d14967vB96/f1dvlyryebXRy6PXnmM3zo7FqLju3A4VuJYV/rUdveDmktVdFnH38rYW3U8zaInTLOY6z3rY7M8O3MW28Uw5V3rLJTBf/yhV1vH8YV9jjGMY5xjGMcD0TcPxW2B6c01sUetkNLpZkqbQY2l+DJjJhwKE0tWt7JMESp0JcFamcTiSrKUZNBUYttorPUYrrgvU/63dHqs3IeI1rgtYeqjlVxtOxQ+FimKYdzMgueSd8Hj43z1K6fRtC8zVDSX81cOJeGGuh7K68SicqLDadyDXK57bM8jRE1tU4G3ZWc99Xry1y/HponLz73Op/4aBg1+Pg//Fg4lqyFd5HMVuNFFc37CpyowEXkADc4X7s0cD6RPq3rXgcVyBxzpz9CoyXjYlLdKgfeyuy076KUjELlM0lxjmkx0ZifRM0EpbSLf/MS5z8WvLf1TNAJ1/46/rCQwlZ6uKgPXs6Al9GzqAZnWmRT0le+vUjZCyNiqgh/LXkc4afdadCQCnu2Gc7b3i4olsL25w/Pc+BosMzMTIEy0eM8qrJl1IKeaAeImloVDUe0wUWSXtewfDu8N7nJaObhek0UgTDoydGRiKhA/QT0sLdrEAJrq9+d9ra3u6+7aZGv73fvtKc9rCU+rCk+bL/ZFu/sn/v8r2x5uzuZw95LItv1r38hKaSt3w/A9S1Uz9vpfb+1R33pLo9tLYZnr4er6lEz2Xsd90nC9njl0X7gV92vLVE1M+Ku2uiURLVSNARqtCokWgiJOr4m+hyjghsYBHIWQOmgLEPiq7GJrORQKFkIxMSttcPkwhQ2GWV3LXtda5NmuoOHSUxmkvi9TyIwKIcTEpJyVUrkXmbNa0qaUQbVGzJhkUeo1dpANgMotEkJu/JlkvP0gvcWjYxj+0MSPDmXc/SgwPqySMCvEgVQNH18PF/vkuQoAsn7ugtC7MOtQGwx1CtyrWbJT3woXOPmBK4vRLCY2H2NlvlxX1uooqRpkRJ91gzn0jpSs3A2vAevvlDy+t8G563j7w1CJap1BNUL8Ls5eBwjrHvd93iBr2NiVN7io5CNC/K2AFqY+FpNUInTWu00hwXy7r35DQAm8jmKKZklP3WeqenAHtbNFkUhn7/oyqZNaitordPCTQu6qiYmKWWRWKicphAZZ6daLM+GNoXz0SCjhV8J8L6FtMh70GKUMMpGsRlMvdFzt/qancZuFgI7lSvdaPb6sdkBA/r8mbM7Pq6txG4h9vj6owxg7xculZwfgtrXx24h7+F4afHEXR/frvToTiISBhfmB+/bbrQFxpD4OMYxjnGMYxwPQOyqwlZK/bfAPyYUlt8DfgU4AnwB2Ac8A/yXfjAjNDI8QSgsN1DXofLSGpyPMKAQlzyJKNZsNlMFXpblwBREnqs0idCllUapKHkqc8/OYSdCtTPlC+pYxlc11sVxMYG+tcYJwSjPWygVVvLp8PCJtKZQydBDCTxaOYu8HOfAlIIGZBWVHE8hkHhXlRRx1As38Ksm7LOPxcvokC01LVH0KnKYEhvHqSLs/+g+xVOPh9Gjx556Bwsy61tMynyw76KIHtOeZDytbVrJ1REvph+gcgDdR9cB/vE2VMd+5kmMzC776lqC0r2Tc1EKzKScVyO1K1RdpuusxY+7MXuefY+eBuDm9R/wwouh2piZCnDWvocncaJops0STE3L9S7BCQmvCtWp6iwmKdjaGpbuhHOYlHFsnZXpc2byWU6dDJXuavNdANy68jrehAppYvZI8hIv8mYiDxr5bBmdY2TmmyzDmYHfOoCpfFLRUy3N/KGDcr4T1BE+74bjnluY5eYd8S+vFVbvTo5xK7FX3+fh2CrpbBgO32mlvJOqe3ZqKj1nJ97d9zLWy5M+NnszkaS2avyx09hKdb1VS01z6Ikhq8znh6RFN660N4rsyvNrZrrjzHX06V4f6+VG9zqGR71GjXsNj3jtRew4YSuljgH/BDjvve8qpf4A+M+Anwf+J+/9F5RS/yvwq8C/3nSDHsIo9IDxG52ifISY1WBOWxudRFIUOvVco+44ZMkD2+BRApXGj0qmVOoReqPJRYc7LxxGh2TQjz3bXpXY63XdSyzy6GhllEoscKfByesKScbGGHT02yZDx/u9S9rSlcxbe+dRTo4yV/RjPzvqh6NwVhYE2QqTIi169uwkZw8GiPbsQkjSpx85xoHTpwFo7T+GilZUVjS/vQMlvTitE+St0KCiC1h06CpS39r5FbwJGc9MPh0eb5yEOnx5vO8Mrm1c1fjwTgCovEFabbk+igjRyxumHVoWUwcemue1KwEKf+G7IXG/L/s6xVyQM1W6wAtkTa5BRFDoSLKsuph+6FeXvZLVKiTkAkn49bXkhNWYadG9HL5gx8+HL197cj/Xr4lHdVmnxZjDps+JEpZ7OMcoo1uTSyKPLHiynFzaNN3FRSAci62r5DY3OT2brpGXO7U1GHVvu1d7/n3eQdwLSLvVmnhLv3kjffF7lahjX3s70Pin3n2Ac4+21tx3/szZHfWVtxM72e52kvtR4IVLQQM9Ju7syhfWJOHNRFa2Cpk/PH+ZC9JZiIn73KMnuPDi5S29fisxLE06KmHH2exvfGVv9rdbSDwDWkqpDJgA3gB+Fvgjefx3gF/Y5T7GMY5xvD0x/j6PYxz3cex42e69f10p9T8CrwFd4CvA3wJ3vE+MpSvAsc22pVDBRML7oVlf1jDCAXCQi3OSQSWykHUOLwpSWWSDZxYnrzeqSN7aSuDuSlm0FeUtMipB+XzeoiFQ51QuVdjELEpMNpYWb2Pl9Iz4M+dGp4pSZQYt+HdTTCMsFuWiOllOJrCoUzqph7V8lEYFZfry3BY9gVUzuaIZHlOEY23UmpYYcrzr2BE+9rOBTT1/MrAf86kZVJrfVfhIjoruZ85BNFZRjTRzjXNpPjuqyWE8uFgBGVQzzEaricCaVnYJ7ztxT8FAJBylbPMOvgqVrtdNdDYvm5pKxbZ2q4Pnij/53LFDHD4YJDxffvk0AMevX+XE1ECqFYFCvSnQK/JZEaUy5fp4gceL3DAxK1KvIl1q+8uQHQFg4dQC3/l6gN2L6YAWNI9+iiOTct3ulCythGM8dGAfRUuMRprha1R1KpJurdG46HUus/LKK2oxp6mrDmVb5Gpdl9vdcG1Wy3ANTx84yOWXXgKgZ6vkinavYi+/z9uJzdy87va8ncZ684/1UPhGJLO3Y/46zgife/Q9b4G971VVvdNtD0Phd3v9esj8fFJr232bZz0UPixLqg89DYuhmnbXvpXu2wwmH66at/K8GKOeHyHxY6ce3RMjkN1A4vPAZ4EzwB3gD4FPj3jqyHkUpdSvAb8GCMwYYFgvicsrn5p/ka2tsjxJYXb7XcoqjnKZAQQb5Uqdw0dmtTMYExnjETJXZALXem2xojGuTIV2InRhJZPkFY2p0G+c1irZZpZ24BwW5UTzTJML5N0QNnjflOiY5DFpVAs8tfSQ48/RhLLU8rbkHorIYJbjtw6MJAWvaxZmAyR+/t0Pc+ihAB3TEog4m0wILa6LEi1vInsenfr8oFAiFoIqE9taJVvHEmRRo5sfQjXOySnIGItvJzEV5bO0EEkvVxNExjnVKl7aGWRHUDr8QDoXiQg1mZEF1LRiciEksbluEFu5tFhwcCnISDRnF/HCvFZM4ablx1ZWOLZ9FZdHLfGMVkvsNZ0Ir/gaMxHO+/DcDMeeDBD/8mpYJExOTdBshONaeeMVpmbCQqPZmqQhWuLdtuiSV/00PmiKPAmyRA3cWtukCe7qkpWlyJQ3VKvhHJeuh+M6vXCEv4y9cVOSy1jhvYq9/T5vn9G+Ua/7XsHUo+DpjSD5vUzUW4XHh+VG72Wi3qvtx6Q8altvuU9Y4pGh/dylL3J+k+1vZ5QrJvH6eAGE5Byh8ac+OGCFty99cc3rtpqoY2xnjOvsuffw3e+GhD07NbVjpvhuIPFPAJe89zd8YCP9W+BpYE4gNYDjrBWySeG9/23v/fu89+/Tavtf8HGMYxx7Gnv4fR4Pn4xjHPcidsNkeQ34oFJqggCh/T3gb4CvAf8JgVn6y8CfbL4pj9IWaj8kgOkT8zvLorlCRlmJL7QdwLVaQSSBm/hb4Uya2c6MRrvoKpWeQB2rbUsiAykLPXGiygXSzmlghK42NbmPXGDmlRthtaxyj45al7pA61gBh7uafhIjMHvtLVqgdO8dCzLL3ZVqP1cttFRhStdMINCuVGnzE/ChcwGVnN+nmDsUVuonHz2DmgnQ7nClSvLZVpD2G0VFsiHpUTuYs1agTJRqjSvBaZh6Wi7yNCp6U8fxchqoKLxiGqTfeEEuUC3Sz75nMMddLeJlLtw5YXZTpVn1zpXvUN/8XrhGVwNZ5Wbz/dy8HVbDx482UUoQhdU7qDK+z3J8aJx8ZmrbYmlRzFTSvHNGc38w9Oi6FZS0XGoXruXE1CyVSNDOz+4HgeqtclSxKhTI2+sBq7/qVek9t/J5KlccLoufE2gLvF5nCief1TiXn9cTnDwaKoUfXLxK0wv6ce9iD7/Pd4/hqnm30qURvt5IrhQGxLJRlfJGle69gr9H7W/Yjevco9F5a7Qc6F7GXs1Zb8YSt9eeXfPcWCWvXPkzQKDroYjEumEP7I1iGAJfH2E/QWzpXrPFtxLxvb+xeGvHn68dL4W9998mkFGeIYyAaOC3gX8G/FOl1I+A/cD/ttN9jGMc43h7Yvx9Hsc47v/Y1ayI9/63gN9ad/fLwPu3vTHr8RqUjzPMA6vMWCX2yj7OxnEjlWrx2rlUbTuppJVSGBnF6tss+WD7qOyliyS1mWtNLHwqSCM2ifJW1TgZG1ud0LRmw9zvtOzrzvU7dGLFqHpkUq3X0ofVOqMphea+ZoMsC/vtWoWX49K5qJPVPvWYvYYTs6Gy+th7w+rsY5/5EAff9Zgc2ZCy1sQBavbJuce3tUNUMkNrlNiNaumLh+Jaev9aJYlQxWB0TemwMvWtdyZ5VONW8aJwFp+HyvAm9Pm9qlFEKVXpRbs+gznvAi9MM4UFIZvFcbd+12JFyUzrCWZPB2LbvsUXwlldeZZXp8N9R7sd1KSMV80u4G8JYitvh3WKWlCMpaVlXnlJ5vinQv/5zLuPcfDJ/wiA2ytTLP8w+FEfOv9OAI6dOcf1S6+F7R87yMFHQh/duRzfFEKd+HkrrdK8vtEKL0RF1wsHU3ZL+sK70IWi6oXzrrIMJVKqk2KzaRUc3x/O8Uev3KLrBoYF9yr29Pu8hdhKdb0ZAW247zxKrnSjGO4lr696l1ZXt9xj3KxSWr/tSCqLspif+/RZTjz9+S3tazdxtx7zbmMr2xyuwmM/+kmZw/7D710m9pqHe/fDVpwbRVQze3g+kMuePH4gSZ9evfg8F74UuC5PffrXgdCnjr3nyTOfuavH9V5HnKu/sfgXO+5h3xfSpF5msB02SYt65yka4QexFjjY1w4lP+reM1Ck8DpJgxoVJUZNEknRCrwkXMRZyVHTkB9HvKKO7KzaJji27oufcW5YFQjYVxYvEGwxGV6vWrehLdrTgBNIXAmxSpuCSs7rVl0zNx9eVxSWwoa3oBBIdCKvOSDa15/7zJN89LMfBqB1WOaOG1NpHjpcg8hANknXOy5wvJpKHtRemwHkrQMEjKtTi0C5PqkHUdX4IswPkoe/nqUgSQr4cpHIPYpscKXygZSsbkH0Ih9ySVMqtgo0iWZne1BLci8Cocsv3wGZXS4OzTE9ERLm2Tz4RzenbuBcOIeO28+USJqqaiW9zjUi+dBTdsP231jq8qPVcO2P+XA933XgPVhZVKxWt+j0w3ktnHwICAzvGdEXV2aKWq53f7lH3hF4XdohuqGxIoBSd0q8kAMrYYb3qz49YaxX7YqeQPVMTAblGyATIlu51OPo0dD6yJ7rQf/+EPPYTWxHpvRur98odiIhOiwdGv8ura7edSZ8eD/rdcVHQd4xSX/8kz+TmN/3Gu4ejmFI+u3YV4zN9hkT97lq8HnYjojKd6/c5Orl+PsSFkCPf/DslqD09fF26ICfPRfaHZF8tpMYs0PGMY5xjGMc43gA4r6osJUikLacGYxyZRn9OhpPxFGsOLQVhLKiepjRKo2SxIrRazcwBCFL80WxQtd4aoEssUXyrla6xmXiPe2l0q7KNBPe6tdkdXBZ0s1QEU41D9HrhCqwdgP1srgeUl4lT+/au6hoiclzMlEtOz0d/v6Dj3+Aj/ynnwBg5sw5MFNyXuJVTRsl0HVg1wspjXzIO1sgWqVDZY1U4MlmWwh2Kg9qZ4CnhEqg8uwcFMfl/qiK1gUZ9fJ1B1OIWYVU614XA/nXujOY6RYFOaV6iYCm0BARC60GamtyfMWhczj/ilyvVUwjnG82E6rQyXnD0pW/AuD2tf1Mzp+V4+oDwXTEVeG4XVkho80sdSbpToT3rPGOgBy4mbNkB0JVdP2VP6cUouHMvtD2qIynX8lMeKtF/2aAskyepTn8rrRGTO3SvHzlPLWOt8PnrOqv0hWjkm6/jyDldG4vhZFFoDUV4P22WWJe1OSOzB/n4mrwCn+QY6eV9VZjO5D4cKwftRqumHeqvvZ3XVUPx9u5343GuoYr7/WksvNnikREu3qxGEk622icK7YWhkll8fV/+qfPbHq8e1VZty99kWfvPLr5E9m5gxvcJwkbpdBGYRSp3+jqGmcj3BH7nTr9qCs8ViBWhQ/JHsikGZ3pAUu81oMet2IwA60lwTSKRnKnynROVYUf2J7odDe1x9UB+u2oNj3R/c4iDD/kdIUbwPaROW6VReXhvpbWZM2QZM/NGD710+FN/vQ/+hwA02efBNHU9vjUV4bVdCl8TIxeJ01up0pUhLzlPq+K9LhXWTrzOE/tfIXq35a3oImfkN64niYQhcH78MOj6zIIrcgZuyjIYoZgygjJ61ZaVKjIzncFzgoLvC5xkatgNFZmuWMrIm++C8Xr4b7uSlpUWBuONZs2NGflHMwbLL4amKLTBw9jq/AcKwuNsu6y3AsLkWUO0DwQoPCD54Lwy6vPPs+Vi18F4HbvAHMHDgPQFLnTKxcvsU9c4bJMU/koxqPpVF05x3B8vdrj+zI332+zUobPUUfaJVWvwgrLvNvvstIJ93d6Jau3ZOKgERJ26+QhmpMhcRw/fYpLb+ydnOKDFNuZw94sUd+NLQ5vtcbcasQf4MfnV/j4JwPs+XedpN/OWA+Dr2eNr78Gd4OssyvPDyw2R0iUDsewG1ec6X7h0heT9CkM3LK2O2O9lRie416frC9e+M6a/0c4HHYnojKGxMcxjnGMYxzjeADivqiwvfdUlUOjsC5WhCpVwDGUGph7KK0pom+1NomIFRXNvFKJBJXnBh2FteTxrJElEw2jwMulUL5C5eLIFGenjUFH5rWz1EJBzqUS7/R7ib1ujE8z4VpHJTWFEnh+ugn/1UcDhPsf//I/4MAjwUPaMSN/M7TM7aKqpIrmIusaBRHGVjpA2en6yO1IemMgxRqGoCNEL1V7uYLKQkWpikdQkbDn28SKXtdBMhNbgfhZK7M/mX+gYwVkk4qcsjawv2HIUKQFWazQO/hSDDmW3kzEOVoo92CpAAAgAElEQVSBZV71b4MKj9eqR7RFaxThWvQbOW05rVs3r9ARJTOfueQx3Zf3uecqlutwn2vNMzd3CID9+wPkfeinfp6//KPfBeDazds88t4PyjFKa6VXs1wKQ7sxRZGJp7dyQUcWsMLwVnVFJW2Djq+ppLLv3Qkw/cqdJZz4qtvMU5fi0FWWGGG6227YV/flmumHAvHt+P6D2J+gtfVOyGXD6lEbKUltZ/Z1FBS+0X7jHPVv/Oav/kRU1OtjuKreDulss9gIBh/MXp9IFXSMn/v8r/Dl3/8/ALjw4mWe/vTe+12vV0gDeGLuxVRlN9/8Ko/Nrn38+xe+k6rsx2Zv8t0d7vsn51dgHOMYxzjGMY4HOO6LChsfxm8cLlXYmSlIo0OJxaXQetD7JBl9GJyJ41zhuU3TAlH8qpXHyONGym7bqzFZ2FZFPrDyNEXyo7ZSvWoDporGGSYdT1/0nW1lkw1l7VyyRVSpxw1NOZb/5hc/xH/xT/9x2FdpcVZIYyaaZXSTCljYT1+OW8aVqIlDxgo76Evj0tjUoJIeGtXyJAvSqG2tGo+AVMoei/cBMfD+DjpafMbXqAKfxQplQA70aZZLJRRD4Qf3S1WOB+XlHI0DUSKrVntUqy8D0DwSjUrA5+FxU7k0OuYmxWxlxuMboW+4fOUVFqvQe2wvtTlyKpDltIzsdVWLJRduzx48jRfS2YW//fcA3HipR9uFY1wqCxbOB/Sj3Qn7nz8wi++Ga9CYmMKIklp7eQVXRx9tUUfLHF4q+0bWSuNcWkbNzGRjMF5o6wFq4/v0eoKgNMOx1l5x9ZVgRLJwbIFm8ZMt37vV/vT62ztRlNrOjOznP3qOf/I//Pfb3sePc8Qe9dENHl8/ujXc016vXLaRzzUEotmzd8LtWPV++dLo596rsa2NiGbfX3rrcce+9tlznwQu7Gh/90XC9nistWijQyIGNHaQptM8tR+4SKkMYyLzOw9uX0AWQQMPVbRX1oPkaV1M8vkAXleD+W8qjc8GRh0QHMBUIyZ3hReos9tZjrsiFyIZzmF8JKuFu/q141NPnwbgH/7Xv0S9Ksmw6sL+KP8iydZZahs+hbbuUnYDnNq9fk2OO0O1JOFmMcmHc4wJwMisudFuwKTPm5giyG2q5ml5VYO4ILB0UD7s17huXOugjLDAXZ3Y55gcXJQ0jUQ3M5B9VRbE4zp6TSufrSXmqXC9pk+8C08QI6llwaAoqW24tkq1kgiLFsZ5MWuYOBzO4daNJVZeC1/41euv0F0JrztwKrDAi8mD6MkAg7vuJAvHQwtg9c1wDX/0g6/TF+GWU+efopTWRkNmQ002wYGHAnx+582VJKDTbE1QR6MPoisXWIHEu71OWhBOzoTXN+bn0vZv3rpGeym8tzovaEkbpu5FuVNPV5juncUep46Ga/TsMxv8Ij3gsR4G38lM9W5jOFFvtv9feHeYzf+N3/zsjmDgzeQ8N4v7DXofPp6NEvVGcfTs40mgXldlctaCQQIfTtzDUqbRb7p9Kbh7DHtdbyRHulsCWiS4QRDbh4ErFwSi29nwM/MW8lmMnTLFx5D4OMYxjnGMYxwPQNwXFbYiVsGkGWan/MCuMcpnAkpGpbyyyeRC4xMxiSzOZg/5APoMGyFxWaI4A8aGVb3OcrJogtHKKGV/LQJUaoomXkbMWjV0pBK0UWbSgC/Cc4tmi0yg0PS47vPODwaZyfZyhx99+88BOPfpv095M0hwtlfDSnLp+lVuXH0l3L5xi2uvhPnbV16+LMfaIG+JKUlrEsQYRWeeohUq7tmJUB1MT2fMzAVI8ODJ93LgaKgKpudCtd5qNckbUYe1i4rjXrrAi9WmUwPIm1jpupp4dX2csx4emFM5qFCZq2gLWXXBiTlIvZyIebXPUQKPW3tNtuXQ0pbwdIiuIZkRa0smmJkK41uzxx6hvxJe3+v1uHNTZuuzsGZfOHsCNRfW/P26xdXLVwBYuRWu92o1QSb2micefZyjRwKk3l8Ns/aGIn3mtHM4US9TRSOtdss6jGeV/X4iknW7HerYRmmJXWtVpibG5PQslViyLi93aIgmQGyNmFWHk5nwm51bLMi42Y9rlP1ypOTodirtrcLfC/P7R45wDRPNNtvvxz/5M+l2nPf9uc//SrpvuIKOkO8Ll8pUAV69PEBK4izxRjGqUpw8c/RtldW81zGAyZ/n+WtvfXxUpf3snUchVbZC+GJQYb/d0qOjIhLNhivtixe+k2RK4Rvb2t59kbBTb9p7XErOOvWrYybQWqX/OK8SCztAsFrul/6yB5UNuXkJNOsExnQ1yMMUkFi43tbJm7on+5/qWbIs/FAvri7R7oYEkScpzizBonmWowvpQwp03jQN/uRLzwHwvb+6gm6EY3z85iq3LgcI9+r1NwC49lqHN5YCNF26jDoed2z/qjYmypHmqxhxcVKZCgbaQCPKnCpF1ghv8ezEaxzc/zUATp0KcMzpd+zjyKnwgT5ybIEDBwJLO5tcSNc5apV75VGpXz3oYceFkk+vkL/xuXHRpXO8QM/YHt6GH0evJ5JcbCbCKk6ZgdVZnaX5b59kazs0psNz9y/M074xJfs4T6cTEvntOBK+mtMXWL9je9y5GgRufDccS69zm3Pv/ggAD73znSDMf1cGuHriwEnuXAqLpv5Sm1xY4mWvRy0z5slBrt+jkgVK3esnxnjiQugsebt7XZA3wry9zvpYEenJhL9QNSoaZYDSu1Wb2eY9d+v6iYnhZD1KhnQ4WW+0CPiXfxB+gL/21bU/uOsT8vOL05vPd7886GeOgkq/8txbHamOnerye18It3/xA610f4RrP3T26t+JHOleRIS8h6HxGM9Vj6z5f/PNr77lOetnr2GtlvrbIUO6Ps6ee8+G8Ph2YgyJj2Mc4xjHOMbxAMR9UmEHtrEmzEQDoDxOYNcsFWsKo6Nv8AAFd86lgizep7G4KGmqMzIflbnCn8yAtnFWt5+kIZV1FHH2Waqlfl6jK3EM65fYSEjKo8SnHUh8lj55Kk+1wt+q1+bVi4FAdJGb5DIn/f/++QW83J6YFbjX5/RdeF1pKkwWCWRCNPN5MjXBg5ZZcaVJpC4jaEDfeHoya95u3+FNQQaeeyVU862/cMyI3Oe5h+Z46qmgdHbu3e/k2KlAcipaMlDoDYkYhx8ozkXinndDoLgJKm0wgMF9H++78rBDRRxDNfE2nkOAfb26AaXIoJKjs1DxuPjmKY+Sz0Fzumbh7DkAVpcqJtqhmulcDyhFt2rQdqGC6qwuU3VDBV33xNilNceZd5xK54Cw45vTYXXeb3fpCks8yxug5TPRq1JLxss1sLaijtvv9+n2BeaOrnOmoI7weuYppH0zPzeXUJtrb4QWScNZtFi8GWeYyOb4cYzNZq53Ao2vN+S4FxGr5j++PCCq/fFzf5xuD/a/M/W0zeL1V19Mt//lq4P7j50K37FnZm9y7tEA1X/iE0+9bdX2KKWz7USAxoU1fuZjvHBJ2nQjqu3hiKzsx2ZvJiLa+uPabYyav95MjjRW1Y/N3hR2+MZEtK3EfZGwldJkpol1fQaZQCVpUa0G2TjCo1qZlCCUGsh3OzcYqXI6/NB6Z/GS8LQ8s2Ub1EqSiasGY0rK0ZeEXYgoiNOeXkfEM2wtGt5hLxCcxuKIV+ZW8CJ8YqbCj4wuDJUkIGvA6yj0oaKTJiZqghtPN/7Ao5PYiC7FslOX1HI9WhTUsi+NxdooNBP1xRWZLAgyr/EiYuKEXb6Kob8aHr/1N3f4zrMB3nvnQ8/ysY+/C4B3fzB80BeOnsLrIc1vP5S8EXnYBJlbvPTDk1yq74Ecq0en1keWz1D35Hx1gIBVvZK0yH2uB71xG29VAwZ/I6M5OyfXs09rLsDMeSPA4G++3OXGcuhH088pZMys3w/v5+ETx3EifNK9dY08CpjIAm/52iq6HvitJnVWDFbg81o02G1dJ5jcViW2HZJNZIs3JwsK4RyUtUstnyxrUEvybzTDcVXLy+QT0pJp5Bj14Lt17SbuBlvH2M5oV3x8lEBKt9tJ299oO9sZ/dpMEnU4RsHn22EUx0T+OgMo/cKLl9ckb3h7WOZxH7tJ4Mlu88zHgMADuFuSXC+ksj52CoePStaTZz7D0yOeCwPWeOxhf//CdxJz/LHZm5se50YxhsTHMY5xjGMc43gA4r6osFEenXm0a1ALUUdp8AIzx8rNA6WIhmRDM9neMYBgpfqtapOqVzXMkZI1SsdVGDH3cK7G2Dw9N4t+0lJFdpe7lGWoopyxaS68luNzfmAE4r3HJ8nJAE+ZvJGWRrauo6Ilhc6phbbeLYUEVTtcrErrID8KojUCoApMFDUxBiUMaucHhLxa2OkNW6ClulO5YkLg9b5A+pXKMeL/nANtqRj/+sWaK28EN6z3/zCQaD76iQ/w8JPvC88t5vAyT6zlWMO1ijPXPkmxph4FPpmWONfDSQWuOYQTEl10McNrlMDgCpf8sn0tgjHWpzlxqy1a3g9X9VkU9nftQrVuKWjHSjefpi+z5C2RMD1+6gzTUlXXq7dpTQfhFNsTL+tOl8zHufmMKs6ik6W5dyeSsJnOKPsB2q57fYjz+lEid9IwL4z0JQaokLYu6QhMihf6ap3TXgnvx9zcHDYKrvyYxbCD13aMPnYTm/ld38v9xrjXkP1wfOW5m2tY6QCf+MS9q7L3crsREh+OSDQbrlIfmw3f+6eG4PBvfuvGPWWJty99cc1M9t1imHQWofGdxLjCHsc4xjGOcYzjAYj7o8L24Kwlz7LU37W2n0Z7bFIkU6mHXTkVlUlBDRS9Yj2nIZl7KHySLHUqPs+RCYlKZ00KqU47dQ1igajqcF+3u0otcp55IyMXgloV56yVRsul9BqqeBCyOKy9S33OfrcTrbnpm2YyDYmVdOkUrSJCAx7lYx9fEACl0/WoXIkyA5TBiTyqkarXek9DHi+ynOnpULUWq6EKXKp7dOWKld6QWuC6x5vL4dy+/PUgG3rlyhI//9nwuvd9+KdpNKMCWqw4GRrxcgnp8KnSDvKz4XaZyIMenbyrlZDlUCTCoSLDRsKdWJQ6o7C9QCpTtaUlpiFubpJOL1RpVtrWrneHqi0GJllFKZaXxx4Os68nH3mIQvbbqwv2TYZ+eKcrva7uSvrMuKqmjrPqdS/1s52gQvv2T5DpUCEv37mVPnNaZGW7qw5bioqdVjipKAtTYMSaNIJKyhsmZkVhbaXD4UPr3AQegCgaxbY8sO/23O1W37GCXVpdfUu/eaN++GZV96he9NtRqa/3644RZ3mHCWgbxfOL4bP0vIyjXXjxMp/7dCB3nXj683t2rLD7Ma+10qWDGfb1MTzStdOe8G5jo972qBieyR6229xO3BcJ2+Ox3qGsJRfmta0VydfZDchlkYhmfdTVDnB31K5WcR5b1wlKVzZDCd7qhBSkDIlsVGcaJzCz9Q4vP7qRAe3wQZwlHGwiROk8emxrch3nlj2RGxWPL/OKQuDo0mRYGx2s+niZPa5jsgKcOGwZo9A2zu2Gx8KcrpDWnBv4eJuBbHhyvq4GvG6VlywLcS4y5lW9QtMII906Yj7NS0uVWN7hh/IHL92g+r/+Qo6h5L0feZ/cjgInegB/e0talSTt7A6IOAjYdI1xBh2105O39jTYm+naKzlGLy0Q276e9NDzbAYrIi9ZM6M1HVcdYSExcdAzsxS2f+dWh7wVHj/y0GkAWtOT1Kuy0MlbrNwWJnt0X1N12n+/26crrRFva2bnww93hNcPTCmaspB5/eWruL4kYeJnrsAK4a/WoOMCp64p4uy8wPvdzCSyna36g8XOAxTDYijbSdwbbStG0Sg2JaFthxS20TZ2E9sRYdlpbCVRbxRfee5mYlb/d/z+jpL2Xs9f3y0i+WxU4gZGMsN3Gx86e5W/vLg1odVn7zzKE3M7fz+2GmNIfBzjGMc4xjGOByDuiwo7SpOiB25bWmVYqXpj5Vg7n6BUhUpzsGifFM50rHSVJotGIkonow9bRggamS0GXVnK+LoMnJCb4ry1dy7JpOIGamrGxKp+4OClNOgonyrbtHWdVkZZo4jTTeEVsi0p6Mh0lkamnGsM1N7EZKPEo4gIgKMW3L3wBYWLsLxsU5XUVagO2yuabi+s+psCkze8pzEbXtPv9jC1VLB1jhdCnhUZ1grDq1eCitiXv/RXTLTCPs4/EWa3tZki6Y36muQnHQl0rg0+4NSeHpoDco4kwxdUfL8n0S76g7fTPLyrBRnwYIp98t7M4+S4Z/cfo6yDruGbb/wtAF1b05gUqVZgcn+Az0+L13Sus0QO1FS4djjH5nSo2nuTDbpi1lI7m+a/cxRGDDuiotnrt1bpShumaLawMutdivNX2V1Oc/N6aopMkAG0SxKwmWx/ujXFqhi/tJo5ZRlNVn5y426w+EbQ9HB1u1fw9XqTkLttdzfVdYTAIyR+Y/HWjk0jRkWs0P/dlw7wOX4f2Do8/nZU1+bQE8lIJMq/wkCqNYyrrZVt/fDTC2tGt+LtnZDPtlpdx9gqAW03cV8kbE/oYWeZSclQ4dKPY5SkDC3a2Nsk9a2VSuqWRBhdKZ36pxabBmiNJBWtNTpqkzoHVXi81+/LfsI2IPQYYx9Va5VkSLVsS2XFUP+2TklWC029Kksq6VHmOqMpPbCqX9Grw5c9MaQ1yWLU6Bovb1EtfVDlLLUKSbRpDDpaiNY1dewbR/a7LulLnz2nSHrqVhKc0prljvRvrcPI/HdDeTJ5jjciFOLBy30vXrzGt7/2fQCOHQuJc9+Rh5OdqUclMkGcorZYvMDUSjdAdMHr9hLayMJIXmPyBZSWL1jvVby9Jdc5/FipZhtlwlCjsvuYOhTdvposXQjzj3euB0j92pUS1w8/9I88+S4OHw29632N8EM6OT3JfDP0rZdu3SKX2f3VqyFx50WLO3cW5XpqdB2nEEq6i6E33pD389r11STfmjcbsC8sOrTYtpVlnTgYrtumF28XOU4Scr8TPg8KmMxFdnYyo+o+mAl7t1B4jK30sEf1opfu3NnRtkbFcF981H7X73u3MWome7dz2qPi6uVL/Lsvhdu/cXYg4bmduNvzhx/bLNGv384gaYaEPezG9dSnfz3dHk7Iw25cu3XmijF55jMj+9Uxnph7kW9tUQ9lp/1r2AIkrpT635VS15VSzw/dt08p9VWl1Evyd17uV0qp/1kp9SOl1HNKqad2fGTjGMc49jzG3+dxjOPBja1U2P8G+FfA7w7d98+B/+C9/xdKqX8u//9nwKeBh+XfB4B/LX83DWUMVW3JdJyBVolAFgk3SiWuEUaBlorRa4U2cipSqVpvyeR1VpHUySKMjWIwW13XSR7TeotJ7lCidIZDR9aX9ugoSRoZ3N6mqtq5BpF1ZoVBXdc1Kg6CK00hkqVZpiiXpeqMpiauImdwLnWsvE08fp2kWm09OF+Uw+qwuo/VsdYGJwQzS5YcyawgB32fUfcGynC5VIdVlmGECBZJa6UaVNurFl6/Gqr8a5eDlOb+ww+ndgbOo4TVHlEOpZooI/Ka2TS1yK/a/suohvQI8gBXazVPLYQ99ARKB+U2F5Vm9Xzy6VateV595SIALz37TEIsZg8HudJu/2aq/I+ePMbBg8Ebu9USwtjcbCI12rpH+0qoppUMvvdXaohz9ThMfG+tZUUMQnQ8rnYXV8brptKbl4uTm5koEuO88jZ9/uqVDrW0YSqpsJe7bfafDHBfuXyb6Yk9Iy79G96G7/NuY30VvN7Na1TE6najqn63c94bEdnuxhofVk0bta3dzGPvFTz+/OI0RwVZjs5iJ7ZRYW+ngt7ONoZh8Ag3n2NtlXs3qHs9PL5ZxAo67utuFfVWY6+Z7JtW2N77bwC31939WeB35PbvAL8wdP/v+hB/BcwppY7s+ijHMY5x7EmMv8/jGMeDGzvtYR/y3r8B4L1/Qyl1UO4/BkOGpHBF7ntj/QaUUr8G/BqEvnCmNBafRou88ygT+8ZSdeNST1QpldTNNApXR9JXOKVcDx5XKJz4WfdiBeQHo1rWerJoyqA0xgx644AQ1mT7djBo7Uy09FQUUnllylNLE7wuZWbc1jQEAQhcN9GN9g2MDytylyrwIo1whQo+ktJk7KyERh5nvJoYmdnOnCPPQgXhTVRHM0S3kwqHFU3sSOZzytOspL/aUqy42P/v4lSoajPxf/ZFC+T9mM01FGEbZezD+oEhiPceJ+cVDT+Ur9A66Hx7PYvtxkHpJbwVzXUfHu/3bmNLeb1dStaT8cOx2u5y+YU/A+DWYpf2nTCuttJbYXo+rLgbouM+PzOHkwuqKsv0ZNiHjWN2vXayvJycb5ITeuuVfJ5uXbzK9Eyo5m23ixH/8HqpRyZISynVeGd5Bd8WXfGyTCYwaipcy0ZzgqwQdAWFlvfpzuISEyYgFj3C9crKCi2fibmJGfJmHPK6J7Gn32eld3+ssUqOVfGoivvtiNmpqZFz3Hs1qrUbo5LN+tnDj9+tGn98fgU4sOHjw7FRBT3q/u30wS9/K5DeNhrb2mm1u74C36ji3o5q2XAMv+YJOcbhEa8Lb257k3eNvSadjfqmjhwg9d7/NvDbAHmeeW0M3jpccm4KtDIIs9EgJiAxo2s1lLz1IKnL45UrsYKhOj8EzUYYWycAF60VWswVcjNwdo7wg8mbiRGl85wqJt8hIltXWNxFpgasZzkW7XySCAWVGOHWqgG7XVYH1jui90ffe1ryuqwlCTTT5LIo8V5jErHOpP3F81beYYRxXmuXpFabYoBRKshzuU9ndEycK+9hbJwPD9s01jEp533+sRkePxbg7RkbNVc7iSDnXY2vQxHnowGLmcTJ47Z3C9ePRDKLF/jcRqer9g9AiGi95T66GZLoaj/8sL38wgu8+fJVuV4tai1fcqeo2+HiLS6Gb8rMwUPMHgg/Ru3lPisrIbm3inDcK2+uUnXCD7LJNI2Th+S4wuOH3vUQNy6GnNWsOqwsBcZ3v9NNMrrujXCObqVH1Q8J12YO1xWovyP31RmNuSCAYuanyGfDe9rCcWp/ELY4PBeO/403O7xxPSxqjp0/xevX9vibv7XY4fc599sVTRmOUdD1Xm5rODZLvDuZ514foxzH3g5p0q1C5kdPnHkL23p93A3q3k4Sv3rx+XXCKGuh7+EYxQKHu7Ox1yfk9Qn7bjD5ZouCYcIbBLb6MIyejus7PxoJ4V948avpdZu5fG0UO53DvhahMfl7Xe6/Agxf4ePA1R3uYxzjGMfbE+Pv8zjG8QDETivs/xv4ZeBfyN8/Gbr/N5VSXyCQU5Yi1Ha38B4qG2Q4B+QsqAXGjrPXrnbJqlAplWajnbXUtchxRljX+yFI25MnWD2y1vRg9EjncWYMpwxeRaOFcHk0fo0HtYlWnDqOVJUJvrdK46rojxyVvaCKdpMuS6/v1qvEg0giYdpTNEIF3NQ5RXNCHg/H1Pc2bV85hbehglBa4bNoUDKkuiZrstwW6CzC8iJXqhS+CDtuK0VTTrvMDTbafsp1ySipqlApLt0wnPhoUBY6cv6UnGstw/SCPLh47vFaeXyUmm0vJZ9s3ZjFi/FKb/mHYV/uBtaExzvdVcoqVKK3hei2uGhhMlSqrqqphLi3eP0mM9Ohsp+fOwbA9PQpjh4OVXOuNa/9TbAQbU2E6za1/xhKmIz9FU/XhkpWz4SKl/p2IukVTUXvB2GV7bzDdWUUUAhjvlxNhMO67ahE4jbOylfdFfrtgBbo9jz5fBiJm5icoTElrY0ivDen/JsUbfmc9Grm9+3d/O2I2OPvs99RRbxXqmj3KoYJZaOq5o2q8berst4NCS0qicXq1157dlN7zK3aZ0Yi20b/Xx/DlWy8HSvt4er6Q2ev8uXf/+Ka44dPrdnWZqSzu1XV6yvqUY/H44p2mrBWdW2YwBafu9PqGraQsJVS/yfwMeCAUuoK8FuEL/YfKKV+FXgN+CV5+r8Hfh74EdABfmUrB6G0plm06HaWUxJWMNAKZ0ioRMWEDFVMjL5GqUFClQNPrloGnxJ1etir5MplvcfoIelRHXWwRbCDDE1k/zYQJBQtcHFuBv7Odd9SizhLzPtaO7Swop2q0+tt6bCSvBsif2laOTr243HJh9tJRs+VGeihG4PVIVk1dI6T88miLKhwAwAajRwlwioD1VEX2MxA5fMEjzeUJkuQePgi1A5ymUGuy5pp8ZvOpsJfyhrvpB/vuijXlisdFh/eGup+nLdWmDz0kqv2IsQ+t74lx9JjZTV80bq9Ba4JNHzzRkim/R4oExKqsR1KYcK39s8zOSsypf2QuO3Vy7ROPAzA/OEFqjsheS9efDXsq1NTNMOPaG2hWYbjnpC2xfWbXQ6fOQ7A6rU3OP7+8GV78Znv49riqby8IudVoaTfbrWnjL2NRJ5fSi2CloY6Sdwe4kYniOGcPh6+1Lf7bQ60wvXsN6dYqUUydZfxtnyfldq2jjg8GIk6xmaJeiOo/W4Q+3aS+V4KqAQnr+BSN5yEN0vE203UMTbqU4+KmOTctW8B0Gatp/fkmaOyzZi4v8Kr65L2+hiezR5OqJsl6FERX/PEmhz8VpnU82eKdN5Pv+cdO+7Jb5qwvff/+QYP/b0Rz/XAb+zoSMYxjnHc8xh/n8cxjgc37gulM7zH1TWGDCvln/M2VYc6Gn64egA3qzzRuI02qWqMr1d6UI1rclSEvBMMnqFkgDZXBU7gy8x7MR6BotWQ/Wq8FsMOX6NE0tQL4cpnOnl3224V4GEAqdoNmjwXIps29FZlrrcukzJWhE9Vzw/Mr22eyGrRe1mbAsR0pEENkZSmsoFDSPIPd8mEw9c1PpNrkKD+nCyS3Qz4hiAS1QS1HIMSApu2FZOtsK3Hzx9i4ZggBrVU0rZCy1yyYgmfNWVf4rCFxotXtOX/b+9bYyXLrvK+tfc5VXUfdR/9fk5Pz3imm5nBxn13os8AACAASURBVAM4xCQOCBkZy2Dy+AGKFCsgoSBQgqJIgJAS/vCDIJBIlICIQJCIR4ICimXJwiPkxEqwDfbYM56xZ5jp6Znp5723+z7q3nqc1175sdbedbq66t66j+5bPTqfNHOrT53HPqfq1Dpr7W9931qYdnB5BmbJrNNU/ra3NtDdkpL3aquB20vvAgCSZXXoik8hPqb9zBlgNGulrIt5zTxOLEj30ZkzF7F4WsvbUY7Fs7J89rg8mae3b6CxICX39moLS2vyFLzUvgEAWF5aQTSnfdRHp5B05Bzb6y3Uoax8ragkaS9I45rpenCD89/J2dMnsHhOCNi1qVnYaTnHVpbizk2ZNj4+IwpucS3C+l2pLNTdLI6dlPJ5hZ0xilE+vyBEyWGlbWB4Vrwb5bKdSubDsB+G+EFl2d//0Y/cUwp/WBgsd2+H577n+wBIyb7MPv+7Tyql4knJur945UxYdvPKK/dl2+2rn8H/xf2ktXJ5ezeZdnns5XK9L5EPMyW5l5H+m2MfC6jMPypUqFChQoVHAhORYTNE+Ul8rSVzM2TCvHPujYfBofc1L1wgasEhmGjE2nrUb84CmIvQ1hXMNFx//taaPPgQO+sQaQabqOlEzdSReatOcoh8W5ftq5NlPW1DypLQP+vbr2pxFAhwWZ6iyHy/cxF00lmJT1EEOG/i0ahjVp+pcv9oZSy48GSmIsxbpyYH6Tw5+fl6lwf1sbhOiHryPqxmxa6Ohp6XiRqhlStpuHBu05rZc2yxeES2f/rpJnQKGk7nVilrwylhC9aAnc5da7ucY4LLJTvlbBmJljrSNEM318y6dUf/WmROyFm33r2DzWXhOWVGshFbW8fmO6Lj3evdARkho504ewI1SZbRVqLAyvI3Mb0gbV1T0dOItQ86T2SbjR6QLKliWc1hWrPtm+/Iuay+fR1fvKqZwDPP4tT7hMz25Pu/Da/9v5fl2tZ8Rpeik8i4bKfmOXiwMzLPf/TsMZx6QjLoBBFaXpGuDZhYxvPmW9cBAM89dxpPzmjr2QajxSXf8QnHXklnB4lyH7fPuMtZ77iZ9aCt57D1d5OFHyTpbL+64tJ/XSZs7R3D5quj6/1l+TnJ4L95Nb0vgx2V0Zaz1/689yvBEMSe/MBQ4pvXH7+A/vH9nPHrr10DXvutsH+f6T7/Qz+NFz/7W9ue47Dx7dS77Y/7zsVP4JmLnwvL39nVkfqYjIDtS+JEwRs5c33zD88StzYK/dKNunowA8jzAnkuP3iBJV7yZ7aGglSmF9vMjUHkyWVFBqs9yJFthH5la3xAzr1mCGxkQKGRWr5ESeECAY5g+mVRJWy5OA4+3Ek3QRyIdVzy71YCHJlwvnFukPiear2nbJYj1weCzFDo3ybOYYOIiU4FWELk+9o7FpES1FhdplJnkNb6nuPOa2x2U0x5xq4+QE1THUUs6252M3S6EvSnIKVjokSY9wBgpkMpnFXWM09Xkadb+nnl6GnPdWvzGrbUoKHTlnPpuMewck3kRpONVbhEpwDmZf+dzS4cC6ls6+4t1NTII50/DqtOZsmWuow15nFnSYLg9HIK05Nz3NyQwLry7jr0OQAZAWvvitvX3Y4ytLsJTEPOYXlzDe/8xesAgGPnzuG7f/TvAwDaLfnhXLt+G00N+N07Od59VUr5Jy/ID8jCmaNY35AHnK20hkKZ6swF5o9Lufbqt4Qpf+1mjHNayUe2id6NTbzXsF2P9E4BfxijvLxsL85e5eXD9rubfY3CIOlsUJhlvwHdB/FxAvfa3HcDkGB45sm9H3MwWPtA/fXr8gBuTn4Yr3/2ynj7unb1nn8PK5f7450Zsqx9td/T/U0AQClQD4EP0pcunw9mIqMIYcMY64Nyph6+FO4NQT48sK+9ks6qkniFChUqVKjwCIA41JUPD1EU8WyzCSIXfKUdTCCbkRYCnMmDpaaJDBrajjNtG0gSyR43U8lE8qwXauIGccjWvSUmyIWSObFBwV6CM0Lkcj8uOW5hg31nTgzWzDrv+awacJq5k40Qqy1izXqyXIrcS3waCmVwuAL1SIltXl3NMuqamRdsAxltmr3EKBCprGbEcTiHggyM9wVVgpwzERh+XYBJSXa6fcPWUVdfz6Q2G6oXzmWY1hYv4+09bQ0LU1IHP3cswoe/UzLJD32nlK5PnZpDFKnhR54jT3U6IxViVZbX0e4u6WezgXRTMoGN7hpWWnVdV0rXd6+to9eRti5OGUfPSFtWj2S91u3X0O1Khjxdb2L6uKSiC/VpHD0m45k/Icdly8jUYjTajDFthLzVuSFZfbsDdLSknm0RWEvPd9QDO4ma6GxJNm+nZtA8J5mwubuGaEvGsHhSSt6z588BmeyrNn8EyaZcu8XTMqZ2r4uVu5JNXb+xhNmm7Gvh9BxWbko2kq5LxlavMd7/nDylT2c3sHxNKhqf+Le//lVm/i5MMKIo4ubc7jLFWr02dml6cLthGJQ2HQc7HWOYfec42KuM6aCd54Nq+zp74TL+0d+R+3mwH9sjZLWl5eXMulz+fjl7+p5thymEDb7vM+sz5y8OXa9cti+3hfnlO7WKjcqwvSFH2aRjJxJcOZtul+RIPT6w8NrQXmufdV/A58J4f/Rnf3NX9/NElMQB0dwgskFcIzL95N+XeGNEITCCgVTnIalGIGU4xyw/6i53IYARjHg0A9LADS0taPB2pgjzviYvwHpsX/lGkYK8BGhfVjzIoDIRbKzSoZYDYzzXcnJsIsBX5F2B1EuWmhoy9iVpFTOJLFQDBiZi1PR8CtIeZhgU3lmM8iDYEjsDZt+H7bW3c1jjBWG4b+ylc9Uucujp3Hmc95Cr9rVFiixRJrkvz3OBzS0Jsm8kDVxbkmv/+jflx+TD3zuLZ5+RH6Yp00BXq3yJlsQ7vQ56qQTJXt5FpyejaW/V0NqQc9zaUrZ31MDUyW+Tc0kYqc4Bb12TmZ92ewWNRVk2f+IYjNXjzs5iXfu3C+UhRMZgZlqvcZRjY0nmw9NrwsBeSw3ixzQIT6Vo3dU5+UIeTvKig/pR+aGMazO4/TUt2dkMVifM65Fs376+ikZPzvf4xRnMqXzrVkvmyG+vrSPRnvEjzSbWVmUMSzevI9MS/TGVUcW8xYtfFz3i599/BvUp39f+3sFgr/a4zOpx9z0uhgXq7bYfnAPfDvs5l8FAPag7vl0g3w2L/MY7r+E/6qTqD75fvn+XLqdD57aHzVVL8JEg7Zb+Cm+sTd23zjAGtn9989rVkYH63mPcD7/c92m/sXb+nu23Y3z3Tn30nkC9E4a5eA0LzK+/dg1XdOrr2Xl5EL90+Txe/Kwcqz0GK34UqpJ4hQoVKlSo8AhgYjJsVgKW07KtJSD3Rh1eCpSK0GPsXBGy8aSXIKp7gprsz1oKfdIgBmuZ29h62B4l9bDcl6QNwak/MqwS1FyBnidvOQRvbF9aJmv9sFAUBeC8VKWqoxGH96VyoGS3HKEX3DuEucKBNB3P8zzImOZBKrQI75PJg4+2zB7IeLqeEc8GvkE9snHoyfZypTV2yIzvp45gIdMJheFAkvN5eU51kKb+ORu4npzbF1+XfS3dbaO9LhnlM5fm0N2Q7ba0b7njNtFTJn2SF+huylg3tnpod+XkrJKwpq0BcnlKX8s30LnyquxrTbJn54DGMSl59zYcmkpG2+x1kWiW3tXy+9zcFOaPSimqMXcUK29Kf2RbfafZGawlUrJuLs4hvaWKYmlLr1WCYlWyeXNyGsefFJb45s01kDLOXUNK8oYc2vL1QvvWGqCGHZsrOk2DaTROSh/23IkFLGoVIF8r0NTX82dEhcnOTeOvv/K/AQCPn1rAwhEZ43sJ5cy2TBQru2GVM9TtiGjj+GXvtJ+dyut7wX6cvQZL4qPeH4VhLHKPUdn3516WjPDmtavARz8CYHQ52me1gGS2gp2zx3JmPe66Hpcunx86nv7xx8eo7NpnzeXS9gcWXruXae5x6v4Mu3fqo3gWL4TXsk9g9doXwjnsFRMRsBkMVzA4yoLOtSNCTed68zDNHgVWNBsXNJrZOIC1/UhFLJi5r20N9HXHfTAtaYJnENEWWdmA/EstHQOuP59uONgi+mCYplnf6pMQgqiXHY3B8J1pictBvofMREFmtPAylWRR6BhrBkidCqr4hwh/EKhkqvPlcQnmQL+dzBGC81dEUWhX41SuW7dnYTTAGDhkiew3jghTNQmI+swBStuA2nqmRY5CH1pmnJdkZazfleMurxisLsmN39IyemYcUv2MUq4h7Uiw21rqwGm5v65zzY5y3FkTtnRSs1i5oc5cq1IWrh9fRGNJyuvmRANFSwJ5Z/kW6vMSPGfmZfwN1HDtqpSWozwGWnq9VPQkoxQN/c6sLreRNpS57a9lNoeetmrNF8dxRufIF779SWzqD2m9Kcd0vRw31uRBYHWL0L0uJe+8o/PaRyKwTuMk66to6BQEJxmMWniub8g+G0mCun7/Nm5v4ejxB6ol/lAwyOweJXBSq9fuKzkPsrb9vw+ifWxc685xjjUs6A9rJxsniA8rie9lPhvYm5RpPzCdvyc4ewwLkuUgXC5zf/6FL2x7rPIc9qhSuUf5oWG7kvdOAii9Ux8NNpj3Mr7lAb+tvxuABPFxy+eN2y+EQF2GZ+XvB1VJvEKFChUqVHgEMBEZNoFgowhsCFaz04jJGx8heFkwBblPQwaWfXpoQ+bsM1IuADJ9xrmXJvXapGRtkAV1DOTsZUoZIcXW7SNbD6QzQxQy6Fxdmmzk4JShxo6DZGrk/UTIhrGwHFzPywQDEvISogxAs7s8N4isNyLxJXuD2JPaCoNct7dM/ccv/Vtni8LLkToHTvUaqOwoWwqMd67VgmuVS3MwJIOtx8KqnolnkUPLxSbD2aYc5H0X1V3q3CxsLNf+yhtX0NFMMlUxl5QMYCSL7LWATBnW3bQLqwz6rCsZaZKsI88lA9nsdNBblszaaL+1SbpY39KKR7OJow35HtiYkGk2W0xLRjE1fQ71afk8k9YmchLGd9KT82scOYXZs+qZu9bC1oawxDd4Rq9Firpm69GUxcxTsm4tquHEohB0CjWGaW92sPH6G3JedzeRZpoJzEvWFS3MoD4rY33rxdeC7GtcI9SOZHoOcr3Onb2AtYuy/yNHm+D80RFOKWNYxjmq9DyuD/ZOme5OPdQ7lc736uW90373Ux4H9tejvVN/thdR8RntU4tdmJN9442dSs7DytujSt6ffyu9Zyz+2IMYll3vBpcun98xyy4zvgd7qWcufgKNkpjKqxvH7nnfE8oA4MrrXyu9cwzY+No961y6fB6NsH5FOqtQoUKFChXe05iIDBsAwITYEQrNRHNjA8EsMl5ulMWbGmIh6eeImTlkzn56mMAyiQsAhsMctM9YC1eAnTfU6BuFZK6An5suNNNtGArzxjkXYvMJoD6tGT5HyDL1487TvvkH980fLHsf775HBxkKA6ZAsItAmrHlZJB7+0xt1YoAsLa2udxAXTlBQVssdJ3BgQHf5sYUvMatSqvGGaFQ4wybIfSaE9fhdN20WNVrP4WFae13niZcfr88JZ8/IfvvtlO0tJc4shEK3Vmm18Khhq1Nmb/rbGzCJUpwyxgwsrzoyjZbKbC+pqpoPYdaQwhoNZ2Ddy0OY826CZJpyVrs/ClE/ipoy1UnL1BoZcGlWVDCy7X/fe7CWajXC7jXDkYi7U05l9nGHCKdo45NDcvX5XqcPXsc63dkncXjQiS7tbKKmt5RebuH1HcgapvedFGgtSbb99I20k25BnVLSJekr4acZPbzC01c+qC0yizONmDTR6+tq5yp7qbNarfHAEZn1WUy2k5jGZX97meefLAFbC+ta3udtx6GUZl2P7OWjPSNtfPAWn8OezuMQx7zWfUwvLLWDFn2qHnrcbFbQlfZpGNQteyvvvammgOPhp/XXmwNvwY3pZAo8+XKT5u5+AlcwOeGrr8TJiZgMxiOOLC8mTjUwr0XNRvb38AAsQpppEmCvPDkLL8CBYlPZoTgXzhlY3PozAZKEqFErlT+VhZvUfRL6WQCQYzYl8SBeEqDYGGQK6krS6U8GzFC/zdM36fbkUHdi6josTJ2sHoSEXI4ZbqTaqizy5EXnhnuEHnSlwVyL4LjtcYNweqyHEDkNcozL8zCntAOtgngtcgpD17kTp8IIqSh9DvTiLHc0nPwuuhZB1YFYzITIUm0zK1Rq9tpo3dXfqzybhtWfzRN7JBaIXKtLKtc6dYWelsScJszCdLcfx56jWsWnVyu8dbqOmragz8Vx+jptceGsNO3VlZw7uLjAIBZ20CrrWPQ78Zm0kahsraN2QYSFVSZ0oeaPG3DdmSsnZS8vTfurBjcvSVkt9ayl1x1WDgiLPLeOpDdlPdnldEe1QnXXhUy3ebKBqKGPjA2moCWvHOWH653v/E6jp38kFy7qQJx5HVKHx3shrldDmDdbmdPQXJYQB71ehjKwXRYQN1Nv/ZeRVa2Q7kPe7Anez+QYCnl3nHZ1uME6VfW5Du7HVt9tyi7au0XZXJY++pn+j3VGsSB4cIq5VK4vw7lh45huPL61/ABFadpX/0M3gnl91/b1ZirkniFChUqVKjwCGAyMmzSdikTB6IXDHt+mTRlQ8w4IngPahvMP1zq4Pum+g1X1FciM6XquFdKIwffD03Uz7BRyrx9wkrkgiQq0O/T7jl54o64QF2z/bqtIdKMzw8/z7JwrAgUys3gAgU86Yz93pHrEGPLodecteUpI9f3ASdG6lNkMnB+jNrLxQQ4rR0bQ8HlK9UTi0wB4zN0sih8OxszIr12NR2LqzkglvpObmdx9462eEVCRJtdINS9nFuWIlPDjs0N2U9ncwPzWlI/duoUZnyZeXoBt1VV7fr6twAAW90Opox3P0Mg5nX0M55rNjHTlKfVlDbR3pIsJjpyGmZK+79bkrGmq1vopWJQ8r6nLiGHbEdNSZULToGefl5xDfXjKjOq+2x1uqGi4ooOzpy4IOd1u416Uz5z3vKEMROIjCcunkRvoIy9dncdnZ6OtRGjXpMsjC2Q+akJbTe7u3wDrXWpEszN1NBSg5RHCTtltAehaPYgXMEGyWF7Gee4mfVuiWjlnuyDLJUPZsyDZentMurBdQM5a+21IWv34TPv73/i3u/JMJnSYcc/iEzbt3W9tH45vPYoq5hdunweGCCwvbpxDIuQcW2XXQOiJvfSpR8Px9yr+cdkBGwAIIPIxtJTDT+vrMHGs7ZhQcYHtgJO54gjMiHwFYGN3Rf/iEwc+rB9FCZnESrsbIOcqOO+Madng4Oi0GdNXIQ5YuODrTNItGE5rxFqOpFZM9N6aikKLdU6cOglBwi5PkD4+fKosPCh3jGFUr7v/U4ch7I/F64/h80UHMdMODEDij0jnQHt651iz4I3IH+N2QR2unUM1nVz19XramG25MDX0gKxMrMTPX4vqcGq0IxjRk3H0pyVm/GJJ5/HY0/JnOyJM6fRUhGV27fWsPL2shwDEmQ7OfdFaRwj18+cdT783eV1nNQHnGPHZ5CrqkySMGqzKmpTl8Dbam9htS3n0NlKkSqLvD6n23QcoOdIaYTZM1K+zu/ofLqdha3JsVqdBDffelvP4SzSVD6bqdO6DeehTD41M41TFx8DAGxuSOC+88Zt1GoyrvqsRaYM/U53A71EHz5Vi3x2toGld2VfZ0+fxOsv79WQbzJwEMG5jGG90zuxzHczjz5srnmwbH9Q2Mnqs4yDKoOXy9SvYBhzfPuS95nzF+9xrfJoX/0MXv1y9551dyrff/6tdGzG+M1rV0PwHhawR+mWj2KL++UfuF//5B7hlJfWL98vkrLxtfs3UvjpAI/nFjf7ZfWF8/izgWs0LqqSeIUKFSpUqPAIYCIybCJCHMVSBteUMSYbsm3fgyyuWD5TLlAoMzsritCzHTJSIngTayIK7HKnfy1MP2smF8rf1piwTvCqZheYxOS4VF73qW6fUyZcKGWkB/Uzg7SUtTtvKkIOTrNt4/u42QaVLTgDa30fdT8T9m5dMIy6Zg0U1RGFvnXNdGFDWV4HIsu139oUjMRn9lQAme9lN6HfvR5rybyw2GorAa5bwG1JNhwreatpGZG6jM1Nz+DpSycBAN/xvKgGnXrsJFjdvNJeiizRYxUJMu2dz/XztIYQRcoutwSXa8lazy93hFs3JUNIO1M4tjij55PDsTyp15WwOL+wgML3mndamG6q5OVx6S/PnAnnODVXw9qmZMNLHSk3ungem3dEYrTIgdVMMoCF48fQbApZLlXCWHt1A8mWPDn3tjLwlErMKvnQ5i44yiTdHLl+DshiNLzJypyU7Hs5wEroy/IcLlGq+SMAIrrP4WocjJu1DrLAB5eNWncYdsrA99s7PQ6G7f+gMulxMIwUNph1901B7s+qgb37Ow+O4RUc3ZYxXv53OWs+KCLaIHxWXFYu24kZXoY/l1fWmlib02u4j/FUGXaFChUqVKjwCGAiMmyAYMjAwcF6bW3ikI34uWgyVnqXAbi8CFmaK1wgbXl/bwcO+3Iu72eaOn+bEyMKWa/tW3FGfcMMq5ldboqg+U2O7u93LlxoATNwcLnXOJdt8jwFsyeH2cCAM8YB2mbGXukMeTgHMuKfDSDkytbEMDovXURFyDq5yJF4gxKtTBScIfJqbsQhGy7IZ6zkKXiIrEPh7UAtgra600y3ARPIcpGzYD9uvYYt57Co7584Poenn5XM+shp6VEuMqBQz3IGI4HvJ8vC52u5pudKXgYelgtY29DrKVmmYRvIWb2tAmvY0M9jEbPHJTM5elLmldvdFjqrst3W1jrmFqTtylufUrqJTPu8W8td3FmWfbVuS4YdzbQQ1+T4UdaViwNgZfkGqCbz+KSVgfXWBgrNtnsbCdr6DZltypjOPnUat26JLvrN6zdgSDKrmeZUqCJ5Ilu6voa5BRnX1NQMesn2pJb3EnajZFZettde7536tAfns8ttZ/vtLx+VvY8y/CjjQWbhZXvOsxcu49Jl+S4OZtYP4rg+u9+JyDUM5bavcgY+jMg2arvyMo/G7RcCme5Z+WnBmfMXw7Ib7/QJa88tboaxe/3w5/A3OKPtYDMXfxr48p/u+tyAMQI2Ef0egE8AWGbm53TZrwH4YQApgCsA/jkzr+t7vwjgJwEUAP4lM//FzsNgXT0CWHuYcw4W0iFIGwLnKnyRZHB5n2DWL4UH9Y9+PzWbfuk59D1TCOKWAKfiLEQEG3lxFg1ajNDbTIaCjKjxLHT0S+3EJDKhAGzore7LmQI5nFdOcSb0O/sfbBAFL/AopmBg4nyQtdw3MOkVSMibhhSIfH+33z+TkPcAWMuI9IpaLaknsJj2UwUmQ907mRUONfVtzvThgikDa5B0bIJjmc46ICEGN+QfzRNN1GckyPnPyDkXSvFyqt6Eg2FIndQa3tglRqqyr1MmAtU0uDvZJ1lCM1h+W/hnoXyri0wDG8dyI8VZgZk52a7TSlCsinDJ3FE5v8b0LNpaBl/52yVkSmScUVGcu60lNGfkocMYE0RY7r59E22VMZ1XidI8z7F267q+TuC0dzqblpL91IUjOKqDTQoG17Rs3IjQVlZ7pyvjf+LbnsT5S0/IOWZt9NZVgWGfeDj38/3YiVi1197r3QTMYb3Ru+nTHnbc3ZDaDqK0/qBL5T5IH2Tv9Dhj3os5SRk+uA7KkQ4G6u2EWfx2ofca1+5lp2tJ2wfpJy99MLhy4UKfkLZW2udi62/C8f0DQfvqZ8Ly3WKckvjvA/jYwLIXADzHzO8H8LcAfhEAiOgZAD8G4Fnd5j+TpyFXqFBhEvD7qO7nChUeSeyYYTPzF4jo8YFlZV21LwH4J/r6kwD+hJkTAFeJ6E0AHwLwxe0PIhlYZDnYSBoHUORtIlUqNC8ALT+6Ig+ZLhGVeqZL/tBe2tRGvgALp+1bhikoqaUuAYw8Hdc4gvGZec1n0jFYMy9yBPa2n7pXIXv5TJhhvSSpMtXYITRlW1IVN6hkqabY5P03xQpFXrGFbzErvISp4/C+RQT2GTRxOHffW10wI/PEt4L6rWt+rKYI19alBnks71tbR6r7qHvjFaqBlfhWcArWrLiX+/I+Y6YhWWttKkYv8xaekjlGBkGNzjkrkqQACo5APk3XVjCKDVyhJW/O4NjbjWpZLmKosih6JgdrA3ligbV1yQyaLXkKXpyZRqb2ne1eDNa2qa2OZNVFbQHQygMjxuqqWmnOy1iPP3YBeddPrWTo+cyja9G+I/3d60ekR3q2WYfxinRZAqOEwV5H9nl3NYapS9Y9HW+hmwtBLUsMqC7XcaEpZL3ZhaPoakVi4847yNKDaSN6KPfzCAxmmOVMd7ce1dthsDw+eNxBudDBMYxzzActuToK5VL5frLtckZbzqaHZdai7HU/qWsY0UzaoEa3O+00ljKkNWr7Uva4GFUSH9bfvRaMO+7NzAcxjt2mL4kfOfVBvKRSCoP93rvBQcxh/wSA/66vz0JueI/rumx7kA+0jFxLvHEUh/nV3PskFxmKIkTmUCqH4/tY4GAT5kELFWYB+iVaa/vz1oVD0PTOOUdNJTr9vHIcGYS6awZkKg4dcg2iwCg3tq/JbZW2bbjvUU3MSLVnm5jB/gFFGdSGuVQm74u4RP6j4hwIjx8E6/pSMb6sHsrrLAFezsUh93sr/Hw7IdXAS2SQFxIQnc1RaNk8VmnUPLJhDtzaBlBI4PPa2zZmNNRL2kQxCg3OPQ1KDRuHvvmCOUxt1LkOG03p6cg16DkW1zSII1lNpVSDiktEIC+jGvW9yqejqSBX21KP7ObU6dBrnnEHzQVhh9e833fXwGgf97mnL6LzihxjoyWlaZpKEM1JkM3XNsGFHnc6htXWBNeRH383E2N2QR4UOImCrGyiJfe1t7uYOXFKziGO0NGHg6zegNMpgEKnBaaemgIK2e+rX38zdBE8YtjuQwAAElhJREFUBOz/fi5hWD/zqEA9rMy8U2A8yMC5G43xUT3fDzOA7zTPvV1AL89RH188OnYJfC9s8L3KqO5mDnvYvPWly+e3FXwZ9V4oV8/3g/swL+tB965ymdv3YZ8tlcp9oN7JG3w77CtgE9EvQaLHH/pFQ1bjIctARD8F4KeAvihIhQoVDg8Hdj+b6n6uUOFBYM8Bm4g+BSGv/AB7WrM8gZdrJ+cA3By2PTP/DoDfAYC4FjPDwDEQsZKrDIKJRq7lVSKEsq4BBTcsx33Gtg3Mb4B9aRkcvJ5jJVYJq7zv1sVK5IqMReG8w5T88NQiRi0S4lLuElifgWumbUxocQYQhZI5KUkrNoTCeDcvF1LzyCBkvRRIZ/2yPXEBZLJujbTnnPpN3wRGrhm6c0DN9DNnGT/D+FI7cfilTT1RzZbIcI4BqGsW2+AutqnEO3KMhpYOcppCbLT8rczAmm0AynruJQ5bqigWqx83xX2FOFDmZzbgshzQ8rcfX8EWiWbgDc7BRslmWs429ToijQkNWweUXT41O4czZ8/oGBM9LYvYauUALdzRUvhRzaqLxGFqVvqpXZ0wM6+SpXrdNm7eweyUqJOZmkWiam51xzDewESnU7I8w9yCku3qBumGZECFyqx2kwy5ryzEdcDKdnmeIO/plEuupDlY1LQPe2N5FXlnb8pI4+JA7+c4DkF9WGY9qvx9UKSznVjeo8Y1DMOy61El9XHNRUbtd3Dd/ZLUdiqfb5dVH188GrLDVzeAnip+7aecuxeMWwofpWQ2WNK+ee1qyJafPT+eiYnHMKLYsMy7rHLm2eNPXvrgyHV2gz0FbCL6GICfB/APmLn8zf00gD8iot8AcAbAUwD+eqf9CUecYdki1RafopfBBLnQfpD2ZXCGAXv9cIOQCwTXLdgQARznsPqj6oVXDAGR9z1kBActLii0RXlhlJwsYi1vx5gWAQwARbh8DsT9wBg0XPRcDAwKrwme58EW1BqD2M+HezETS+FBI0YRtMJ90sIwYa6aYYIIiyUbpgMK3w5nAKvrRmyCrSdrqdYwgf1UAQEmtJgRcv+Tq8IqBEJHpwUMtWGUsc1q34nGNNZVf/vW8m3YRqLjFoZ0ETdgfNsYCuRdz5ZOkao0aK4lYMOMTeevMbCoZXeKZXyZyxEp8/pofR5bul1uEzit1sw2VRglLYIW/ZRpoKvypmmqgXEjgdOAbajAkXn5Yev2VAfczaKjOt4Li3OYnlUd+SSB6cq4rbZ9JddbQKaOYjPT6Kyu6zWSBw2XpSgyuS7rtR6OLqqWeFzDZlvW7WmQ7hXruHlb58bTAtm89pI8ABz0/VzGfiU8H0aJeS+Bcb9uXKOC917sN8fBXnTHfbA5e+HyPbKa+zn+sDGsrN29T0/8YcEH3GHB+J7gPn8nWGWWUd7OPxyszR0bur/yQ4Uv9//lLsc7TlvXHwP4PgDHiOg6gH8HYZHWAbygweNLzPwvmPlVIvofAL4JKa39DHu2VoUKFQ4d1f1cocKji3FY4j8+ZPHvbrP+rwD4lV2NgoVglXIOl/XLYr4kHVqnwUF0xMGF0i+4b/LlVU2EQa4EM1g19UBf9MRlcJHPejkQl3KS9eUY3mPbhAw7jYFa7I8rGWO3KMBqseXSHDaW5V5shU0GzjzznILnd54ApBms70V2ziLWFTIGjH5EXvqFCVAytxqU+WzdwRWeBe6Jd4BRklRGDv2JAz2+M332uzFgo+5TzDCeya6yoTEb5Coba3OgR2oKopKZCXrI/Pm+3UakZeDZmhAzonoWhGqSwkITbPSSDijr6jF8tcGFz7NjGTW9trF+Hk1bQ5aoB/X8DKZqkk0XWQ/ttjy51pR53tpqIfLJiktQ+L5y4w1aUrRXhKA2d/EYuCGf/fFF2Wd6NEGrJ4NpnlzE0jdkXUrbYerEqjtb1DSY0ZL4ytIanJIDXUdZ+0WG7rT0gbt2hKIpVQKXMh57n/Rcb9yV920R4Z1r8ki/Bga3dxbSGAcP5X4u4UG4ae0Ve8leR5WmH5T5x3bvH0TWPS7LvExKu/HOa/0+41K/8zBIJn5s6HujIMd5sMJA5dL6GfT9rHfqh/be1+OUzssktMFSfq/0/o21O9grKnZIhQoVKlSo8AhgQqRJGZynYGdDpstwyNh7BPftKH3vtEERJDzLc9DBtdo5WM2yMupLnnqJUEsEoxmyISd92ZAMlr1/JbwsKCF3fl7ZAr7hzPd5O4tC58MditCyRFZJXEUG51uNswJkfYuWgaN7fZCBHIUeK0OBms6NF7rP2BgUwbSkb3CSgIMBCQWLURNa0Cz1VctIs0uDAnDeftPAqb1lQYB1XsJVs2pOkWsbU8rAlO4r9fag3TS0MbncwVyXwcxOaQ/10eOwKgubkUGSyrq9XoJE54tzr3hmKcyzk0uwWkjmf0SJf3e7OTL9oOdTgxkl9KUmQk+z9XXtfc6TDJl6YBsuAJ0jdkbnzes58o23AQBrKwa2JuvOnpAMG7dXESnhq2h1QdprjmIK2mYdzgvGwekHvbW8hEZNr7fvwstEfheQHv9EOQMrd24GQ5nzF4U0N9ucx9r1WwCATjcB8kfH/IOZDyWz3su88vzs7I7tUQeZTY+LYcc8aCOSYeddzrrvJaXJfPazagIyiuS1m+P6Y20nR7qTUtm43tkPE+WKhM/QgX42fwN7I5wBkxKwWcq71nIgkhXMoXztA1HGDOsJWzAg64OwcMIR/i8a3D7sima2vq/BsmZjcagC0CtcEGGJXYSeHriurlecF+hpabhm+37TrDKqZBku03VRwIue+tm+WhyBNQCmWYYYvkydBfnUvmKqDT3fDRuHPm1fRs8ch5WNtaAgc5qFXnNWFrsB+g9AjvpSr8pUyywhCubaEaDnyDAI5HLrRV4cnPZWR2zB2qftZVILxF5VFowUTv2kr9dkR5FZD85iESxSpYn3ug7druh3F049w4u+T3jNTcMp6ayl2vG2FmN9UwPf9XU8duqkHreFttdLV6/qNN8EJ0L6akSzmLbyI+Gs/PA1TA11loC8cf0moveJZ3dPpUKn6jXYRX+Nc1x84kkAQHvlJtZb8oPWSfte5tyWY51sMPJcttvSB6iubYSHubMXziH3BLhOCwuzor0+e0QY6avrd7GlLPOe68A05GHlvYz9EMxGBbJRjG6PcTS7tzvWQQfz7R46HrRrGDC6ZO6D9+deln97965BvLpxbOzAVA7cw9y6yigH4fLrYSzycWRIh22/20BfZnqX+62BfqAOMqe3Xwjrl6cbdouqJF6hQoUKFSo8ApiMDBvSh8wFwWrfrzRS30uiiiMLVkUotn1HJ+a+qYc3vrCG+q1eLhK/bQCsGV8tJmhCh8gSjCeaWaCmY2DN0Qk2yJVaMPLgZy1Zjy0SgJNwHpHp93cDQEQmtGe53CFVqUwQofBGIbbfPhVBSsC9LEPdVxE05bVkUPisOHcwxl8vU3r68u1wBrl32IINSmN9U5R+tQAZB1MQNq7vjJV74pxFzfjpChZzaAA17YEuKIPTHvo8Z19px11thZhdnsXRBe91HqFItexfJGgr0dBnxY6KQBpzpgi+0F7NLs0YqgCKpVYLTt9/4twxzOhFaPfkCTeOItQwE66h//w31Dlsq7eCo1p1sZtbuHtFzDtOP3FOPoN2jqNPaAa/1gEtSNbRnL6AuUzK12s3luX9pSVY/VLNnj+H3qbYAPAdKW034lnUFh+Xc8jb2Lgj21089z5Mn5WMZWlFMo13rnwLrIQ+RzHavaF6JROPYepl5ff2WjrfKdsc1nO9m2x4Nz3TZexXeW3Ycf2yh+HNXcZ2rWCjstcyiWsvBiL7desap2Q+rnf2YCY/mIE/t7h5Tx92OPcL343eKem79u1wsm0/I9+ruQr1NRIOD1EU8ezCAmLiwOYGObAGAF+flRJwmKjtl7zhQnAMcqJUQ6Q92xkIkacdh6jWny+31gWhDEszwR6TNDJnVMDLfkxbi1hdlnrarExOSuEAkHLWd/bSg9moHubbszQP7GGGhS/c+1I/ECHWIJhR0Z+P1t5qNhzK52CC8Vrk5OB0TtT3XhNbZMH2k2GMF1zxJf0kiLgQRNwEAIrChH3kyjyP6zEifwnJlErtWvKOYhRa9mdi1Kfk9fS0zAmfPd7AXFMfJCxCybtICasajO4saxnpToZUg3eBApk+zNRUM9wYA4rkM1qAhdPnzrlmDY89Lj3Vx+fEYWu6OYdC+7Q31zqYmZMfPFLXLbe1ghmdzphxEW53JcjSiW8HAJw+fgJRXR9OMmBuQUrW7sgMNt5Y0Wsj16WGCPaozsvdWA7WpmKCJcIp7Y5ajBrAP2vFsxFWtId987ocf33jLta3ZLskZaQ6LfCV19/6KjN/FyYYURRxc253UpSDwXtYwCsHsP0Ert0KmIwax6j3HxaGabOPez47rVcO0sOkRY8vDi9dA7in9Ou3HxeDx/H92cMeDoY9NIwqa5f7qcsoB+/Befnt3tsJnhHug/gra82hQfrlb7y6q/u5KolXqFChQoUKjwAmIsMmohUAbQB7b1B78DiGyR3fJI8NqMa3HwyO7QIzHz+swYwDItoE8Pphj2MbTPLnDUz2+CZ5bMCjN75d3c8TEbABgIi+Msmlvkke3ySPDajGtx9M8thGYdLHXI1v75jksQHv/fFVJfEKFSpUqFDhEUAVsCtUqFChQoVHAJMUsH/nsAewAyZ5fJM8NqAa334wyWMbhUkfczW+vWOSxwa8x8c3MXPYFSpUqFChQoXRmKQMu0KFChUqVKgwAocesInoY0T0OhG9SUS/MAHjOU9EnyeibxHRq0T0r3T5LxPRDSL6uv738UMc49tE9A0dx1d02REieoGI3tC/i4cwrkul6/N1ImoR0c8d5rUjot8jomUieqW0bOi1IsF/0O/iy0T0/CGN79eI6DUdw58T0YIuf5yIuqXr+NsPeny7xSTdz9W9vO+xVffz/sd2sPcyMx/afxB76isAngBQA/ASgGcOeUynATyvr5sA/hbAMwB+GcC/Ocyxlcb4NoBjA8v+PYBf0Ne/AOBXJ+CzvQ3gwmFeOwAfAfA8gFd2ulYAPg7gsxDht+8B8OVDGt8PAoj09a+Wxvd4eb1J+2/S7ufqXj7wz7a6n3c/tgO9lw87w/4QgDeZ+S0W66s/AfDJwxwQM99i5hf19SaAbwE4e5hjGhOfBPAH+voPAPzoIY4FAH4AwBVmfucwB8HMXwCwOrB41LX6JID/yoIvAVggotMPe3zM/Dn2PrDAlwCce5BjOEBM1P1c3csHiup+3sPYDvpePuyAfRZAWaT1OibohiKixwF8EMCXddHPamnj9w6rTKVgAJ8joq8S0U/pspPMfAuQHyoAJw5tdIIfA/DHpX9PyrUDRl+rSfw+/gQkS/C4SERfI6L/Q0R//7AGNQKTeP0AVPfyAaC6n/ePfd/Lhx2waciyiaCtE9EsgP8J4OeYuQXgtwA8CeA7ANwC8OuHOLzvZebnAfwQgJ8hoo8c4ljuAxHVAPwIgD/VRZN07bbDRH0fieiXAOQA/lAX3QLwGDN/EMC/BvBHRDR3WOMbgom6fh7Vvbw/VPfz/nFQ9/JhB+zrAMpeZ+cA3DyksQQQUQy5wf+Qmf8MAJh5iZkLZnYA/guk/HcoYOab+ncZwJ/rWJZ8uUf/Lh/W+CA/Pi8y8xIwWddOMepaTcz3kYg+BeATAP4p66QXMyfMfFdffxUyX/z0YYxvBCbm+nlU9/KBoLqf94GDvJcPO2D/DYCniOiiPsX9GIBPH+aAiIgA/C6AbzHzb5SWl+c+/iGAVwa3fRggohkiavrXEFLDK5Dr9ild7VMA/tdhjE/x4yiVzybl2pUw6lp9GsA/U3bp9wDY8KW2hwki+hiAnwfwI8zcKS0/TiR+qET0BICnALz1sMe3DSbqfq7u5QNDdT/vEQd+Lz9I1tyYzLqPQ9ibVwD80gSM5+9ByiYvA/i6/vdxAP8NwDd0+acBnD6k8T0BYd++BOBVf80AHAXwlwDe0L9HDml80wDuApgvLTu0awf5obkFIIM8cf/kqGsFKaH9J/0ufgPAdx3S+N6EzL35799v67r/WD/zlwC8COCHD+Mz3uF8JuZ+ru7lAxljdT/vb2wHei9XSmcVKlSoUKHCI4DDLolXqFChQoUKFcZAFbArVKhQoUKFRwBVwK5QoUKFChUeAVQBu0KFChUqVHgEUAXsChUqVKhQ4RFAFbArVKhQoUKFRwBVwK5QoUKFChUeAVQBu0KFChUqVHgE8P8BpC0A0jOY7b0AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ======= Experiment with these parameters ================\n",
+ "# We can try different values for these parameters\n",
+ "K = 16\n",
+ "max_iters = 10\n",
+ "\n",
+ "# Load an image of a bird\n",
+ "# Any png image can be read in here\n",
+ "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n",
+ "# ==========================================================\n",
+ "\n",
+ "# Divide by 255 so that all values are in the range 0 - 1\n",
+ "A /= 255\n",
+ "\n",
+ "# Reshape the image into an Nx3 matrix where N = number of pixels.\n",
+ "# Each row will contain the Red, Green and Blue pixel values\n",
+ "# This gives us our dataset matrix X that we will use K-Means on.\n",
+ "X = A.reshape(-1, 3)\n",
+ "\n",
+ "# When using K-Means, it is important to randomly initialize centroids\n",
+ "# You should complete the code in kMeansInitCentroids above before proceeding\n",
+ "initial_centroids = kMeansInitCentroids(X, K)\n",
+ "\n",
+ "# Run K-Means\n",
+ "centroids, idx = runkMeans(X, initial_centroids,\n",
+ " findClosestCentroids,\n",
+ " computeCentroids,\n",
+ " max_iters)\n",
+ "\n",
+ "# We can now recover the image from the indices (idx) by mapping each pixel\n",
+ "# (specified by its index in idx) to the centroid value\n",
+ "# Reshape the recovered image into proper dimensions\n",
+ "X_recovered = centroids[idx, :].reshape(A.shape)\n",
+ "\n",
+ "# Display the original image, rescale back by 255\n",
+ "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n",
+ "ax[0].imshow(A*255)\n",
+ "ax[0].set_title('Original')\n",
+ "ax[0].grid(False)\n",
+ "\n",
+ "# Display compressed image, rescale back by 255\n",
+ "ax[1].imshow(X_recovered*255)\n",
+ "ax[1].set_title('Compressed, with %d colors' % K)\n",
+ "ax[1].grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Principal Component Analysis
\n",
+ "In this exercise we will use principle component analysis (PCA) to perform dimensionality reduction. We will first experiment with an example 2D dataset to get intuition on how PCA works, then use it on a bigger dataset of 5000 face images.\n",
+ "\n",
+ "The following cell will plot the 2D training data. In this part of the exercise we will visualize what happens as we use PCA to reduce data from 2D to 1D. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load the dataset into the variable X \n",
+ "data = loadmat(os.path.join('Data', 'ex7data1.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Visualize the example dataset\n",
+ "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n",
+ "pyplot.axis([0.5, 6.5, 2, 8])\n",
+ "pyplot.gca().set_aspect('equal')\n",
+ "pyplot.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now implement PCA. This consists of two steps: First, we compute the covariance matrix of the data. Then we use the SVD function to compute the eigenvectors. These will correspond to the principle components of variation in the data. \n",
+ "\n",
+ "Before using PCA, it is importan to first normalize the data by subtracting the mean value of each feature from the dataset, and scaling each dimension so that they are in the same range. After doing so we can run PCA and plot the corrosponding principle components. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pca(X):\n",
+ " \"\"\"\n",
+ " Run principal component analysis.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset to be used for computing PCA. It has dimensions (m x n)\n",
+ " where m is the number of examples (observations) and n is \n",
+ " the number of features.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " U : array_like\n",
+ " The eigenvectors, representing the computed principal components\n",
+ " of X. U has dimensions (n x n) where each column is a single \n",
+ " principal component.\n",
+ " \n",
+ " S : array_like\n",
+ " A vector of size n, contaning the singular values for each\n",
+ " principal component. Note this is the diagonal of the matrix we \n",
+ " mentioned in class.\n",
+ " \"\"\"\n",
+ " # Useful values\n",
+ " m, n = X.shape\n",
+ " \n",
+ " U = np.zeros(n)\n",
+ " S = np.zeros(n)\n",
+ "\n",
+ " X_trans = X.transpose()\n",
+ " Sigma = (1/m)*(X_trans.dot(X))\n",
+ " U, S, V = np.linalg.svd(Sigma)\n",
+ "\n",
+ " return U, S"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def featureNormalize(X):\n",
+ " \"\"\"\n",
+ " Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+ " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+ " working with learning algorithms.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+ " and n is the number of dimensions for each example.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " X_norm : array_like\n",
+ " The normalized input dataset.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of size n corresponding to the mean for each dimension across all examples.\n",
+ "\n",
+ " sigma : array_like\n",
+ " A vector of size n corresponding to the standard deviations for each dimension across\n",
+ " all examples.\n",
+ " \"\"\"\n",
+ " mu = np.mean(X, axis=0)\n",
+ " X_norm = X - mu\n",
+ "\n",
+ " sigma = np.std(X_norm, axis=0, ddof=1)\n",
+ " X_norm /= sigma\n",
+ " return X_norm, mu, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top eigenvector: U[:, 0] = [-0.707107 -0.707107]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Before running PCA, it is important to first normalize X\n",
+ "X_norm, mu, sigma = featureNormalize(X)\n",
+ "\n",
+ "# Run PCA\n",
+ "U, S = pca(X_norm)\n",
+ "\n",
+ "# Draw the eigenvectors centered at mean of data. These lines show the\n",
+ "# directions of maximum variations in the dataset.\n",
+ "fig, ax = pyplot.subplots()\n",
+ "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n",
+ "\n",
+ "for i in range(2):\n",
+ " ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n",
+ " head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n",
+ "\n",
+ "ax.axis([0.5, 6.5, 2, 8])\n",
+ "ax.set_aspect('equal')\n",
+ "ax.grid(False)\n",
+ "\n",
+ "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After computing the principal components, we can use them to reduce the feature dimension of our dataset by projecting each example onto a lower dimensional space. In this part of the exercise, we will use the eigenvectors to project our dataset onto a 1-dimensional space. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def projectData(X, U, K):\n",
+ " \"\"\"\n",
+ " Computes the reduced data representation when projecting only \n",
+ " on to the top K eigenvectors.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input dataset of shape (m x n). The dataset is assumed to be \n",
+ " normalized.\n",
+ " \n",
+ " U : array_like\n",
+ " The computed eigenvectors using PCA. This is a matrix of \n",
+ " shape (n x n). Each column in the matrix represents a single\n",
+ " eigenvector (or a single principal component).\n",
+ " \n",
+ " K : int\n",
+ " Number of dimensions to project onto. Must be smaller than n.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " Z : array_like\n",
+ " The projects of the dataset onto the top K eigenvectors. \n",
+ " This will be a matrix of shape (m x k).\n",
+ " \"\"\"\n",
+ " Z = np.zeros((X.shape[0], K))\n",
+ " m = X.shape[0]\n",
+ "\n",
+ " for i in range(m):\n",
+ " for j in range(K):\n",
+ " x = X[i, :].transpose()\n",
+ " Z[i,j] = np.dot(x.transpose(), U[:, j])\n",
+ "\n",
+ " return Z"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will test our function by projecting the first example onto the first dimension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Projection of the first example: 1.481274\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Project the data onto K = 1 dimension\n",
+ "K = 1\n",
+ "Z = projectData(X_norm, U, K)\n",
+ "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After projecting the data onto the lower dimensional space, we can appoximately recover the data by projecting them back onto the original high dimensional space. We will do so for the first example we saw and create a figure to visualize the process."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def recoverData(Z, U, K):\n",
+ " \"\"\"\n",
+ " Recovers an approximation of the original data when using the \n",
+ " projected data.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " Z : array_like\n",
+ " The reduced data after applying PCA. This is a matrix\n",
+ " of shape (m x K).\n",
+ " \n",
+ " U : array_like\n",
+ " The eigenvectors (principal components) computed by PCA.\n",
+ " This is a matrix of shape (n x n) where each column represents\n",
+ " a single eigenvector.\n",
+ " \n",
+ " K : int\n",
+ " The number of principal components retained\n",
+ " (should be less than n).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " X_rec : array_like\n",
+ " The recovered data after transformation back to the original \n",
+ " dataset space. This is a matrix of shape (m x n), where m is \n",
+ " the number of examples and n is the dimensions (number of\n",
+ " features) of original datatset.\n",
+ " \"\"\"\n",
+ " X_rec = np.zeros((Z.shape[0], U.shape[0]))\n",
+ " m, n = X.shape\n",
+ "\n",
+ " for i in range(m):\n",
+ " for j in range(n):\n",
+ " v = Z[i,:]\n",
+ " X_rec[i,j] = np.dot(v, U[j, :K])\n",
+ "\n",
+ " # =============================================================\n",
+ " return X_rec"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Approximation of the first example: [-1.047419 -1.047419]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X_rec = recoverData(Z, U, K)\n",
+ "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n",
+ "\n",
+ "# Plot the normalized dataset (returned from featureNormalize)\n",
+ "fig, ax = pyplot.subplots(figsize=(5, 5))\n",
+ "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n",
+ "ax.set_aspect('equal')\n",
+ "ax.grid(False)\n",
+ "pyplot.axis([-3, 2.75, -3, 2.75])\n",
+ "\n",
+ "# Draw lines connecting the projected points to the original points\n",
+ "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n",
+ "for xnorm, xrec in zip(X_norm, X_rec):\n",
+ " ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now run PCA on face images to see how it can be used in practice for dimension reduction. We have a dataset of face images, each 32 x 32 in grayscale. The following cells will load and visualize the first 100 of these face images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+ " \"\"\"\n",
+ " Displays 2D data in a nice grid.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input data of size (m x n) where m is the number of examples and n is the number of\n",
+ " features.\n",
+ "\n",
+ " example_width : int, optional\n",
+ " THe width of each 2-D image in pixels. If not provided, the image is assumed to be square,\n",
+ " and the width is the floor of the square root of total number of pixels.\n",
+ "\n",
+ " figsize : tuple, optional\n",
+ " A 2-element tuple indicating the width and height of figure in inches.\n",
+ " \"\"\"\n",
+ " # Compute rows, cols\n",
+ " if X.ndim == 2:\n",
+ " m, n = X.shape\n",
+ " elif X.ndim == 1:\n",
+ " n = X.size\n",
+ " m = 1\n",
+ " X = X[None] # Promote to a 2 dimensional array\n",
+ " else:\n",
+ " raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+ "\n",
+ " example_width = example_width or int(np.round(np.sqrt(n)))\n",
+ " example_height = int(n / example_width)\n",
+ "\n",
+ " # Compute number of items to display\n",
+ " display_rows = int(np.floor(np.sqrt(m)))\n",
+ " display_cols = int(np.ceil(m / display_rows))\n",
+ "\n",
+ " fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n",
+ " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+ "\n",
+ " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+ "\n",
+ " for i, ax in enumerate(ax_array):\n",
+ " ax.imshow(X[i].reshape(example_height, example_width, order='F'), cmap='gray')\n",
+ " ax.axis('off')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load Face dataset\n",
+ "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Display the first 100 faces in the dataset\n",
+ "displayData(X[:100, :], figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run PCA on the face dataset, we first normalize the dataset by subtracting the mean of each feature from the data matrix X. After running PCA we visualize the principal components by reshaping each into a 32 x 32 matrix that corrosponds to the pixels in the original dataset. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# normalize X by subtracting the mean value from each feature\n",
+ "X_norm, mu, sigma = featureNormalize(X)\n",
+ "\n",
+ "# Run PCA\n",
+ "U, S = pca(X_norm)\n",
+ "\n",
+ "# Visualize the top 36 eigenvectors found\n",
+ "displayData(U[:, :36].T, figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the principle components, we can use them to reduce the dimension of the face dataset. This allows us to use our learning algorithm with a smaller input size, helping to speed it up. The following cell will project the face dataset onto only the first 100 principle components. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The projected data Z has a shape of: (5000, 100)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Project images to the eigen space using the top k eigenvectors \n",
+ "# If you are applying a machine learning algorithm \n",
+ "K = 100\n",
+ "Z = projectData(X_norm, U, K)\n",
+ "\n",
+ "print('The projected data Z has a shape of: ', Z.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Project images to the eigen space using the top K eigen vectors and \n",
+ "# visualize only using those K dimensions\n",
+ "# Compare to the original input, which is also displayed\n",
+ "K = 100\n",
+ "X_rec = recoverData(Z, U, K)\n",
+ "\n",
+ "# Display normalized data\n",
+ "displayData(X_norm[:100, :], figsize=(6, 6))\n",
+ "pyplot.gcf().suptitle('Original faces')\n",
+ "\n",
+ "# Display reconstructed data from only k eigenfaces\n",
+ "displayData(X_rec[:100, :], figsize=(6, 6))\n",
+ "pyplot.gcf().suptitle('Recovered faces')\n",
+ "pass"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex7/.ipynb_checkpoints/ex7-checkpoint.ipynb b/ex7/.ipynb_checkpoints/ex7-checkpoint.ipynb
new file mode 100644
index 0000000..9c495c2
--- /dev/null
+++ b/ex7/.ipynb_checkpoints/ex7-checkpoint.ipynb
@@ -0,0 +1,5471 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 7: K-means Clustering and Principal Component Analysis
\n",
+ "
Introduction
\n",
+ "In this exercise, we will implement the K-means clustering algorithm and apply it to compress an image. In the second part, we will use principle component analasys to find a low-dimensional representation of face images. To begein we import necessary libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import regular expressions to process emails\n",
+ "import re\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "import matplotlib as mpl\n",
+ "\n",
+ "from IPython.display import HTML, display, clear_output\n",
+ "\n",
+ "try:\n",
+ " pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n",
+ "except ValueError:\n",
+ " pyplot.rcParams[\"animation.html\"] = \"html5\"\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from matplotlib.animation import FuncAnimation\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 K-means Clustering
\n",
+ "In this exercise, we will implement the K-means algorithm and use it for image compression. We begin with an example 2D dataset that will help us gain an intuition of how the K-means algorithm works. After the, we will use K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common.\n",
+ "\n",
+ "The algorithm works by, with an initial set of centroids, assigning each data point to its closest centroid. This will be accomplishes in the findClosestCentroids function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def findClosestCentroids(X, centroids):\n",
+ " \"\"\"\n",
+ " Computes the centroid memberships for every example.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of size (m, n) where each row is a single example. \n",
+ " That is, we have m examples each of n dimensions.\n",
+ " \n",
+ " centroids : array_like\n",
+ " The k-means centroids of size (K, n). K is the number\n",
+ " of clusters, and n is the the data dimension.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " idx : array_like\n",
+ " A vector of size (m, ) which holds the centroids assignment for each\n",
+ " example (row) in the dataset X.\n",
+ " \"\"\"\n",
+ " K = centroids.shape[0]\n",
+ " m = X.shape[0]\n",
+ " idx = np.zeros(X.shape[0], dtype=int)\n",
+ " \n",
+ " for i in range(m):\n",
+ " tempX = X[i,:]\n",
+ " tempSums = np.zeros(K)\n",
+ " for j in range(K):\n",
+ " tempCentroid = centroids[j,:]\n",
+ " tempDiff = tempX - tempCentroid\n",
+ " tempDiff = np.square(tempDiff)\n",
+ " tempSums[j] = np.sum(tempDiff)\n",
+ " idx[i] = np.argmin(tempSums)\n",
+ "\n",
+ " return idx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell tests our code (we should see the closest centroids appear as [0 2 1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Closest centroids for the first 3 examples:\n",
+ "[0 2 1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load an example dataset that we will be using\n",
+ "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Select an initial set of centroids\n",
+ "K = 3 # 3 Centroids\n",
+ "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+ "\n",
+ "# Find the closest centroids for the examples using the initial_centroids\n",
+ "idx = findClosestCentroids(X, initial_centroids)\n",
+ "\n",
+ "print('Closest centroids for the first 3 examples:')\n",
+ "print(idx[:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The next step in the algorithm computes, for each centroid, the average of the points assigned to it. This will be accomplished in the computeCentroids function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def computeCentroids(X, idx, K):\n",
+ " \"\"\"\n",
+ " Returns the new centroids by computing the means of the data points\n",
+ " assigned to each centroid.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The datset where each row is a single data point. That is, it \n",
+ " is a matrix of size (m, n) where there are m datapoints each\n",
+ " having n dimensions. \n",
+ " \n",
+ " idx : array_like \n",
+ " A vector (size m) of centroid assignments (i.e. each entry in range [0 ... K-1])\n",
+ " for each example.\n",
+ " \n",
+ " K : int\n",
+ " Number of clusters\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " A matrix of size (K, n) where each row is the mean of the data \n",
+ " points assigned to it.\n",
+ " \"\"\"\n",
+ " # Useful variables\n",
+ " m, n = X.shape\n",
+ " centroids = np.zeros((K, n))\n",
+ " \n",
+ " for i in range(K):\n",
+ " # Find examples which fall into cluster k\n",
+ " sel = np.argwhere(idx==i)\n",
+ " centroids[i,:] = np.mean(X[sel,:], axis=0)\n",
+ "\n",
+ " return centroids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will test this function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Centroids computed after initial finding of closest centroids:\n",
+ "[[2.42830111 3.15792418]\n",
+ " [5.81350331 2.63365645]\n",
+ " [7.11938687 3.6166844 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute means based on the closest centroids found in the previous part.\n",
+ "centroids = computeCentroids(X, idx, K)\n",
+ "\n",
+ "print('Centroids computed after initial finding of closest centroids:')\n",
+ "print(centroids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have all the pieces necessary to run the K-means algorithm, as all we do is repeat the last two steps for a set number of iterations. As we do so, the means will converge to the centers of any clusters in our dataset. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotProgresskMeans(i, X, centroid_history, idx_history):\n",
+ " \"\"\"\n",
+ " A helper function that displays the progress of k-Means as it is running. It is intended for use\n",
+ " only with 2D data. It plots data points with colors assigned to each centroid. With the\n",
+ " previous centroids, it also plots a line between the previous locations and current locations\n",
+ " of the centroids.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " i : int\n",
+ " Current iteration number of k-means. Used for matplotlib animation function.\n",
+ "\n",
+ " X : array_like\n",
+ " The dataset, which is a matrix (m x n). Note since the plot only supports 2D data, n should\n",
+ " be equal to 2.\n",
+ "\n",
+ " centroid_history : list\n",
+ " A list of computed centroids for all iteration.\n",
+ "\n",
+ " idx_history : list\n",
+ " A list of computed assigned indices for all iterations.\n",
+ " \"\"\"\n",
+ " K = centroid_history[0].shape[0]\n",
+ " pyplot.gcf().clf()\n",
+ " cmap = pyplot.cm.rainbow\n",
+ " norm = mpl.colors.Normalize(vmin=0, vmax=2)\n",
+ "\n",
+ " for k in range(K):\n",
+ " current = np.stack([c[k, :] for c in centroid_history[:i+1]], axis=0)\n",
+ " pyplot.plot(current[:, 0], current[:, 1],\n",
+ " '-Xk',\n",
+ " mec='k',\n",
+ " lw=2,\n",
+ " ms=10,\n",
+ " mfc=cmap(norm(k)),\n",
+ " mew=2)\n",
+ "\n",
+ " pyplot.scatter(X[:, 0], X[:, 1],\n",
+ " c=idx_history[i],\n",
+ " cmap=cmap,\n",
+ " marker='o',\n",
+ " s=8**2,\n",
+ " linewidths=1,)\n",
+ " pyplot.grid(False)\n",
+ " pyplot.title('Iteration number %d' % (i+1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def runkMeans(X, centroids, findClosestCentroids, computeCentroids,\n",
+ " max_iters=10, plot_progress=False):\n",
+ " \"\"\"\n",
+ " Runs the K-means algorithm.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The data set of size (m, n). Each row of X is a single example of n dimensions. The\n",
+ " data set is a total of m examples.\n",
+ "\n",
+ " centroids : array_like\n",
+ " Initial centroid location for each clusters. This is a matrix of size (K, n). K is the total\n",
+ " number of clusters and n is the dimensions of each data point.\n",
+ "\n",
+ " findClosestCentroids : func\n",
+ " A function (implemented by student) reference which computes the cluster assignment for\n",
+ " each example.\n",
+ "\n",
+ " computeCentroids : func\n",
+ " A function(implemented by student) reference which computes the centroid of each cluster.\n",
+ "\n",
+ " max_iters : int, optional\n",
+ " Specifies the total number of interactions of K-Means to execute.\n",
+ "\n",
+ " plot_progress : bool, optional\n",
+ " A flag that indicates if the function should also plot its progress as the learning happens.\n",
+ " This is set to false by default.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " A (K x n) matrix of the computed (updated) centroids.\n",
+ " idx : array_like\n",
+ " A vector of size (m,) for cluster assignment for each example in the dataset. Each entry\n",
+ " in idx is within the range [0 ... K-1].\n",
+ "\n",
+ " anim : FuncAnimation, optional\n",
+ " A matplotlib animation object which can be used to embed a video within the jupyter\n",
+ " notebook. This is only returned if `plot_progress` is `True`.\n",
+ " \"\"\"\n",
+ " K = centroids.shape[0]\n",
+ " idx = None\n",
+ " idx_history = []\n",
+ " centroid_history = []\n",
+ "\n",
+ " for i in range(max_iters):\n",
+ " idx = findClosestCentroids(X, centroids)\n",
+ "\n",
+ " if plot_progress:\n",
+ " idx_history.append(idx)\n",
+ " centroid_history.append(centroids)\n",
+ "\n",
+ " centroids = computeCentroids(X, idx, K)\n",
+ "\n",
+ " if plot_progress:\n",
+ " fig = pyplot.figure()\n",
+ " anim = FuncAnimation(fig, plotProgresskMeans,\n",
+ " frames=max_iters,\n",
+ " interval=500,\n",
+ " repeat_delay=2,\n",
+ " fargs=(X, centroid_history, idx_history))\n",
+ " return centroids, idx, anim\n",
+ "\n",
+ " return centroids, idx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will run K-means on our dataset and show each step along the way to give an intuition for how the algorithm works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load an example dataset\n",
+ "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+ "\n",
+ "# Settings for running K-Means\n",
+ "K = 3\n",
+ "max_iters = 10\n",
+ "\n",
+ "# For consistency, here we set centroids to specific values\n",
+ "# but in practice you want to generate them automatically, such as by\n",
+ "# settings them to be random examples (as can be seen in\n",
+ "# kMeansInitCentroids).\n",
+ "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+ "\n",
+ "\n",
+ "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n",
+ "# the progress of K-Means\n",
+ "centroids, idx, anim = runkMeans(X, initial_centroids,\n",
+ " findClosestCentroids, computeCentroids, max_iters, True)\n",
+ "anim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The initial assignments of centroids for the previous dataset were predetermined. However, in practice a good strategy for initializing the centroids is to select random examples from the training set. We will now create a function to do just that. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def kMeansInitCentroids(X, K):\n",
+ " \"\"\"\n",
+ " This function initializes K centroids that are to be used in K-means on the dataset x.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like \n",
+ " The dataset of size (m x n).\n",
+ " \n",
+ " K : int\n",
+ " The number of clusters.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " Centroids of the clusters. This is a matrix of size (K x n).\n",
+ " \"\"\"\n",
+ " m, n = X.shape\n",
+ " centroids = np.zeros((K, n))\n",
+ "\n",
+ " # Initialize the centroids to be random examples\n",
+ "\n",
+ " # Randomly reorder the indices of examples\n",
+ " randidx = np.random.permutation(X.shape[0])\n",
+ " # Take the first K examples as centroids\n",
+ " centroids = X[randidx[:K], :]\n",
+ "\n",
+ " return centroids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this exercise, you will apply K-means to image compression. We will use the image below as an example (property of Frank Wouters with permission to this class).\n",
+ "\n",
+ "\n",
+ "In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often referred to as the RGB encoding. Our image contains thousands of colors, and in this part of the exercise, we will reduce the number of colors to 16 colors.\n",
+ "\n",
+ "By making this reduction, it is possible to represent (compress) the photo in an efficient way. Specifically, we only need to store the RGB values of the 16 selected colors, and for each pixel in the image we now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n",
+ "\n",
+ "In this exercise, we will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image. Concretely, we will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3-dimensional RGB space. Once we have computed the cluster centroids on the image, we will then use the 16 colors to replace the pixels in the original image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ======= Experiment with these parameters ================\n",
+ "# We can try different values for these parameters\n",
+ "K = 16\n",
+ "max_iters = 10\n",
+ "\n",
+ "# Load an image of a bird\n",
+ "# Any png image can be read in here\n",
+ "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n",
+ "# ==========================================================\n",
+ "\n",
+ "# Divide by 255 so that all values are in the range 0 - 1\n",
+ "A /= 255\n",
+ "\n",
+ "# Reshape the image into an Nx3 matrix where N = number of pixels.\n",
+ "# Each row will contain the Red, Green and Blue pixel values\n",
+ "# This gives us our dataset matrix X that we will use K-Means on.\n",
+ "X = A.reshape(-1, 3)\n",
+ "\n",
+ "# When using K-Means, it is important to randomly initialize centroids\n",
+ "# You should complete the code in kMeansInitCentroids above before proceeding\n",
+ "initial_centroids = kMeansInitCentroids(X, K)\n",
+ "\n",
+ "# Run K-Means\n",
+ "centroids, idx = runkMeans(X, initial_centroids,\n",
+ " findClosestCentroids,\n",
+ " computeCentroids,\n",
+ " max_iters)\n",
+ "\n",
+ "# We can now recover the image from the indices (idx) by mapping each pixel\n",
+ "# (specified by its index in idx) to the centroid value\n",
+ "# Reshape the recovered image into proper dimensions\n",
+ "X_recovered = centroids[idx, :].reshape(A.shape)\n",
+ "\n",
+ "# Display the original image, rescale back by 255\n",
+ "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n",
+ "ax[0].imshow(A*255)\n",
+ "ax[0].set_title('Original')\n",
+ "ax[0].grid(False)\n",
+ "\n",
+ "# Display compressed image, rescale back by 255\n",
+ "ax[1].imshow(X_recovered*255)\n",
+ "ax[1].set_title('Compressed, with %d colors' % K)\n",
+ "ax[1].grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Principal Component Analysis
\n",
+ "In this exercise we will use principle component analysis (PCA) to perform dimensionality reduction. We will first experiment with an example 2D dataset to get intuition on how PCA works, then use it on a bigger dataset of 5000 face images.\n",
+ "\n",
+ "The following cell will plot the 2D training data. In this part of the exercise we will visualize what happens as we use PCA to reduce data from 2D to 1D. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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Y8rvGW0kdsEaJeMYZoxHrkaNbkOZ7cs+tde6u1Z/TaZ15Votkmn8ITTuc7PDRTD5wOqWkDlRUIg4N/YXXRr/jtSDt7EqSREsdwjRVu5qftLnQBweP9wULTnCz8E8EcVdSB6l1Il7nUPahoU+2bEHitJy16bDxWK1qKyFMU3Ui6yW1SVJSByhOIg4NXeJnnvmalr+E7ezJnUS/MY8tddE0S+qhDGfT+lpt7nR703sOHvw4Tzyxmt27n2p6X/w9uXdRO0lyOfAEpdIEpdJE2ydJXnTReiYmJqlWr428J287gxaJik8ykuQKqbgFHgsWLE6kwKTbQ+clXUrqjCRZaRV3YcaHPvQBdu9+ikOHDrJ791Ns2XJ9R2c96xzpsCmpM5LkCqksWs40Dp2XhER1tru5NFDWWtKVVlkXeEhvoQUd4YnzCHvDDZ/juuu2xDoXSy2nvCgq27u51FLHFzV3enhJZHJb90px0KSlttrryRodHfWZmcIejpm6SqXCypWr2LdvG413DLmXcnkdO3fep8GoPmVmOzzisEo9fgcolC2AJJ+U1AEqwjnZkh0ldYDyfk62ZEtJHaAQtgCS/FJSB6h1YUoFs/ewf/9zxdh8XhIVO6nNbNDMfmZmd6UZkLSqELsbeC3ub6Ba3cH8TQSzOu1CwtJOS3058Fhagchh0YUpPwDeDdwFfJ7CbD4viYqV1GZ2GvB2YCLdcGROowqxUul8zMbQVJc0E7el/hLwCeCFqBvMbMzMZsxsZteuXYkEVzTzD2WL0xdesWIFW7Zc/+LKqoULj8P9kqbvUa1uYOvWL6u17mMtk9rM1gJPu/uOZve5+1Z3H3X30aVLlyYWYFE021A/bl847lRXtbpH/et+FlU/OndROznsSeBx4ClgH3Bzs69R7feRklqR1d62ReFubyvdo5tVWu6+2d1Pc/eXARcCP3D3i9L6kCmipMo+46zBrg17rI/9PaV4NE/dA52UfTbqf+/e/QxDQ1tpthlCLak3Nfye0h/aSmp3/5G7r00rmKJq3heuAOPA63n22acZGVnG2rXnc+aZrzmm/33bbafywgtVjjvu7cCVzF+DDZuBdcBN1Ka6QKWk/UktdQ9El33eDayidqrlduB59uzZzre//XKee86oVt/A0XPRBw58BzMYGpqg9jh/9ImY8zdDUClpP1JS90DjvnAFuBjYBlzL/OSFL1ArMLm4ft9853Do0CWcfvorKJU2AAepjV9ez+EWukbb9PYnJXUPNC773AI0HzyDDcCxA13V6gYef/xX2qZXGlJS90Djss9bgOaDZ7WkbjTQtZx9+57peJvedotgJGei5rq6uTRP3dj8/cjAYp1HBYNNj7Rp93youOdKS9jQWVrh6eb8q04PQg/hAHhJRrOk1uN3RtorJJmv876y9j7rE1HZ3s2llrq1eGdKn+Bwjx8+S/ljPjQ00vEjsk6rLA7UUoen2Wb+tUKSdwBvpNZSDwOrGBzcyne/e0fHG/Nr77P+oKTO0NFrps2GgZWY/Qj4Zv36d0qlj1Muw5133sp5553X8ftp77P+oKTO2Pw10y+8cJDZ2Z1s3HgOIyPrEz86J8lD+SRcOnQ+MHNJvmXL9Yl+30qlwu7dz1CtbqX2aN/45I/aINx9ib639JZa6j4wt0HDbbedCtxAbeHHVehc6WJSUhdcpVLhggsuZt++bVSr1wIfoLbw43lqC0Fq/fh3vetpnY5ZEErqgms8N72C2gKQp4CDlEof4cQTT1ALXRBK6hzppGZb53L1HyV1TnS6caHmpvuPkprwVy0d2y+Ov4m/5qb7T98ndRJb96atm5ptzU33oaj60W6uvNR+52XVUjc123n5GaU9qPa7sbysWuqmX9ysxlxz08XU10mdl5HhbvvFjc7lSrL8VMJitZY8WaOjoz4zM5P4903awMAg7gdoXi1bZWBgmEOHDvYqrGNs3DjOxMRwfZCssVJpM2NjBxIvL5UwmdkOdx9t9Fpft9R5GRlufl41aJNBma+vkzovI8PqF0s7+jqp89QCql8scfV1nxpq89QXXHAx1eoGqtUNwHLgCUqlCUqlCaamblLCSHC66lOb2UIze8DMHjazR83smuRDzI5aQCmali21mRmwyN33mlkJ+ClwubtHrqTPU0stkkfNWuqWO5/Uq1f21v9Zql/JP7OLSCJiDZSZ2aCZPQQ8DXzf3e9PN6xjhb7oQiQUsZLa3Q+5+1nAacBrzezMo+8xszEzmzGzmV27diUaZB4WXYiEou3RbzO7Gvi9u38h6p4k+9SVSoWVK1exb982ojbLK5fXsXPnfZqnlb7R7ej3UjM7sf73YeDNwC+SDTFaXhZdiIQizuP3qcAPzWwn8B/U+tR3pRvWYXlZdCESijij3zuBV/Ugloa0HY9Ie4IvE83LoguRUASf1HlZdCESiuCTOk+LLkRCEHxSa9mhSHuCT2rQoguRdvT90kuRPNJ2RiJ9REktUjBKapGCUVKLFIySWqRglNQiBaOkFikYJbVIwSipRQpGSS1SMEpqkYJRUosUjJJapGCU1CIFo6QWKRgltUjBKKlFCkZJLVIwSmqRglFSixSMklqkYJTUIgUT5yjbPzSzH5rZY2b2qJld3ovARKQzLU+9BA4CV7r7g2a2GNhhZt939/9MOTYR6UDLltrd/8fdH6z/fQ/wGPDStAMTkc7EaalfZGYvo3ZW9f0NXhsDxur/3Gtmv+w2OGAJENLB04qnudDigfBiSiqeyEPbYx+7Y2bHAz8G/trdb08gqDjvORN1tEgWFE9zocUD4cXUi3hijX6bWQn4FvD1XiW0iHQmzui3AZPAY+5+ffohiUg34rTUq4H3AueZ2UP1620pxzVna4/eJy7F01xo8UB4MaUeTypH2YpIdlRRJlIwSmqRggkyqc3sy2b2tJn9PIBYgiuTNbOFZvaAmT1cj+marGMCMLNBM/uZmd0VQCyPm9kj9TGgmQDiOdHMpszsF/XfpXNSe68Q+9Rmdi6wF7jJ3c/MOJZTgVPnl8kCf5ZlmWx9RmKRu++tTzf+FLjc3e/LKqZ6XB8FRoERd1+bcSyPA6PuHkThiZl9FfiJu0+Y2QKg7O7PpPFeQbbU7v5vwP9lHQeEWSbrNXvr/yzVr0w/nc3sNODtwESWcYTIzEaAc6lNDePuz6eV0BBoUoeqWZlsr9UfdR8Cnga+7+5Zx/Ql4BPACxnHMceB75nZjnoJc5ZeDuwCvlLvnkyY2aK03kxJHVO9TPZbwBXu/mzW8bj7IXc/CzgNeK2ZZdZNMbO1wNPuviOrGBpY7e6vBtYAm+pduqwMAa8G/tHdXwX8HrgqrTdTUscQcpls/THuR8BbMwxjNbCu3o+9lVqh0s0ZxoO7/7b+59PAHcBrMwznSeDJeU9TU9SSPBVK6hZCLJM1s6VmdmL978PAm4FfZBWPu29299Pc/WXAhcAP3P2irOIxs0X1QU3qj7l/AmQ2k+LuTwH/bWavqP+nNwGpDbS2tfSyV8zsG8AfA0vM7EnganefzCicuTLZR+p9WIBPuft0RvEAnAp81cwGqX0wf9PdM59GCsgy4I7a5zFDwC3u/p1sQ+Iy4Ov1ke9fAe9P642CnNISkc7p8VukYJTUIgWjpBYpGCW1SMEoqUUKRkktUjBKapGC+X8HRMm0p9LX3wAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load the dataset into the variable X \n",
+ "data = loadmat(os.path.join('Data', 'ex7data1.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Visualize the example dataset\n",
+ "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n",
+ "pyplot.axis([0.5, 6.5, 2, 8])\n",
+ "pyplot.gca().set_aspect('equal')\n",
+ "pyplot.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now implement PCA. This consists of two steps: First, we compute the covariance matrix of the data. Then we use the SVD function to compute the eigenvectors. These will correspond to the principle components of variation in the data. \n",
+ "\n",
+ "Before using PCA, it is importan to first normalize the data by subtracting the mean value of each feature from the dataset, and scaling each dimension so that they are in the same range. After doing so we can run PCA and plot the corrosponding principle components. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pca(X):\n",
+ " \"\"\"\n",
+ " Run principal component analysis.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset to be used for computing PCA. It has dimensions (m x n)\n",
+ " where m is the number of examples (observations) and n is \n",
+ " the number of features.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " U : array_like\n",
+ " The eigenvectors, representing the computed principal components\n",
+ " of X. U has dimensions (n x n) where each column is a single \n",
+ " principal component.\n",
+ " \n",
+ " S : array_like\n",
+ " A vector of size n, contaning the singular values for each\n",
+ " principal component. Note this is the diagonal of the matrix we \n",
+ " mentioned in class.\n",
+ " \"\"\"\n",
+ " # Useful values\n",
+ " m, n = X.shape\n",
+ " \n",
+ " U = np.zeros(n)\n",
+ " S = np.zeros(n)\n",
+ "\n",
+ " X_trans = X.transpose()\n",
+ " Sigma = (1/m)*(X_trans.dot(X))\n",
+ " U, S, V = np.linalg.svd(Sigma)\n",
+ "\n",
+ " return U, S"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def featureNormalize(X):\n",
+ " \"\"\"\n",
+ " Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+ " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+ " working with learning algorithms.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+ " and n is the number of dimensions for each example.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " X_norm : array_like\n",
+ " The normalized input dataset.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of size n corresponding to the mean for each dimension across all examples.\n",
+ "\n",
+ " sigma : array_like\n",
+ " A vector of size n corresponding to the standard deviations for each dimension across\n",
+ " all examples.\n",
+ " \"\"\"\n",
+ " mu = np.mean(X, axis=0)\n",
+ " X_norm = X - mu\n",
+ "\n",
+ " sigma = np.std(X_norm, axis=0, ddof=1)\n",
+ " X_norm /= sigma\n",
+ " return X_norm, mu, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top eigenvector: U[:, 0] = [-0.707107 -0.707107]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Before running PCA, it is important to first normalize X\n",
+ "X_norm, mu, sigma = featureNormalize(X)\n",
+ "\n",
+ "# Run PCA\n",
+ "U, S = pca(X_norm)\n",
+ "\n",
+ "# Draw the eigenvectors centered at mean of data. These lines show the\n",
+ "# directions of maximum variations in the dataset.\n",
+ "fig, ax = pyplot.subplots()\n",
+ "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n",
+ "\n",
+ "for i in range(2):\n",
+ " ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n",
+ " head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n",
+ "\n",
+ "ax.axis([0.5, 6.5, 2, 8])\n",
+ "ax.set_aspect('equal')\n",
+ "ax.grid(False)\n",
+ "\n",
+ "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After computing the principal components, we can use them to reduce the feature dimension of our dataset by projecting each example onto a lower dimensional space. In this part of the exercise, we will use the eigenvectors to project our dataset onto a 1-dimensional space. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def projectData(X, U, K):\n",
+ " \"\"\"\n",
+ " Computes the reduced data representation when projecting only \n",
+ " on to the top K eigenvectors.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input dataset of shape (m x n). The dataset is assumed to be \n",
+ " normalized.\n",
+ " \n",
+ " U : array_like\n",
+ " The computed eigenvectors using PCA. This is a matrix of \n",
+ " shape (n x n). Each column in the matrix represents a single\n",
+ " eigenvector (or a single principal component).\n",
+ " \n",
+ " K : int\n",
+ " Number of dimensions to project onto. Must be smaller than n.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " Z : array_like\n",
+ " The projects of the dataset onto the top K eigenvectors. \n",
+ " This will be a matrix of shape (m x k).\n",
+ " \"\"\"\n",
+ " Z = np.zeros((X.shape[0], K))\n",
+ " m = X.shape[0]\n",
+ "\n",
+ " for i in range(m):\n",
+ " for j in range(K):\n",
+ " x = X[i, :].transpose()\n",
+ " Z[i,j] = np.dot(x.transpose(), U[:, j])\n",
+ "\n",
+ " return Z"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will test our function by projecting the first example onto the first dimension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Projection of the first example: 1.481274\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Project the data onto K = 1 dimension\n",
+ "K = 1\n",
+ "Z = projectData(X_norm, U, K)\n",
+ "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After projecting the data onto the lower dimensional space, we can appoximately recover the data by projecting them back onto the original high dimensional space. We will do so for the first example we saw and create a figure to visualize the process."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def recoverData(Z, U, K):\n",
+ " \"\"\"\n",
+ " Recovers an approximation of the original data when using the \n",
+ " projected data.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " Z : array_like\n",
+ " The reduced data after applying PCA. This is a matrix\n",
+ " of shape (m x K).\n",
+ " \n",
+ " U : array_like\n",
+ " The eigenvectors (principal components) computed by PCA.\n",
+ " This is a matrix of shape (n x n) where each column represents\n",
+ " a single eigenvector.\n",
+ " \n",
+ " K : int\n",
+ " The number of principal components retained\n",
+ " (should be less than n).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " X_rec : array_like\n",
+ " The recovered data after transformation back to the original \n",
+ " dataset space. This is a matrix of shape (m x n), where m is \n",
+ " the number of examples and n is the dimensions (number of\n",
+ " features) of original datatset.\n",
+ " \"\"\"\n",
+ " X_rec = np.zeros((Z.shape[0], U.shape[0]))\n",
+ " m, n = X.shape\n",
+ "\n",
+ " for i in range(m):\n",
+ " for j in range(n):\n",
+ " v = Z[i,:]\n",
+ " X_rec[i,j] = np.dot(v, U[j, :K])\n",
+ "\n",
+ " # =============================================================\n",
+ " return X_rec"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Approximation of the first example: [-1.047419 -1.047419]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X_rec = recoverData(Z, U, K)\n",
+ "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n",
+ "\n",
+ "# Plot the normalized dataset (returned from featureNormalize)\n",
+ "fig, ax = pyplot.subplots(figsize=(5, 5))\n",
+ "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n",
+ "ax.set_aspect('equal')\n",
+ "ax.grid(False)\n",
+ "pyplot.axis([-3, 2.75, -3, 2.75])\n",
+ "\n",
+ "# Draw lines connecting the projected points to the original points\n",
+ "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n",
+ "for xnorm, xrec in zip(X_norm, X_rec):\n",
+ " ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now run PCA on face images to see how it can be used in practice for dimension reduction. We have a dataset of face images, each 32 x 32 in grayscale. The following cells will load and visualize the first 100 of these face images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+ " \"\"\"\n",
+ " Displays 2D data in a nice grid.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input data of size (m x n) where m is the number of examples and n is the number of\n",
+ " features.\n",
+ "\n",
+ " example_width : int, optional\n",
+ " THe width of each 2-D image in pixels. If not provided, the image is assumed to be square,\n",
+ " and the width is the floor of the square root of total number of pixels.\n",
+ "\n",
+ " figsize : tuple, optional\n",
+ " A 2-element tuple indicating the width and height of figure in inches.\n",
+ " \"\"\"\n",
+ " # Compute rows, cols\n",
+ " if X.ndim == 2:\n",
+ " m, n = X.shape\n",
+ " elif X.ndim == 1:\n",
+ " n = X.size\n",
+ " m = 1\n",
+ " X = X[None] # Promote to a 2 dimensional array\n",
+ " else:\n",
+ " raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+ "\n",
+ " example_width = example_width or int(np.round(np.sqrt(n)))\n",
+ " example_height = int(n / example_width)\n",
+ "\n",
+ " # Compute number of items to display\n",
+ " display_rows = int(np.floor(np.sqrt(m)))\n",
+ " display_cols = int(np.ceil(m / display_rows))\n",
+ "\n",
+ " fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n",
+ " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+ "\n",
+ " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+ "\n",
+ " for i, ax in enumerate(ax_array):\n",
+ " ax.imshow(X[i].reshape(example_height, example_width, order='F'), cmap='gray')\n",
+ " ax.axis('off')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load Face dataset\n",
+ "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Display the first 100 faces in the dataset\n",
+ "displayData(X[:100, :], figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run PCA on the face dataset, we first normalize the dataset by subtracting the mean of each feature from the data matrix X. After running PCA we visualize the principal components by reshaping each into a 32 x 32 matrix that corrosponds to the pixels in the original dataset. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# normalize X by subtracting the mean value from each feature\n",
+ "X_norm, mu, sigma = featureNormalize(X)\n",
+ "\n",
+ "# Run PCA\n",
+ "U, S = pca(X_norm)\n",
+ "\n",
+ "# Visualize the top 36 eigenvectors found\n",
+ "displayData(U[:, :36].T, figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the principle components, we can use them to reduce the dimension of the face dataset. This allows us to use our learning algorithm with a smaller input size, helping to speed it up. The following cell will project the face dataset onto only the first 100 principle components. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The projected data Z has a shape of: (5000, 100)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Project images to the eigen space using the top k eigenvectors \n",
+ "# If you are applying a machine learning algorithm \n",
+ "K = 100\n",
+ "Z = projectData(X_norm, U, K)\n",
+ "\n",
+ "print('The projected data Z has a shape of: ', Z.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Project images to the eigen space using the top K eigen vectors and \n",
+ "# visualize only using those K dimensions\n",
+ "# Compare to the original input, which is also displayed\n",
+ "K = 100\n",
+ "X_rec = recoverData(Z, U, K)\n",
+ "\n",
+ "# Display normalized data\n",
+ "displayData(X_norm[:100, :], figsize=(6, 6))\n",
+ "pyplot.gcf().suptitle('Original faces')\n",
+ "\n",
+ "# Display reconstructed data from only k eigenfaces\n",
+ "displayData(X_rec[:100, :], figsize=(6, 6))\n",
+ "pyplot.gcf().suptitle('Recovered faces')\n",
+ "pass"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex7/Data/bird_small.mat b/ex7/Data/bird_small.mat
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diff --git a/ex7/ex7.ipynb b/ex7/ex7.ipynb
new file mode 100644
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--- /dev/null
+++ b/ex7/ex7.ipynb
@@ -0,0 +1,5471 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 7: K-means Clustering and Principal Component Analysis
\n",
+ "
Introduction
\n",
+ "In this exercise, we will implement the K-means clustering algorithm and apply it to compress an image. In the second part, we will use principle component analasys to find a low-dimensional representation of face images. To begein we import necessary libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Import regular expressions to process emails\n",
+ "import re\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "import matplotlib as mpl\n",
+ "\n",
+ "from IPython.display import HTML, display, clear_output\n",
+ "\n",
+ "try:\n",
+ " pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n",
+ "except ValueError:\n",
+ " pyplot.rcParams[\"animation.html\"] = \"html5\"\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from matplotlib.animation import FuncAnimation\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 K-means Clustering
\n",
+ "In this exercise, we will implement the K-means algorithm and use it for image compression. We begin with an example 2D dataset that will help us gain an intuition of how the K-means algorithm works. After the, we will use K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common.\n",
+ "\n",
+ "The algorithm works by, with an initial set of centroids, assigning each data point to its closest centroid. This will be accomplishes in the findClosestCentroids function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def findClosestCentroids(X, centroids):\n",
+ " \"\"\"\n",
+ " Computes the centroid memberships for every example.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of size (m, n) where each row is a single example. \n",
+ " That is, we have m examples each of n dimensions.\n",
+ " \n",
+ " centroids : array_like\n",
+ " The k-means centroids of size (K, n). K is the number\n",
+ " of clusters, and n is the the data dimension.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " idx : array_like\n",
+ " A vector of size (m, ) which holds the centroids assignment for each\n",
+ " example (row) in the dataset X.\n",
+ " \"\"\"\n",
+ " K = centroids.shape[0]\n",
+ " m = X.shape[0]\n",
+ " idx = np.zeros(X.shape[0], dtype=int)\n",
+ " \n",
+ " for i in range(m):\n",
+ " tempX = X[i,:]\n",
+ " tempSums = np.zeros(K)\n",
+ " for j in range(K):\n",
+ " tempCentroid = centroids[j,:]\n",
+ " tempDiff = tempX - tempCentroid\n",
+ " tempDiff = np.square(tempDiff)\n",
+ " tempSums[j] = np.sum(tempDiff)\n",
+ " idx[i] = np.argmin(tempSums)\n",
+ "\n",
+ " return idx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell tests our code (we should see the closest centroids appear as [0 2 1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Closest centroids for the first 3 examples:\n",
+ "[0 2 1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load an example dataset that we will be using\n",
+ "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Select an initial set of centroids\n",
+ "K = 3 # 3 Centroids\n",
+ "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+ "\n",
+ "# Find the closest centroids for the examples using the initial_centroids\n",
+ "idx = findClosestCentroids(X, initial_centroids)\n",
+ "\n",
+ "print('Closest centroids for the first 3 examples:')\n",
+ "print(idx[:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The next step in the algorithm computes, for each centroid, the average of the points assigned to it. This will be accomplished in the computeCentroids function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def computeCentroids(X, idx, K):\n",
+ " \"\"\"\n",
+ " Returns the new centroids by computing the means of the data points\n",
+ " assigned to each centroid.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The datset where each row is a single data point. That is, it \n",
+ " is a matrix of size (m, n) where there are m datapoints each\n",
+ " having n dimensions. \n",
+ " \n",
+ " idx : array_like \n",
+ " A vector (size m) of centroid assignments (i.e. each entry in range [0 ... K-1])\n",
+ " for each example.\n",
+ " \n",
+ " K : int\n",
+ " Number of clusters\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " A matrix of size (K, n) where each row is the mean of the data \n",
+ " points assigned to it.\n",
+ " \"\"\"\n",
+ " # Useful variables\n",
+ " m, n = X.shape\n",
+ " centroids = np.zeros((K, n))\n",
+ " \n",
+ " for i in range(K):\n",
+ " # Find examples which fall into cluster k\n",
+ " sel = np.argwhere(idx==i)\n",
+ " centroids[i,:] = np.mean(X[sel,:], axis=0)\n",
+ "\n",
+ " return centroids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will test this function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Centroids computed after initial finding of closest centroids:\n",
+ "[[2.42830111 3.15792418]\n",
+ " [5.81350331 2.63365645]\n",
+ " [7.11938687 3.6166844 ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute means based on the closest centroids found in the previous part.\n",
+ "centroids = computeCentroids(X, idx, K)\n",
+ "\n",
+ "print('Centroids computed after initial finding of closest centroids:')\n",
+ "print(centroids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have all the pieces necessary to run the K-means algorithm, as all we do is repeat the last two steps for a set number of iterations. As we do so, the means will converge to the centers of any clusters in our dataset. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotProgresskMeans(i, X, centroid_history, idx_history):\n",
+ " \"\"\"\n",
+ " A helper function that displays the progress of k-Means as it is running. It is intended for use\n",
+ " only with 2D data. It plots data points with colors assigned to each centroid. With the\n",
+ " previous centroids, it also plots a line between the previous locations and current locations\n",
+ " of the centroids.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " i : int\n",
+ " Current iteration number of k-means. Used for matplotlib animation function.\n",
+ "\n",
+ " X : array_like\n",
+ " The dataset, which is a matrix (m x n). Note since the plot only supports 2D data, n should\n",
+ " be equal to 2.\n",
+ "\n",
+ " centroid_history : list\n",
+ " A list of computed centroids for all iteration.\n",
+ "\n",
+ " idx_history : list\n",
+ " A list of computed assigned indices for all iterations.\n",
+ " \"\"\"\n",
+ " K = centroid_history[0].shape[0]\n",
+ " pyplot.gcf().clf()\n",
+ " cmap = pyplot.cm.rainbow\n",
+ " norm = mpl.colors.Normalize(vmin=0, vmax=2)\n",
+ "\n",
+ " for k in range(K):\n",
+ " current = np.stack([c[k, :] for c in centroid_history[:i+1]], axis=0)\n",
+ " pyplot.plot(current[:, 0], current[:, 1],\n",
+ " '-Xk',\n",
+ " mec='k',\n",
+ " lw=2,\n",
+ " ms=10,\n",
+ " mfc=cmap(norm(k)),\n",
+ " mew=2)\n",
+ "\n",
+ " pyplot.scatter(X[:, 0], X[:, 1],\n",
+ " c=idx_history[i],\n",
+ " cmap=cmap,\n",
+ " marker='o',\n",
+ " s=8**2,\n",
+ " linewidths=1,)\n",
+ " pyplot.grid(False)\n",
+ " pyplot.title('Iteration number %d' % (i+1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def runkMeans(X, centroids, findClosestCentroids, computeCentroids,\n",
+ " max_iters=10, plot_progress=False):\n",
+ " \"\"\"\n",
+ " Runs the K-means algorithm.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The data set of size (m, n). Each row of X is a single example of n dimensions. The\n",
+ " data set is a total of m examples.\n",
+ "\n",
+ " centroids : array_like\n",
+ " Initial centroid location for each clusters. This is a matrix of size (K, n). K is the total\n",
+ " number of clusters and n is the dimensions of each data point.\n",
+ "\n",
+ " findClosestCentroids : func\n",
+ " A function (implemented by student) reference which computes the cluster assignment for\n",
+ " each example.\n",
+ "\n",
+ " computeCentroids : func\n",
+ " A function(implemented by student) reference which computes the centroid of each cluster.\n",
+ "\n",
+ " max_iters : int, optional\n",
+ " Specifies the total number of interactions of K-Means to execute.\n",
+ "\n",
+ " plot_progress : bool, optional\n",
+ " A flag that indicates if the function should also plot its progress as the learning happens.\n",
+ " This is set to false by default.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " A (K x n) matrix of the computed (updated) centroids.\n",
+ " idx : array_like\n",
+ " A vector of size (m,) for cluster assignment for each example in the dataset. Each entry\n",
+ " in idx is within the range [0 ... K-1].\n",
+ "\n",
+ " anim : FuncAnimation, optional\n",
+ " A matplotlib animation object which can be used to embed a video within the jupyter\n",
+ " notebook. This is only returned if `plot_progress` is `True`.\n",
+ " \"\"\"\n",
+ " K = centroids.shape[0]\n",
+ " idx = None\n",
+ " idx_history = []\n",
+ " centroid_history = []\n",
+ "\n",
+ " for i in range(max_iters):\n",
+ " idx = findClosestCentroids(X, centroids)\n",
+ "\n",
+ " if plot_progress:\n",
+ " idx_history.append(idx)\n",
+ " centroid_history.append(centroids)\n",
+ "\n",
+ " centroids = computeCentroids(X, idx, K)\n",
+ "\n",
+ " if plot_progress:\n",
+ " fig = pyplot.figure()\n",
+ " anim = FuncAnimation(fig, plotProgresskMeans,\n",
+ " frames=max_iters,\n",
+ " interval=500,\n",
+ " repeat_delay=2,\n",
+ " fargs=(X, centroid_history, idx_history))\n",
+ " return centroids, idx, anim\n",
+ "\n",
+ " return centroids, idx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will run K-means on our dataset and show each step along the way to give an intuition for how the algorithm works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ ],
+ "text/plain": [
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+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load an example dataset\n",
+ "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+ "\n",
+ "# Settings for running K-Means\n",
+ "K = 3\n",
+ "max_iters = 10\n",
+ "\n",
+ "# For consistency, here we set centroids to specific values\n",
+ "# but in practice you want to generate them automatically, such as by\n",
+ "# settings them to be random examples (as can be seen in\n",
+ "# kMeansInitCentroids).\n",
+ "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+ "\n",
+ "\n",
+ "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n",
+ "# the progress of K-Means\n",
+ "centroids, idx, anim = runkMeans(X, initial_centroids,\n",
+ " findClosestCentroids, computeCentroids, max_iters, True)\n",
+ "anim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The initial assignments of centroids for the previous dataset were predetermined. However, in practice a good strategy for initializing the centroids is to select random examples from the training set. We will now create a function to do just that. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def kMeansInitCentroids(X, K):\n",
+ " \"\"\"\n",
+ " This function initializes K centroids that are to be used in K-means on the dataset x.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like \n",
+ " The dataset of size (m x n).\n",
+ " \n",
+ " K : int\n",
+ " The number of clusters.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " centroids : array_like\n",
+ " Centroids of the clusters. This is a matrix of size (K x n).\n",
+ " \"\"\"\n",
+ " m, n = X.shape\n",
+ " centroids = np.zeros((K, n))\n",
+ "\n",
+ " # Initialize the centroids to be random examples\n",
+ "\n",
+ " # Randomly reorder the indices of examples\n",
+ " randidx = np.random.permutation(X.shape[0])\n",
+ " # Take the first K examples as centroids\n",
+ " centroids = X[randidx[:K], :]\n",
+ "\n",
+ " return centroids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this exercise, you will apply K-means to image compression. We will use the image below as an example (property of Frank Wouters with permission to this class).\n",
+ "\n",
+ "\n",
+ "In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often referred to as the RGB encoding. Our image contains thousands of colors, and in this part of the exercise, we will reduce the number of colors to 16 colors.\n",
+ "\n",
+ "By making this reduction, it is possible to represent (compress) the photo in an efficient way. Specifically, we only need to store the RGB values of the 16 selected colors, and for each pixel in the image we now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n",
+ "\n",
+ "In this exercise, we will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image. Concretely, we will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3-dimensional RGB space. Once we have computed the cluster centroids on the image, we will then use the 16 colors to replace the pixels in the original image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ======= Experiment with these parameters ================\n",
+ "# We can try different values for these parameters\n",
+ "K = 16\n",
+ "max_iters = 10\n",
+ "\n",
+ "# Load an image of a bird\n",
+ "# Any png image can be read in here\n",
+ "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n",
+ "# ==========================================================\n",
+ "\n",
+ "# Divide by 255 so that all values are in the range 0 - 1\n",
+ "A /= 255\n",
+ "\n",
+ "# Reshape the image into an Nx3 matrix where N = number of pixels.\n",
+ "# Each row will contain the Red, Green and Blue pixel values\n",
+ "# This gives us our dataset matrix X that we will use K-Means on.\n",
+ "X = A.reshape(-1, 3)\n",
+ "\n",
+ "# When using K-Means, it is important to randomly initialize centroids\n",
+ "# You should complete the code in kMeansInitCentroids above before proceeding\n",
+ "initial_centroids = kMeansInitCentroids(X, K)\n",
+ "\n",
+ "# Run K-Means\n",
+ "centroids, idx = runkMeans(X, initial_centroids,\n",
+ " findClosestCentroids,\n",
+ " computeCentroids,\n",
+ " max_iters)\n",
+ "\n",
+ "# We can now recover the image from the indices (idx) by mapping each pixel\n",
+ "# (specified by its index in idx) to the centroid value\n",
+ "# Reshape the recovered image into proper dimensions\n",
+ "X_recovered = centroids[idx, :].reshape(A.shape)\n",
+ "\n",
+ "# Display the original image, rescale back by 255\n",
+ "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n",
+ "ax[0].imshow(A*255)\n",
+ "ax[0].set_title('Original')\n",
+ "ax[0].grid(False)\n",
+ "\n",
+ "# Display compressed image, rescale back by 255\n",
+ "ax[1].imshow(X_recovered*255)\n",
+ "ax[1].set_title('Compressed, with %d colors' % K)\n",
+ "ax[1].grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Principal Component Analysis
\n",
+ "In this exercise we will use principle component analysis (PCA) to perform dimensionality reduction. We will first experiment with an example 2D dataset to get intuition on how PCA works, then use it on a bigger dataset of 5000 face images.\n",
+ "\n",
+ "The following cell will plot the 2D training data. In this part of the exercise we will visualize what happens as we use PCA to reduce data from 2D to 1D. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load the dataset into the variable X \n",
+ "data = loadmat(os.path.join('Data', 'ex7data1.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Visualize the example dataset\n",
+ "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n",
+ "pyplot.axis([0.5, 6.5, 2, 8])\n",
+ "pyplot.gca().set_aspect('equal')\n",
+ "pyplot.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now implement PCA. This consists of two steps: First, we compute the covariance matrix of the data. Then we use the SVD function to compute the eigenvectors. These will correspond to the principle components of variation in the data. \n",
+ "\n",
+ "Before using PCA, it is importan to first normalize the data by subtracting the mean value of each feature from the dataset, and scaling each dimension so that they are in the same range. After doing so we can run PCA and plot the corrosponding principle components. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pca(X):\n",
+ " \"\"\"\n",
+ " Run principal component analysis.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset to be used for computing PCA. It has dimensions (m x n)\n",
+ " where m is the number of examples (observations) and n is \n",
+ " the number of features.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " U : array_like\n",
+ " The eigenvectors, representing the computed principal components\n",
+ " of X. U has dimensions (n x n) where each column is a single \n",
+ " principal component.\n",
+ " \n",
+ " S : array_like\n",
+ " A vector of size n, contaning the singular values for each\n",
+ " principal component. Note this is the diagonal of the matrix we \n",
+ " mentioned in class.\n",
+ " \"\"\"\n",
+ " # Useful values\n",
+ " m, n = X.shape\n",
+ " \n",
+ " U = np.zeros(n)\n",
+ " S = np.zeros(n)\n",
+ "\n",
+ " X_trans = X.transpose()\n",
+ " Sigma = (1/m)*(X_trans.dot(X))\n",
+ " U, S, V = np.linalg.svd(Sigma)\n",
+ "\n",
+ " return U, S"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def featureNormalize(X):\n",
+ " \"\"\"\n",
+ " Normalizes the features in X returns a normalized version of X where the mean value of each\n",
+ " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n",
+ " working with learning algorithms.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " An dataset which is a (m x n) matrix, where m is the number of examples,\n",
+ " and n is the number of dimensions for each example.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " X_norm : array_like\n",
+ " The normalized input dataset.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of size n corresponding to the mean for each dimension across all examples.\n",
+ "\n",
+ " sigma : array_like\n",
+ " A vector of size n corresponding to the standard deviations for each dimension across\n",
+ " all examples.\n",
+ " \"\"\"\n",
+ " mu = np.mean(X, axis=0)\n",
+ " X_norm = X - mu\n",
+ "\n",
+ " sigma = np.std(X_norm, axis=0, ddof=1)\n",
+ " X_norm /= sigma\n",
+ " return X_norm, mu, sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top eigenvector: U[:, 0] = [-0.707107 -0.707107]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Before running PCA, it is important to first normalize X\n",
+ "X_norm, mu, sigma = featureNormalize(X)\n",
+ "\n",
+ "# Run PCA\n",
+ "U, S = pca(X_norm)\n",
+ "\n",
+ "# Draw the eigenvectors centered at mean of data. These lines show the\n",
+ "# directions of maximum variations in the dataset.\n",
+ "fig, ax = pyplot.subplots()\n",
+ "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n",
+ "\n",
+ "for i in range(2):\n",
+ " ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n",
+ " head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n",
+ "\n",
+ "ax.axis([0.5, 6.5, 2, 8])\n",
+ "ax.set_aspect('equal')\n",
+ "ax.grid(False)\n",
+ "\n",
+ "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After computing the principal components, we can use them to reduce the feature dimension of our dataset by projecting each example onto a lower dimensional space. In this part of the exercise, we will use the eigenvectors to project our dataset onto a 1-dimensional space. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def projectData(X, U, K):\n",
+ " \"\"\"\n",
+ " Computes the reduced data representation when projecting only \n",
+ " on to the top K eigenvectors.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input dataset of shape (m x n). The dataset is assumed to be \n",
+ " normalized.\n",
+ " \n",
+ " U : array_like\n",
+ " The computed eigenvectors using PCA. This is a matrix of \n",
+ " shape (n x n). Each column in the matrix represents a single\n",
+ " eigenvector (or a single principal component).\n",
+ " \n",
+ " K : int\n",
+ " Number of dimensions to project onto. Must be smaller than n.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " Z : array_like\n",
+ " The projects of the dataset onto the top K eigenvectors. \n",
+ " This will be a matrix of shape (m x k).\n",
+ " \"\"\"\n",
+ " Z = np.zeros((X.shape[0], K))\n",
+ " m = X.shape[0]\n",
+ "\n",
+ " for i in range(m):\n",
+ " for j in range(K):\n",
+ " x = X[i, :].transpose()\n",
+ " Z[i,j] = np.dot(x.transpose(), U[:, j])\n",
+ "\n",
+ " return Z"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will test our function by projecting the first example onto the first dimension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Projection of the first example: 1.481274\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Project the data onto K = 1 dimension\n",
+ "K = 1\n",
+ "Z = projectData(X_norm, U, K)\n",
+ "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After projecting the data onto the lower dimensional space, we can appoximately recover the data by projecting them back onto the original high dimensional space. We will do so for the first example we saw and create a figure to visualize the process."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def recoverData(Z, U, K):\n",
+ " \"\"\"\n",
+ " Recovers an approximation of the original data when using the \n",
+ " projected data.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " Z : array_like\n",
+ " The reduced data after applying PCA. This is a matrix\n",
+ " of shape (m x K).\n",
+ " \n",
+ " U : array_like\n",
+ " The eigenvectors (principal components) computed by PCA.\n",
+ " This is a matrix of shape (n x n) where each column represents\n",
+ " a single eigenvector.\n",
+ " \n",
+ " K : int\n",
+ " The number of principal components retained\n",
+ " (should be less than n).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " X_rec : array_like\n",
+ " The recovered data after transformation back to the original \n",
+ " dataset space. This is a matrix of shape (m x n), where m is \n",
+ " the number of examples and n is the dimensions (number of\n",
+ " features) of original datatset.\n",
+ " \"\"\"\n",
+ " X_rec = np.zeros((Z.shape[0], U.shape[0]))\n",
+ " m, n = X.shape\n",
+ "\n",
+ " for i in range(m):\n",
+ " for j in range(n):\n",
+ " v = Z[i,:]\n",
+ " X_rec[i,j] = np.dot(v, U[j, :K])\n",
+ "\n",
+ " # =============================================================\n",
+ " return X_rec"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Approximation of the first example: [-1.047419 -1.047419]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "X_rec = recoverData(Z, U, K)\n",
+ "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n",
+ "\n",
+ "# Plot the normalized dataset (returned from featureNormalize)\n",
+ "fig, ax = pyplot.subplots(figsize=(5, 5))\n",
+ "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n",
+ "ax.set_aspect('equal')\n",
+ "ax.grid(False)\n",
+ "pyplot.axis([-3, 2.75, -3, 2.75])\n",
+ "\n",
+ "# Draw lines connecting the projected points to the original points\n",
+ "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n",
+ "for xnorm, xrec in zip(X_norm, X_rec):\n",
+ " ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will now run PCA on face images to see how it can be used in practice for dimension reduction. We have a dataset of face images, each 32 x 32 in grayscale. The following cells will load and visualize the first 100 of these face images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def displayData(X, example_width=None, figsize=(10, 10)):\n",
+ " \"\"\"\n",
+ " Displays 2D data in a nice grid.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The input data of size (m x n) where m is the number of examples and n is the number of\n",
+ " features.\n",
+ "\n",
+ " example_width : int, optional\n",
+ " THe width of each 2-D image in pixels. If not provided, the image is assumed to be square,\n",
+ " and the width is the floor of the square root of total number of pixels.\n",
+ "\n",
+ " figsize : tuple, optional\n",
+ " A 2-element tuple indicating the width and height of figure in inches.\n",
+ " \"\"\"\n",
+ " # Compute rows, cols\n",
+ " if X.ndim == 2:\n",
+ " m, n = X.shape\n",
+ " elif X.ndim == 1:\n",
+ " n = X.size\n",
+ " m = 1\n",
+ " X = X[None] # Promote to a 2 dimensional array\n",
+ " else:\n",
+ " raise IndexError('Input X should be 1 or 2 dimensional.')\n",
+ "\n",
+ " example_width = example_width or int(np.round(np.sqrt(n)))\n",
+ " example_height = int(n / example_width)\n",
+ "\n",
+ " # Compute number of items to display\n",
+ " display_rows = int(np.floor(np.sqrt(m)))\n",
+ " display_cols = int(np.ceil(m / display_rows))\n",
+ "\n",
+ " fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n",
+ " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n",
+ "\n",
+ " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n",
+ "\n",
+ " for i, ax in enumerate(ax_array):\n",
+ " ax.imshow(X[i].reshape(example_height, example_width, order='F'), cmap='gray')\n",
+ " ax.axis('off')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load Face dataset\n",
+ "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n",
+ "X = data['X']\n",
+ "\n",
+ "# Display the first 100 faces in the dataset\n",
+ "displayData(X[:100, :], figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run PCA on the face dataset, we first normalize the dataset by subtracting the mean of each feature from the data matrix X. After running PCA we visualize the principal components by reshaping each into a 32 x 32 matrix that corrosponds to the pixels in the original dataset. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# normalize X by subtracting the mean value from each feature\n",
+ "X_norm, mu, sigma = featureNormalize(X)\n",
+ "\n",
+ "# Run PCA\n",
+ "U, S = pca(X_norm)\n",
+ "\n",
+ "# Visualize the top 36 eigenvectors found\n",
+ "displayData(U[:, :36].T, figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the principle components, we can use them to reduce the dimension of the face dataset. This allows us to use our learning algorithm with a smaller input size, helping to speed it up. The following cell will project the face dataset onto only the first 100 principle components. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The projected data Z has a shape of: (5000, 100)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Project images to the eigen space using the top k eigenvectors \n",
+ "# If you are applying a machine learning algorithm \n",
+ "K = 100\n",
+ "Z = projectData(X_norm, U, K)\n",
+ "\n",
+ "print('The projected data Z has a shape of: ', Z.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Project images to the eigen space using the top K eigen vectors and \n",
+ "# visualize only using those K dimensions\n",
+ "# Compare to the original input, which is also displayed\n",
+ "K = 100\n",
+ "X_rec = recoverData(Z, U, K)\n",
+ "\n",
+ "# Display normalized data\n",
+ "displayData(X_norm[:100, :], figsize=(6, 6))\n",
+ "pyplot.gcf().suptitle('Original faces')\n",
+ "\n",
+ "# Display reconstructed data from only k eigenfaces\n",
+ "displayData(X_rec[:100, :], figsize=(6, 6))\n",
+ "pyplot.gcf().suptitle('Recovered faces')\n",
+ "pass"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex8/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/ex8/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..7dd4071
--- /dev/null
+++ b/ex8/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,969 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 8: Anomaly Detection and Recommender Systems
\n",
+ "
Introduction
\n",
+ "In this exercise, we will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, we will use collaborative filtering to build a recommender system for movies. To begin, we import necessary libraries. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "from os.path import join\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "import matplotlib as mpl\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Anomaly detection
\n",
+ "In this exercise, we will implement an anomaly detection algorithm to detect anomalous behavior in server computers. The features measure the throughput (mb/s) and latency (ms) of response of each server. Whil operating, we collected m = 307 example of how they were behaving. We suspect the vast majority are \"normal\", but there may be some anomalous examples.\n",
+ "\n",
+ "We will use a Gaussian model to detect anomalous example in the dataset. We begin on a 2D dataset to visualize what the algorithm is doing. The following cell will visualize the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# The following command loads the dataset.\n",
+ "data = loadmat(os.path.join('Data', 'ex8data1.mat'))\n",
+ "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Visualize the example dataset\n",
+ "pyplot.plot(X[:, 0], X[:, 1], 'bx', mew=2, mec='k', ms=6)\n",
+ "pyplot.axis([0, 30, 0, 30])\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To perform anomaly detection, we first need to fit a model to the data's distribution. To do so, we need to estimate the mean and variance parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def estimateGaussian(X):\n",
+ " \"\"\"\n",
+ " This function estimates the parameters of a Gaussian distribution\n",
+ " using a provided dataset.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x n) with each n-dimensional \n",
+ " data point in one row, and each total of m data points.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " mu : array_like \n",
+ " A vector of shape (n,) containing the means of each dimension.\n",
+ " \n",
+ " sigma2 : array_like\n",
+ " A vector of shape (n,) containing the computed\n",
+ " variances of each dimension.\n",
+ " \"\"\"\n",
+ " m, n = X.shape\n",
+ " mu = np.zeros(n)\n",
+ " sigma2 = np.zeros(n)\n",
+ "\n",
+ " mu = np.mean(X, axis=0)\n",
+ " sigma2 = np.var(X, axis=0)\n",
+ " return mu, sigma2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cells visualize this distribution and how our dataset falls into it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def multivariateGaussian(X, mu, Sigma2):\n",
+ " \"\"\"\n",
+ " Computes the probability density function of the multivariate gaussian distribution.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x n). Where there are m examples of n-dimensions.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of shape (n,) contains the means for each dimension (feature).\n",
+ "\n",
+ " Sigma2 : array_like\n",
+ " Either a vector of shape (n,) containing the variances of independent features\n",
+ " (i.e. it is the diagonal of the correlation matrix), or the full\n",
+ " correlation matrix of shape (n x n) which can represent dependent features.\n",
+ "\n",
+ " Returns\n",
+ " ------\n",
+ " p : array_like\n",
+ " A vector of shape (m,) which contains the computed probabilities at each of the\n",
+ " provided examples.\n",
+ " \"\"\"\n",
+ " k = mu.size\n",
+ "\n",
+ " # if sigma is given as a diagonal, compute the matrix\n",
+ " if Sigma2.ndim == 1:\n",
+ " Sigma2 = np.diag(Sigma2)\n",
+ "\n",
+ " X = X - mu\n",
+ " p = (2 * np.pi) ** (- k / 2) * np.linalg.det(Sigma2) ** (-0.5)\\\n",
+ " * np.exp(-0.5 * np.sum(np.dot(X, np.linalg.pinv(Sigma2)) * X, axis=1))\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualizeFit(X, mu, sigma2):\n",
+ " \"\"\"\n",
+ " Visualize the dataset and its estimated distribution.\n",
+ " This visualization shows you the probability density function of the Gaussian distribution.\n",
+ " Each example has a location (x1, x2) that depends on its feature values.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x 2). Where there are m examples of 2-dimensions. We need at most\n",
+ " 2-D features to be able to visualize the distribution.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of shape (n,) contains the means for each dimension (feature).\n",
+ "\n",
+ " sigma2 : array_like\n",
+ " Either a vector of shape (n,) containing the variances of independent features\n",
+ " (i.e. it is the diagonal of the correlation matrix), or the full\n",
+ " correlation matrix of shape (n x n) which can represent dependent features.\n",
+ " \"\"\"\n",
+ "\n",
+ " X1, X2 = np.meshgrid(np.arange(0, 35.5, 0.5), np.arange(0, 35.5, 0.5))\n",
+ " Z = multivariateGaussian(np.stack([X1.ravel(), X2.ravel()], axis=1), mu, sigma2)\n",
+ " Z = Z.reshape(X1.shape)\n",
+ "\n",
+ " pyplot.plot(X[:, 0], X[:, 1], 'bx', mec='b', mew=2, ms=8)\n",
+ "\n",
+ " if np.all(abs(Z) != np.inf):\n",
+ " pyplot.contour(X1, X2, Z, levels=10**(np.arange(-20., 1, 3)), zorder=100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Estimate my and sigma2\n",
+ "mu, sigma2 = estimateGaussian(X)\n",
+ "\n",
+ "# Returns the density of the multivariate normal at each data point (row) \n",
+ "# of X\n",
+ "p = multivariateGaussian(X, mu, sigma2)\n",
+ "\n",
+ "# Visualize the fit\n",
+ "visualizeFit(X, mu, sigma2)\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pyplot.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have estimated the Gaussian parameters, we can investigate which examples have a very high probability given this distribution and which examples have a very low probability. The low probability examples are more liekly to be the anomalies in our dataset. One way to determine which examples are anomalies is to select a threshold based on a cross-validation set. In this part of the exercise, we will implement an algorithm to select the threshold epsilon using the F1 score on a cross validation set. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def selectThreshold(yval, pval):\n",
+ " \"\"\"\n",
+ " Find the best threshold (epsilon) to use for selecting outliers based\n",
+ " on the results from a validation set and the ground truth.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " yval : array_like\n",
+ " The ground truth labels of shape (m, ).\n",
+ " \n",
+ " pval : array_like\n",
+ " The precomputed vector of probabilities based on mu and sigma2 parameters. It's shape is also (m, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " bestEpsilon : array_like\n",
+ " A vector of shape (n,) corresponding to the threshold value.\n",
+ " \n",
+ " bestF1 : float\n",
+ " The value for the best F1 score.\n",
+ " \"\"\"\n",
+ " bestEpsilon = 0\n",
+ " bestF1 = 0\n",
+ " F1 = 0\n",
+ " \n",
+ " for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n",
+ " predictions = (pval < epsilon)\n",
+ " tp = np.sum((predictions == 1) & (yval == 1))\n",
+ " fp = np.sum((predictions == 1) & (yval == 0))\n",
+ " fn = np.sum((predictions == 0) & (yval == 1))\n",
+ " prec = tp / (tp + fp)\n",
+ " rec = tp / (tp + fn)\n",
+ " F1 = (2*prec*rec) / (prec + rec)\n",
+ "\n",
+ " if F1 > bestF1:\n",
+ " bestF1 = F1\n",
+ " bestEpsilon = epsilon\n",
+ "\n",
+ " return bestEpsilon, bestF1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will run our threshold selection function and circle the anomalies in the plot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best epsilon found using cross-validation: 9.00e-05\n",
+ "Best F1 on Cross Validation Set: 0.875000\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+ "\n",
+ "epsilon, F1 = selectThreshold(yval, pval)\n",
+ "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+ "print('Best F1 on Cross Validation Set: %f' % F1)\n",
+ "\n",
+ "# Find the outliers in the training set and plot the\n",
+ "outliers = p < epsilon\n",
+ "\n",
+ "# Visualize the fit\n",
+ "visualizeFit(X, mu, sigma2)\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pyplot.tight_layout()\n",
+ "\n",
+ "# Draw a red circle around those outliers\n",
+ "pyplot.plot(X[outliers, 0], X[outliers, 1], 'ro', ms=10, mfc='None', mew=2)\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have run our code successfuly on a small, low dimension dataset, we can apply it to higher dimensions. The following cell will do so on a dataset where each example is described by 11 features. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best epsilon found using cross-validation: 1.38e-18\n",
+ "Best F1 on Cross Validation Set : 0.615385\n",
+ "\n",
+ "\n",
+ "# Outliers found: 117\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Loads the second dataset. You should now have the\n",
+ "# variables X, Xval, yval in your environment\n",
+ "data = loadmat(os.path.join('Data', 'ex8data2.mat'))\n",
+ "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Apply the same steps to the larger dataset\n",
+ "mu, sigma2 = estimateGaussian(X)\n",
+ "\n",
+ "# Training set \n",
+ "p = multivariateGaussian(X, mu, sigma2)\n",
+ "\n",
+ "# Cross-validation set\n",
+ "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+ "\n",
+ "# Find the best threshold\n",
+ "epsilon, F1 = selectThreshold(yval, pval)\n",
+ "\n",
+ "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+ "print('Best F1 on Cross Validation Set : %f\\n' % F1)\n",
+ "print('\\n# Outliers found: %d' % np.sum(p < epsilon))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Recommender Systems
\n",
+ "In this part of the exercise, we will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings. This dataset consists of ratings on a scale of 1 to 5. The dataset has 943 users and 1682 movies. \n",
+ "\n",
+ "To begin, we load and plot the dataset ex8_movies.mat."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average rating for movie 1 (Toy Story): 3.878319 / 5\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load data\n",
+ "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+ "Y, R = data['Y'], data['R']\n",
+ "\n",
+ "# Y is a 1682x943 matrix, containing ratings (1-5) of \n",
+ "# 1682 movies on 943 users\n",
+ "\n",
+ "# R is a 1682x943 matrix, where R(i,j) = 1 \n",
+ "# if and only if user j gave a rating to movie i\n",
+ "\n",
+ "# From the matrix, we can compute statistics like average rating.\n",
+ "print('Average rating for movie 1 (Toy Story): %f / 5' %\n",
+ " np.mean(Y[0, R[0, :] == 1]))\n",
+ "\n",
+ "# We can \"visualize\" the ratings matrix by plotting it with imshow\n",
+ "pyplot.figure(figsize=(8, 8))\n",
+ "pyplot.imshow(Y)\n",
+ "pyplot.ylabel('Movies')\n",
+ "pyplot.xlabel('Users')\n",
+ "pyplot.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We begin constructing our collaberative filtering algorithm by implementing the regularized cost function which returns our cost and cost gradient. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cofiCostFunc(params, Y, R, num_users, num_movies,\n",
+ " num_features, lambda_=0.0):\n",
+ " \"\"\"\n",
+ " Collaborative filtering cost function.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " params : array_like\n",
+ " The parameters which will be optimized. This is a one\n",
+ " dimensional vector of shape (num_movies x num_users, 1). It is the \n",
+ " concatenation of the feature vectors X and parameters Theta.\n",
+ " \n",
+ " Y : array_like\n",
+ " A matrix of shape (num_movies x num_users) of user ratings of movies.\n",
+ " \n",
+ " R : array_like\n",
+ " A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n",
+ " i-th movie was rated by the j-th user.\n",
+ " \n",
+ " num_users : int\n",
+ " Total number of users.\n",
+ " \n",
+ " num_movies : int\n",
+ " Total number of movies.\n",
+ " \n",
+ " num_features : int\n",
+ " Number of features to learn.\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization coefficient.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The value of the cost function at the given params.\n",
+ " \n",
+ " grad : array_like\n",
+ " The gradient vector of the cost function at the given params.\n",
+ " grad has a shape (num_movies x num_users, 1)\n",
+ " \"\"\"\n",
+ " # Unfold the U and W matrices from params\n",
+ " X = params[:num_movies*num_features].reshape(num_movies, num_features)\n",
+ " Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n",
+ "\n",
+ " J = 0\n",
+ " X_grad = np.zeros(X.shape)\n",
+ " Theta_grad = np.zeros(Theta.shape)\n",
+ "\n",
+ " predMovieRatings = np.dot(X, Theta.transpose())\n",
+ " predMovieError = predMovieRatings - Y\n",
+ " error_factor = np.multiply(predMovieError, R)\n",
+ " J = (.5)*np.sum(np.sum(np.square(error_factor)))\n",
+ " X_grad = error_factor.dot(Theta)\n",
+ " Theta_grad = np.dot(error_factor.transpose(), X)\n",
+ " J += (lambda_/2)*np.sum(np.sum(np.square(Theta))) + (lambda_/2)*np.sum(np.sum(np.square(X)))\n",
+ " X_grad += lambda_*X\n",
+ " Theta_grad += lambda_*Theta\n",
+ " \n",
+ " grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can confirm our gradient vector is correct by comparing it to a numerically computed alternative."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def computeNumericalGradient(J, theta, e=1e-4):\n",
+ " \"\"\"\n",
+ " Computes the gradient using \"finite differences\" and gives us a numerical estimate of the\n",
+ " gradient.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " J : func\n",
+ " The cost function which will be used to estimate its numerical gradient.\n",
+ "\n",
+ " theta : array_like\n",
+ " The one dimensional unrolled network parameters. The numerical gradient is computed at\n",
+ " those given parameters.\n",
+ "\n",
+ " e : float (optional)\n",
+ " The value to use for epsilon for computing the finite difference.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " numgrad : array_like\n",
+ " The numerical gradient with respect to theta. Has same shape as theta.\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " The following code implements numerical gradient checking, and\n",
+ " returns the numerical gradient. It sets `numgrad[i]` to (a numerical\n",
+ " approximation of) the partial derivative of J with respect to the\n",
+ " i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should\n",
+ " be the (approximately) the partial derivative of J with respect\n",
+ " to theta[i].)\n",
+ " \"\"\"\n",
+ " numgrad = np.zeros(theta.shape)\n",
+ " perturb = np.diag(e * np.ones(theta.shape))\n",
+ " for i in range(theta.size):\n",
+ " loss1, _ = J(theta - perturb[:, i])\n",
+ " loss2, _ = J(theta + perturb[:, i])\n",
+ " numgrad[i] = (loss2 - loss1)/(2*e)\n",
+ " return numgrad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def checkCostFunction(cofiCostFunc, lambda_=0.):\n",
+ " \"\"\"\n",
+ " Creates a collaborative filtering problem to check your cost function and gradients.\n",
+ " It will output the analytical gradients produced by your code and the numerical gradients\n",
+ " (computed using computeNumericalGradient). These two gradient computations should result\n",
+ " in very similar values.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " cofiCostFunc: func\n",
+ " Implementation of the cost function.\n",
+ "\n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \"\"\"\n",
+ " # Create small problem\n",
+ " X_t = np.random.rand(4, 3)\n",
+ " Theta_t = np.random.rand(5, 3)\n",
+ "\n",
+ " # Zap out most entries\n",
+ " Y = np.dot(X_t, Theta_t.T)\n",
+ " Y[np.random.rand(*Y.shape) > 0.5] = 0\n",
+ " R = np.zeros(Y.shape)\n",
+ " R[Y != 0] = 1\n",
+ "\n",
+ " # Run Gradient Checking\n",
+ " X = np.random.randn(*X_t.shape)\n",
+ " Theta = np.random.randn(*Theta_t.shape)\n",
+ " num_movies, num_users = Y.shape\n",
+ " num_features = Theta_t.shape[1]\n",
+ "\n",
+ " params = np.concatenate([X.ravel(), Theta.ravel()])\n",
+ " numgrad = computeNumericalGradient(\n",
+ " lambda x: cofiCostFunc(x, Y, R, num_users, num_movies, num_features, lambda_), params)\n",
+ "\n",
+ " cost, grad = cofiCostFunc(params, Y, R, num_users,num_movies, num_features, lambda_)\n",
+ "\n",
+ " print(np.stack([numgrad, grad], axis=1))\n",
+ " print('\\nThe above two columns you get should be very similar.'\n",
+ " '(Left-Your Numerical Gradient, Right-Analytical Gradient)')\n",
+ "\n",
+ " diff = np.linalg.norm(numgrad-grad)/np.linalg.norm(numgrad+grad)\n",
+ " print('If your cost function implementation is correct, then '\n",
+ " 'the relative difference will be small (less than 1e-9).')\n",
+ " print('\\nRelative Difference: %g' % diff)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ -1.30245609 -1.30245609]\n",
+ " [ 2.24453275 2.24453275]\n",
+ " [ -2.32584117 -2.32584117]\n",
+ " [ -1.16890419 -1.16890419]\n",
+ " [ -7.60517509 -7.60517509]\n",
+ " [ -3.08489651 -3.08489651]\n",
+ " [ 2.74351052 2.74351052]\n",
+ " [ 0.39549628 0.39549628]\n",
+ " [ 1.23984089 1.23984089]\n",
+ " [ 9.76761153 9.76761153]\n",
+ " [ -1.81070958 -1.81070958]\n",
+ " [ -3.32270793 -3.32270793]\n",
+ " [-11.72043052 -11.72043052]\n",
+ " [ -0.9098094 -0.9098094 ]\n",
+ " [ -1.03173915 -1.03173915]\n",
+ " [ -0.58357716 -0.58357716]\n",
+ " [ 1.30289496 1.30289496]\n",
+ " [ -3.95392858 -3.95392858]\n",
+ " [ 2.31196568 2.31196568]\n",
+ " [ 5.89449544 5.89449544]\n",
+ " [ 2.10919126 2.10919126]\n",
+ " [-11.96684902 -11.96684902]\n",
+ " [ 2.26736518 2.26736518]\n",
+ " [ -0.34033361 -0.34033361]\n",
+ " [ -5.95019407 -5.95019407]\n",
+ " [ -0.86876709 -0.86876709]\n",
+ " [ 0.8207698 0.8207698 ]]\n",
+ "\n",
+ "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+ "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+ "\n",
+ "Relative Difference: 2.81077e-12\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Check gradients by running checkCostFunction\n",
+ "checkCostFunction(cofiCostFunc, 1.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working collaborative filtering cost function and gradient, we can start training our algorithm to make movie recommendations. The following cells will take in your user ratings, train the model, and make recommendations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def loadMovieList():\n",
+ " \"\"\"\n",
+ " Reads the fixed movie list in movie_ids.txt and returns a list of movie names.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " movieNames : list\n",
+ " A list of strings, representing all movie names.\n",
+ " \"\"\"\n",
+ " # Read the fixed movieulary list\n",
+ " with open(join('Data', 'movie_ids.txt'), encoding='ISO-8859-1') as fid:\n",
+ " movies = fid.readlines()\n",
+ "\n",
+ " movieNames = []\n",
+ " for movie in movies:\n",
+ " parts = movie.split()\n",
+ " movieNames.append(' '.join(parts[1:]).strip())\n",
+ " return movieNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "New user ratings:\n",
+ "-----------------\n",
+ "Rated 4 stars: Toy Story (1995)\n",
+ "Rated 3 stars: Twelve Monkeys (1995)\n",
+ "Rated 5 stars: Usual Suspects, The (1995)\n",
+ "Rated 4 stars: Outbreak (1995)\n",
+ "Rated 5 stars: Shawshank Redemption, The (1994)\n",
+ "Rated 3 stars: While You Were Sleeping (1995)\n",
+ "Rated 5 stars: Forrest Gump (1994)\n",
+ "Rated 2 stars: Silence of the Lambs, The (1991)\n",
+ "Rated 4 stars: Alien (1979)\n",
+ "Rated 5 stars: Die Hard 2 (1990)\n",
+ "Rated 5 stars: Sphere (1998)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Before we will train the collaborative filtering model, we will first\n",
+ "# add ratings that correspond to a new user that we just observed. This\n",
+ "# part of the code will also allow you to put in your own ratings for the\n",
+ "# movies in our dataset!\n",
+ "movieList = loadMovieList()\n",
+ "n_m = len(movieList)\n",
+ "\n",
+ "# Initialize my ratings\n",
+ "my_ratings = np.zeros(n_m)\n",
+ "\n",
+ "# Check the file movie_idx.txt for id of each movie in our dataset\n",
+ "# For example, Toy Story (1995) has ID 1, so to rate it \"4\", you can set\n",
+ "# Note that the index here is ID-1, since we start index from 0.\n",
+ "my_ratings[0] = 4\n",
+ "\n",
+ "# Or suppose did not enjoy Silence of the Lambs (1991), you can set\n",
+ "my_ratings[97] = 2\n",
+ "\n",
+ "# We have selected a few movies we liked / did not like and the ratings we\n",
+ "# gave are as follows:\n",
+ "my_ratings[6] = 3\n",
+ "my_ratings[11]= 5\n",
+ "my_ratings[53] = 4\n",
+ "my_ratings[63] = 5\n",
+ "my_ratings[65] = 3\n",
+ "my_ratings[68] = 5\n",
+ "my_ratings[182] = 4\n",
+ "my_ratings[225] = 5\n",
+ "my_ratings[354] = 5\n",
+ "\n",
+ "print('New user ratings:')\n",
+ "print('-----------------')\n",
+ "for i in range(len(my_ratings)):\n",
+ " if my_ratings[i] > 0:\n",
+ " print('Rated %d stars: %s' % (my_ratings[i], movieList[i]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def normalizeRatings(Y, R):\n",
+ " \"\"\"\n",
+ " Preprocess data by subtracting mean rating for every movie (every row).\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " Y : array_like\n",
+ " The user ratings for all movies. A matrix of shape (num_movies x num_users).\n",
+ "\n",
+ " R : array_like\n",
+ " Indicator matrix for movies rated by users. A matrix of shape (num_movies x num_users).\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " Ynorm : array_like\n",
+ " A matrix of same shape as Y, after mean normalization.\n",
+ "\n",
+ " Ymean : array_like\n",
+ " A vector of shape (num_movies, ) containing the mean rating for each movie.\n",
+ " \"\"\"\n",
+ " m, n = Y.shape\n",
+ " Ymean = np.zeros(m)\n",
+ " Ynorm = np.zeros(Y.shape)\n",
+ "\n",
+ " for i in range(m):\n",
+ " idx = R[i, :] == 1\n",
+ " Ymean[i] = np.mean(Y[i, idx])\n",
+ " Ynorm[i, idx] = Y[i, idx] - Ymean[i]\n",
+ "\n",
+ " return Ynorm, Ymean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Recommender system learning completed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now, we will train the collaborative filtering model on a movie rating \n",
+ "# dataset of 1682 movies and 943 users\n",
+ "\n",
+ "# Load data\n",
+ "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+ "Y, R = data['Y'], data['R']\n",
+ "\n",
+ "# Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by \n",
+ "# 943 users\n",
+ "\n",
+ "# R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a\n",
+ "# rating to movie i\n",
+ "\n",
+ "# Add our own ratings to the data matrix\n",
+ "Y = np.hstack([my_ratings[:, None], Y])\n",
+ "R = np.hstack([(my_ratings > 0)[:, None], R])\n",
+ "\n",
+ "# Normalize Ratings\n",
+ "Ynorm, Ymean = normalizeRatings(Y, R)\n",
+ "\n",
+ "# Useful Values\n",
+ "num_movies, num_users = Y.shape\n",
+ "num_features = 10\n",
+ "\n",
+ "# Set Initial Parameters (Theta, X)\n",
+ "X = np.random.randn(num_movies, num_features)\n",
+ "Theta = np.random.randn(num_users, num_features)\n",
+ "\n",
+ "initial_parameters = np.concatenate([X.ravel(), Theta.ravel()])\n",
+ "\n",
+ "# Set options for scipy.optimize.minimize\n",
+ "options = {'maxiter': 100}\n",
+ "\n",
+ "# Set Regularization\n",
+ "lambda_ = 10\n",
+ "res = optimize.minimize(lambda x: cofiCostFunc(x, Ynorm, R, num_users,\n",
+ " num_movies, num_features, lambda_),\n",
+ " initial_parameters,\n",
+ " method='TNC',\n",
+ " jac=True,\n",
+ " options=options)\n",
+ "theta = res.x\n",
+ "\n",
+ "# Unfold the returned theta back into U and W\n",
+ "X = theta[:num_movies*num_features].reshape(num_movies, num_features)\n",
+ "Theta = theta[num_movies*num_features:].reshape(num_users, num_features)\n",
+ "\n",
+ "print('Recommender system learning completed.')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top recommendations for you:\n",
+ "----------------------------\n",
+ "Predicting rating 5.0 for movie Saint of Fort Washington, The (1993)\n",
+ "Predicting rating 5.0 for movie Prefontaine (1997)\n",
+ "Predicting rating 5.0 for movie Star Kid (1997)\n",
+ "Predicting rating 5.0 for movie Great Day in Harlem, A (1994)\n",
+ "Predicting rating 5.0 for movie Santa with Muscles (1996)\n",
+ "Predicting rating 5.0 for movie Someone Else's America (1995)\n",
+ "Predicting rating 5.0 for movie Aiqing wansui (1994)\n",
+ "Predicting rating 5.0 for movie Entertaining Angels: The Dorothy Day Story (1996)\n",
+ "Predicting rating 5.0 for movie They Made Me a Criminal (1939)\n",
+ "Predicting rating 5.0 for movie Marlene Dietrich: Shadow and Light (1996)\n",
+ "\n",
+ "Original ratings provided:\n",
+ "--------------------------\n",
+ "Rated 4 for Toy Story (1995)\n",
+ "Rated 3 for Twelve Monkeys (1995)\n",
+ "Rated 5 for Usual Suspects, The (1995)\n",
+ "Rated 4 for Outbreak (1995)\n",
+ "Rated 5 for Shawshank Redemption, The (1994)\n",
+ "Rated 3 for While You Were Sleeping (1995)\n",
+ "Rated 5 for Forrest Gump (1994)\n",
+ "Rated 2 for Silence of the Lambs, The (1991)\n",
+ "Rated 4 for Alien (1979)\n",
+ "Rated 5 for Die Hard 2 (1990)\n",
+ "Rated 5 for Sphere (1998)\n"
+ ]
+ }
+ ],
+ "source": [
+ "p = np.dot(X, Theta.T)\n",
+ "my_predictions = p[:, 0] + Ymean\n",
+ "\n",
+ "movieList = loadMovieList()\n",
+ "\n",
+ "ix = np.argsort(my_predictions)[::-1]\n",
+ "\n",
+ "print('Top recommendations for you:')\n",
+ "print('----------------------------')\n",
+ "for i in range(10):\n",
+ " j = ix[i]\n",
+ " print('Predicting rating %.1f for movie %s' % (my_predictions[j], movieList[j]))\n",
+ "\n",
+ "print('\\nOriginal ratings provided:')\n",
+ "print('--------------------------')\n",
+ "for i in range(len(my_ratings)):\n",
+ " if my_ratings[i] > 0:\n",
+ " print('Rated %d for %s' % (my_ratings[i], movieList[i]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex8/.ipynb_checkpoints/ex8-checkpoint.ipynb b/ex8/.ipynb_checkpoints/ex8-checkpoint.ipynb
new file mode 100644
index 0000000..7b1eb39
--- /dev/null
+++ b/ex8/.ipynb_checkpoints/ex8-checkpoint.ipynb
@@ -0,0 +1,969 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 8: Anomaly Detection and Recommender Systems
\n",
+ "
Introduction
\n",
+ "In this exercise, we will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, we will use collaborative filtering to build a recommender system for movies. To begin, we import necessary libraries. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "from os.path import join\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "import matplotlib as mpl\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Anomaly detection
\n",
+ "In this exercise, we will implement an anomaly detection algorithm to detect anomalous behavior in server computers. The features measure the throughput (mb/s) and latency (ms) of response of each server. Whil operating, we collected m = 307 example of how they were behaving. We suspect the vast majority are \"normal\", but there may be some anomalous examples.\n",
+ "\n",
+ "We will use a Gaussian model to detect anomalous example in the dataset. We begin on a 2D dataset to visualize what the algorithm is doing. The following cell will visualize the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# The following command loads the dataset.\n",
+ "data = loadmat(os.path.join('Data', 'ex8data1.mat'))\n",
+ "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Visualize the example dataset\n",
+ "pyplot.plot(X[:, 0], X[:, 1], 'bx', mew=2, mec='k', ms=6)\n",
+ "pyplot.axis([0, 30, 0, 30])\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To perform anomaly detection, we first need to fit a model to the data's distribution. To do so, we need to estimate the mean and variance parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def estimateGaussian(X):\n",
+ " \"\"\"\n",
+ " This function estimates the parameters of a Gaussian distribution\n",
+ " using a provided dataset.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x n) with each n-dimensional \n",
+ " data point in one row, and each total of m data points.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " mu : array_like \n",
+ " A vector of shape (n,) containing the means of each dimension.\n",
+ " \n",
+ " sigma2 : array_like\n",
+ " A vector of shape (n,) containing the computed\n",
+ " variances of each dimension.\n",
+ " \"\"\"\n",
+ " m, n = X.shape\n",
+ " mu = np.zeros(n)\n",
+ " sigma2 = np.zeros(n)\n",
+ "\n",
+ " mu = np.mean(X, axis=0)\n",
+ " sigma2 = np.var(X, axis=0)\n",
+ " return mu, sigma2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cells visualize this distribution and how our dataset falls into it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def multivariateGaussian(X, mu, Sigma2):\n",
+ " \"\"\"\n",
+ " Computes the probability density function of the multivariate gaussian distribution.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x n). Where there are m examples of n-dimensions.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of shape (n,) contains the means for each dimension (feature).\n",
+ "\n",
+ " Sigma2 : array_like\n",
+ " Either a vector of shape (n,) containing the variances of independent features\n",
+ " (i.e. it is the diagonal of the correlation matrix), or the full\n",
+ " correlation matrix of shape (n x n) which can represent dependent features.\n",
+ "\n",
+ " Returns\n",
+ " ------\n",
+ " p : array_like\n",
+ " A vector of shape (m,) which contains the computed probabilities at each of the\n",
+ " provided examples.\n",
+ " \"\"\"\n",
+ " k = mu.size\n",
+ "\n",
+ " # if sigma is given as a diagonal, compute the matrix\n",
+ " if Sigma2.ndim == 1:\n",
+ " Sigma2 = np.diag(Sigma2)\n",
+ "\n",
+ " X = X - mu\n",
+ " p = (2 * np.pi) ** (- k / 2) * np.linalg.det(Sigma2) ** (-0.5)\\\n",
+ " * np.exp(-0.5 * np.sum(np.dot(X, np.linalg.pinv(Sigma2)) * X, axis=1))\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualizeFit(X, mu, sigma2):\n",
+ " \"\"\"\n",
+ " Visualize the dataset and its estimated distribution.\n",
+ " This visualization shows you the probability density function of the Gaussian distribution.\n",
+ " Each example has a location (x1, x2) that depends on its feature values.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x 2). Where there are m examples of 2-dimensions. We need at most\n",
+ " 2-D features to be able to visualize the distribution.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of shape (n,) contains the means for each dimension (feature).\n",
+ "\n",
+ " sigma2 : array_like\n",
+ " Either a vector of shape (n,) containing the variances of independent features\n",
+ " (i.e. it is the diagonal of the correlation matrix), or the full\n",
+ " correlation matrix of shape (n x n) which can represent dependent features.\n",
+ " \"\"\"\n",
+ "\n",
+ " X1, X2 = np.meshgrid(np.arange(0, 35.5, 0.5), np.arange(0, 35.5, 0.5))\n",
+ " Z = multivariateGaussian(np.stack([X1.ravel(), X2.ravel()], axis=1), mu, sigma2)\n",
+ " Z = Z.reshape(X1.shape)\n",
+ "\n",
+ " pyplot.plot(X[:, 0], X[:, 1], 'bx', mec='b', mew=2, ms=8)\n",
+ "\n",
+ " if np.all(abs(Z) != np.inf):\n",
+ " pyplot.contour(X1, X2, Z, levels=10**(np.arange(-20., 1, 3)), zorder=100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Estimate my and sigma2\n",
+ "mu, sigma2 = estimateGaussian(X)\n",
+ "\n",
+ "# Returns the density of the multivariate normal at each data point (row) \n",
+ "# of X\n",
+ "p = multivariateGaussian(X, mu, sigma2)\n",
+ "\n",
+ "# Visualize the fit\n",
+ "visualizeFit(X, mu, sigma2)\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pyplot.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have estimated the Gaussian parameters, we can investigate which examples have a very high probability given this distribution and which examples have a very low probability. The low probability examples are more liekly to be the anomalies in our dataset. One way to determine which examples are anomalies is to select a threshold based on a cross-validation set. In this part of the exercise, we will implement an algorithm to select the threshold epsilon using the F1 score on a cross validation set. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def selectThreshold(yval, pval):\n",
+ " \"\"\"\n",
+ " Find the best threshold (epsilon) to use for selecting outliers based\n",
+ " on the results from a validation set and the ground truth.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " yval : array_like\n",
+ " The ground truth labels of shape (m, ).\n",
+ " \n",
+ " pval : array_like\n",
+ " The precomputed vector of probabilities based on mu and sigma2 parameters. It's shape is also (m, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " bestEpsilon : array_like\n",
+ " A vector of shape (n,) corresponding to the threshold value.\n",
+ " \n",
+ " bestF1 : float\n",
+ " The value for the best F1 score.\n",
+ " \"\"\"\n",
+ " bestEpsilon = 0\n",
+ " bestF1 = 0\n",
+ " F1 = 0\n",
+ " \n",
+ " for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n",
+ " predictions = (pval < epsilon)\n",
+ " tp = np.sum((predictions == 1) & (yval == 1))\n",
+ " fp = np.sum((predictions == 1) & (yval == 0))\n",
+ " fn = np.sum((predictions == 0) & (yval == 1))\n",
+ " prec = tp / (tp + fp)\n",
+ " rec = tp / (tp + fn)\n",
+ " F1 = (2*prec*rec) / (prec + rec)\n",
+ "\n",
+ " if F1 > bestF1:\n",
+ " bestF1 = F1\n",
+ " bestEpsilon = epsilon\n",
+ "\n",
+ " return bestEpsilon, bestF1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will run our threshold selection function and circle the anomalies in the plot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best epsilon found using cross-validation: 9.00e-05\n",
+ "Best F1 on Cross Validation Set: 0.875000\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+ "\n",
+ "epsilon, F1 = selectThreshold(yval, pval)\n",
+ "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+ "print('Best F1 on Cross Validation Set: %f' % F1)\n",
+ "\n",
+ "# Find the outliers in the training set and plot the\n",
+ "outliers = p < epsilon\n",
+ "\n",
+ "# Visualize the fit\n",
+ "visualizeFit(X, mu, sigma2)\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pyplot.tight_layout()\n",
+ "\n",
+ "# Draw a red circle around those outliers\n",
+ "pyplot.plot(X[outliers, 0], X[outliers, 1], 'ro', ms=10, mfc='None', mew=2)\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have run our code successfuly on a small, low dimension dataset, we can apply it to higher dimensions. The following cell will do so on a dataset where each example is described by 11 features. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best epsilon found using cross-validation: 1.38e-18\n",
+ "Best F1 on Cross Validation Set : 0.615385\n",
+ "\n",
+ "\n",
+ "# Outliers found: 117\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Loads the second dataset. You should now have the\n",
+ "# variables X, Xval, yval in your environment\n",
+ "data = loadmat(os.path.join('Data', 'ex8data2.mat'))\n",
+ "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Apply the same steps to the larger dataset\n",
+ "mu, sigma2 = estimateGaussian(X)\n",
+ "\n",
+ "# Training set \n",
+ "p = multivariateGaussian(X, mu, sigma2)\n",
+ "\n",
+ "# Cross-validation set\n",
+ "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+ "\n",
+ "# Find the best threshold\n",
+ "epsilon, F1 = selectThreshold(yval, pval)\n",
+ "\n",
+ "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+ "print('Best F1 on Cross Validation Set : %f\\n' % F1)\n",
+ "print('\\n# Outliers found: %d' % np.sum(p < epsilon))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Recommender Systems
\n",
+ "In this part of the exercise, we will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings. This dataset consists of ratings on a scale of 1 to 5. The dataset has 943 users and 1682 movies. \n",
+ "\n",
+ "To begin, we load and plot the dataset ex8_movies.mat."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average rating for movie 1 (Toy Story): 3.878319 / 5\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load data\n",
+ "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+ "Y, R = data['Y'], data['R']\n",
+ "\n",
+ "# Y is a 1682x943 matrix, containing ratings (1-5) of \n",
+ "# 1682 movies on 943 users\n",
+ "\n",
+ "# R is a 1682x943 matrix, where R(i,j) = 1 \n",
+ "# if and only if user j gave a rating to movie i\n",
+ "\n",
+ "# From the matrix, we can compute statistics like average rating.\n",
+ "print('Average rating for movie 1 (Toy Story): %f / 5' %\n",
+ " np.mean(Y[0, R[0, :] == 1]))\n",
+ "\n",
+ "# We can \"visualize\" the ratings matrix by plotting it with imshow\n",
+ "pyplot.figure(figsize=(8, 8))\n",
+ "pyplot.imshow(Y)\n",
+ "pyplot.ylabel('Movies')\n",
+ "pyplot.xlabel('Users')\n",
+ "pyplot.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We begin constructing our collaberative filtering algorithm by implementing the regularized cost function which returns our cost and cost gradient. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cofiCostFunc(params, Y, R, num_users, num_movies,\n",
+ " num_features, lambda_=0.0):\n",
+ " \"\"\"\n",
+ " Collaborative filtering cost function.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " params : array_like\n",
+ " The parameters which will be optimized. This is a one\n",
+ " dimensional vector of shape (num_movies x num_users, 1). It is the \n",
+ " concatenation of the feature vectors X and parameters Theta.\n",
+ " \n",
+ " Y : array_like\n",
+ " A matrix of shape (num_movies x num_users) of user ratings of movies.\n",
+ " \n",
+ " R : array_like\n",
+ " A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n",
+ " i-th movie was rated by the j-th user.\n",
+ " \n",
+ " num_users : int\n",
+ " Total number of users.\n",
+ " \n",
+ " num_movies : int\n",
+ " Total number of movies.\n",
+ " \n",
+ " num_features : int\n",
+ " Number of features to learn.\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization coefficient.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The value of the cost function at the given params.\n",
+ " \n",
+ " grad : array_like\n",
+ " The gradient vector of the cost function at the given params.\n",
+ " grad has a shape (num_movies x num_users, 1)\n",
+ " \"\"\"\n",
+ " # Unfold the U and W matrices from params\n",
+ " X = params[:num_movies*num_features].reshape(num_movies, num_features)\n",
+ " Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n",
+ "\n",
+ " J = 0\n",
+ " X_grad = np.zeros(X.shape)\n",
+ " Theta_grad = np.zeros(Theta.shape)\n",
+ "\n",
+ " predMovieRatings = np.dot(X, Theta.transpose())\n",
+ " predMovieError = predMovieRatings - Y\n",
+ " error_factor = np.multiply(predMovieError, R)\n",
+ " J = (.5)*np.sum(np.sum(np.square(error_factor)))\n",
+ " X_grad = error_factor.dot(Theta)\n",
+ " Theta_grad = np.dot(error_factor.transpose(), X)\n",
+ " J += (lambda_/2)*np.sum(np.sum(np.square(Theta))) + (lambda_/2)*np.sum(np.sum(np.square(X)))\n",
+ " X_grad += lambda_*X\n",
+ " Theta_grad += lambda_*Theta\n",
+ " \n",
+ " grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can confirm our gradient vector is correct by comparing it to a numerically computed alternative."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def computeNumericalGradient(J, theta, e=1e-4):\n",
+ " \"\"\"\n",
+ " Computes the gradient using \"finite differences\" and gives us a numerical estimate of the\n",
+ " gradient.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " J : func\n",
+ " The cost function which will be used to estimate its numerical gradient.\n",
+ "\n",
+ " theta : array_like\n",
+ " The one dimensional unrolled network parameters. The numerical gradient is computed at\n",
+ " those given parameters.\n",
+ "\n",
+ " e : float (optional)\n",
+ " The value to use for epsilon for computing the finite difference.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " numgrad : array_like\n",
+ " The numerical gradient with respect to theta. Has same shape as theta.\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " The following code implements numerical gradient checking, and\n",
+ " returns the numerical gradient. It sets `numgrad[i]` to (a numerical\n",
+ " approximation of) the partial derivative of J with respect to the\n",
+ " i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should\n",
+ " be the (approximately) the partial derivative of J with respect\n",
+ " to theta[i].)\n",
+ " \"\"\"\n",
+ " numgrad = np.zeros(theta.shape)\n",
+ " perturb = np.diag(e * np.ones(theta.shape))\n",
+ " for i in range(theta.size):\n",
+ " loss1, _ = J(theta - perturb[:, i])\n",
+ " loss2, _ = J(theta + perturb[:, i])\n",
+ " numgrad[i] = (loss2 - loss1)/(2*e)\n",
+ " return numgrad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def checkCostFunction(cofiCostFunc, lambda_=0.):\n",
+ " \"\"\"\n",
+ " Creates a collaborative filtering problem to check your cost function and gradients.\n",
+ " It will output the analytical gradients produced by your code and the numerical gradients\n",
+ " (computed using computeNumericalGradient). These two gradient computations should result\n",
+ " in very similar values.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " cofiCostFunc: func\n",
+ " Implementation of the cost function.\n",
+ "\n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \"\"\"\n",
+ " # Create small problem\n",
+ " X_t = np.random.rand(4, 3)\n",
+ " Theta_t = np.random.rand(5, 3)\n",
+ "\n",
+ " # Zap out most entries\n",
+ " Y = np.dot(X_t, Theta_t.T)\n",
+ " Y[np.random.rand(*Y.shape) > 0.5] = 0\n",
+ " R = np.zeros(Y.shape)\n",
+ " R[Y != 0] = 1\n",
+ "\n",
+ " # Run Gradient Checking\n",
+ " X = np.random.randn(*X_t.shape)\n",
+ " Theta = np.random.randn(*Theta_t.shape)\n",
+ " num_movies, num_users = Y.shape\n",
+ " num_features = Theta_t.shape[1]\n",
+ "\n",
+ " params = np.concatenate([X.ravel(), Theta.ravel()])\n",
+ " numgrad = computeNumericalGradient(\n",
+ " lambda x: cofiCostFunc(x, Y, R, num_users, num_movies, num_features, lambda_), params)\n",
+ "\n",
+ " cost, grad = cofiCostFunc(params, Y, R, num_users,num_movies, num_features, lambda_)\n",
+ "\n",
+ " print(np.stack([numgrad, grad], axis=1))\n",
+ " print('\\nThe above two columns you get should be very similar.'\n",
+ " '(Left-Your Numerical Gradient, Right-Analytical Gradient)')\n",
+ "\n",
+ " diff = np.linalg.norm(numgrad-grad)/np.linalg.norm(numgrad+grad)\n",
+ " print('If your cost function implementation is correct, then '\n",
+ " 'the relative difference will be small (less than 1e-9).')\n",
+ " print('\\nRelative Difference: %g' % diff)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 13.86445373 13.86445373]\n",
+ " [ -0.50346339 -0.50346339]\n",
+ " [ -0.25274807 -0.25274807]\n",
+ " [ -7.16541284 -7.16541284]\n",
+ " [ 3.10313028 3.10313028]\n",
+ " [ 0.56829314 0.56829314]\n",
+ " [ -1.04192584 -1.04192584]\n",
+ " [ 2.07795586 2.07795586]\n",
+ " [ -0.68986789 -0.68986789]\n",
+ " [ 1.86079356 1.86079356]\n",
+ " [ -2.40363355 -2.40363355]\n",
+ " [ -0.20257324 -0.20257324]\n",
+ " [ -2.35627103 -2.35627103]\n",
+ " [ 0.99584362 0.99584362]\n",
+ " [ -0.94845546 -0.94845546]\n",
+ " [ 6.87482798 6.87482798]\n",
+ " [ -1.90959988 -1.90959988]\n",
+ " [ 0.30039997 0.30039997]\n",
+ " [ 1.99317192 1.99317192]\n",
+ " [ -0.71867099 -0.71867099]\n",
+ " [ -0.13605671 -0.13605671]\n",
+ " [-19.15609492 -19.15609492]\n",
+ " [ 6.2449762 6.2449762 ]\n",
+ " [ 0.34766409 0.34766409]\n",
+ " [ 3.77041093 3.77041093]\n",
+ " [ -4.92417175 -4.92417175]\n",
+ " [ 0.16311157 0.16311157]]\n",
+ "\n",
+ "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+ "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+ "\n",
+ "Relative Difference: 2.96831e-12\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Check gradients by running checkCostFunction\n",
+ "checkCostFunction(cofiCostFunc, 1.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working collaborative filtering cost function and gradient, we can start training our algorithm to make movie recommendations. The following cells will take in your user ratings, train the model, and make recommendations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def loadMovieList():\n",
+ " \"\"\"\n",
+ " Reads the fixed movie list in movie_ids.txt and returns a list of movie names.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " movieNames : list\n",
+ " A list of strings, representing all movie names.\n",
+ " \"\"\"\n",
+ " # Read the fixed movieulary list\n",
+ " with open(join('Data', 'movie_ids.txt'), encoding='ISO-8859-1') as fid:\n",
+ " movies = fid.readlines()\n",
+ "\n",
+ " movieNames = []\n",
+ " for movie in movies:\n",
+ " parts = movie.split()\n",
+ " movieNames.append(' '.join(parts[1:]).strip())\n",
+ " return movieNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "New user ratings:\n",
+ "-----------------\n",
+ "Rated 4 stars: Toy Story (1995)\n",
+ "Rated 3 stars: Twelve Monkeys (1995)\n",
+ "Rated 5 stars: Usual Suspects, The (1995)\n",
+ "Rated 4 stars: Outbreak (1995)\n",
+ "Rated 5 stars: Shawshank Redemption, The (1994)\n",
+ "Rated 3 stars: While You Were Sleeping (1995)\n",
+ "Rated 5 stars: Forrest Gump (1994)\n",
+ "Rated 2 stars: Silence of the Lambs, The (1991)\n",
+ "Rated 4 stars: Alien (1979)\n",
+ "Rated 5 stars: Die Hard 2 (1990)\n",
+ "Rated 5 stars: Sphere (1998)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Before we will train the collaborative filtering model, we will first\n",
+ "# add ratings that correspond to a new user that we just observed. This\n",
+ "# part of the code will also allow you to put in your own ratings for the\n",
+ "# movies in our dataset!\n",
+ "movieList = loadMovieList()\n",
+ "n_m = len(movieList)\n",
+ "\n",
+ "# Initialize my ratings\n",
+ "my_ratings = np.zeros(n_m)\n",
+ "\n",
+ "# Check the file movie_idx.txt for id of each movie in our dataset\n",
+ "# For example, Toy Story (1995) has ID 1, so to rate it \"4\", you can set\n",
+ "# Note that the index here is ID-1, since we start index from 0.\n",
+ "my_ratings[0] = 4\n",
+ "\n",
+ "# Or suppose did not enjoy Silence of the Lambs (1991), you can set\n",
+ "my_ratings[97] = 2\n",
+ "\n",
+ "# We have selected a few movies we liked / did not like and the ratings we\n",
+ "# gave are as follows:\n",
+ "my_ratings[6] = 3\n",
+ "my_ratings[11]= 5\n",
+ "my_ratings[53] = 4\n",
+ "my_ratings[63] = 5\n",
+ "my_ratings[65] = 3\n",
+ "my_ratings[68] = 5\n",
+ "my_ratings[182] = 4\n",
+ "my_ratings[225] = 5\n",
+ "my_ratings[354] = 5\n",
+ "\n",
+ "print('New user ratings:')\n",
+ "print('-----------------')\n",
+ "for i in range(len(my_ratings)):\n",
+ " if my_ratings[i] > 0:\n",
+ " print('Rated %d stars: %s' % (my_ratings[i], movieList[i]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def normalizeRatings(Y, R):\n",
+ " \"\"\"\n",
+ " Preprocess data by subtracting mean rating for every movie (every row).\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " Y : array_like\n",
+ " The user ratings for all movies. A matrix of shape (num_movies x num_users).\n",
+ "\n",
+ " R : array_like\n",
+ " Indicator matrix for movies rated by users. A matrix of shape (num_movies x num_users).\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " Ynorm : array_like\n",
+ " A matrix of same shape as Y, after mean normalization.\n",
+ "\n",
+ " Ymean : array_like\n",
+ " A vector of shape (num_movies, ) containing the mean rating for each movie.\n",
+ " \"\"\"\n",
+ " m, n = Y.shape\n",
+ " Ymean = np.zeros(m)\n",
+ " Ynorm = np.zeros(Y.shape)\n",
+ "\n",
+ " for i in range(m):\n",
+ " idx = R[i, :] == 1\n",
+ " Ymean[i] = np.mean(Y[i, idx])\n",
+ " Ynorm[i, idx] = Y[i, idx] - Ymean[i]\n",
+ "\n",
+ " return Ynorm, Ymean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Recommender system learning completed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now, we will train the collaborative filtering model on a movie rating \n",
+ "# dataset of 1682 movies and 943 users\n",
+ "\n",
+ "# Load data\n",
+ "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+ "Y, R = data['Y'], data['R']\n",
+ "\n",
+ "# Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by \n",
+ "# 943 users\n",
+ "\n",
+ "# R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a\n",
+ "# rating to movie i\n",
+ "\n",
+ "# Add our own ratings to the data matrix\n",
+ "Y = np.hstack([my_ratings[:, None], Y])\n",
+ "R = np.hstack([(my_ratings > 0)[:, None], R])\n",
+ "\n",
+ "# Normalize Ratings\n",
+ "Ynorm, Ymean = normalizeRatings(Y, R)\n",
+ "\n",
+ "# Useful Values\n",
+ "num_movies, num_users = Y.shape\n",
+ "num_features = 10\n",
+ "\n",
+ "# Set Initial Parameters (Theta, X)\n",
+ "X = np.random.randn(num_movies, num_features)\n",
+ "Theta = np.random.randn(num_users, num_features)\n",
+ "\n",
+ "initial_parameters = np.concatenate([X.ravel(), Theta.ravel()])\n",
+ "\n",
+ "# Set options for scipy.optimize.minimize\n",
+ "options = {'maxiter': 100}\n",
+ "\n",
+ "# Set Regularization\n",
+ "lambda_ = 10\n",
+ "res = optimize.minimize(lambda x: cofiCostFunc(x, Ynorm, R, num_users,\n",
+ " num_movies, num_features, lambda_),\n",
+ " initial_parameters,\n",
+ " method='TNC',\n",
+ " jac=True,\n",
+ " options=options)\n",
+ "theta = res.x\n",
+ "\n",
+ "# Unfold the returned theta back into U and W\n",
+ "X = theta[:num_movies*num_features].reshape(num_movies, num_features)\n",
+ "Theta = theta[num_movies*num_features:].reshape(num_users, num_features)\n",
+ "\n",
+ "print('Recommender system learning completed.')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top recommendations for you:\n",
+ "----------------------------\n",
+ "Predicting rating 5.0 for movie Great Day in Harlem, A (1994)\n",
+ "Predicting rating 5.0 for movie Saint of Fort Washington, The (1993)\n",
+ "Predicting rating 5.0 for movie Entertaining Angels: The Dorothy Day Story (1996)\n",
+ "Predicting rating 5.0 for movie Santa with Muscles (1996)\n",
+ "Predicting rating 5.0 for movie Star Kid (1997)\n",
+ "Predicting rating 5.0 for movie They Made Me a Criminal (1939)\n",
+ "Predicting rating 5.0 for movie Aiqing wansui (1994)\n",
+ "Predicting rating 5.0 for movie Prefontaine (1997)\n",
+ "Predicting rating 5.0 for movie Marlene Dietrich: Shadow and Light (1996)\n",
+ "Predicting rating 5.0 for movie Someone Else's America (1995)\n",
+ "\n",
+ "Original ratings provided:\n",
+ "--------------------------\n",
+ "Rated 4 for Toy Story (1995)\n",
+ "Rated 3 for Twelve Monkeys (1995)\n",
+ "Rated 5 for Usual Suspects, The (1995)\n",
+ "Rated 4 for Outbreak (1995)\n",
+ "Rated 5 for Shawshank Redemption, The (1994)\n",
+ "Rated 3 for While You Were Sleeping (1995)\n",
+ "Rated 5 for Forrest Gump (1994)\n",
+ "Rated 2 for Silence of the Lambs, The (1991)\n",
+ "Rated 4 for Alien (1979)\n",
+ "Rated 5 for Die Hard 2 (1990)\n",
+ "Rated 5 for Sphere (1998)\n"
+ ]
+ }
+ ],
+ "source": [
+ "p = np.dot(X, Theta.T)\n",
+ "my_predictions = p[:, 0] + Ymean\n",
+ "\n",
+ "movieList = loadMovieList()\n",
+ "\n",
+ "ix = np.argsort(my_predictions)[::-1]\n",
+ "\n",
+ "print('Top recommendations for you:')\n",
+ "print('----------------------------')\n",
+ "for i in range(10):\n",
+ " j = ix[i]\n",
+ " print('Predicting rating %.1f for movie %s' % (my_predictions[j], movieList[j]))\n",
+ "\n",
+ "print('\\nOriginal ratings provided:')\n",
+ "print('--------------------------')\n",
+ "for i in range(len(my_ratings)):\n",
+ " if my_ratings[i] > 0:\n",
+ " print('Rated %d for %s' % (my_ratings[i], movieList[i]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ex8/Data/ex8_movieParams.mat b/ex8/Data/ex8_movieParams.mat
new file mode 100644
index 0000000..2dea689
Binary files /dev/null and b/ex8/Data/ex8_movieParams.mat differ
diff --git a/ex8/Data/ex8_movies.mat b/ex8/Data/ex8_movies.mat
new file mode 100644
index 0000000..31ecd00
Binary files /dev/null and b/ex8/Data/ex8_movies.mat differ
diff --git a/ex8/Data/ex8data1.mat b/ex8/Data/ex8data1.mat
new file mode 100644
index 0000000..1f08123
Binary files /dev/null and b/ex8/Data/ex8data1.mat differ
diff --git a/ex8/Data/ex8data2.mat b/ex8/Data/ex8data2.mat
new file mode 100644
index 0000000..fe48db3
Binary files /dev/null and b/ex8/Data/ex8data2.mat differ
diff --git a/ex8/Data/movie_ids.txt b/ex8/Data/movie_ids.txt
new file mode 100644
index 0000000..392427a
--- /dev/null
+++ b/ex8/Data/movie_ids.txt
@@ -0,0 +1,1682 @@
+1 Toy Story (1995)
+2 GoldenEye (1995)
+3 Four Rooms (1995)
+4 Get Shorty (1995)
+5 Copycat (1995)
+6 Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)
+7 Twelve Monkeys (1995)
+8 Babe (1995)
+9 Dead Man Walking (1995)
+10 Richard III (1995)
+11 Seven (Se7en) (1995)
+12 Usual Suspects, The (1995)
+13 Mighty Aphrodite (1995)
+14 Postino, Il (1994)
+15 Mr. Holland's Opus (1995)
+16 French Twist (Gazon maudit) (1995)
+17 From Dusk Till Dawn (1996)
+18 White Balloon, The (1995)
+19 Antonia's Line (1995)
+20 Angels and Insects (1995)
+21 Muppet Treasure Island (1996)
+22 Braveheart (1995)
+23 Taxi Driver (1976)
+24 Rumble in the Bronx (1995)
+25 Birdcage, The (1996)
+26 Brothers McMullen, The (1995)
+27 Bad Boys (1995)
+28 Apollo 13 (1995)
+29 Batman Forever (1995)
+30 Belle de jour (1967)
+31 Crimson Tide (1995)
+32 Crumb (1994)
+33 Desperado (1995)
+34 Doom Generation, The (1995)
+35 Free Willy 2: The Adventure Home (1995)
+36 Mad Love (1995)
+37 Nadja (1994)
+38 Net, The (1995)
+39 Strange Days (1995)
+40 To Wong Foo, Thanks for Everything! Julie Newmar (1995)
+41 Billy Madison (1995)
+42 Clerks (1994)
+43 Disclosure (1994)
+44 Dolores Claiborne (1994)
+45 Eat Drink Man Woman (1994)
+46 Exotica (1994)
+47 Ed Wood (1994)
+48 Hoop Dreams (1994)
+49 I.Q. (1994)
+50 Star Wars (1977)
+51 Legends of the Fall (1994)
+52 Madness of King George, The (1994)
+53 Natural Born Killers (1994)
+54 Outbreak (1995)
+55 Professional, The (1994)
+56 Pulp Fiction (1994)
+57 Priest (1994)
+58 Quiz Show (1994)
+59 Three Colors: Red (1994)
+60 Three Colors: Blue (1993)
+61 Three Colors: White (1994)
+62 Stargate (1994)
+63 Santa Clause, The (1994)
+64 Shawshank Redemption, The (1994)
+65 What's Eating Gilbert Grape (1993)
+66 While You Were Sleeping (1995)
+67 Ace Ventura: Pet Detective (1994)
+68 Crow, The (1994)
+69 Forrest Gump (1994)
+70 Four Weddings and a Funeral (1994)
+71 Lion King, The (1994)
+72 Mask, The (1994)
+73 Maverick (1994)
+74 Faster Pussycat! Kill! Kill! (1965)
+75 Brother Minister: The Assassination of Malcolm X (1994)
+76 Carlito's Way (1993)
+77 Firm, The (1993)
+78 Free Willy (1993)
+79 Fugitive, The (1993)
+80 Hot Shots! Part Deux (1993)
+81 Hudsucker Proxy, The (1994)
+82 Jurassic Park (1993)
+83 Much Ado About Nothing (1993)
+84 Robert A. Heinlein's The Puppet Masters (1994)
+85 Ref, The (1994)
+86 Remains of the Day, The (1993)
+87 Searching for Bobby Fischer (1993)
+88 Sleepless in Seattle (1993)
+89 Blade Runner (1982)
+90 So I Married an Axe Murderer (1993)
+91 Nightmare Before Christmas, The (1993)
+92 True Romance (1993)
+93 Welcome to the Dollhouse (1995)
+94 Home Alone (1990)
+95 Aladdin (1992)
+96 Terminator 2: Judgment Day (1991)
+97 Dances with Wolves (1990)
+98 Silence of the Lambs, The (1991)
+99 Snow White and the Seven Dwarfs (1937)
+100 Fargo (1996)
+101 Heavy Metal (1981)
+102 Aristocats, The (1970)
+103 All Dogs Go to Heaven 2 (1996)
+104 Theodore Rex (1995)
+105 Sgt. Bilko (1996)
+106 Diabolique (1996)
+107 Moll Flanders (1996)
+108 Kids in the Hall: Brain Candy (1996)
+109 Mystery Science Theater 3000: The Movie (1996)
+110 Operation Dumbo Drop (1995)
+111 Truth About Cats & Dogs, The (1996)
+112 Flipper (1996)
+113 Horseman on the Roof, The (Hussard sur le toit, Le) (1995)
+114 Wallace & Gromit: The Best of Aardman Animation (1996)
+115 Haunted World of Edward D. Wood Jr., The (1995)
+116 Cold Comfort Farm (1995)
+117 Rock, The (1996)
+118 Twister (1996)
+119 Maya Lin: A Strong Clear Vision (1994)
+120 Striptease (1996)
+121 Independence Day (ID4) (1996)
+122 Cable Guy, The (1996)
+123 Frighteners, The (1996)
+124 Lone Star (1996)
+125 Phenomenon (1996)
+126 Spitfire Grill, The (1996)
+127 Godfather, The (1972)
+128 Supercop (1992)
+129 Bound (1996)
+130 Kansas City (1996)
+131 Breakfast at Tiffany's (1961)
+132 Wizard of Oz, The (1939)
+133 Gone with the Wind (1939)
+134 Citizen Kane (1941)
+135 2001: A Space Odyssey (1968)
+136 Mr. Smith Goes to Washington (1939)
+137 Big Night (1996)
+138 D3: The Mighty Ducks (1996)
+139 Love Bug, The (1969)
+140 Homeward Bound: The Incredible Journey (1993)
+141 20,000 Leagues Under the Sea (1954)
+142 Bedknobs and Broomsticks (1971)
+143 Sound of Music, The (1965)
+144 Die Hard (1988)
+145 Lawnmower Man, The (1992)
+146 Unhook the Stars (1996)
+147 Long Kiss Goodnight, The (1996)
+148 Ghost and the Darkness, The (1996)
+149 Jude (1996)
+150 Swingers (1996)
+151 Willy Wonka and the Chocolate Factory (1971)
+152 Sleeper (1973)
+153 Fish Called Wanda, A (1988)
+154 Monty Python's Life of Brian (1979)
+155 Dirty Dancing (1987)
+156 Reservoir Dogs (1992)
+157 Platoon (1986)
+158 Weekend at Bernie's (1989)
+159 Basic Instinct (1992)
+160 Glengarry Glen Ross (1992)
+161 Top Gun (1986)
+162 On Golden Pond (1981)
+163 Return of the Pink Panther, The (1974)
+164 Abyss, The (1989)
+165 Jean de Florette (1986)
+166 Manon of the Spring (Manon des sources) (1986)
+167 Private Benjamin (1980)
+168 Monty Python and the Holy Grail (1974)
+169 Wrong Trousers, The (1993)
+170 Cinema Paradiso (1988)
+171 Delicatessen (1991)
+172 Empire Strikes Back, The (1980)
+173 Princess Bride, The (1987)
+174 Raiders of the Lost Ark (1981)
+175 Brazil (1985)
+176 Aliens (1986)
+177 Good, The Bad and The Ugly, The (1966)
+178 12 Angry Men (1957)
+179 Clockwork Orange, A (1971)
+180 Apocalypse Now (1979)
+181 Return of the Jedi (1983)
+182 GoodFellas (1990)
+183 Alien (1979)
+184 Army of Darkness (1993)
+185 Psycho (1960)
+186 Blues Brothers, The (1980)
+187 Godfather: Part II, The (1974)
+188 Full Metal Jacket (1987)
+189 Grand Day Out, A (1992)
+190 Henry V (1989)
+191 Amadeus (1984)
+192 Raging Bull (1980)
+193 Right Stuff, The (1983)
+194 Sting, The (1973)
+195 Terminator, The (1984)
+196 Dead Poets Society (1989)
+197 Graduate, The (1967)
+198 Nikita (La Femme Nikita) (1990)
+199 Bridge on the River Kwai, The (1957)
+200 Shining, The (1980)
+201 Evil Dead II (1987)
+202 Groundhog Day (1993)
+203 Unforgiven (1992)
+204 Back to the Future (1985)
+205 Patton (1970)
+206 Akira (1988)
+207 Cyrano de Bergerac (1990)
+208 Young Frankenstein (1974)
+209 This Is Spinal Tap (1984)
+210 Indiana Jones and the Last Crusade (1989)
+211 M*A*S*H (1970)
+212 Unbearable Lightness of Being, The (1988)
+213 Room with a View, A (1986)
+214 Pink Floyd - The Wall (1982)
+215 Field of Dreams (1989)
+216 When Harry Met Sally... (1989)
+217 Bram Stoker's Dracula (1992)
+218 Cape Fear (1991)
+219 Nightmare on Elm Street, A (1984)
+220 Mirror Has Two Faces, The (1996)
+221 Breaking the Waves (1996)
+222 Star Trek: First Contact (1996)
+223 Sling Blade (1996)
+224 Ridicule (1996)
+225 101 Dalmatians (1996)
+226 Die Hard 2 (1990)
+227 Star Trek VI: The Undiscovered Country (1991)
+228 Star Trek: The Wrath of Khan (1982)
+229 Star Trek III: The Search for Spock (1984)
+230 Star Trek IV: The Voyage Home (1986)
+231 Batman Returns (1992)
+232 Young Guns (1988)
+233 Under Siege (1992)
+234 Jaws (1975)
+235 Mars Attacks! (1996)
+236 Citizen Ruth (1996)
+237 Jerry Maguire (1996)
+238 Raising Arizona (1987)
+239 Sneakers (1992)
+240 Beavis and Butt-head Do America (1996)
+241 Last of the Mohicans, The (1992)
+242 Kolya (1996)
+243 Jungle2Jungle (1997)
+244 Smilla's Sense of Snow (1997)
+245 Devil's Own, The (1997)
+246 Chasing Amy (1997)
+247 Turbo: A Power Rangers Movie (1997)
+248 Grosse Pointe Blank (1997)
+249 Austin Powers: International Man of Mystery (1997)
+250 Fifth Element, The (1997)
+251 Shall We Dance? (1996)
+252 Lost World: Jurassic Park, The (1997)
+253 Pillow Book, The (1995)
+254 Batman & Robin (1997)
+255 My Best Friend's Wedding (1997)
+256 When the Cats Away (Chacun cherche son chat) (1996)
+257 Men in Black (1997)
+258 Contact (1997)
+259 George of the Jungle (1997)
+260 Event Horizon (1997)
+261 Air Bud (1997)
+262 In the Company of Men (1997)
+263 Steel (1997)
+264 Mimic (1997)
+265 Hunt for Red October, The (1990)
+266 Kull the Conqueror (1997)
+267 unknown
+268 Chasing Amy (1997)
+269 Full Monty, The (1997)
+270 Gattaca (1997)
+271 Starship Troopers (1997)
+272 Good Will Hunting (1997)
+273 Heat (1995)
+274 Sabrina (1995)
+275 Sense and Sensibility (1995)
+276 Leaving Las Vegas (1995)
+277 Restoration (1995)
+278 Bed of Roses (1996)
+279 Once Upon a Time... When We Were Colored (1995)
+280 Up Close and Personal (1996)
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+1625 Nightwatch (1997)
+1626 Nobody Loves Me (Keiner liebt mich) (1994)
+1627 Wife, The (1995)
+1628 Lamerica (1994)
+1629 Nico Icon (1995)
+1630 Silence of the Palace, The (Saimt el Qusur) (1994)
+1631 Slingshot, The (1993)
+1632 Land and Freedom (Tierra y libertad) (1995)
+1633 kldum klaka (Cold Fever) (1994)
+1634 Etz Hadomim Tafus (Under the Domin Tree) (1994)
+1635 Two Friends (1986)
+1636 Brothers in Trouble (1995)
+1637 Girls Town (1996)
+1638 Normal Life (1996)
+1639 Bitter Sugar (Azucar Amargo) (1996)
+1640 Eighth Day, The (1996)
+1641 Dadetown (1995)
+1642 Some Mother's Son (1996)
+1643 Angel Baby (1995)
+1644 Sudden Manhattan (1996)
+1645 Butcher Boy, The (1998)
+1646 Men With Guns (1997)
+1647 Hana-bi (1997)
+1648 Niagara, Niagara (1997)
+1649 Big One, The (1997)
+1650 Butcher Boy, The (1998)
+1651 Spanish Prisoner, The (1997)
+1652 Temptress Moon (Feng Yue) (1996)
+1653 Entertaining Angels: The Dorothy Day Story (1996)
+1654 Chairman of the Board (1998)
+1655 Favor, The (1994)
+1656 Little City (1998)
+1657 Target (1995)
+1658 Substance of Fire, The (1996)
+1659 Getting Away With Murder (1996)
+1660 Small Faces (1995)
+1661 New Age, The (1994)
+1662 Rough Magic (1995)
+1663 Nothing Personal (1995)
+1664 8 Heads in a Duffel Bag (1997)
+1665 Brother's Kiss, A (1997)
+1666 Ripe (1996)
+1667 Next Step, The (1995)
+1668 Wedding Bell Blues (1996)
+1669 MURDER and murder (1996)
+1670 Tainted (1998)
+1671 Further Gesture, A (1996)
+1672 Kika (1993)
+1673 Mirage (1995)
+1674 Mamma Roma (1962)
+1675 Sunchaser, The (1996)
+1676 War at Home, The (1996)
+1677 Sweet Nothing (1995)
+1678 Mat' i syn (1997)
+1679 B. Monkey (1998)
+1680 Sliding Doors (1998)
+1681 You So Crazy (1994)
+1682 Scream of Stone (Schrei aus Stein) (1991)
diff --git a/ex8/Figures/gaussian_fit.png b/ex8/Figures/gaussian_fit.png
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diff --git a/ex8/ex8.ipynb b/ex8/ex8.ipynb
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+++ b/ex8/ex8.ipynb
@@ -0,0 +1,969 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
Programming Exercise 8: Anomaly Detection and Recommender Systems
\n",
+ "
Introduction
\n",
+ "In this exercise, we will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, we will use collaborative filtering to build a recommender system for movies. To begin, we import necessary libraries. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# used for manipulating directory paths\n",
+ "import os\n",
+ "from os.path import join\n",
+ "\n",
+ "# Scientific and vector computation for python\n",
+ "import numpy as np\n",
+ "\n",
+ "# Plotting library\n",
+ "from matplotlib import pyplot\n",
+ "import matplotlib as mpl\n",
+ "\n",
+ "# Optimization module in scipy\n",
+ "from scipy import optimize\n",
+ "\n",
+ "# will be used to load MATLAB mat datafile format\n",
+ "from scipy.io import loadmat\n",
+ "\n",
+ "# tells matplotlib to embed plots within the notebook\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
1 Anomaly detection
\n",
+ "In this exercise, we will implement an anomaly detection algorithm to detect anomalous behavior in server computers. The features measure the throughput (mb/s) and latency (ms) of response of each server. Whil operating, we collected m = 307 example of how they were behaving. We suspect the vast majority are \"normal\", but there may be some anomalous examples.\n",
+ "\n",
+ "We will use a Gaussian model to detect anomalous example in the dataset. We begin on a 2D dataset to visualize what the algorithm is doing. The following cell will visualize the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# The following command loads the dataset.\n",
+ "data = loadmat(os.path.join('Data', 'ex8data1.mat'))\n",
+ "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Visualize the example dataset\n",
+ "pyplot.plot(X[:, 0], X[:, 1], 'bx', mew=2, mec='k', ms=6)\n",
+ "pyplot.axis([0, 30, 0, 30])\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To perform anomaly detection, we first need to fit a model to the data's distribution. To do so, we need to estimate the mean and variance parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def estimateGaussian(X):\n",
+ " \"\"\"\n",
+ " This function estimates the parameters of a Gaussian distribution\n",
+ " using a provided dataset.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x n) with each n-dimensional \n",
+ " data point in one row, and each total of m data points.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " mu : array_like \n",
+ " A vector of shape (n,) containing the means of each dimension.\n",
+ " \n",
+ " sigma2 : array_like\n",
+ " A vector of shape (n,) containing the computed\n",
+ " variances of each dimension.\n",
+ " \"\"\"\n",
+ " m, n = X.shape\n",
+ " mu = np.zeros(n)\n",
+ " sigma2 = np.zeros(n)\n",
+ "\n",
+ " mu = np.mean(X, axis=0)\n",
+ " sigma2 = np.var(X, axis=0)\n",
+ " return mu, sigma2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cells visualize this distribution and how our dataset falls into it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def multivariateGaussian(X, mu, Sigma2):\n",
+ " \"\"\"\n",
+ " Computes the probability density function of the multivariate gaussian distribution.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x n). Where there are m examples of n-dimensions.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of shape (n,) contains the means for each dimension (feature).\n",
+ "\n",
+ " Sigma2 : array_like\n",
+ " Either a vector of shape (n,) containing the variances of independent features\n",
+ " (i.e. it is the diagonal of the correlation matrix), or the full\n",
+ " correlation matrix of shape (n x n) which can represent dependent features.\n",
+ "\n",
+ " Returns\n",
+ " ------\n",
+ " p : array_like\n",
+ " A vector of shape (m,) which contains the computed probabilities at each of the\n",
+ " provided examples.\n",
+ " \"\"\"\n",
+ " k = mu.size\n",
+ "\n",
+ " # if sigma is given as a diagonal, compute the matrix\n",
+ " if Sigma2.ndim == 1:\n",
+ " Sigma2 = np.diag(Sigma2)\n",
+ "\n",
+ " X = X - mu\n",
+ " p = (2 * np.pi) ** (- k / 2) * np.linalg.det(Sigma2) ** (-0.5)\\\n",
+ " * np.exp(-0.5 * np.sum(np.dot(X, np.linalg.pinv(Sigma2)) * X, axis=1))\n",
+ " return p"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualizeFit(X, mu, sigma2):\n",
+ " \"\"\"\n",
+ " Visualize the dataset and its estimated distribution.\n",
+ " This visualization shows you the probability density function of the Gaussian distribution.\n",
+ " Each example has a location (x1, x2) that depends on its feature values.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " X : array_like\n",
+ " The dataset of shape (m x 2). Where there are m examples of 2-dimensions. We need at most\n",
+ " 2-D features to be able to visualize the distribution.\n",
+ "\n",
+ " mu : array_like\n",
+ " A vector of shape (n,) contains the means for each dimension (feature).\n",
+ "\n",
+ " sigma2 : array_like\n",
+ " Either a vector of shape (n,) containing the variances of independent features\n",
+ " (i.e. it is the diagonal of the correlation matrix), or the full\n",
+ " correlation matrix of shape (n x n) which can represent dependent features.\n",
+ " \"\"\"\n",
+ "\n",
+ " X1, X2 = np.meshgrid(np.arange(0, 35.5, 0.5), np.arange(0, 35.5, 0.5))\n",
+ " Z = multivariateGaussian(np.stack([X1.ravel(), X2.ravel()], axis=1), mu, sigma2)\n",
+ " Z = Z.reshape(X1.shape)\n",
+ "\n",
+ " pyplot.plot(X[:, 0], X[:, 1], 'bx', mec='b', mew=2, ms=8)\n",
+ "\n",
+ " if np.all(abs(Z) != np.inf):\n",
+ " pyplot.contour(X1, X2, Z, levels=10**(np.arange(-20., 1, 3)), zorder=100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Estimate my and sigma2\n",
+ "mu, sigma2 = estimateGaussian(X)\n",
+ "\n",
+ "# Returns the density of the multivariate normal at each data point (row) \n",
+ "# of X\n",
+ "p = multivariateGaussian(X, mu, sigma2)\n",
+ "\n",
+ "# Visualize the fit\n",
+ "visualizeFit(X, mu, sigma2)\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pyplot.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have estimated the Gaussian parameters, we can investigate which examples have a very high probability given this distribution and which examples have a very low probability. The low probability examples are more liekly to be the anomalies in our dataset. One way to determine which examples are anomalies is to select a threshold based on a cross-validation set. In this part of the exercise, we will implement an algorithm to select the threshold epsilon using the F1 score on a cross validation set. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def selectThreshold(yval, pval):\n",
+ " \"\"\"\n",
+ " Find the best threshold (epsilon) to use for selecting outliers based\n",
+ " on the results from a validation set and the ground truth.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " yval : array_like\n",
+ " The ground truth labels of shape (m, ).\n",
+ " \n",
+ " pval : array_like\n",
+ " The precomputed vector of probabilities based on mu and sigma2 parameters. It's shape is also (m, ).\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " bestEpsilon : array_like\n",
+ " A vector of shape (n,) corresponding to the threshold value.\n",
+ " \n",
+ " bestF1 : float\n",
+ " The value for the best F1 score.\n",
+ " \"\"\"\n",
+ " bestEpsilon = 0\n",
+ " bestF1 = 0\n",
+ " F1 = 0\n",
+ " \n",
+ " for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n",
+ " predictions = (pval < epsilon)\n",
+ " tp = np.sum((predictions == 1) & (yval == 1))\n",
+ " fp = np.sum((predictions == 1) & (yval == 0))\n",
+ " fn = np.sum((predictions == 0) & (yval == 1))\n",
+ " prec = tp / (tp + fp)\n",
+ " rec = tp / (tp + fn)\n",
+ " F1 = (2*prec*rec) / (prec + rec)\n",
+ "\n",
+ " if F1 > bestF1:\n",
+ " bestF1 = F1\n",
+ " bestEpsilon = epsilon\n",
+ "\n",
+ " return bestEpsilon, bestF1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cell will run our threshold selection function and circle the anomalies in the plot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best epsilon found using cross-validation: 9.00e-05\n",
+ "Best F1 on Cross Validation Set: 0.875000\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+ "\n",
+ "epsilon, F1 = selectThreshold(yval, pval)\n",
+ "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+ "print('Best F1 on Cross Validation Set: %f' % F1)\n",
+ "\n",
+ "# Find the outliers in the training set and plot the\n",
+ "outliers = p < epsilon\n",
+ "\n",
+ "# Visualize the fit\n",
+ "visualizeFit(X, mu, sigma2)\n",
+ "pyplot.xlabel('Latency (ms)')\n",
+ "pyplot.ylabel('Throughput (mb/s)')\n",
+ "pyplot.tight_layout()\n",
+ "\n",
+ "# Draw a red circle around those outliers\n",
+ "pyplot.plot(X[outliers, 0], X[outliers, 1], 'ro', ms=10, mfc='None', mew=2)\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have run our code successfuly on a small, low dimension dataset, we can apply it to higher dimensions. The following cell will do so on a dataset where each example is described by 11 features. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best epsilon found using cross-validation: 1.38e-18\n",
+ "Best F1 on Cross Validation Set : 0.615385\n",
+ "\n",
+ "\n",
+ "# Outliers found: 117\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Loads the second dataset. You should now have the\n",
+ "# variables X, Xval, yval in your environment\n",
+ "data = loadmat(os.path.join('Data', 'ex8data2.mat'))\n",
+ "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+ "\n",
+ "# Apply the same steps to the larger dataset\n",
+ "mu, sigma2 = estimateGaussian(X)\n",
+ "\n",
+ "# Training set \n",
+ "p = multivariateGaussian(X, mu, sigma2)\n",
+ "\n",
+ "# Cross-validation set\n",
+ "pval = multivariateGaussian(Xval, mu, sigma2)\n",
+ "\n",
+ "# Find the best threshold\n",
+ "epsilon, F1 = selectThreshold(yval, pval)\n",
+ "\n",
+ "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+ "print('Best F1 on Cross Validation Set : %f\\n' % F1)\n",
+ "print('\\n# Outliers found: %d' % np.sum(p < epsilon))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
2 Recommender Systems
\n",
+ "In this part of the exercise, we will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings. This dataset consists of ratings on a scale of 1 to 5. The dataset has 943 users and 1682 movies. \n",
+ "\n",
+ "To begin, we load and plot the dataset ex8_movies.mat."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average rating for movie 1 (Toy Story): 3.878319 / 5\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load data\n",
+ "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+ "Y, R = data['Y'], data['R']\n",
+ "\n",
+ "# Y is a 1682x943 matrix, containing ratings (1-5) of \n",
+ "# 1682 movies on 943 users\n",
+ "\n",
+ "# R is a 1682x943 matrix, where R(i,j) = 1 \n",
+ "# if and only if user j gave a rating to movie i\n",
+ "\n",
+ "# From the matrix, we can compute statistics like average rating.\n",
+ "print('Average rating for movie 1 (Toy Story): %f / 5' %\n",
+ " np.mean(Y[0, R[0, :] == 1]))\n",
+ "\n",
+ "# We can \"visualize\" the ratings matrix by plotting it with imshow\n",
+ "pyplot.figure(figsize=(8, 8))\n",
+ "pyplot.imshow(Y)\n",
+ "pyplot.ylabel('Movies')\n",
+ "pyplot.xlabel('Users')\n",
+ "pyplot.grid(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We begin constructing our collaberative filtering algorithm by implementing the regularized cost function which returns our cost and cost gradient. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cofiCostFunc(params, Y, R, num_users, num_movies,\n",
+ " num_features, lambda_=0.0):\n",
+ " \"\"\"\n",
+ " Collaborative filtering cost function.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " params : array_like\n",
+ " The parameters which will be optimized. This is a one\n",
+ " dimensional vector of shape (num_movies x num_users, 1). It is the \n",
+ " concatenation of the feature vectors X and parameters Theta.\n",
+ " \n",
+ " Y : array_like\n",
+ " A matrix of shape (num_movies x num_users) of user ratings of movies.\n",
+ " \n",
+ " R : array_like\n",
+ " A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n",
+ " i-th movie was rated by the j-th user.\n",
+ " \n",
+ " num_users : int\n",
+ " Total number of users.\n",
+ " \n",
+ " num_movies : int\n",
+ " Total number of movies.\n",
+ " \n",
+ " num_features : int\n",
+ " Number of features to learn.\n",
+ " \n",
+ " lambda_ : float, optional\n",
+ " The regularization coefficient.\n",
+ " \n",
+ " Returns\n",
+ " -------\n",
+ " J : float\n",
+ " The value of the cost function at the given params.\n",
+ " \n",
+ " grad : array_like\n",
+ " The gradient vector of the cost function at the given params.\n",
+ " grad has a shape (num_movies x num_users, 1)\n",
+ " \"\"\"\n",
+ " # Unfold the U and W matrices from params\n",
+ " X = params[:num_movies*num_features].reshape(num_movies, num_features)\n",
+ " Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n",
+ "\n",
+ " J = 0\n",
+ " X_grad = np.zeros(X.shape)\n",
+ " Theta_grad = np.zeros(Theta.shape)\n",
+ "\n",
+ " predMovieRatings = np.dot(X, Theta.transpose())\n",
+ " predMovieError = predMovieRatings - Y\n",
+ " error_factor = np.multiply(predMovieError, R)\n",
+ " J = (.5)*np.sum(np.sum(np.square(error_factor)))\n",
+ " X_grad = error_factor.dot(Theta)\n",
+ " Theta_grad = np.dot(error_factor.transpose(), X)\n",
+ " J += (lambda_/2)*np.sum(np.sum(np.square(Theta))) + (lambda_/2)*np.sum(np.sum(np.square(X)))\n",
+ " X_grad += lambda_*X\n",
+ " Theta_grad += lambda_*Theta\n",
+ " \n",
+ " grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n",
+ " return J, grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can confirm our gradient vector is correct by comparing it to a numerically computed alternative."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def computeNumericalGradient(J, theta, e=1e-4):\n",
+ " \"\"\"\n",
+ " Computes the gradient using \"finite differences\" and gives us a numerical estimate of the\n",
+ " gradient.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " J : func\n",
+ " The cost function which will be used to estimate its numerical gradient.\n",
+ "\n",
+ " theta : array_like\n",
+ " The one dimensional unrolled network parameters. The numerical gradient is computed at\n",
+ " those given parameters.\n",
+ "\n",
+ " e : float (optional)\n",
+ " The value to use for epsilon for computing the finite difference.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " numgrad : array_like\n",
+ " The numerical gradient with respect to theta. Has same shape as theta.\n",
+ "\n",
+ " Notes\n",
+ " -----\n",
+ " The following code implements numerical gradient checking, and\n",
+ " returns the numerical gradient. It sets `numgrad[i]` to (a numerical\n",
+ " approximation of) the partial derivative of J with respect to the\n",
+ " i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should\n",
+ " be the (approximately) the partial derivative of J with respect\n",
+ " to theta[i].)\n",
+ " \"\"\"\n",
+ " numgrad = np.zeros(theta.shape)\n",
+ " perturb = np.diag(e * np.ones(theta.shape))\n",
+ " for i in range(theta.size):\n",
+ " loss1, _ = J(theta - perturb[:, i])\n",
+ " loss2, _ = J(theta + perturb[:, i])\n",
+ " numgrad[i] = (loss2 - loss1)/(2*e)\n",
+ " return numgrad"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def checkCostFunction(cofiCostFunc, lambda_=0.):\n",
+ " \"\"\"\n",
+ " Creates a collaborative filtering problem to check your cost function and gradients.\n",
+ " It will output the analytical gradients produced by your code and the numerical gradients\n",
+ " (computed using computeNumericalGradient). These two gradient computations should result\n",
+ " in very similar values.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " cofiCostFunc: func\n",
+ " Implementation of the cost function.\n",
+ "\n",
+ " lambda_ : float, optional\n",
+ " The regularization parameter.\n",
+ " \"\"\"\n",
+ " # Create small problem\n",
+ " X_t = np.random.rand(4, 3)\n",
+ " Theta_t = np.random.rand(5, 3)\n",
+ "\n",
+ " # Zap out most entries\n",
+ " Y = np.dot(X_t, Theta_t.T)\n",
+ " Y[np.random.rand(*Y.shape) > 0.5] = 0\n",
+ " R = np.zeros(Y.shape)\n",
+ " R[Y != 0] = 1\n",
+ "\n",
+ " # Run Gradient Checking\n",
+ " X = np.random.randn(*X_t.shape)\n",
+ " Theta = np.random.randn(*Theta_t.shape)\n",
+ " num_movies, num_users = Y.shape\n",
+ " num_features = Theta_t.shape[1]\n",
+ "\n",
+ " params = np.concatenate([X.ravel(), Theta.ravel()])\n",
+ " numgrad = computeNumericalGradient(\n",
+ " lambda x: cofiCostFunc(x, Y, R, num_users, num_movies, num_features, lambda_), params)\n",
+ "\n",
+ " cost, grad = cofiCostFunc(params, Y, R, num_users,num_movies, num_features, lambda_)\n",
+ "\n",
+ " print(np.stack([numgrad, grad], axis=1))\n",
+ " print('\\nThe above two columns you get should be very similar.'\n",
+ " '(Left-Your Numerical Gradient, Right-Analytical Gradient)')\n",
+ "\n",
+ " diff = np.linalg.norm(numgrad-grad)/np.linalg.norm(numgrad+grad)\n",
+ " print('If your cost function implementation is correct, then '\n",
+ " 'the relative difference will be small (less than 1e-9).')\n",
+ " print('\\nRelative Difference: %g' % diff)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 13.86445373 13.86445373]\n",
+ " [ -0.50346339 -0.50346339]\n",
+ " [ -0.25274807 -0.25274807]\n",
+ " [ -7.16541284 -7.16541284]\n",
+ " [ 3.10313028 3.10313028]\n",
+ " [ 0.56829314 0.56829314]\n",
+ " [ -1.04192584 -1.04192584]\n",
+ " [ 2.07795586 2.07795586]\n",
+ " [ -0.68986789 -0.68986789]\n",
+ " [ 1.86079356 1.86079356]\n",
+ " [ -2.40363355 -2.40363355]\n",
+ " [ -0.20257324 -0.20257324]\n",
+ " [ -2.35627103 -2.35627103]\n",
+ " [ 0.99584362 0.99584362]\n",
+ " [ -0.94845546 -0.94845546]\n",
+ " [ 6.87482798 6.87482798]\n",
+ " [ -1.90959988 -1.90959988]\n",
+ " [ 0.30039997 0.30039997]\n",
+ " [ 1.99317192 1.99317192]\n",
+ " [ -0.71867099 -0.71867099]\n",
+ " [ -0.13605671 -0.13605671]\n",
+ " [-19.15609492 -19.15609492]\n",
+ " [ 6.2449762 6.2449762 ]\n",
+ " [ 0.34766409 0.34766409]\n",
+ " [ 3.77041093 3.77041093]\n",
+ " [ -4.92417175 -4.92417175]\n",
+ " [ 0.16311157 0.16311157]]\n",
+ "\n",
+ "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+ "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+ "\n",
+ "Relative Difference: 2.96831e-12\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Check gradients by running checkCostFunction\n",
+ "checkCostFunction(cofiCostFunc, 1.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have a working collaborative filtering cost function and gradient, we can start training our algorithm to make movie recommendations. The following cells will take in your user ratings, train the model, and make recommendations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def loadMovieList():\n",
+ " \"\"\"\n",
+ " Reads the fixed movie list in movie_ids.txt and returns a list of movie names.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " movieNames : list\n",
+ " A list of strings, representing all movie names.\n",
+ " \"\"\"\n",
+ " # Read the fixed movieulary list\n",
+ " with open(join('Data', 'movie_ids.txt'), encoding='ISO-8859-1') as fid:\n",
+ " movies = fid.readlines()\n",
+ "\n",
+ " movieNames = []\n",
+ " for movie in movies:\n",
+ " parts = movie.split()\n",
+ " movieNames.append(' '.join(parts[1:]).strip())\n",
+ " return movieNames"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "New user ratings:\n",
+ "-----------------\n",
+ "Rated 4 stars: Toy Story (1995)\n",
+ "Rated 3 stars: Twelve Monkeys (1995)\n",
+ "Rated 5 stars: Usual Suspects, The (1995)\n",
+ "Rated 4 stars: Outbreak (1995)\n",
+ "Rated 5 stars: Shawshank Redemption, The (1994)\n",
+ "Rated 3 stars: While You Were Sleeping (1995)\n",
+ "Rated 5 stars: Forrest Gump (1994)\n",
+ "Rated 2 stars: Silence of the Lambs, The (1991)\n",
+ "Rated 4 stars: Alien (1979)\n",
+ "Rated 5 stars: Die Hard 2 (1990)\n",
+ "Rated 5 stars: Sphere (1998)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Before we will train the collaborative filtering model, we will first\n",
+ "# add ratings that correspond to a new user that we just observed. This\n",
+ "# part of the code will also allow you to put in your own ratings for the\n",
+ "# movies in our dataset!\n",
+ "movieList = loadMovieList()\n",
+ "n_m = len(movieList)\n",
+ "\n",
+ "# Initialize my ratings\n",
+ "my_ratings = np.zeros(n_m)\n",
+ "\n",
+ "# Check the file movie_idx.txt for id of each movie in our dataset\n",
+ "# For example, Toy Story (1995) has ID 1, so to rate it \"4\", you can set\n",
+ "# Note that the index here is ID-1, since we start index from 0.\n",
+ "my_ratings[0] = 4\n",
+ "\n",
+ "# Or suppose did not enjoy Silence of the Lambs (1991), you can set\n",
+ "my_ratings[97] = 2\n",
+ "\n",
+ "# We have selected a few movies we liked / did not like and the ratings we\n",
+ "# gave are as follows:\n",
+ "my_ratings[6] = 3\n",
+ "my_ratings[11]= 5\n",
+ "my_ratings[53] = 4\n",
+ "my_ratings[63] = 5\n",
+ "my_ratings[65] = 3\n",
+ "my_ratings[68] = 5\n",
+ "my_ratings[182] = 4\n",
+ "my_ratings[225] = 5\n",
+ "my_ratings[354] = 5\n",
+ "\n",
+ "print('New user ratings:')\n",
+ "print('-----------------')\n",
+ "for i in range(len(my_ratings)):\n",
+ " if my_ratings[i] > 0:\n",
+ " print('Rated %d stars: %s' % (my_ratings[i], movieList[i]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def normalizeRatings(Y, R):\n",
+ " \"\"\"\n",
+ " Preprocess data by subtracting mean rating for every movie (every row).\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " Y : array_like\n",
+ " The user ratings for all movies. A matrix of shape (num_movies x num_users).\n",
+ "\n",
+ " R : array_like\n",
+ " Indicator matrix for movies rated by users. A matrix of shape (num_movies x num_users).\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " Ynorm : array_like\n",
+ " A matrix of same shape as Y, after mean normalization.\n",
+ "\n",
+ " Ymean : array_like\n",
+ " A vector of shape (num_movies, ) containing the mean rating for each movie.\n",
+ " \"\"\"\n",
+ " m, n = Y.shape\n",
+ " Ymean = np.zeros(m)\n",
+ " Ynorm = np.zeros(Y.shape)\n",
+ "\n",
+ " for i in range(m):\n",
+ " idx = R[i, :] == 1\n",
+ " Ymean[i] = np.mean(Y[i, idx])\n",
+ " Ynorm[i, idx] = Y[i, idx] - Ymean[i]\n",
+ "\n",
+ " return Ynorm, Ymean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Recommender system learning completed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Now, we will train the collaborative filtering model on a movie rating \n",
+ "# dataset of 1682 movies and 943 users\n",
+ "\n",
+ "# Load data\n",
+ "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+ "Y, R = data['Y'], data['R']\n",
+ "\n",
+ "# Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by \n",
+ "# 943 users\n",
+ "\n",
+ "# R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a\n",
+ "# rating to movie i\n",
+ "\n",
+ "# Add our own ratings to the data matrix\n",
+ "Y = np.hstack([my_ratings[:, None], Y])\n",
+ "R = np.hstack([(my_ratings > 0)[:, None], R])\n",
+ "\n",
+ "# Normalize Ratings\n",
+ "Ynorm, Ymean = normalizeRatings(Y, R)\n",
+ "\n",
+ "# Useful Values\n",
+ "num_movies, num_users = Y.shape\n",
+ "num_features = 10\n",
+ "\n",
+ "# Set Initial Parameters (Theta, X)\n",
+ "X = np.random.randn(num_movies, num_features)\n",
+ "Theta = np.random.randn(num_users, num_features)\n",
+ "\n",
+ "initial_parameters = np.concatenate([X.ravel(), Theta.ravel()])\n",
+ "\n",
+ "# Set options for scipy.optimize.minimize\n",
+ "options = {'maxiter': 100}\n",
+ "\n",
+ "# Set Regularization\n",
+ "lambda_ = 10\n",
+ "res = optimize.minimize(lambda x: cofiCostFunc(x, Ynorm, R, num_users,\n",
+ " num_movies, num_features, lambda_),\n",
+ " initial_parameters,\n",
+ " method='TNC',\n",
+ " jac=True,\n",
+ " options=options)\n",
+ "theta = res.x\n",
+ "\n",
+ "# Unfold the returned theta back into U and W\n",
+ "X = theta[:num_movies*num_features].reshape(num_movies, num_features)\n",
+ "Theta = theta[num_movies*num_features:].reshape(num_users, num_features)\n",
+ "\n",
+ "print('Recommender system learning completed.')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top recommendations for you:\n",
+ "----------------------------\n",
+ "Predicting rating 5.0 for movie Great Day in Harlem, A (1994)\n",
+ "Predicting rating 5.0 for movie Saint of Fort Washington, The (1993)\n",
+ "Predicting rating 5.0 for movie Entertaining Angels: The Dorothy Day Story (1996)\n",
+ "Predicting rating 5.0 for movie Santa with Muscles (1996)\n",
+ "Predicting rating 5.0 for movie Star Kid (1997)\n",
+ "Predicting rating 5.0 for movie They Made Me a Criminal (1939)\n",
+ "Predicting rating 5.0 for movie Aiqing wansui (1994)\n",
+ "Predicting rating 5.0 for movie Prefontaine (1997)\n",
+ "Predicting rating 5.0 for movie Marlene Dietrich: Shadow and Light (1996)\n",
+ "Predicting rating 5.0 for movie Someone Else's America (1995)\n",
+ "\n",
+ "Original ratings provided:\n",
+ "--------------------------\n",
+ "Rated 4 for Toy Story (1995)\n",
+ "Rated 3 for Twelve Monkeys (1995)\n",
+ "Rated 5 for Usual Suspects, The (1995)\n",
+ "Rated 4 for Outbreak (1995)\n",
+ "Rated 5 for Shawshank Redemption, The (1994)\n",
+ "Rated 3 for While You Were Sleeping (1995)\n",
+ "Rated 5 for Forrest Gump (1994)\n",
+ "Rated 2 for Silence of the Lambs, The (1991)\n",
+ "Rated 4 for Alien (1979)\n",
+ "Rated 5 for Die Hard 2 (1990)\n",
+ "Rated 5 for Sphere (1998)\n"
+ ]
+ }
+ ],
+ "source": [
+ "p = np.dot(X, Theta.T)\n",
+ "my_predictions = p[:, 0] + Ymean\n",
+ "\n",
+ "movieList = loadMovieList()\n",
+ "\n",
+ "ix = np.argsort(my_predictions)[::-1]\n",
+ "\n",
+ "print('Top recommendations for you:')\n",
+ "print('----------------------------')\n",
+ "for i in range(10):\n",
+ " j = ix[i]\n",
+ " print('Predicting rating %.1f for movie %s' % (my_predictions[j], movieList[j]))\n",
+ "\n",
+ "print('\\nOriginal ratings provided:')\n",
+ "print('--------------------------')\n",
+ "for i in range(len(my_ratings)):\n",
+ " if my_ratings[i] > 0:\n",
+ " print('Rated %d for %s' % (my_ratings[i], movieList[i]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
![](\"Figures/svm_c1.png\"/)
![](\"Figures/svm_c100.png\"/)
![](\"Figures/email.png\")
\n",
+ "\n",
+ "The result of these preprocessing steps is shown in the figure below. \n",
+ "\n",
+ "![\"email](\"Figures/email_cleaned.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After preprocessing the emails, we have a list of words for each email. The next step is to choose which words we would like to use in our classifier and which we would want to leave out.\n",
+ "\n",
+ "For this exercise, we have chosen only the most frequently occuring words as our set of words considered (the vocabulary list). Since words that occur rarely in the training set are only in a few emails, they might cause the\n",
+ "model to overfit our training set. The complete vocabulary list is in the file `vocab.txt` (inside the `Data` directory for this exercise) and also shown in the figure below.\n",
+ "\n",
+ "![\"Vocab\"](\"Figures/vocab.png\")
\n",
+ "\n",
+ "Our vocabulary list was selected by choosing all words which occur at least a 100 times in the spam corpus,\n",
+ "resulting in a list of 1899 words. In practice, a vocabulary list with about 10,000 to 50,000 words is often used.\n",
+ "Given the vocabulary list, we can now map each word in the preprocessed emails into a list of word indices that contains the index of the word in the vocabulary dictionary. The figure below shows the mapping for the sample email. Specifically, in the sample email, the word “anyone” was first normalized to “anyon” and then mapped onto the index 86 in the vocabulary list.\n",
+ "\n",
+ "![\"word](\"Figures/word_indices.png\")
\n",
+ "\n",
+ "Our task now is to complete the code in the function `processEmail` to perform this mapping. In the code, we have a string `word` which is a single word from the processed email. We need to look up the word in the vocabulary list `vocabList`. If the word exists in the list, we will add the index of the word into the `word_indices` variable. If the word does not exist, and is therefore not in the vocabulary, we will skip the word.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def getVocabList():\n",
+ " \"\"\"\n",
+ " Reads the fixed vocabulary list in vocab.txt and returns a cell array of the words\n",
+ " % vocabList = GETVOCABLIST() reads the fixed vocabulary list in vocab.txt\n",
+ " % and returns a cell array of the words in vocabList.\n",
+ "\n",
+ " :return:\n",
+ " \"\"\"\n",
+ " vocabList = np.genfromtxt(join('Data', 'vocab.txt'), dtype=object)\n",
+ " return list(vocabList[:, 1].astype(str))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PorterStemmer:\n",
+ " \"\"\"\n",
+ " Porter Stemming Algorithm\n",
+ "\n",
+ " This is the Porter stemming algorithm, ported to Python from the\n",
+ " version coded up in ANSI C by the author. It may be be regarded\n",
+ " as canonical, in that it follows the algorithm presented in\n",
+ "\n",
+ " Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,\n",
+ " no. 3, pp 130-137,\n",
+ "\n",
+ " only differing from it at the points maked --DEPARTURE-- below.\n",
+ "\n",
+ " See also http://www.tartarus.org/~martin/PorterStemmer\n",
+ "\n",
+ " The algorithm as described in the paper could be exactly replicated\n",
+ " by adjusting the points of DEPARTURE, but this is barely necessary,\n",
+ " because (a) the points of DEPARTURE are definitely improvements, and\n",
+ " (b) no encoding of the Porter stemmer I have seen is anything like\n",
+ " as exact as this version, even with the points of DEPARTURE!\n",
+ "\n",
+ " Vivake Gupta (v@nano.com)\n",
+ "\n",
+ " Release 1: January 2001\n",
+ "\n",
+ " Further adjustments by Santiago Bruno (bananabruno@gmail.com)\n",
+ " to allow word input not restricted to one word per line, leading\n",
+ " to:\n",
+ "\n",
+ " release 2: July 2008\n",
+ " \"\"\"\n",
+ " def __init__(self):\n",
+ " \"\"\"\n",
+ " The main part of the stemming algorithm starts here.\n",
+ " b is a buffer holding a word to be stemmed. The letters are in b[k0],\n",
+ " b[k0+1] ... ending at b[k]. In fact k0 = 0 in this demo program. k is\n",
+ " readjusted downwards as the stemming progresses. Zero termination is\n",
+ " not in fact used in the algorithm.\n",
+ "\n",
+ " Note that only lower case sequences are stemmed. Forcing to lower case\n",
+ " should be done before stem(...) is called.\n",
+ " \"\"\"\n",
+ " self.b = \"\" # buffer for word to be stemmed\n",
+ " self.k = 0\n",
+ " self.k0 = 0\n",
+ " self.j = 0 # j is a general offset into the string\n",
+ "\n",
+ " def cons(self, i):\n",
+ " \"\"\"cons(i) is TRUE <=> b[i] is a consonant.\"\"\"\n",
+ " if self.b[i] in 'aeiou':\n",
+ " return 0\n",
+ " if self.b[i] == 'y':\n",
+ " if i == self.k0:\n",
+ " return 1\n",
+ " else:\n",
+ " return not self.cons(i - 1)\n",
+ " return 1\n",
+ "\n",
+ " def m(self):\n",
+ " \"\"\"\n",
+ " m() measures the number of consonant sequences between k0 and j.\n",
+ " if c is a consonant sequence and v a vowel sequence, and <..>\n",
+ " indicates arbitrary presence,\n",
+ "\n",
+ "