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Add entirety of Stanford ML course exercises

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machine-learning-ex1/ex1.pdf Normal file
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function J = computeCost(X, y, theta)
%COMPUTECOST Compute cost for linear regression
% J = COMPUTECOST(X, y, theta) computes the cost of using theta as the
% parameter for linear regression to fit the data points in X and y
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta
% You should set J to the cost.
h = X * theta;
J = h-y;
J = J.^2;
J = sum(J);
J = J / (2*m);
% =========================================================================
end

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function J = computeCostMulti(X, y, theta)
%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
% J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
% parameter for linear regression to fit the data points in X and y
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta
% You should set J to the cost.
h = X * theta;
J = h-y;
J = J.^2;
J = sum(J);
J = J / (2*m);
% =========================================================================
end

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machine-learning-ex1/ex1/ex1.m Normal file
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%% Machine Learning Online Class - Exercise 1: Linear Regression
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% linear exercise. You will need to complete the following functions
% in this exericse:
%
% warmUpExercise.m
% plotData.m
% gradientDescent.m
% computeCost.m
% gradientDescentMulti.m
% computeCostMulti.m
% featureNormalize.m
% normalEqn.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
%
% x refers to the population size in 10,000s
% y refers to the profit in $10,000s
%
%% Initialization
clear ; close all; clc
%% ==================== Part 1: Basic Function ====================
% Complete warmUpExercise.m
fprintf('Running warmUpExercise ... \n');
fprintf('5x5 Identity Matrix: \n');
warmUpExercise()
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ======================= Part 2: Plotting =======================
fprintf('Plotting Data ...\n')
data = load('ex1data1.txt');
X = data(:, 1); y = data(:, 2);
m = length(y); % number of training examples
% Plot Data
% Note: You have to complete the code in plotData.m
plotData(X, y);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% =================== Part 3: Cost and Gradient descent ===================
X = [ones(m, 1), data(:,1)]; % Add a column of ones to x
theta = zeros(2, 1); % initialize fitting parameters
% Some gradient descent settings
iterations = 1500;
alpha = 0.01;
fprintf('\nTesting the cost function ...\n')
% compute and display initial cost
J = computeCost(X, y, theta);
fprintf('With theta = [0 ; 0]\nCost computed = %f\n', J);
fprintf('Expected cost value (approx) 32.07\n');
% further testing of the cost function
J = computeCost(X, y, [-1 ; 2]);
fprintf('\nWith theta = [-1 ; 2]\nCost computed = %f\n', J);
fprintf('Expected cost value (approx) 54.24\n');
fprintf('Program paused. Press enter to continue.\n');
pause;
fprintf('\nRunning Gradient Descent ...\n')
% run gradient descent
theta = gradientDescent(X, y, theta, alpha, iterations);
% print theta to screen
fprintf('Theta found by gradient descent:\n');
fprintf('%f\n', theta);
fprintf('Expected theta values (approx)\n');
fprintf(' -3.6303\n 1.1664\n\n');
% Plot the linear fit
hold on; % keep previous plot visible
plot(X(:,2), X*theta, '-')
legend('Training data', 'Linear regression')
hold off % don't overlay any more plots on this figure
% Predict values for population sizes of 35,000 and 70,000
predict1 = [1, 3.5] *theta;
fprintf('For population = 35,000, we predict a profit of %f\n',...
predict1*10000);
predict2 = [1, 7] * theta;
fprintf('For population = 70,000, we predict a profit of %f\n',...
predict2*10000);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ============= Part 4: Visualizing J(theta_0, theta_1) =============
fprintf('Visualizing J(theta_0, theta_1) ...\n')
% Grid over which we will calculate J
theta0_vals = linspace(-10, 10, 100);
theta1_vals = linspace(-1, 4, 100);
% initialize J_vals to a matrix of 0's
J_vals = zeros(length(theta0_vals), length(theta1_vals));
% Fill out J_vals
for i = 1:length(theta0_vals)
for j = 1:length(theta1_vals)
t = [theta0_vals(i); theta1_vals(j)];
J_vals(i,j) = computeCost(X, y, t);
end
end
% Because of the way meshgrids work in the surf command, we need to
% transpose J_vals before calling surf, or else the axes will be flipped
J_vals = J_vals';
% Surface plot
figure;
surf(theta0_vals, theta1_vals, J_vals)
xlabel('\theta_0'); ylabel('\theta_1');
% Contour plot
figure;
% Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100
contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20))
xlabel('\theta_0'); ylabel('\theta_1');
hold on;
plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2);

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machine-learning-ex1/ex1/ex1_multi.m Normal file
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%% Machine Learning Online Class
% Exercise 1: Linear regression with multiple variables
%
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% linear regression exercise.
%
% You will need to complete the following functions in this
% exericse:
%
% warmUpExercise.m
% plotData.m
% gradientDescent.m
% computeCost.m
% gradientDescentMulti.m
% computeCostMulti.m
% featureNormalize.m
% normalEqn.m
%
% For this part of the exercise, you will need to change some
% parts of the code below for various experiments (e.g., changing
% learning rates).
%
%% Initialization
%% ================ Part 1: Feature Normalization ================
%% Clear and Close Figures
clear ; close all; clc
fprintf('Loading data ...\n');
%% Load Data
data = load('ex1data2.txt');
X = data(:, 1:2);
y = data(:, 3);
m = length(y);
% Print out some data points
fprintf('First 10 examples from the dataset: \n');
fprintf(' x = [%.0f %.0f], y = %.0f \n', [X(1:10,:) y(1:10,:)]');
fprintf('Program paused. Press enter to continue.\n');
pause;
% Scale features and set them to zero mean
fprintf('Normalizing Features ...\n');
[X mu sigma] = featureNormalize(X);
% Add intercept term to X
X = [ones(m, 1) X];
%% ================ Part 2: Gradient Descent ================
% ====================== YOUR CODE HERE ======================
% Instructions: We have provided you with the following starter
% code that runs gradient descent with a particular
% learning rate (alpha).
%
% Your task is to first make sure that your functions -
% computeCost and gradientDescent already work with
% this starter code and support multiple variables.
%
% After that, try running gradient descent with
% different values of alpha and see which one gives
% you the best result.
%
% Finally, you should complete the code at the end
% to predict the price of a 1650 sq-ft, 3 br house.
%
% Hint: By using the 'hold on' command, you can plot multiple
% graphs on the same figure.
%
% Hint: At prediction, make sure you do the same feature normalization.
%
fprintf('Running gradient descent ...\n');
% Choose some alpha value
alpha = .3;
num_iters = 50;
% Init Theta and Run Gradient Descent
theta = zeros(3, 1);
[theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters);
% Plot the convergence graph
figure;
plot(1:numel(J_history), J_history, '-b', 'LineWidth', 2);
xlabel('Number of iterations');
ylabel('Cost J');
% Display gradient descent's result
fprintf('Theta computed from gradient descent: \n');
fprintf(' %f \n', theta);
fprintf('\n');
% Estimate the price of a 1650 sq-ft, 3 br house
% ====================== YOUR CODE HERE ======================
% Recall that the first column of X is all-ones. Thus, it does
% not need to be normalized.
price = 0; % You should change this
% ============================================================
fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
'(using gradient descent):\n $%f\n'], price);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ================ Part 3: Normal Equations ================
fprintf('Solving with normal equations...\n');
% ====================== YOUR CODE HERE ======================
% Instructions: The following code computes the closed form
% solution for linear regression using the normal
% equations. You should complete the code in
% normalEqn.m
%
% After doing so, you should complete this code
% to predict the price of a 1650 sq-ft, 3 br house.
%
%% Load Data
data = csvread('ex1data2.txt');
X = data(:, 1:2);
y = data(:, 3);
m = length(y);
% Add intercept term to X
X = [ones(m, 1) X];
% Calculate the parameters from the normal equation
theta = normalEqn(X, y);
% Display normal equation's result
fprintf('Theta computed from the normal equations: \n');
fprintf(' %f \n', theta);
fprintf('\n');
% Estimate the price of a 1650 sq-ft, 3 br house
% ====================== YOUR CODE HERE ======================
price = 0; % You should change this
% ============================================================
fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
'(using normal equations):\n $%f\n'], price);

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machine-learning-ex1/ex1/ex1data1.txt Normal file
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6.1101,17.592
5.5277,9.1302
8.5186,13.662
7.0032,11.854
5.8598,6.8233
8.3829,11.886
7.4764,4.3483
8.5781,12
6.4862,6.5987
5.0546,3.8166
5.7107,3.2522
14.164,15.505
5.734,3.1551
8.4084,7.2258
5.6407,0.71618
5.3794,3.5129
6.3654,5.3048
5.1301,0.56077
6.4296,3.6518
7.0708,5.3893
6.1891,3.1386
20.27,21.767
5.4901,4.263
6.3261,5.1875
5.5649,3.0825
18.945,22.638
12.828,13.501
10.957,7.0467
13.176,14.692
22.203,24.147
5.2524,-1.22
6.5894,5.9966
9.2482,12.134
5.8918,1.8495
8.2111,6.5426
7.9334,4.5623
8.0959,4.1164
5.6063,3.3928
12.836,10.117
6.3534,5.4974
5.4069,0.55657
6.8825,3.9115
11.708,5.3854
5.7737,2.4406
7.8247,6.7318
7.0931,1.0463
5.0702,5.1337
5.8014,1.844
11.7,8.0043
5.5416,1.0179
7.5402,6.7504
5.3077,1.8396
7.4239,4.2885
7.6031,4.9981
6.3328,1.4233
6.3589,-1.4211
6.2742,2.4756
5.6397,4.6042
9.3102,3.9624
9.4536,5.4141
8.8254,5.1694
5.1793,-0.74279
21.279,17.929
14.908,12.054
18.959,17.054
7.2182,4.8852
8.2951,5.7442
10.236,7.7754
5.4994,1.0173
20.341,20.992
10.136,6.6799
7.3345,4.0259
6.0062,1.2784
7.2259,3.3411
5.0269,-2.6807
6.5479,0.29678
7.5386,3.8845
5.0365,5.7014
10.274,6.7526
5.1077,2.0576
5.7292,0.47953
5.1884,0.20421
6.3557,0.67861
9.7687,7.5435
6.5159,5.3436
8.5172,4.2415
9.1802,6.7981
6.002,0.92695
5.5204,0.152
5.0594,2.8214
5.7077,1.8451
7.6366,4.2959
5.8707,7.2029
5.3054,1.9869
8.2934,0.14454
13.394,9.0551
5.4369,0.61705

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machine-learning-ex1/ex1/ex1data2.txt Normal file
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2104,3,399900
1600,3,329900
2400,3,369000
1416,2,232000
3000,4,539900
1985,4,299900
1534,3,314900
1427,3,198999
1380,3,212000
1494,3,242500
1940,4,239999
2000,3,347000
1890,3,329999
4478,5,699900
1268,3,259900
2300,4,449900
1320,2,299900
1236,3,199900
2609,4,499998
3031,4,599000
1767,3,252900
1888,2,255000
1604,3,242900
1962,4,259900
3890,3,573900
1100,3,249900
1458,3,464500
2526,3,469000
2200,3,475000
2637,3,299900
1839,2,349900
1000,1,169900
2040,4,314900
3137,3,579900
1811,4,285900
1437,3,249900
1239,3,229900
2132,4,345000
4215,4,549000
2162,4,287000
1664,2,368500
2238,3,329900
2567,4,314000
1200,3,299000
852,2,179900
1852,4,299900
1203,3,239500

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function [X_norm, mu, sigma] = featureNormalize(X)
%FEATURENORMALIZE Normalizes the features in X
% FEATURENORMALIZE(X) returns a normalized version of X where
% the mean value of each feature is 0 and the standard deviation
% is 1. This is often a good preprocessing step to do when
% working with learning algorithms.
% You need to set these values correctly
X_norm = X;
mu = zeros(1, size(X, 2));
sigma = zeros(1, size(X, 2));
m = size(X,1);
n = size(X,2);
% ====================== YOUR CODE HERE ======================
% Instructions: First, for each feature dimension, compute the mean
% of the feature and subtract it from the dataset,
% storing the mean value in mu. Next, compute the
% standard deviation of each feature and divide
% each feature by it's standard deviation, storing
% the standard deviation in sigma.
%
% Note that X is a matrix where each column is a
% feature and each row is an example. You need
% to perform the normalization separately for
% each feature.
%
% Hint: You might find the 'mean' and 'std' functions useful.
%
mu = mean(X);
sigma = std(X);
tempMu = zeros(size(X));
for i=1:m
tempMu(i,:) = mu;
endfor
X_norm = X_norm - tempMu;
for i=1:n
X_norm(:,i) = X_norm(:,i)/sigma(i);
endfor
% ============================================================
end

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function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESCENT(X, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha
% Initialize some useful values
m = length(y); % number of training examples
n = length(theta); % number of features plus 1
J_history = zeros(num_iters, 1);
for iter = 1:num_iters
% ====================== YOUR CODE HERE ======================
% Instructions: Perform a single gradient step on the parameter vector
% theta.
%
% Hint: While debugging, it can be useful to print out the values
% of the cost function (computeCost) and gradient here.
%
tempTheta = theta;
temp = 0;
for j = 1:n
temp = temp + (X*theta-y)'*X(:,j);
temp = temp*alpha;
temp = temp/m;
tempTheta(j) = tempTheta(j) - temp;
temp = 0;
endfor
theta = tempTheta;
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCost(X, y, theta);
end
end

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function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
% theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha
% Initialize some useful values
m = length(y); % number of training examples
n = length(theta);
J_history = zeros(num_iters, 1);
for iter = 1:num_iters
% ====================== YOUR CODE HERE ======================
% Instructions: Perform a single gradient step on the parameter vector
% theta.
%
% Hint: While debugging, it can be useful to print out the values
% of the cost function (computeCostMulti) and gradient here.
%
tempTheta = theta;
temp = 0;
for j = 1:n
temp = temp + (X*theta-y)'*X(:,j);
temp = temp*alpha;
temp = temp/m;
tempTheta(j) = tempTheta(j) - temp;
temp = 0;
endfor
theta = tempTheta;
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCostMulti(X, y, theta);
end
end

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machine-learning-ex1/ex1/lib/jsonlab/AUTHORS.txt Normal file
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The author of "jsonlab" toolbox is Qianqian Fang. Qianqian
is currently an Assistant Professor at Massachusetts General Hospital,
Harvard Medical School.
Address: Martinos Center for Biomedical Imaging,
Massachusetts General Hospital,
Harvard Medical School
Bldg 149, 13th St, Charlestown, MA 02129, USA
URL: http://nmr.mgh.harvard.edu/~fangq/
Email: <fangq at nmr.mgh.harvard.edu> or <fangqq at gmail.com>
The script loadjson.m was built upon previous works by
- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
date: 2009/11/02
- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
date: 2009/03/22
- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565
date: 2008/07/03
This toolbox contains patches submitted by the following contributors:
- Blake Johnson <bjohnso at bbn.com>
part of revision 341
- Niclas Borlin <Niclas.Borlin at cs.umu.se>
various fixes in revision 394, including
- loadjson crashes for all-zero sparse matrix.
- loadjson crashes for empty sparse matrix.
- Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson.
- loadjson crashes for sparse real column vector.
- loadjson crashes for sparse complex column vector.
- Data is corrupted by savejson for sparse real row vector.
- savejson crashes for sparse complex row vector.
- Yul Kang <yul.kang.on at gmail.com>
patches for svn revision 415.
- savejson saves an empty cell array as [] instead of null
- loadjson differentiates an empty struct from an empty array

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============================================================================
JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave
----------------------------------------------------------------------------
JSONlab ChangeLog (key features marked by *):
== JSONlab 1.0 (codename: Optimus - Final), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2015/01/02 polish help info for all major functions, update examples, finalize 1.0
2014/12/19 fix a bug to strictly respect NoRowBracket in savejson
== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/11/22 show progress bar in loadjson ('ShowProgress')
2014/11/17 add Compact option in savejson to output compact JSON format ('Compact')
2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels
2014/09/18 start official github mirror: https://github.com/fangq/jsonlab
== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8
2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson
2014/08/04 escape special characters in a JSON string
2014/02/16 fix a bug when saving ubjson files
== JSONlab 0.9.9 (codename: Optimus - beta), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/01/22 use binary read and write in saveubjson and loadubjson
== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang)
== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson
== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin)
== JSONlab 0.9.0 (codename: Rodimus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson
2012/06/01 support JSONP in savejson
2012/05/25 fix the empty cell bug (reported by Cyril Davin)
2012/04/05 savejson can save to a file (suggested by Patrick Rapin)
== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS
2012/01/25 patch to handle root-less objects, contributed by Blake Johnson
== JSONlab 0.8.0 (codename: Sentiel), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab
2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer
2011/12/22 *accept sequence of 'param',value input in savejson and loadjson
2011/11/18 fix struct array bug reported by Mykel Kochenderfer
== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration
2011/10/20 loadjson supports JSON collections - concatenated JSON objects
== JSONlab 0.5.0 (codename: Nexus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/16 package and release jsonlab 0.5.0
2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug
2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level
2011/10/10 create jsonlab project, start jsonlab website, add online documentation
2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support
2011/10/06 *savejson works for structs, cells and arrays
2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m

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machine-learning-ex1/ex1/lib/jsonlab/LICENSE_BSD.txt Normal file
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Copyright 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are
permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list
of conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those of the
authors and should not be interpreted as representing official policies, either expressed
or implied, of the copyright holders.

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===============================================================================
= JSONLab =
= An open-source MATLAB/Octave JSON encoder and decoder =
===============================================================================
*Copyright (C) 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>
*License: BSD License, see License_BSD.txt for details
*Version: 1.0 (Optimus - Final)
-------------------------------------------------------------------------------
Table of Content:
I. Introduction
II. Installation
III.Using JSONLab
IV. Known Issues and TODOs
V. Contribution and feedback
-------------------------------------------------------------------------------
I. Introduction
JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable,
human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format
to represent complex and hierarchical data. It is as powerful as
[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely
used for data-exchange in applications, and is essential for the wild success
of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and
[http://en.wikipedia.org/wiki/Web_2.0 Web2.0].
UBJSON (Universal Binary JSON) is a binary JSON format, specifically
optimized for compact file size and better performance while keeping
the semantics as simple as the text-based JSON format. Using the UBJSON
format allows to wrap complex binary data in a flexible and extensible
structure, making it possible to process complex and large dataset
without accuracy loss due to text conversions.
We envision that both JSON and its binary version will serve as part of
the mainstream data-exchange formats for scientific research in the future.
It will provide the flexibility and generality achieved by other popular
general-purpose file specifications, such as
[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly
reduced complexity and enhanced performance.
JSONLab is a free and open-source implementation of a JSON/UBJSON encoder
and a decoder in the native MATLAB language. It can be used to convert a MATLAB
data structure (array, struct, cell, struct array and cell array) into
JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB
data structure. JSONLab supports both MATLAB and
[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-------------------------------------------------------------------------------
II. Installation
The installation of JSONLab is no different than any other simple
MATLAB toolbox. You only need to download/unzip the JSONLab package
to a folder, and add the folder's path to MATLAB/Octave's path list
by using the following command:
addpath('/path/to/jsonlab');
If you want to add this path permanently, you need to type "pathtool",
browse to the jsonlab root folder and add to the list, then click "Save".
Then, run "rehash" in MATLAB, and type "which loadjson", if you see an
output, that means JSONLab is installed for MATLAB/Octave.
-------------------------------------------------------------------------------
III.Using JSONLab
JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder,
and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and
two equivallent functions -- loadubjson and saveubjson for the binary
JSON. The detailed help info for the four functions can be found below:
=== loadjson.m ===
<pre>
data=loadjson(fname,opt)
or
data=loadjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09, including previous works from
Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
created on 2009/11/02
François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
created on 2009/03/22
Joel Feenstra:
http://www.mathworks.com/matlabcentral/fileexchange/20565
created on 2008/07/03
$Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a JSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.FastArrayParser [1|0 or integer]: if set to 1, use a
speed-optimized array parser when loading an
array object. The fast array parser may
collapse block arrays into a single large
array similar to rules defined in cell2mat; 0 to
use a legacy parser; if set to a larger-than-1
value, this option will specify the minimum
dimension to enable the fast array parser. For
example, if the input is a 3D array, setting
FastArrayParser to 1 will return a 3D array;
setting to 2 will return a cell array of 2D
arrays; setting to 3 will return to a 2D cell
array of 1D vectors; setting to 4 will return a
3D cell array.
opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
dat=loadjson(['examples' filesep 'example1.json'])
dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
</pre>
=== savejson.m ===
<pre>
json=savejson(rootname,obj,filename)
or
json=savejson(rootname,obj,opt)
json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
Object Notation) string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09
$Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array).
filename: a string for the file name to save the output JSON data.
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.FloatFormat ['%.10g'|string]: format to show each numeric element
of a 1D/2D array;
opt.ArrayIndent [1|0]: if 1, output explicit data array with
precedent indentation; if 0, no indentation
opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [0|1]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
to represent +/-Inf. The matched pattern is '([-+]*)Inf'
and $1 represents the sign. For those who want to use
1e999 to represent Inf, they can set opt.Inf to '$11e999'
opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
to represent NaN
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSONP='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a string in the JSON format (see http://json.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
savejson('jmesh',jsonmesh)
savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
</pre>
=== loadubjson.m ===
<pre>
data=loadubjson(fname,opt)
or
data=loadubjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/01
$Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a UBJSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.IntEndian [B|L]: specify the endianness of the integer fields
in the UBJSON input data. B - Big-Endian format for
integers (as required in the UBJSON specification);
L - input integer fields are in Little-Endian order.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
obj=struct('string','value','array',[1 2 3]);
ubjdata=saveubjson('obj',obj);
dat=loadubjson(ubjdata)
dat=loadubjson(['examples' filesep 'example1.ubj'])
dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
</pre>
=== saveubjson.m ===
<pre>
json=saveubjson(rootname,obj,filename)
or
json=saveubjson(rootname,obj,opt)
json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a Universal
Binary JSON (UBJSON) binary string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/17
$Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array)
filename: a string for the file name to save the output UBJSON data
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [1|0]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSON='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a binary string in the UBJSON format (see http://ubjson.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
saveubjson('jsonmesh',jsonmesh)
saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
</pre>
=== examples ===
Under the "examples" folder, you can find several scripts to demonstrate the
basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you
will see the conversions from MATLAB data structure to JSON text and backward.
In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet
and validate the loadjson/savejson functions for regression testing purposes.
Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson
and loadubjson pairs for various matlab data structures.
Please run these examples and understand how JSONLab works before you use
it to process your data.
-------------------------------------------------------------------------------
IV. Known Issues and TODOs
JSONLab has several known limitations. We are striving to make it more general
and robust. Hopefully in a few future releases, the limitations become less.
Here are the known issues:
# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays;
# When processing names containing multi-byte characters, Octave and MATLAB \
can give different field-names; you can use feature('DefaultCharacterSet','latin1') \
in MATLAB to get consistant results
# savejson can not handle class and dataset.
# saveubjson converts a logical array into a uint8 ([U]) array
# an unofficial N-D array count syntax is implemented in saveubjson. We are \
actively communicating with the UBJSON spec maintainer to investigate the \
possibility of making it upstream
# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \
files, however, it can parse all UBJSON files produced by saveubjson.
-------------------------------------------------------------------------------
V. Contribution and feedback
JSONLab is an open-source project. This means you can not only use it and modify
it as you wish, but also you can contribute your changes back to JSONLab so
that everyone else can enjoy the improvement. For anyone who want to contribute,
please download JSONLab source code from it's subversion repository by using the
following command:
svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab
You can make changes to the files as needed. Once you are satisfied with your
changes, and ready to share it with others, please cd the root directory of
JSONLab, and type
svn diff > yourname_featurename.patch
You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at
the email address shown in the beginning of this file. Qianqian will review
the changes and commit it to the subversion if they are satisfactory.
We appreciate any suggestions and feedbacks from you. Please use iso2mesh's
mailing list to report any questions you may have with JSONLab:
http://groups.google.com/group/iso2mesh-users?hl=en&pli=1
(Subscription to the mailing list is needed in order to post messages).

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function val=jsonopt(key,default,varargin)
%
% val=jsonopt(key,default,optstruct)
%
% setting options based on a struct. The struct can be produced
% by varargin2struct from a list of 'param','value' pairs
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
%
% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $
%
% input:
% key: a string with which one look up a value from a struct
% default: if the key does not exist, return default
% optstruct: a struct where each sub-field is a key
%
% output:
% val: if key exists, val=optstruct.key; otherwise val=default
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
val=default;
if(nargin<=2) return; end
opt=varargin{1};
if(isstruct(opt) && isfield(opt,key))
val=getfield(opt,key);
end

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function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

528
machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m Normal file
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@ -0,0 +1,528 @@
function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

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function s=mergestruct(s1,s2)
%
% s=mergestruct(s1,s2)
%
% merge two struct objects into one
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% s1,s2: a struct object, s1 and s2 can not be arrays
%
% output:
% s: the merged struct object. fields in s1 and s2 will be combined in s.
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(~isstruct(s1) || ~isstruct(s2))
error('input parameters contain non-struct');
end
if(length(s1)>1 || length(s2)>1)
error('can not merge struct arrays');
end
fn=fieldnames(s2);
s=s1;
for i=1:length(fn)
s=setfield(s,fn{i},getfield(s2,fn{i}));
end

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function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end

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function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/17
%
% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array)
% filename: a string for the file name to save the output UBJSON data
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [1|0]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSON='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a binary string in the UBJSON format (see http://ubjson.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% saveubjson('jsonmesh',jsonmesh)
% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
json=obj2ubjson(rootname,obj,rootlevel,opt);
if(~rootisarray)
json=['{' json '}'];
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=[jsonp '(' json ')'];
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2ubjson(name,item,level,varargin)
if(iscell(item))
txt=cell2ubjson(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2ubjson(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2ubjson(name,item,level,varargin{:});
else
txt=mat2ubjson(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2ubjson(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item); % let's handle 1D cell first
if(len>1)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) '[']; name='';
else
txt='[';
end
elseif(len==0)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) 'Z']; name='';
else
txt='Z';
end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=struct2ubjson(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=[txt S_(checkname(name,varargin{:})) '{'];
else
txt=[txt '{'];
end
if(~isempty(names))
for e=1:length(names)
txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
end
end
txt=[txt '}'];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=str2ubjson(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
val=item(e,:);
if(len==1)
obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
if(isempty(name)) obj=['',S_(val),'']; end
txt=[txt,'',obj];
else
txt=[txt,'',['',S_(val),'']];
end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=mat2ubjson(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
cid=I_(uint32(max(size(item))));
if(isempty(name))
txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
else
if(isempty(item))
txt=[S_(checkname(name,varargin{:})),'Z'];
return;
else
txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
end
end
else
if(isempty(name))
txt=matdata2ubjson(item,level+1,varargin{:});
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
txt=[S_(checkname(name,varargin{:})) numtxt];
else
txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
end
end
return;
end
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=[txt,S_('_ArrayIsComplex_'),'T'];
end
txt=[txt,S_('_ArrayIsSparse_'),'T'];
if(size(item,1)==1)
% Row vector, store only column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([iy(:),data'],level+2,varargin{:})];
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,data],level+2,varargin{:})];
else
% General case, store row and column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,iy,data],level+2,varargin{:})];
end
else
if(isreal(item))
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson(item(:)',level+2,varargin{:})];
else
txt=[txt,S_('_ArrayIsComplex_'),'T'];
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
end
end
txt=[txt,'}'];
%%-------------------------------------------------------------------------
function txt=matdata2ubjson(mat,level,varargin)
if(isempty(mat))
txt='Z';
return;
end
if(size(mat,1)==1)
level=level-1;
end
type='';
hasnegtive=(mat<0);
if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
if(isempty(hasnegtive))
if(max(mat(:))<=2^8)
type='U';
end
end
if(isempty(type))
% todo - need to consider negative ones separately
id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
if(isempty(find(id)))
error('high-precision data is not yet supported');
end
key='iIlL';
type=key(find(id));
end
txt=[I_a(mat(:),type,size(mat))];
elseif(islogical(mat))
logicalval='FT';
if(numel(mat)==1)
txt=logicalval(mat+1);
else
txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
end
else
if(numel(mat)==1)
txt=['[' D_(mat) ']'];
else
txt=D_a(mat(:),'D',size(mat));
end
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function val=S_(str)
if(length(str)==1)
val=['C' str];
else
val=['S' I_(int32(length(str))) str];
end
%%-------------------------------------------------------------------------
function val=I_(num)
if(~isinteger(num))
error('input is not an integer');
end
if(num>=0 && num<255)
val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
return;
end
key='iIlL';
cid={'int8','int16','int32','int64'};
for i=1:4
if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
return;
end
end
error('unsupported integer');
%%-------------------------------------------------------------------------
function val=D_(num)
if(~isfloat(num))
error('input is not a float');
end
if(isa(num,'single'))
val=['d' data2byte(num,'uint8')];
else
val=['D' data2byte(num,'uint8')];
end
%%-------------------------------------------------------------------------
function data=I_a(num,type,dim,format)
id=find(ismember('iUIlL',type));
if(id==0)
error('unsupported integer array');
end
% based on UBJSON specs, all integer types are stored in big endian format
if(id==1)
data=data2byte(swapbytes(int8(num)),'uint8');
blen=1;
elseif(id==2)
data=data2byte(swapbytes(uint8(num)),'uint8');
blen=1;
elseif(id==3)
data=data2byte(swapbytes(int16(num)),'uint8');
blen=2;
elseif(id==4)
data=data2byte(swapbytes(int32(num)),'uint8');
blen=4;
elseif(id==5)
data=data2byte(swapbytes(int64(num)),'uint8');
blen=8;
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
end
data=['[' data(:)'];
else
data=reshape(data,blen,numel(data)/blen);
data(2:blen+1,:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function data=D_a(num,type,dim,format)
id=find(ismember('dD',type));
if(id==0)
error('unsupported float array');
end
if(id==1)
data=data2byte(single(num),'uint8');
elseif(id==2)
data=data2byte(double(num),'uint8');
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
end
data=['[' data];
else
data=reshape(data,(id*4),length(data)/(id*4));
data(2:(id*4+1),:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function bytes=data2byte(varargin)
bytes=typecast(varargin{:});
bytes=bytes(:)';

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machine-learning-ex1/ex1/lib/jsonlab/varargin2struct.m Normal file
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function opt=varargin2struct(varargin)
%
% opt=varargin2struct('param1',value1,'param2',value2,...)
% or
% opt=varargin2struct(...,optstruct,...)
%
% convert a series of input parameters into a structure
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% 'param', value: the input parameters should be pairs of a string and a value
% optstruct: if a parameter is a struct, the fields will be merged to the output struct
%
% output:
% opt: a struct where opt.param1=value1, opt.param2=value2 ...
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
len=length(varargin);
opt=struct;
if(len==0) return; end
i=1;
while(i<=len)
if(isstruct(varargin{i}))
opt=mergestruct(opt,varargin{i});
elseif(ischar(varargin{i}) && i<len)
opt=setfield(opt,varargin{i},varargin{i+1});
i=i+1;
else
error('input must be in the form of ...,''name'',value,... pairs or structs');
end
i=i+1;
end

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machine-learning-ex1/ex1/lib/makeValidFieldName.m Normal file
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function str = makeValidFieldName(str)
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
isoct=exist('OCTAVE_VERSION','builtin');
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end

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function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = promptToken('', '', tokenFile);
end
if isempty(token)
fprintf('!! Submission Cancelled\n');
return
end
try
response = submitParts(conf, email, token, parts);
catch
e = lasterror();
fprintf('\n!! Submission failed: %s\n', e.message);
fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
fprintf('\nPlease correct your code and resubmit.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
elseif isfield(response, 'errorCode')
fprintf('!! Submission failed: %s\n', response.message);
else
showFeedback(parts, response);
save(tokenFile, 'email', 'token');
end
end
function [email token] = promptToken(email, existingToken, tokenFile)
if (~isempty(email) && ~isempty(existingToken))
prompt = sprintf( ...
'Use token from last successful submission (%s)? (Y/n): ', ...
email);
reenter = input(prompt, 's');
if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
token = existingToken;
return;
else
delete(tokenFile);
end
end
email = input('Login (email address): ', 's');
token = input('Token: ', 's');
end
function isValid = isValidPartOptionIndex(partOptions, i)
isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
end
function response = submitParts(conf, email, token, parts)
body = makePostBody(conf, email, token, parts);
submissionUrl = submissionUrl();
responseBody = getResponse(submissionUrl, body);
jsonResponse = validateResponse(responseBody);
response = loadjson(jsonResponse);
end
function body = makePostBody(conf, email, token, parts)
bodyStruct.assignmentSlug = conf.assignmentSlug;
bodyStruct.submitterEmail = email;
bodyStruct.secret = token;
bodyStruct.parts = makePartsStruct(conf, parts);
opt.Compact = 1;
body = savejson('', bodyStruct, opt);
end
function partsStruct = makePartsStruct(conf, parts)
for part = parts
partId = part{:}.id;
fieldName = makeValidFieldName(partId);
outputStruct.output = conf.output(partId);
partsStruct.(fieldName) = outputStruct;
end
end
function [parts] = parts(conf)
parts = {};
for partArray = conf.partArrays
part.id = partArray{:}{1};
part.sourceFiles = partArray{:}{2};
part.name = partArray{:}{3};
parts{end + 1} = part;
end
end
function showFeedback(parts, response)
fprintf('== \n');
fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
for part = parts
score = '';
partFeedback = '';
partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
end
evaluation = response.evaluation;
totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
fprintf('== --------------------------------\n');
fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
fprintf('== \n');
end
% use urlread or curl to send submit results to the grader and get a response
function response = getResponse(url, body)
% try using urlread() and a secure connection
params = {'jsonBody', body};
[response, success] = urlread(url, 'post', params);
if (success == 0)
% urlread didn't work, try curl & the peer certificate patch
if ispc
% testing note: use 'jsonBody =' for a test case
json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
else
% it's linux/OS X, so use the other form
json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
end
% get the response body for the peer certificate patch method
[code, response] = system(json_command);
% test the success code
if (code ~= 0)
fprintf('[error] submission with curl() was not successful\n');
end
end
end
% validate the grader's response
function response = validateResponse(resp)
% test if the response is json or an HTML page
isJson = length(resp) > 0 && resp(1) == '{';
isHtml = findstr(lower(resp), '<html');
if (isJson)
response = resp;
elseif (isHtml)
% the response is html, so it's probably an error message
printHTMLContents(resp);
error('Grader response is an HTML message');
else
error('Grader sent no response');
end
end
% parse a HTML response and print it's contents
function printHTMLContents(response)
strippedResponse = regexprep(response, '<[^>]+>', ' ');
strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
fprintf(strippedResponse);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end

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machine-learning-ex1/ex1/normalEqn.m Normal file
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function [theta] = normalEqn(X, y)
%NORMALEQN Computes the closed-form solution to linear regression
% NORMALEQN(X,y) computes the closed-form solution to linear
% regression using the normal equations.
theta = zeros(size(X, 2), 1);
% ====================== YOUR CODE HERE ======================
% Instructions: Complete the code to compute the closed form solution
% to linear regression and put the result in theta.
%
% ---------------------- Sample Solution ----------------------
theta = inv(X'*X)*X'*y;
% -------------------------------------------------------------
% ============================================================
end

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function plotData(x, y)
%PLOTDATA Plots the data points x and y into a new figure
% PLOTDATA(x,y) plots the data points and gives the figure axes labels of
% population and profit.
figure; % open a new figure window
% ====================== YOUR CODE HERE ======================
% Instructions: Plot the training data into a figure using the
% "figure" and "plot" commands. Set the axes labels using
% the "xlabel" and "ylabel" commands. Assume the
% population and revenue data have been passed in
% as the x and y arguments of this function.
%
% Hint: You can use the 'rx' option with plot to have the markers
% appear as red crosses. Furthermore, you can make the
% markers larger by using plot(..., 'rx', 'MarkerSize', 10);
plot(x, y, 'rx', 'markersize', 10); # Plot the data
ylabel('Profit in $10,000s'); # Label y-axis
xlabel('Population of City in 10,000s'); # Label x-axis
% ============================================================
end

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function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m' }, ...
'Computing Cost (for One Variable)', ...
}, ...
{ ...
'3', ...
{ 'gradientDescent.m' }, ...
'Gradient Descent (for One Variable)', ...
}, ...
{ ...
'4', ...
{ 'featureNormalize.m' }, ...
'Feature Normalization', ...
}, ...
{ ...
'5', ...
{ 'computeCostMulti.m' }, ...
'Computing Cost (for Multiple Variables)', ...
}, ...
{ ...
'6', ...
{ 'gradientDescentMulti.m' }, ...
'Gradient Descent (for Multiple Variables)', ...
}, ...
{ ...
'7', ...
{ 'normalEqn.m' }, ...
'Normal Equations', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId)
% Random Test Cases
X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))'];
Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2));
X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25];
Y2 = Y1.^0.5 + Y1;
if partId == '1'
out = sprintf('%0.5f ', warmUpExercise());
elseif partId == '2'
out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]'));
elseif partId == '3'
out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10));
elseif partId == '4'
out = sprintf('%0.5f ', featureNormalize(X2(:,2:4)));
elseif partId == '5'
out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]'));
elseif partId == '6'
out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10));
elseif partId == '7'
out = sprintf('%0.5f ', normalEqn(X2, Y2));
end
end

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# Created by Octave 4.4.1, Sun Aug 11 21:41:32 2019 GMT <unknown@LAPTOP-8PSOVU3K>
# name: email
# type: sq_string
# elements: 1
# length: 17
tsb1995@gmail.com
# name: token
# type: sq_string
# elements: 1
# length: 16
F1n31VQPbofugvf0

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machine-learning-ex1/ex1/warmUpExercise.m Normal file
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function A = warmUpExercise()
%WARMUPEXERCISE Example function in octave
% A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix
A = [];
% ============= YOUR CODE HERE ==============
% Instructions: Return the 5x5 identity matrix
% In octave, we return values by defining which variables
% represent the return values (at the top of the file)
% and then set them accordingly.
A = eye(5);
% ===========================================
end

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function [J, grad] = costFunction(theta, X, y)
%COSTFUNCTION Compute cost and gradient for logistic regression
% J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
% parameter for logistic regression and the gradient of the cost
% w.r.t. to the parameters.
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
grad = zeros(size(theta));
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
% You should set J to the cost.
% Compute the partial derivatives and set grad to the partial
% derivatives of the cost w.r.t. each parameter in theta
%
% Note: grad should have the same dimensions as theta
%
h = sigmoid(X*theta);
J = (1/m)*(-y'*log(h)-(1-y)'*log(1-h));
grad = (1/m)*X'*(sigmoid(X*theta)-y);
% =============================================================
end

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machine-learning-ex2/ex2/costFunctionReg.m Normal file
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function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
% J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
% theta as the parameter for regularized logistic regression and the
% gradient of the cost w.r.t. to the parameters.
% Initialize some useful values
m = length(y); % number of training examples
n = length(theta);
% You need to return the following variables correctly
J = 0;
grad = zeros(size(theta));
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
% You should set J to the cost.
% Compute the partial derivatives and set grad to the partial
% derivatives of the cost w.r.t. each parameter in theta
tempTheta = theta(1);
h = sigmoid(X*theta);
theta(1) = 0;
J = (1/m)*(-y'*log(h)-(1-y)'*log(1-h));
J = J + (lambda/(2*m))*sum(theta.^2);
theta(1) = tempTheta;
grad = (1/m)*X'*(sigmoid(X*theta)-y);
for i=2:n
grad(i) = grad(i) + (lambda/m)*theta(i);
endfor
% =============================================================
end

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%% Machine Learning Online Class - Exercise 2: Logistic Regression
%
% Instructions
% ------------
%
% This file contains code that helps you get started on the logistic
% regression exercise. You will need to complete the following functions
% in this exericse:
%
% sigmoid.m
% costFunction.m
% predict.m
% costFunctionReg.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
%
%% Initialization
clear ; close all; clc
%% Load Data
% The first two columns contains the exam scores and the third column
% contains the label.
data = load('ex2data1.txt');
X = data(:, [1, 2]); y = data(:, 3);
%% ==================== Part 1: Plotting ====================
% We start the exercise by first plotting the data to understand the
% the problem we are working with.
fprintf(['Plotting data with + indicating (y = 1) examples and o ' ...
'indicating (y = 0) examples.\n']);
plotData(X, y);
% Put some labels
hold on;
% Labels and Legend
xlabel('Exam 1 score')
ylabel('Exam 2 score')
% Specified in plot order
legend('Admitted', 'Not admitted')
hold off;
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============ Part 2: Compute Cost and Gradient ============
% In this part of the exercise, you will implement the cost and gradient
% for logistic regression. You neeed to complete the code in
% costFunction.m
% Setup the data matrix appropriately, and add ones for the intercept term
[m, n] = size(X);
% Add intercept term to x and X_test
X = [ones(m, 1) X];
% Initialize fitting parameters
initial_theta = zeros(n + 1, 1);
% Compute and display initial cost and gradient
[cost, grad] = costFunction(initial_theta, X, y);
fprintf('Cost at initial theta (zeros): %f\n', cost);
fprintf('Expected cost (approx): 0.693\n');
fprintf('Gradient at initial theta (zeros): \n');
fprintf(' %f \n', grad);
fprintf('Expected gradients (approx):\n -0.1000\n -12.0092\n -11.2628\n');
% Compute and display cost and gradient with non-zero theta
test_theta = [-24; 0.2; 0.2];
[cost, grad] = costFunction(test_theta, X, y);
fprintf('\nCost at test theta: %f\n', cost);
fprintf('Expected cost (approx): 0.218\n');
fprintf('Gradient at test theta: \n');
fprintf(' %f \n', grad);
fprintf('Expected gradients (approx):\n 0.043\n 2.566\n 2.647\n');
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============= Part 3: Optimizing using fminunc =============
% In this exercise, you will use a built-in function (fminunc) to find the
% optimal parameters theta.
% Set options for fminunc
options = optimset('GradObj', 'on', 'MaxIter', 400);
% Run fminunc to obtain the optimal theta
% This function will return theta and the cost
[theta, cost] = ...
fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
% Print theta to screen
fprintf('Cost at theta found by fminunc: %f\n', cost);
fprintf('Expected cost (approx): 0.203\n');
fprintf('theta: \n');
fprintf(' %f \n', theta);
fprintf('Expected theta (approx):\n');
fprintf(' -25.161\n 0.206\n 0.201\n');
% Plot Boundary
plotDecisionBoundary(theta, X, y);
% Put some labels
hold on;
% Labels and Legend
xlabel('Exam 1 score')
ylabel('Exam 2 score')
% Specified in plot order
legend('Admitted', 'Not admitted')
hold off;
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============== Part 4: Predict and Accuracies ==============
% After learning the parameters, you'll like to use it to predict the outcomes
% on unseen data. In this part, you will use the logistic regression model
% to predict the probability that a student with score 45 on exam 1 and
% score 85 on exam 2 will be admitted.
%
% Furthermore, you will compute the training and test set accuracies of
% our model.
%
% Your task is to complete the code in predict.m
% Predict probability for a student with score 45 on exam 1
% and score 85 on exam 2
prob = sigmoid([1 45 85] * theta);
fprintf(['For a student with scores 45 and 85, we predict an admission ' ...
'probability of %f\n'], prob);
fprintf('Expected value: 0.775 +/- 0.002\n\n');
% Compute accuracy on our training set
p = predict(theta, X);
fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
fprintf('Expected accuracy (approx): 89.0\n');
fprintf('\n');

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%% Machine Learning Online Class - Exercise 2: Logistic Regression
%
% Instructions
% ------------
%
% This file contains code that helps you get started on the second part
% of the exercise which covers regularization with logistic regression.
%
% You will need to complete the following functions in this exericse:
%
% sigmoid.m
% costFunction.m
% predict.m
% costFunctionReg.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
%
%% Initialization
clear ; close all; clc
%% Load Data
% The first two columns contains the X values and the third column
% contains the label (y).
data = load('ex2data2.txt');
X = data(:, [1, 2]); y = data(:, 3);
plotData(X, y);
% Put some labels
hold on;
% Labels and Legend
xlabel('Microchip Test 1')
ylabel('Microchip Test 2')
% Specified in plot order
legend('y = 1', 'y = 0')
hold off;
%% =========== Part 1: Regularized Logistic Regression ============
% In this part, you are given a dataset with data points that are not
% linearly separable. However, you would still like to use logistic
% regression to classify the data points.
%
% To do so, you introduce more features to use -- in particular, you add
% polynomial features to our data matrix (similar to polynomial
% regression).
%
% Add Polynomial Features
% Note that mapFeature also adds a column of ones for us, so the intercept
% term is handled
X = mapFeature(X(:,1), X(:,2));
% Initialize fitting parameters
initial_theta = zeros(size(X, 2), 1);
% Set regularization parameter lambda to 1
lambda = 1;
% Compute and display initial cost and gradient for regularized logistic
% regression
[cost, grad] = costFunctionReg(initial_theta, X, y, lambda);
fprintf('Cost at initial theta (zeros): %f\n', cost);
fprintf('Expected cost (approx): 0.693\n');
fprintf('Gradient at initial theta (zeros) - first five values only:\n');
fprintf(' %f \n', grad(1:5));
fprintf('Expected gradients (approx) - first five values only:\n');
fprintf(' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n');
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
% Compute and display cost and gradient
% with all-ones theta and lambda = 10
test_theta = ones(size(X,2),1);
[cost, grad] = costFunctionReg(test_theta, X, y, 10);
fprintf('\nCost at test theta (with lambda = 10): %f\n', cost);
fprintf('Expected cost (approx): 3.16\n');
fprintf('Gradient at test theta - first five values only:\n');
fprintf(' %f \n', grad(1:5));
fprintf('Expected gradients (approx) - first five values only:\n');
fprintf(' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n');
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ============= Part 2: Regularization and Accuracies =============
% Optional Exercise:
% In this part, you will get to try different values of lambda and
% see how regularization affects the decision coundart
%
% Try the following values of lambda (0, 1, 10, 100).
%
% How does the decision boundary change when you vary lambda? How does
% the training set accuracy vary?
%
% Initialize fitting parameters
initial_theta = zeros(size(X, 2), 1);
% Set regularization parameter lambda to 1 (you should vary this)
lambda = 1;
% Set Options
options = optimset('GradObj', 'on', 'MaxIter', 400);
% Optimize
[theta, J, exit_flag] = ...
fminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);
% Plot Boundary
plotDecisionBoundary(theta, X, y);
hold on;
title(sprintf('lambda = %g', lambda))
% Labels and Legend
xlabel('Microchip Test 1')
ylabel('Microchip Test 2')
legend('y = 1', 'y = 0', 'Decision boundary')
hold off;
% Compute accuracy on our training set
p = predict(theta, X);
fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
fprintf('Expected accuracy (with lambda = 1): 83.1 (approx)\n');

100
machine-learning-ex2/ex2/ex2data1.txt Normal file
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@ -0,0 +1,100 @@
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118
machine-learning-ex2/ex2/ex2data2.txt Normal file
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@ -0,0 +1,118 @@
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0.51786,-0.5943,0
0.46601,-0.41886,0
0.35081,-0.57968,0
0.28744,-0.76974,0
0.085829,-0.75512,0
0.14919,-0.57968,0
-0.13306,-0.4481,0
-0.40956,-0.41155,0
-0.39228,-0.25804,0
-0.74366,-0.25804,0
-0.69758,0.041667,0
-0.75518,0.2902,0
-0.69758,0.68494,0
-0.4038,0.70687,0
-0.38076,0.91886,0
-0.50749,0.90424,0
-0.54781,0.70687,0
0.10311,0.77997,0
0.057028,0.91886,0
-0.10426,0.99196,0
-0.081221,1.1089,0
0.28744,1.087,0
0.39689,0.82383,0
0.63882,0.88962,0
0.82316,0.66301,0
0.67339,0.64108,0
1.0709,0.10015,0
-0.046659,-0.57968,0
-0.23675,-0.63816,0
-0.15035,-0.36769,0
-0.49021,-0.3019,0
-0.46717,-0.13377,0
-0.28859,-0.060673,0
-0.61118,-0.067982,0
-0.66302,-0.21418,0
-0.59965,-0.41886,0
-0.72638,-0.082602,0
-0.83007,0.31213,0
-0.72062,0.53874,0
-0.59389,0.49488,0
-0.48445,0.99927,0
-0.0063364,0.99927,0
0.63265,-0.030612,0

41
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The author of "jsonlab" toolbox is Qianqian Fang. Qianqian
is currently an Assistant Professor at Massachusetts General Hospital,
Harvard Medical School.
Address: Martinos Center for Biomedical Imaging,
Massachusetts General Hospital,
Harvard Medical School
Bldg 149, 13th St, Charlestown, MA 02129, USA
URL: http://nmr.mgh.harvard.edu/~fangq/
Email: <fangq at nmr.mgh.harvard.edu> or <fangqq at gmail.com>
The script loadjson.m was built upon previous works by
- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
date: 2009/11/02
- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
date: 2009/03/22
- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565
date: 2008/07/03
This toolbox contains patches submitted by the following contributors:
- Blake Johnson <bjohnso at bbn.com>
part of revision 341
- Niclas Borlin <Niclas.Borlin at cs.umu.se>
various fixes in revision 394, including
- loadjson crashes for all-zero sparse matrix.
- loadjson crashes for empty sparse matrix.
- Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson.
- loadjson crashes for sparse real column vector.
- loadjson crashes for sparse complex column vector.
- Data is corrupted by savejson for sparse real row vector.
- savejson crashes for sparse complex row vector.
- Yul Kang <yul.kang.on at gmail.com>
patches for svn revision 415.
- savejson saves an empty cell array as [] instead of null
- loadjson differentiates an empty struct from an empty array

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============================================================================
JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave
----------------------------------------------------------------------------
JSONlab ChangeLog (key features marked by *):
== JSONlab 1.0 (codename: Optimus - Final), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2015/01/02 polish help info for all major functions, update examples, finalize 1.0
2014/12/19 fix a bug to strictly respect NoRowBracket in savejson
== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/11/22 show progress bar in loadjson ('ShowProgress')
2014/11/17 add Compact option in savejson to output compact JSON format ('Compact')
2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels
2014/09/18 start official github mirror: https://github.com/fangq/jsonlab
== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8
2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson
2014/08/04 escape special characters in a JSON string
2014/02/16 fix a bug when saving ubjson files
== JSONlab 0.9.9 (codename: Optimus - beta), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/01/22 use binary read and write in saveubjson and loadubjson
== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang)
== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson
== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin)
== JSONlab 0.9.0 (codename: Rodimus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson
2012/06/01 support JSONP in savejson
2012/05/25 fix the empty cell bug (reported by Cyril Davin)
2012/04/05 savejson can save to a file (suggested by Patrick Rapin)
== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS
2012/01/25 patch to handle root-less objects, contributed by Blake Johnson
== JSONlab 0.8.0 (codename: Sentiel), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab
2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer
2011/12/22 *accept sequence of 'param',value input in savejson and loadjson
2011/11/18 fix struct array bug reported by Mykel Kochenderfer
== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration
2011/10/20 loadjson supports JSON collections - concatenated JSON objects
== JSONlab 0.5.0 (codename: Nexus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/16 package and release jsonlab 0.5.0
2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug
2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level
2011/10/10 create jsonlab project, start jsonlab website, add online documentation
2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support
2011/10/06 *savejson works for structs, cells and arrays
2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m

25
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Copyright 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are
permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list
of conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those of the
authors and should not be interpreted as representing official policies, either expressed
or implied, of the copyright holders.

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===============================================================================
= JSONLab =
= An open-source MATLAB/Octave JSON encoder and decoder =
===============================================================================
*Copyright (C) 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>
*License: BSD License, see License_BSD.txt for details
*Version: 1.0 (Optimus - Final)
-------------------------------------------------------------------------------
Table of Content:
I. Introduction
II. Installation
III.Using JSONLab
IV. Known Issues and TODOs
V. Contribution and feedback
-------------------------------------------------------------------------------
I. Introduction
JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable,
human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format
to represent complex and hierarchical data. It is as powerful as
[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely
used for data-exchange in applications, and is essential for the wild success
of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and
[http://en.wikipedia.org/wiki/Web_2.0 Web2.0].
UBJSON (Universal Binary JSON) is a binary JSON format, specifically
optimized for compact file size and better performance while keeping
the semantics as simple as the text-based JSON format. Using the UBJSON
format allows to wrap complex binary data in a flexible and extensible
structure, making it possible to process complex and large dataset
without accuracy loss due to text conversions.
We envision that both JSON and its binary version will serve as part of
the mainstream data-exchange formats for scientific research in the future.
It will provide the flexibility and generality achieved by other popular
general-purpose file specifications, such as
[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly
reduced complexity and enhanced performance.
JSONLab is a free and open-source implementation of a JSON/UBJSON encoder
and a decoder in the native MATLAB language. It can be used to convert a MATLAB
data structure (array, struct, cell, struct array and cell array) into
JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB
data structure. JSONLab supports both MATLAB and
[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-------------------------------------------------------------------------------
II. Installation
The installation of JSONLab is no different than any other simple
MATLAB toolbox. You only need to download/unzip the JSONLab package
to a folder, and add the folder's path to MATLAB/Octave's path list
by using the following command:
addpath('/path/to/jsonlab');
If you want to add this path permanently, you need to type "pathtool",
browse to the jsonlab root folder and add to the list, then click "Save".
Then, run "rehash" in MATLAB, and type "which loadjson", if you see an
output, that means JSONLab is installed for MATLAB/Octave.
-------------------------------------------------------------------------------
III.Using JSONLab
JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder,
and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and
two equivallent functions -- loadubjson and saveubjson for the binary
JSON. The detailed help info for the four functions can be found below:
=== loadjson.m ===
<pre>
data=loadjson(fname,opt)
or
data=loadjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09, including previous works from
Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
created on 2009/11/02
François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
created on 2009/03/22
Joel Feenstra:
http://www.mathworks.com/matlabcentral/fileexchange/20565
created on 2008/07/03
$Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a JSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.FastArrayParser [1|0 or integer]: if set to 1, use a
speed-optimized array parser when loading an
array object. The fast array parser may
collapse block arrays into a single large
array similar to rules defined in cell2mat; 0 to
use a legacy parser; if set to a larger-than-1
value, this option will specify the minimum
dimension to enable the fast array parser. For
example, if the input is a 3D array, setting
FastArrayParser to 1 will return a 3D array;
setting to 2 will return a cell array of 2D
arrays; setting to 3 will return to a 2D cell
array of 1D vectors; setting to 4 will return a
3D cell array.
opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
dat=loadjson(['examples' filesep 'example1.json'])
dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
</pre>
=== savejson.m ===
<pre>
json=savejson(rootname,obj,filename)
or
json=savejson(rootname,obj,opt)
json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
Object Notation) string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09
$Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array).
filename: a string for the file name to save the output JSON data.
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.FloatFormat ['%.10g'|string]: format to show each numeric element
of a 1D/2D array;
opt.ArrayIndent [1|0]: if 1, output explicit data array with
precedent indentation; if 0, no indentation
opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [0|1]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
to represent +/-Inf. The matched pattern is '([-+]*)Inf'
and $1 represents the sign. For those who want to use
1e999 to represent Inf, they can set opt.Inf to '$11e999'
opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
to represent NaN
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSONP='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a string in the JSON format (see http://json.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
savejson('jmesh',jsonmesh)
savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
</pre>
=== loadubjson.m ===
<pre>
data=loadubjson(fname,opt)
or
data=loadubjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/01
$Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a UBJSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.IntEndian [B|L]: specify the endianness of the integer fields
in the UBJSON input data. B - Big-Endian format for
integers (as required in the UBJSON specification);
L - input integer fields are in Little-Endian order.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
obj=struct('string','value','array',[1 2 3]);
ubjdata=saveubjson('obj',obj);
dat=loadubjson(ubjdata)
dat=loadubjson(['examples' filesep 'example1.ubj'])
dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
</pre>
=== saveubjson.m ===
<pre>
json=saveubjson(rootname,obj,filename)
or
json=saveubjson(rootname,obj,opt)
json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a Universal
Binary JSON (UBJSON) binary string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/17
$Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array)
filename: a string for the file name to save the output UBJSON data
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [1|0]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSON='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a binary string in the UBJSON format (see http://ubjson.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
saveubjson('jsonmesh',jsonmesh)
saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
</pre>
=== examples ===
Under the "examples" folder, you can find several scripts to demonstrate the
basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you
will see the conversions from MATLAB data structure to JSON text and backward.
In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet
and validate the loadjson/savejson functions for regression testing purposes.
Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson
and loadubjson pairs for various matlab data structures.
Please run these examples and understand how JSONLab works before you use
it to process your data.
-------------------------------------------------------------------------------
IV. Known Issues and TODOs
JSONLab has several known limitations. We are striving to make it more general
and robust. Hopefully in a few future releases, the limitations become less.
Here are the known issues:
# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays;
# When processing names containing multi-byte characters, Octave and MATLAB \
can give different field-names; you can use feature('DefaultCharacterSet','latin1') \
in MATLAB to get consistant results
# savejson can not handle class and dataset.
# saveubjson converts a logical array into a uint8 ([U]) array
# an unofficial N-D array count syntax is implemented in saveubjson. We are \
actively communicating with the UBJSON spec maintainer to investigate the \
possibility of making it upstream
# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \
files, however, it can parse all UBJSON files produced by saveubjson.
-------------------------------------------------------------------------------
V. Contribution and feedback
JSONLab is an open-source project. This means you can not only use it and modify
it as you wish, but also you can contribute your changes back to JSONLab so
that everyone else can enjoy the improvement. For anyone who want to contribute,
please download JSONLab source code from it's subversion repository by using the
following command:
svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab
You can make changes to the files as needed. Once you are satisfied with your
changes, and ready to share it with others, please cd the root directory of
JSONLab, and type
svn diff > yourname_featurename.patch
You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at
the email address shown in the beginning of this file. Qianqian will review
the changes and commit it to the subversion if they are satisfactory.
We appreciate any suggestions and feedbacks from you. Please use iso2mesh's
mailing list to report any questions you may have with JSONLab:
http://groups.google.com/group/iso2mesh-users?hl=en&pli=1
(Subscription to the mailing list is needed in order to post messages).

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function val=jsonopt(key,default,varargin)
%
% val=jsonopt(key,default,optstruct)
%
% setting options based on a struct. The struct can be produced
% by varargin2struct from a list of 'param','value' pairs
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
%
% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $
%
% input:
% key: a string with which one look up a value from a struct
% default: if the key does not exist, return default
% optstruct: a struct where each sub-field is a key
%
% output:
% val: if key exists, val=optstruct.key; otherwise val=default
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
val=default;
if(nargin<=2) return; end
opt=varargin{1};
if(isstruct(opt) && isfield(opt,key))
val=getfield(opt,key);
end

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function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

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function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

33
machine-learning-ex2/ex2/lib/jsonlab/mergestruct.m Normal file
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function s=mergestruct(s1,s2)
%
% s=mergestruct(s1,s2)
%
% merge two struct objects into one
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% s1,s2: a struct object, s1 and s2 can not be arrays
%
% output:
% s: the merged struct object. fields in s1 and s2 will be combined in s.
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(~isstruct(s1) || ~isstruct(s2))
error('input parameters contain non-struct');
end
if(length(s1)>1 || length(s2)>1)
error('can not merge struct arrays');
end
fn=fieldnames(s2);
s=s1;
for i=1:length(fn)
s=setfield(s,fn{i},getfield(s2,fn{i}));
end

475
machine-learning-ex2/ex2/lib/jsonlab/savejson.m Normal file
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function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end

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machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m Normal file
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function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/17
%
% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array)
% filename: a string for the file name to save the output UBJSON data
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [1|0]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSON='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a binary string in the UBJSON format (see http://ubjson.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% saveubjson('jsonmesh',jsonmesh)
% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
json=obj2ubjson(rootname,obj,rootlevel,opt);
if(~rootisarray)
json=['{' json '}'];
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=[jsonp '(' json ')'];
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2ubjson(name,item,level,varargin)
if(iscell(item))
txt=cell2ubjson(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2ubjson(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2ubjson(name,item,level,varargin{:});
else
txt=mat2ubjson(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2ubjson(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item); % let's handle 1D cell first
if(len>1)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) '[']; name='';
else
txt='[';
end
elseif(len==0)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) 'Z']; name='';
else
txt='Z';
end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=struct2ubjson(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=[txt S_(checkname(name,varargin{:})) '{'];
else
txt=[txt '{'];
end
if(~isempty(names))
for e=1:length(names)
txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
end
end
txt=[txt '}'];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=str2ubjson(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
val=item(e,:);
if(len==1)
obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
if(isempty(name)) obj=['',S_(val),'']; end
txt=[txt,'',obj];
else
txt=[txt,'',['',S_(val),'']];
end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=mat2ubjson(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
cid=I_(uint32(max(size(item))));
if(isempty(name))
txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
else
if(isempty(item))
txt=[S_(checkname(name,varargin{:})),'Z'];
return;
else
txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
end
end
else
if(isempty(name))
txt=matdata2ubjson(item,level+1,varargin{:});
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
txt=[S_(checkname(name,varargin{:})) numtxt];
else
txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
end
end
return;
end
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=[txt,S_('_ArrayIsComplex_'),'T'];
end
txt=[txt,S_('_ArrayIsSparse_'),'T'];
if(size(item,1)==1)
% Row vector, store only column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([iy(:),data'],level+2,varargin{:})];
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,data],level+2,varargin{:})];
else
% General case, store row and column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,iy,data],level+2,varargin{:})];
end
else
if(isreal(item))
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson(item(:)',level+2,varargin{:})];
else
txt=[txt,S_('_ArrayIsComplex_'),'T'];
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
end
end
txt=[txt,'}'];
%%-------------------------------------------------------------------------
function txt=matdata2ubjson(mat,level,varargin)
if(isempty(mat))
txt='Z';
return;
end
if(size(mat,1)==1)
level=level-1;
end
type='';
hasnegtive=(mat<0);
if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
if(isempty(hasnegtive))
if(max(mat(:))<=2^8)
type='U';
end
end
if(isempty(type))
% todo - need to consider negative ones separately
id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
if(isempty(find(id)))
error('high-precision data is not yet supported');
end
key='iIlL';
type=key(find(id));
end
txt=[I_a(mat(:),type,size(mat))];
elseif(islogical(mat))
logicalval='FT';
if(numel(mat)==1)
txt=logicalval(mat+1);
else
txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
end
else
if(numel(mat)==1)
txt=['[' D_(mat) ']'];
else
txt=D_a(mat(:),'D',size(mat));
end
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function val=S_(str)
if(length(str)==1)
val=['C' str];
else
val=['S' I_(int32(length(str))) str];
end
%%-------------------------------------------------------------------------
function val=I_(num)
if(~isinteger(num))
error('input is not an integer');
end
if(num>=0 && num<255)
val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
return;
end
key='iIlL';
cid={'int8','int16','int32','int64'};
for i=1:4
if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
return;
end
end
error('unsupported integer');
%%-------------------------------------------------------------------------
function val=D_(num)
if(~isfloat(num))
error('input is not a float');
end
if(isa(num,'single'))
val=['d' data2byte(num,'uint8')];
else
val=['D' data2byte(num,'uint8')];
end
%%-------------------------------------------------------------------------
function data=I_a(num,type,dim,format)
id=find(ismember('iUIlL',type));
if(id==0)
error('unsupported integer array');
end
% based on UBJSON specs, all integer types are stored in big endian format
if(id==1)
data=data2byte(swapbytes(int8(num)),'uint8');
blen=1;
elseif(id==2)
data=data2byte(swapbytes(uint8(num)),'uint8');
blen=1;
elseif(id==3)
data=data2byte(swapbytes(int16(num)),'uint8');
blen=2;
elseif(id==4)
data=data2byte(swapbytes(int32(num)),'uint8');
blen=4;
elseif(id==5)
data=data2byte(swapbytes(int64(num)),'uint8');
blen=8;
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
end
data=['[' data(:)'];
else
data=reshape(data,blen,numel(data)/blen);
data(2:blen+1,:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function data=D_a(num,type,dim,format)
id=find(ismember('dD',type));
if(id==0)
error('unsupported float array');
end
if(id==1)
data=data2byte(single(num),'uint8');
elseif(id==2)
data=data2byte(double(num),'uint8');
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
end
data=['[' data];
else
data=reshape(data,(id*4),length(data)/(id*4));
data(2:(id*4+1),:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function bytes=data2byte(varargin)
bytes=typecast(varargin{:});
bytes=bytes(:)';

40
machine-learning-ex2/ex2/lib/jsonlab/varargin2struct.m Normal file
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function opt=varargin2struct(varargin)
%
% opt=varargin2struct('param1',value1,'param2',value2,...)
% or
% opt=varargin2struct(...,optstruct,...)
%
% convert a series of input parameters into a structure
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% 'param', value: the input parameters should be pairs of a string and a value
% optstruct: if a parameter is a struct, the fields will be merged to the output struct
%
% output:
% opt: a struct where opt.param1=value1, opt.param2=value2 ...
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
len=length(varargin);
opt=struct;
if(len==0) return; end
i=1;
while(i<=len)
if(isstruct(varargin{i}))
opt=mergestruct(opt,varargin{i});
elseif(ischar(varargin{i}) && i<len)
opt=setfield(opt,varargin{i},varargin{i+1});
i=i+1;
else
error('input must be in the form of ...,''name'',value,... pairs or structs');
end
i=i+1;
end

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function str = makeValidFieldName(str)
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
isoct=exist('OCTAVE_VERSION','builtin');
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end

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function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = promptToken('', '', tokenFile);
end
if isempty(token)
fprintf('!! Submission Cancelled\n');
return
end
try
response = submitParts(conf, email, token, parts);
catch
e = lasterror();
fprintf('\n!! Submission failed: %s\n', e.message);
fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
fprintf('\nPlease correct your code and resubmit.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
elseif isfield(response, 'errorCode')
fprintf('!! Submission failed: %s\n', response.message);
else
showFeedback(parts, response);
save(tokenFile, 'email', 'token');
end
end
function [email token] = promptToken(email, existingToken, tokenFile)
if (~isempty(email) && ~isempty(existingToken))
prompt = sprintf( ...
'Use token from last successful submission (%s)? (Y/n): ', ...
email);
reenter = input(prompt, 's');
if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
token = existingToken;
return;
else
delete(tokenFile);
end
end
email = input('Login (email address): ', 's');
token = input('Token: ', 's');
end
function isValid = isValidPartOptionIndex(partOptions, i)
isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
end
function response = submitParts(conf, email, token, parts)
body = makePostBody(conf, email, token, parts);
submissionUrl = submissionUrl();
responseBody = getResponse(submissionUrl, body);
jsonResponse = validateResponse(responseBody);
response = loadjson(jsonResponse);
end
function body = makePostBody(conf, email, token, parts)
bodyStruct.assignmentSlug = conf.assignmentSlug;
bodyStruct.submitterEmail = email;
bodyStruct.secret = token;
bodyStruct.parts = makePartsStruct(conf, parts);
opt.Compact = 1;
body = savejson('', bodyStruct, opt);
end
function partsStruct = makePartsStruct(conf, parts)
for part = parts
partId = part{:}.id;
fieldName = makeValidFieldName(partId);
outputStruct.output = conf.output(partId);
partsStruct.(fieldName) = outputStruct;
end
end
function [parts] = parts(conf)
parts = {};
for partArray = conf.partArrays
part.id = partArray{:}{1};
part.sourceFiles = partArray{:}{2};
part.name = partArray{:}{3};
parts{end + 1} = part;
end
end
function showFeedback(parts, response)
fprintf('== \n');
fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
for part = parts
score = '';
partFeedback = '';
partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
end
evaluation = response.evaluation;
totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
fprintf('== --------------------------------\n');
fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
fprintf('== \n');
end
% use urlread or curl to send submit results to the grader and get a response
function response = getResponse(url, body)
% try using urlread() and a secure connection
params = {'jsonBody', body};
[response, success] = urlread(url, 'post', params);
if (success == 0)
% urlread didn't work, try curl & the peer certificate patch
if ispc
% testing note: use 'jsonBody =' for a test case
json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
else
% it's linux/OS X, so use the other form
json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
end
% get the response body for the peer certificate patch method
[code, response] = system(json_command);
% test the success code
if (code ~= 0)
fprintf('[error] submission with curl() was not successful\n');
end
end
end
% validate the grader's response
function response = validateResponse(resp)
% test if the response is json or an HTML page
isJson = length(resp) > 0 && resp(1) == '{';
isHtml = findstr(lower(resp), '<html');
if (isJson)
response = resp;
elseif (isHtml)
% the response is html, so it's probably an error message
printHTMLContents(resp);
error('Grader response is an HTML message');
else
error('Grader sent no response');
end
end
% parse a HTML response and print it's contents
function printHTMLContents(response)
strippedResponse = regexprep(response, '<[^>]+>', ' ');
strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
fprintf(strippedResponse);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end

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function out = mapFeature(X1, X2)
% MAPFEATURE Feature mapping function to polynomial features
%
% MAPFEATURE(X1, X2) maps the two input features
% to quadratic features used in the regularization exercise.
%
% Returns a new feature array with more features, comprising of
% X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc..
%
% Inputs X1, X2 must be the same size
%
degree = 6;
out = ones(size(X1(:,1)));
for i = 1:degree
for j = 0:i
out(:, end+1) = (X1.^(i-j)).*(X2.^j);
end
end
end

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function plotData(X, y)
%PLOTDATA Plots the data points X and y into a new figure
% PLOTDATA(x,y) plots the data points with + for the positive examples
% and o for the negative examples. X is assumed to be a Mx2 matrix.
% Create New Figure
figure; hold on;
% ====================== YOUR CODE HERE ======================
% Instructions: Plot the positive and negative examples on a
% 2D plot, using the option 'k+' for the positive
% examples and 'ko' for the negative examples.
%
% Seperate between positive and negative ( 0 or 1 admission) to later represent on graph
pos = find(y == 1);
neg = find(y == 0);
% Setup Plots note: X(pos,1) finds all rows with positive output thanks to
% our find function returning a vector containing the indeces of each
plot(X(pos,1), X(pos,2), 'k+', 'LineWidth', 2, 'MarkerSize', 7);
plot(X(neg,1), X(neg,2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);
% =========================================================================
hold off;
end

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function plotDecisionBoundary(theta, X, y)
%PLOTDECISIONBOUNDARY Plots the data points X and y into a new figure with
%the decision boundary defined by theta
% PLOTDECISIONBOUNDARY(theta, X,y) plots the data points with + for the
% positive examples and o for the negative examples. X is assumed to be
% a either
% 1) Mx3 matrix, where the first column is an all-ones column for the
% intercept.
% 2) MxN, N>3 matrix, where the first column is all-ones
% Plot Data
plotData(X(:,2:3), y);
hold on
if size(X, 2) <= 3
% Only need 2 points to define a line, so choose two endpoints
plot_x = [min(X(:,2))-2, max(X(:,2))+2];
% Calculate the decision boundary line
plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1));
% Plot, and adjust axes for better viewing
plot(plot_x, plot_y)
% Legend, specific for the exercise
legend('Admitted', 'Not admitted', 'Decision Boundary')
axis([30, 100, 30, 100])
else
% Here is the grid range
u = linspace(-1, 1.5, 50);
v = linspace(-1, 1.5, 50);
z = zeros(length(u), length(v));
% Evaluate z = theta*x over the grid
for i = 1:length(u)
for j = 1:length(v)
z(i,j) = mapFeature(u(i), v(j))*theta;
end
end
z = z'; % important to transpose z before calling contour
% Plot z = 0
% Notice you need to specify the range [0, 0]
contour(u, v, z, [0, 0], 'LineWidth', 2)
end
hold off
end

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function p = predict(theta, X)
%PREDICT Predict whether the label is 0 or 1 using learned logistic
%regression parameters theta
% p = PREDICT(theta, X) computes the predictions for X using a
% threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)
m = size(X, 1); % Number of training examples
% You need to return the following variables correctly
p = zeros(m, 1);
% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
% your learned logistic regression parameters.
% You should set p to a vector of 0's and 1's
%
temp = sigmoid(X*theta);
for i=1:m
if (temp(i)>=0.5)
p(i) = 1;
endif
endfor
% =========================================================================
end

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function g = sigmoid(z)
%SIGMOID Compute sigmoid function
% g = SIGMOID(z) computes the sigmoid of z.
% You need to return the following variables correctly
g = zeros(size(z));
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the sigmoid of each value of z (z can be a matrix,
% vector or scalar).
g = 1 + exp(-1*z);
g = g.^(-1);
% =============================================================
end

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function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic Regression Cost', ...
}, ...
{ ...
'3', ...
{ 'costFunction.m' }, ...
'Logistic Regression Gradient', ...
}, ...
{ ...
'4', ...
{ 'predict.m' }, ...
'Predict', ...
}, ...
{ ...
'5', ...
{ 'costFunctionReg.m' }, ...
'Regularized Logistic Regression Cost', ...
}, ...
{ ...
'6', ...
{ 'costFunctionReg.m' }, ...
'Regularized Logistic Regression Gradient', ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxstring)
% Random Test Cases
X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
y = sin(X(:,1) + X(:,2)) > 0;
if partId == '1'
out = sprintf('%0.5f ', sigmoid(X));
elseif partId == '2'
out = sprintf('%0.5f ', costFunction([0.25 0.5 -0.5]', X, y));
elseif partId == '3'
[cost, grad] = costFunction([0.25 0.5 -0.5]', X, y);
out = sprintf('%0.5f ', grad);
elseif partId == '4'
out = sprintf('%0.5f ', predict([0.25 0.5 -0.5]', X));
elseif partId == '5'
out = sprintf('%0.5f ', costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1));
elseif partId == '6'
[cost, grad] = costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1);
out = sprintf('%0.5f ', grad);
end
end

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# Created by Octave 4.4.1, Wed Aug 14 14:40:02 2019 GMT <unknown@LAPTOP-8PSOVU3K>
# name: email
# type: sq_string
# elements: 1
# length: 17
tsb1995@gmail.com
# name: token
# type: sq_string
# elements: 1
# length: 16
dyGPwTWBjVLKRja1

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function [h, display_array] = displayData(X, example_width)
%DISPLAYDATA Display 2D data in a nice grid
% [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
% stored in X in a nice grid. It returns the figure handle h and the
% displayed array if requested.
% Set example_width automatically if not passed in
if ~exist('example_width', 'var') || isempty(example_width)
example_width = round(sqrt(size(X, 2)));
end
% Gray Image
colormap(gray);
% Compute rows, cols
[m n] = size(X);
example_height = (n / example_width);
% Compute number of items to display
display_rows = floor(sqrt(m));
display_cols = ceil(m / display_rows);
% Between images padding
pad = 1;
% Setup blank display
display_array = - ones(pad + display_rows * (example_height + pad), ...
pad + display_cols * (example_width + pad));
% Copy each example into a patch on the display array
curr_ex = 1;
for j = 1:display_rows
for i = 1:display_cols
if curr_ex > m,
break;
end
% Copy the patch
% Get the max value of the patch
max_val = max(abs(X(curr_ex, :)));
display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...
pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...
reshape(X(curr_ex, :), example_height, example_width) / max_val;
curr_ex = curr_ex + 1;
end
if curr_ex > m,
break;
end
end
% Display Image
h = imagesc(display_array, [-1 1]);
% Do not show axis
axis image off
drawnow;
end

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%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% linear exercise. You will need to complete the following functions
% in this exericse:
%
% lrCostFunction.m (logistic regression cost function)
% oneVsAll.m
% predictOneVsAll.m
% predict.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
%
%% Initialization
clear ; close all; clc
%% Setup the parameters you will use for this part of the exercise
input_layer_size = 400; % 20x20 Input Images of Digits
num_labels = 10; % 10 labels, from 1 to 10
% (note that we have mapped "0" to label 10)
%% =========== Part 1: Loading and Visualizing Data =============
% We start the exercise by first loading and visualizing the dataset.
% You will be working with a dataset that contains handwritten digits.
%
% Load Training Data
fprintf('Loading and Visualizing Data ...\n')
load('ex3data1.mat'); % training data stored in arrays X, y
m = size(X, 1);
% Randomly select 100 data points to display
rand_indices = randperm(m);
sel = X(rand_indices(1:100), :);
displayData(sel);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ============ Part 2a: Vectorize Logistic Regression ============
% In this part of the exercise, you will reuse your logistic regression
% code from the last exercise. You task here is to make sure that your
% regularized logistic regression implementation is vectorized. After
% that, you will implement one-vs-all classification for the handwritten
% digit dataset.
%
% Test case for lrCostFunction
fprintf('\nTesting lrCostFunction() with regularization');
theta_t = [-2; -1; 1; 2];
X_t = [ones(5,1) reshape(1:15,5,3)/10];
y_t = ([1;0;1;0;1] >= 0.5);
lambda_t = 3;
[J grad] = lrCostFunction(theta_t, X_t, y_t, lambda_t);
fprintf('\nCost: %f\n', J);
fprintf('Expected cost: 2.534819\n');
fprintf('Gradients:\n');
fprintf(' %f \n', grad);
fprintf('Expected gradients:\n');
fprintf(' 0.146561\n -0.548558\n 0.724722\n 1.398003\n');
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ============ Part 2b: One-vs-All Training ============
fprintf('\nTraining One-vs-All Logistic Regression...\n')
lambda = 0.1;
[all_theta] = oneVsAll(X, y, num_labels, lambda);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ================ Part 3: Predict for One-Vs-All ================
pred = predictOneVsAll(all_theta, X);
fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);

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%% Machine Learning Online Class - Exercise 3 | Part 2: Neural Networks
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% linear exercise. You will need to complete the following functions
% in this exericse:
%
% lrCostFunction.m (logistic regression cost function)
% oneVsAll.m
% predictOneVsAll.m
% predict.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
%
%% Initialization
clear ; close all; clc
%% Setup the parameters you will use for this exercise
input_layer_size = 400; % 20x20 Input Images of Digits
hidden_layer_size = 25; % 25 hidden units
num_labels = 10; % 10 labels, from 1 to 10
% (note that we have mapped "0" to label 10)
%% =========== Part 1: Loading and Visualizing Data =============
% We start the exercise by first loading and visualizing the dataset.
% You will be working with a dataset that contains handwritten digits.
%
% Load Training Data
fprintf('Loading and Visualizing Data ...\n')
load('ex3data1.mat');
m = size(X, 1);
% Randomly select 100 data points to display
sel = randperm(size(X, 1));
sel = sel(1:100);
displayData(X(sel, :));
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ================ Part 2: Loading Pameters ================
% In this part of the exercise, we load some pre-initialized
% neural network parameters.
fprintf('\nLoading Saved Neural Network Parameters ...\n')
% Load the weights into variables Theta1 and Theta2
load('ex3weights.mat');
%% ================= Part 3: Implement Predict =================
% After training the neural network, we would like to use it to predict
% the labels. You will now implement the "predict" function to use the
% neural network to predict the labels of the training set. This lets
% you compute the training set accuracy.
pred = predict(Theta1, Theta2, X);
fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
fprintf('Program paused. Press enter to continue.\n');
pause;
% To give you an idea of the network's output, you can also run
% through the examples one at the a time to see what it is predicting.
% Randomly permute examples
rp = randperm(m);
for i = 1:m
% Display
fprintf('\nDisplaying Example Image\n');
displayData(X(rp(i), :));
pred = predict(Theta1, Theta2, X(rp(i),:));
fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10));
% Pause with quit option
s = input('Paused - press enter to continue, q to exit:','s');
if s == 'q'
break
end
end

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function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
% Minimize a continuous differentialble multivariate function. Starting point
% is given by "X" (D by 1), and the function named in the string "f", must
% return a function value and a vector of partial derivatives. The Polack-
% Ribiere flavour of conjugate gradients is used to compute search directions,
% and a line search using quadratic and cubic polynomial approximations and the
% Wolfe-Powell stopping criteria is used together with the slope ratio method
% for guessing initial step sizes. Additionally a bunch of checks are made to
% make sure that exploration is taking place and that extrapolation will not
% be unboundedly large. The "length" gives the length of the run: if it is
% positive, it gives the maximum number of line searches, if negative its
% absolute gives the maximum allowed number of function evaluations. You can
% (optionally) give "length" a second component, which will indicate the
% reduction in function value to be expected in the first line-search (defaults
% to 1.0). The function returns when either its length is up, or if no further
% progress can be made (ie, we are at a minimum, or so close that due to
% numerical problems, we cannot get any closer). If the function terminates
% within a few iterations, it could be an indication that the function value
% and derivatives are not consistent (ie, there may be a bug in the
% implementation of your "f" function). The function returns the found
% solution "X", a vector of function values "fX" indicating the progress made
% and "i" the number of iterations (line searches or function evaluations,
% depending on the sign of "length") used.
%
% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
%
% See also: checkgrad
%
% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13
%
%
% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen
%
% Permission is granted for anyone to copy, use, or modify these
% programs and accompanying documents for purposes of research or
% education, provided this copyright notice is retained, and note is
% made of any changes that have been made.
%
% These programs and documents are distributed without any warranty,
% express or implied. As the programs were written for research
% purposes only, they have not been tested to the degree that would be
% advisable in any important application. All use of these programs is
% entirely at the user's own risk.
%
% [ml-class] Changes Made:
% 1) Function name and argument specifications
% 2) Output display
%
% Read options
if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter')
length = options.MaxIter;
else
length = 100;
end
RHO = 0.01; % a bunch of constants for line searches
SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions
INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket
EXT = 3.0; % extrapolate maximum 3 times the current bracket
MAX = 20; % max 20 function evaluations per line search
RATIO = 100; % maximum allowed slope ratio
argstr = ['feval(f, X']; % compose string used to call function
for i = 1:(nargin - 3)
argstr = [argstr, ',P', int2str(i)];
end
argstr = [argstr, ')'];
if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end
S=['Iteration '];
i = 0; % zero the run length counter
ls_failed = 0; % no previous line search has failed
fX = [];
[f1 df1] = eval(argstr); % get function value and gradient
i = i + (length<0); % count epochs?!
s = -df1; % search direction is steepest
d1 = -s'*s; % this is the slope
z1 = red/(1-d1); % initial step is red/(|s|+1)
while i < abs(length) % while not finished
i = i + (length>0); % count iterations?!
X0 = X; f0 = f1; df0 = df1; % make a copy of current values
X = X + z1*s; % begin line search
[f2 df2] = eval(argstr);
i = i + (length<0); % count epochs?!
d2 = df2'*s;
f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1
if length>0, M = MAX; else M = min(MAX, -length-i); end
success = 0; limit = -1; % initialize quanteties
while 1
while ((f2 > f1+z1*RHO*d1) || (d2 > -SIG*d1)) && (M > 0)
limit = z1; % tighten the bracket
if f2 > f1
z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit
else
A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit
B = 3*(f3-f2)-z3*(d3+2*d2);
z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok!
end
if isnan(z2) || isinf(z2)
z2 = z3/2; % if we had a numerical problem then bisect
end
z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits
z1 = z1 + z2; % update the step
X = X + z2*s;
[f2 df2] = eval(argstr);
M = M - 1; i = i + (length<0); % count epochs?!
d2 = df2'*s;
z3 = z3-z2; % z3 is now relative to the location of z2
end
if f2 > f1+z1*RHO*d1 || d2 > -SIG*d1
break; % this is a failure
elseif d2 > SIG*d1
success = 1; break; % success
elseif M == 0
break; % failure
end
A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation
B = 3*(f3-f2)-z3*(d3+2*d2);
z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok!
if ~isreal(z2) || isnan(z2) || isinf(z2) || z2 < 0 % num prob or wrong sign?
if limit < -0.5 % if we have no upper limit
z2 = z1 * (EXT-1); % the extrapolate the maximum amount
else
z2 = (limit-z1)/2; % otherwise bisect
end
elseif (limit > -0.5) && (z2+z1 > limit) % extraplation beyond max?
z2 = (limit-z1)/2; % bisect
elseif (limit < -0.5) && (z2+z1 > z1*EXT) % extrapolation beyond limit
z2 = z1*(EXT-1.0); % set to extrapolation limit
elseif z2 < -z3*INT
z2 = -z3*INT;
elseif (limit > -0.5) && (z2 < (limit-z1)*(1.0-INT)) % too close to limit?
z2 = (limit-z1)*(1.0-INT);
end
f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2
z1 = z1 + z2; X = X + z2*s; % update current estimates
[f2 df2] = eval(argstr);
M = M - 1; i = i + (length<0); % count epochs?!
d2 = df2'*s;
end % end of line search
if success % if line search succeeded
f1 = f2; fX = [fX' f1]';
fprintf('%s %4i | Cost: %4.6e\r', S, i, f1);
s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction
tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
d2 = df1'*s;
if d2 > 0 % new slope must be negative
s = -df1; % otherwise use steepest direction
d2 = -s'*s;
end
z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO
d1 = d2;
ls_failed = 0; % this line search did not fail
else
X = X0; f1 = f0; df1 = df0; % restore point from before failed line search
if ls_failed || i > abs(length) % line search failed twice in a row
break; % or we ran out of time, so we give up
end
tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
s = -df1; % try steepest
d1 = -s'*s;
z1 = 1/(1-d1);
ls_failed = 1; % this line search failed
end
if exist('OCTAVE_VERSION')
fflush(stdout);
end
end
fprintf('\n');

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The author of "jsonlab" toolbox is Qianqian Fang. Qianqian
is currently an Assistant Professor at Massachusetts General Hospital,
Harvard Medical School.
Address: Martinos Center for Biomedical Imaging,
Massachusetts General Hospital,
Harvard Medical School
Bldg 149, 13th St, Charlestown, MA 02129, USA
URL: http://nmr.mgh.harvard.edu/~fangq/
Email: <fangq at nmr.mgh.harvard.edu> or <fangqq at gmail.com>
The script loadjson.m was built upon previous works by
- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
date: 2009/11/02
- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
date: 2009/03/22
- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565
date: 2008/07/03
This toolbox contains patches submitted by the following contributors:
- Blake Johnson <bjohnso at bbn.com>
part of revision 341
- Niclas Borlin <Niclas.Borlin at cs.umu.se>
various fixes in revision 394, including
- loadjson crashes for all-zero sparse matrix.
- loadjson crashes for empty sparse matrix.
- Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson.
- loadjson crashes for sparse real column vector.
- loadjson crashes for sparse complex column vector.
- Data is corrupted by savejson for sparse real row vector.
- savejson crashes for sparse complex row vector.
- Yul Kang <yul.kang.on at gmail.com>
patches for svn revision 415.
- savejson saves an empty cell array as [] instead of null
- loadjson differentiates an empty struct from an empty array

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============================================================================
JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave
----------------------------------------------------------------------------
JSONlab ChangeLog (key features marked by *):
== JSONlab 1.0 (codename: Optimus - Final), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2015/01/02 polish help info for all major functions, update examples, finalize 1.0
2014/12/19 fix a bug to strictly respect NoRowBracket in savejson
== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/11/22 show progress bar in loadjson ('ShowProgress')
2014/11/17 add Compact option in savejson to output compact JSON format ('Compact')
2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels
2014/09/18 start official github mirror: https://github.com/fangq/jsonlab
== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8
2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson
2014/08/04 escape special characters in a JSON string
2014/02/16 fix a bug when saving ubjson files
== JSONlab 0.9.9 (codename: Optimus - beta), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/01/22 use binary read and write in saveubjson and loadubjson
== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang)
== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson
== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin)
== JSONlab 0.9.0 (codename: Rodimus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson
2012/06/01 support JSONP in savejson
2012/05/25 fix the empty cell bug (reported by Cyril Davin)
2012/04/05 savejson can save to a file (suggested by Patrick Rapin)
== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS
2012/01/25 patch to handle root-less objects, contributed by Blake Johnson
== JSONlab 0.8.0 (codename: Sentiel), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab
2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer
2011/12/22 *accept sequence of 'param',value input in savejson and loadjson
2011/11/18 fix struct array bug reported by Mykel Kochenderfer
== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration
2011/10/20 loadjson supports JSON collections - concatenated JSON objects
== JSONlab 0.5.0 (codename: Nexus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/16 package and release jsonlab 0.5.0
2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug
2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level
2011/10/10 create jsonlab project, start jsonlab website, add online documentation
2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support
2011/10/06 *savejson works for structs, cells and arrays
2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m

25
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Copyright 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are
permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list
of conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those of the
authors and should not be interpreted as representing official policies, either expressed
or implied, of the copyright holders.

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===============================================================================
= JSONLab =
= An open-source MATLAB/Octave JSON encoder and decoder =
===============================================================================
*Copyright (C) 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>
*License: BSD License, see License_BSD.txt for details
*Version: 1.0 (Optimus - Final)
-------------------------------------------------------------------------------
Table of Content:
I. Introduction
II. Installation
III.Using JSONLab
IV. Known Issues and TODOs
V. Contribution and feedback
-------------------------------------------------------------------------------
I. Introduction
JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable,
human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format
to represent complex and hierarchical data. It is as powerful as
[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely
used for data-exchange in applications, and is essential for the wild success
of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and
[http://en.wikipedia.org/wiki/Web_2.0 Web2.0].
UBJSON (Universal Binary JSON) is a binary JSON format, specifically
optimized for compact file size and better performance while keeping
the semantics as simple as the text-based JSON format. Using the UBJSON
format allows to wrap complex binary data in a flexible and extensible
structure, making it possible to process complex and large dataset
without accuracy loss due to text conversions.
We envision that both JSON and its binary version will serve as part of
the mainstream data-exchange formats for scientific research in the future.
It will provide the flexibility and generality achieved by other popular
general-purpose file specifications, such as
[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly
reduced complexity and enhanced performance.
JSONLab is a free and open-source implementation of a JSON/UBJSON encoder
and a decoder in the native MATLAB language. It can be used to convert a MATLAB
data structure (array, struct, cell, struct array and cell array) into
JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB
data structure. JSONLab supports both MATLAB and
[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-------------------------------------------------------------------------------
II. Installation
The installation of JSONLab is no different than any other simple
MATLAB toolbox. You only need to download/unzip the JSONLab package
to a folder, and add the folder's path to MATLAB/Octave's path list
by using the following command:
addpath('/path/to/jsonlab');
If you want to add this path permanently, you need to type "pathtool",
browse to the jsonlab root folder and add to the list, then click "Save".
Then, run "rehash" in MATLAB, and type "which loadjson", if you see an
output, that means JSONLab is installed for MATLAB/Octave.
-------------------------------------------------------------------------------
III.Using JSONLab
JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder,
and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and
two equivallent functions -- loadubjson and saveubjson for the binary
JSON. The detailed help info for the four functions can be found below:
=== loadjson.m ===
<pre>
data=loadjson(fname,opt)
or
data=loadjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09, including previous works from
Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
created on 2009/11/02
François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
created on 2009/03/22
Joel Feenstra:
http://www.mathworks.com/matlabcentral/fileexchange/20565
created on 2008/07/03
$Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a JSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.FastArrayParser [1|0 or integer]: if set to 1, use a
speed-optimized array parser when loading an
array object. The fast array parser may
collapse block arrays into a single large
array similar to rules defined in cell2mat; 0 to
use a legacy parser; if set to a larger-than-1
value, this option will specify the minimum
dimension to enable the fast array parser. For
example, if the input is a 3D array, setting
FastArrayParser to 1 will return a 3D array;
setting to 2 will return a cell array of 2D
arrays; setting to 3 will return to a 2D cell
array of 1D vectors; setting to 4 will return a
3D cell array.
opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
dat=loadjson(['examples' filesep 'example1.json'])
dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
</pre>
=== savejson.m ===
<pre>
json=savejson(rootname,obj,filename)
or
json=savejson(rootname,obj,opt)
json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
Object Notation) string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09
$Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array).
filename: a string for the file name to save the output JSON data.
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.FloatFormat ['%.10g'|string]: format to show each numeric element
of a 1D/2D array;
opt.ArrayIndent [1|0]: if 1, output explicit data array with
precedent indentation; if 0, no indentation
opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [0|1]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
to represent +/-Inf. The matched pattern is '([-+]*)Inf'
and $1 represents the sign. For those who want to use
1e999 to represent Inf, they can set opt.Inf to '$11e999'
opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
to represent NaN
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSONP='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a string in the JSON format (see http://json.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
savejson('jmesh',jsonmesh)
savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
</pre>
=== loadubjson.m ===
<pre>
data=loadubjson(fname,opt)
or
data=loadubjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/01
$Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a UBJSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.IntEndian [B|L]: specify the endianness of the integer fields
in the UBJSON input data. B - Big-Endian format for
integers (as required in the UBJSON specification);
L - input integer fields are in Little-Endian order.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
obj=struct('string','value','array',[1 2 3]);
ubjdata=saveubjson('obj',obj);
dat=loadubjson(ubjdata)
dat=loadubjson(['examples' filesep 'example1.ubj'])
dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
</pre>
=== saveubjson.m ===
<pre>
json=saveubjson(rootname,obj,filename)
or
json=saveubjson(rootname,obj,opt)
json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a Universal
Binary JSON (UBJSON) binary string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/17
$Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array)
filename: a string for the file name to save the output UBJSON data
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [1|0]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSON='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a binary string in the UBJSON format (see http://ubjson.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
saveubjson('jsonmesh',jsonmesh)
saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
</pre>
=== examples ===
Under the "examples" folder, you can find several scripts to demonstrate the
basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you
will see the conversions from MATLAB data structure to JSON text and backward.
In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet
and validate the loadjson/savejson functions for regression testing purposes.
Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson
and loadubjson pairs for various matlab data structures.
Please run these examples and understand how JSONLab works before you use
it to process your data.
-------------------------------------------------------------------------------
IV. Known Issues and TODOs
JSONLab has several known limitations. We are striving to make it more general
and robust. Hopefully in a few future releases, the limitations become less.
Here are the known issues:
# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays;
# When processing names containing multi-byte characters, Octave and MATLAB \
can give different field-names; you can use feature('DefaultCharacterSet','latin1') \
in MATLAB to get consistant results
# savejson can not handle class and dataset.
# saveubjson converts a logical array into a uint8 ([U]) array
# an unofficial N-D array count syntax is implemented in saveubjson. We are \
actively communicating with the UBJSON spec maintainer to investigate the \
possibility of making it upstream
# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \
files, however, it can parse all UBJSON files produced by saveubjson.
-------------------------------------------------------------------------------
V. Contribution and feedback
JSONLab is an open-source project. This means you can not only use it and modify
it as you wish, but also you can contribute your changes back to JSONLab so
that everyone else can enjoy the improvement. For anyone who want to contribute,
please download JSONLab source code from it's subversion repository by using the
following command:
svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab
You can make changes to the files as needed. Once you are satisfied with your
changes, and ready to share it with others, please cd the root directory of
JSONLab, and type
svn diff > yourname_featurename.patch
You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at
the email address shown in the beginning of this file. Qianqian will review
the changes and commit it to the subversion if they are satisfactory.
We appreciate any suggestions and feedbacks from you. Please use iso2mesh's
mailing list to report any questions you may have with JSONLab:
http://groups.google.com/group/iso2mesh-users?hl=en&pli=1
(Subscription to the mailing list is needed in order to post messages).

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function val=jsonopt(key,default,varargin)
%
% val=jsonopt(key,default,optstruct)
%
% setting options based on a struct. The struct can be produced
% by varargin2struct from a list of 'param','value' pairs
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
%
% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $
%
% input:
% key: a string with which one look up a value from a struct
% default: if the key does not exist, return default
% optstruct: a struct where each sub-field is a key
%
% output:
% val: if key exists, val=optstruct.key; otherwise val=default
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
val=default;
if(nargin<=2) return; end
opt=varargin{1};
if(isstruct(opt) && isfield(opt,key))
val=getfield(opt,key);
end

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function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

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function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

33
machine-learning-ex3/ex3/lib/jsonlab/mergestruct.m Normal file
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function s=mergestruct(s1,s2)
%
% s=mergestruct(s1,s2)
%
% merge two struct objects into one
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% s1,s2: a struct object, s1 and s2 can not be arrays
%
% output:
% s: the merged struct object. fields in s1 and s2 will be combined in s.
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(~isstruct(s1) || ~isstruct(s2))
error('input parameters contain non-struct');
end
if(length(s1)>1 || length(s2)>1)
error('can not merge struct arrays');
end
fn=fieldnames(s2);
s=s1;
for i=1:length(fn)
s=setfield(s,fn{i},getfield(s2,fn{i}));
end

475
machine-learning-ex3/ex3/lib/jsonlab/savejson.m Normal file
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function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end

504
machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m Normal file
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@ -0,0 +1,504 @@
function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/17
%
% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array)
% filename: a string for the file name to save the output UBJSON data
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [1|0]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSON='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a binary string in the UBJSON format (see http://ubjson.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% saveubjson('jsonmesh',jsonmesh)
% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
json=obj2ubjson(rootname,obj,rootlevel,opt);
if(~rootisarray)
json=['{' json '}'];
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=[jsonp '(' json ')'];
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2ubjson(name,item,level,varargin)
if(iscell(item))
txt=cell2ubjson(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2ubjson(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2ubjson(name,item,level,varargin{:});
else
txt=mat2ubjson(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2ubjson(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item); % let's handle 1D cell first
if(len>1)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) '[']; name='';
else
txt='[';
end
elseif(len==0)
if(~isempty(name))
txt=[S_(checkname(name,varargin{:})) 'Z']; name='';
else
txt='Z';
end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=struct2ubjson(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
for j=1:dim(2)
if(dim(1)>1) txt=[txt '[']; end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=[txt S_(checkname(name,varargin{:})) '{'];
else
txt=[txt '{'];
end
if(~isempty(names))
for e=1:length(names)
txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
end
end
txt=[txt '}'];
end
if(dim(1)>1) txt=[txt ']']; end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=str2ubjson(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
if(~isempty(name))
if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
else
if(len>1) txt='['; end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
val=item(e,:);
if(len==1)
obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
if(isempty(name)) obj=['',S_(val),'']; end
txt=[txt,'',obj];
else
txt=[txt,'',['',S_(val),'']];
end
end
if(len>1) txt=[txt ']']; end
%%-------------------------------------------------------------------------
function txt=mat2ubjson(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
cid=I_(uint32(max(size(item))));
if(isempty(name))
txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
else
if(isempty(item))
txt=[S_(checkname(name,varargin{:})),'Z'];
return;
else
txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
end
end
else
if(isempty(name))
txt=matdata2ubjson(item,level+1,varargin{:});
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
txt=[S_(checkname(name,varargin{:})) numtxt];
else
txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
end
end
return;
end
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=[txt,S_('_ArrayIsComplex_'),'T'];
end
txt=[txt,S_('_ArrayIsSparse_'),'T'];
if(size(item,1)==1)
% Row vector, store only column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([iy(:),data'],level+2,varargin{:})];
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,data],level+2,varargin{:})];
else
% General case, store row and column indices.
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([ix,iy,data],level+2,varargin{:})];
end
else
if(isreal(item))
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson(item(:)',level+2,varargin{:})];
else
txt=[txt,S_('_ArrayIsComplex_'),'T'];
txt=[txt,S_('_ArrayData_'),...
matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
end
end
txt=[txt,'}'];
%%-------------------------------------------------------------------------
function txt=matdata2ubjson(mat,level,varargin)
if(isempty(mat))
txt='Z';
return;
end
if(size(mat,1)==1)
level=level-1;
end
type='';
hasnegtive=(mat<0);
if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
if(isempty(hasnegtive))
if(max(mat(:))<=2^8)
type='U';
end
end
if(isempty(type))
% todo - need to consider negative ones separately
id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
if(isempty(find(id)))
error('high-precision data is not yet supported');
end
key='iIlL';
type=key(find(id));
end
txt=[I_a(mat(:),type,size(mat))];
elseif(islogical(mat))
logicalval='FT';
if(numel(mat)==1)
txt=logicalval(mat+1);
else
txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
end
else
if(numel(mat)==1)
txt=['[' D_(mat) ']'];
else
txt=D_a(mat(:),'D',size(mat));
end
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function val=S_(str)
if(length(str)==1)
val=['C' str];
else
val=['S' I_(int32(length(str))) str];
end
%%-------------------------------------------------------------------------
function val=I_(num)
if(~isinteger(num))
error('input is not an integer');
end
if(num>=0 && num<255)
val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
return;
end
key='iIlL';
cid={'int8','int16','int32','int64'};
for i=1:4
if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
return;
end
end
error('unsupported integer');
%%-------------------------------------------------------------------------
function val=D_(num)
if(~isfloat(num))
error('input is not a float');
end
if(isa(num,'single'))
val=['d' data2byte(num,'uint8')];
else
val=['D' data2byte(num,'uint8')];
end
%%-------------------------------------------------------------------------
function data=I_a(num,type,dim,format)
id=find(ismember('iUIlL',type));
if(id==0)
error('unsupported integer array');
end
% based on UBJSON specs, all integer types are stored in big endian format
if(id==1)
data=data2byte(swapbytes(int8(num)),'uint8');
blen=1;
elseif(id==2)
data=data2byte(swapbytes(uint8(num)),'uint8');
blen=1;
elseif(id==3)
data=data2byte(swapbytes(int16(num)),'uint8');
blen=2;
elseif(id==4)
data=data2byte(swapbytes(int32(num)),'uint8');
blen=4;
elseif(id==5)
data=data2byte(swapbytes(int64(num)),'uint8');
blen=8;
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
end
data=['[' data(:)'];
else
data=reshape(data,blen,numel(data)/blen);
data(2:blen+1,:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function data=D_a(num,type,dim,format)
id=find(ismember('dD',type));
if(id==0)
error('unsupported float array');
end
if(id==1)
data=data2byte(single(num),'uint8');
elseif(id==2)
data=data2byte(double(num),'uint8');
end
if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
format='opt';
end
if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
cid=I_(uint32(max(dim)));
data=['$' type '#' I_a(dim,cid(1)) data(:)'];
else
data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
end
data=['[' data];
else
data=reshape(data,(id*4),length(data)/(id*4));
data(2:(id*4+1),:)=data;
data(1,:)=type;
data=data(:)';
data=['[' data(:)' ']'];
end
%%-------------------------------------------------------------------------
function bytes=data2byte(varargin)
bytes=typecast(varargin{:});
bytes=bytes(:)';

40
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function opt=varargin2struct(varargin)
%
% opt=varargin2struct('param1',value1,'param2',value2,...)
% or
% opt=varargin2struct(...,optstruct,...)
%
% convert a series of input parameters into a structure
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% 'param', value: the input parameters should be pairs of a string and a value
% optstruct: if a parameter is a struct, the fields will be merged to the output struct
%
% output:
% opt: a struct where opt.param1=value1, opt.param2=value2 ...
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
len=length(varargin);
opt=struct;
if(len==0) return; end
i=1;
while(i<=len)
if(isstruct(varargin{i}))
opt=mergestruct(opt,varargin{i});
elseif(ischar(varargin{i}) && i<len)
opt=setfield(opt,varargin{i},varargin{i+1});
i=i+1;
else
error('input must be in the form of ...,''name'',value,... pairs or structs');
end
i=i+1;
end

30
machine-learning-ex3/ex3/lib/makeValidFieldName.m Normal file
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function str = makeValidFieldName(str)
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
isoct=exist('OCTAVE_VERSION','builtin');
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end

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function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = promptToken('', '', tokenFile);
end
if isempty(token)
fprintf('!! Submission Cancelled\n');
return
end
try
response = submitParts(conf, email, token, parts);
catch
e = lasterror();
fprintf('\n!! Submission failed: %s\n', e.message);
fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
fprintf('\nPlease correct your code and resubmit.\n');
return
end
if isfield(response, 'errorMessage')
fprintf('!! Submission failed: %s\n', response.errorMessage);
elseif isfield(response, 'errorCode')
fprintf('!! Submission failed: %s\n', response.message);
else
showFeedback(parts, response);
save(tokenFile, 'email', 'token');
end
end
function [email token] = promptToken(email, existingToken, tokenFile)
if (~isempty(email) && ~isempty(existingToken))
prompt = sprintf( ...
'Use token from last successful submission (%s)? (Y/n): ', ...
email);
reenter = input(prompt, 's');
if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
token = existingToken;
return;
else
delete(tokenFile);
end
end
email = input('Login (email address): ', 's');
token = input('Token: ', 's');
end
function isValid = isValidPartOptionIndex(partOptions, i)
isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
end
function response = submitParts(conf, email, token, parts)
body = makePostBody(conf, email, token, parts);
submissionUrl = submissionUrl();
responseBody = getResponse(submissionUrl, body);
jsonResponse = validateResponse(responseBody);
response = loadjson(jsonResponse);
end
function body = makePostBody(conf, email, token, parts)
bodyStruct.assignmentSlug = conf.assignmentSlug;
bodyStruct.submitterEmail = email;
bodyStruct.secret = token;
bodyStruct.parts = makePartsStruct(conf, parts);
opt.Compact = 1;
body = savejson('', bodyStruct, opt);
end
function partsStruct = makePartsStruct(conf, parts)
for part = parts
partId = part{:}.id;
fieldName = makeValidFieldName(partId);
outputStruct.output = conf.output(partId);
partsStruct.(fieldName) = outputStruct;
end
end
function [parts] = parts(conf)
parts = {};
for partArray = conf.partArrays
part.id = partArray{:}{1};
part.sourceFiles = partArray{:}{2};
part.name = partArray{:}{3};
parts{end + 1} = part;
end
end
function showFeedback(parts, response)
fprintf('== \n');
fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
for part = parts
score = '';
partFeedback = '';
partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
end
evaluation = response.evaluation;
totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
fprintf('== --------------------------------\n');
fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
fprintf('== \n');
end
% use urlread or curl to send submit results to the grader and get a response
function response = getResponse(url, body)
% try using urlread() and a secure connection
params = {'jsonBody', body};
[response, success] = urlread(url, 'post', params);
if (success == 0)
% urlread didn't work, try curl & the peer certificate patch
if ispc
% testing note: use 'jsonBody =' for a test case
json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
else
% it's linux/OS X, so use the other form
json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
end
% get the response body for the peer certificate patch method
[code, response] = system(json_command);
% test the success code
if (code ~= 0)
fprintf('[error] submission with curl() was not successful\n');
end
end
end
% validate the grader's response
function response = validateResponse(resp)
% test if the response is json or an HTML page
isJson = length(resp) > 0 && resp(1) == '{';
isHtml = findstr(lower(resp), '<html');
if (isJson)
response = resp;
elseif (isHtml)
% the response is html, so it's probably an error message
printHTMLContents(resp);
error('Grader response is an HTML message');
else
error('Grader sent no response');
end
end
% parse a HTML response and print it's contents
function printHTMLContents(response)
strippedResponse = regexprep(response, '<[^>]+>', ' ');
strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
fprintf(strippedResponse);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Service configuration
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function submissionUrl = submissionUrl()
submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
end

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function [J, grad] = lrCostFunction(theta, X, y, lambda)
%LRCOSTFUNCTION Compute cost and gradient for logistic regression with
%regularization
% J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using
% theta as the parameter for regularized logistic regression and the
% gradient of the cost w.r.t. to the parameters.
% Initialize some useful values
m = length(y); % number of training examples
n = length(theta);
% You need to return the following variables correctly
J = 0;
grad = zeros(size(theta));
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
% You should set J to the cost.
% Compute the partial derivatives and set grad to the partial
% derivatives of the cost w.r.t. each parameter in theta
%
% Hint: The computation of the cost function and gradients can be
% efficiently vectorized. For example, consider the computation
%
% sigmoid(X * theta)
%
% Each row of the resulting matrix will contain the value of the
% prediction for that example. You can make use of this to vectorize
% the cost function and gradient computations.
%
% Hint: When computing the gradient of the regularized cost function,
% there're many possible vectorized solutions, but one solution
% looks like:
% grad = (unregularized gradient for logistic regression)
% temp = theta;
% temp(1) = 0; % because we don't add anything for j = 0
% grad = grad + YOUR_CODE_HERE (using the temp variable)
%
h = sigmoid(X*theta);
J = (1/m)*(-y'*log(h)-(1-y)'*log(1-h));
grad = (1/m)*X'*(sigmoid(X*theta)-y);
J = J + (lambda/(2*m))*sum(theta(2:end).^2);
grad(2:end) = grad(2:end) + (lambda/m)*theta(2:end);
% =============================================================
grad = grad(:);
end

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machine-learning-ex3/ex3/oneVsAll.m Normal file
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function [all_theta] = oneVsAll(X, y, num_labels, lambda)
%ONEVSALL trains multiple logistic regression classifiers and returns all
%the classifiers in a matrix all_theta, where the i-th row of all_theta
%corresponds to the classifier for label i
% [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
% logistic regression classifiers and returns each of these classifiers
% in a matrix all_theta, where the i-th row of all_theta corresponds
% to the classifier for label i
% Some useful variables
m = size(X, 1);
n = size(X, 2);
% You need to return the following variables correctly
all_theta = zeros(num_labels, n + 1);
% Add ones to the X data matrix
X = [ones(m, 1) X];
% ====================== YOUR CODE HERE ======================
% Instructions: You should complete the following code to train num_labels
% logistic regression classifiers with regularization
% parameter lambda.
%
% Hint: theta(:) will return a column vector.
%
% Hint: You can use y == c to obtain a vector of 1's and 0's that tell you
% whether the ground truth is true/false for this class.
%
% Note: For this assignment, we recommend using fmincg to optimize the cost
% function. It is okay to use a for-loop (for c = 1:num_labels) to
% loop over the different classes.
%
% fmincg works similarly to fminunc, but is more efficient when we
% are dealing with large number of parameters.
%
% Example Code for fmincg:
%
% % Set Initial theta
% initial_theta = zeros(n + 1, 1);
%
% % Set options for fminunc
% options = optimset('GradObj', 'on', 'MaxIter', 50);
%
% % Run fmincg to obtain the optimal theta
% % This function will return theta and the cost
% [theta] = ...
% fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
% initial_theta, options);
%
for c=1:num_labels
initial_theta = zeros(n+1,1);
options = optimset('GradObj', 'on', 'MaxIter', 50);
[theta] = ...
fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
initial_theta, options);
all_theta(c,:) = theta;
endfor
% =========================================================================
end

43
machine-learning-ex3/ex3/predict.m Normal file
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function p = predict(Theta1, Theta2, X)
%PREDICT Predict the label of an input given a trained neural network
% p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
% trained weights of a neural network (Theta1, Theta2)
% Useful values
m = size(X, 1);
num_labels = size(Theta2, 1);
% You need to return the following variables correctly
p = zeros(size(X, 1), 1);
% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
% your learned neural network. You should set p to a
% vector containing labels between 1 to num_labels.
%
% Hint: The max function might come in useful. In particular, the max
% function can also return the index of the max element, for more
% information see 'help max'. If your examples are in rows, then, you
% can use max(A, [], 2) to obtain the max for each row.
%
X = [ones(rows(X), 1) X];
z2 = (Theta1*X')';
a2 = sigmoid(z2);
a2 = [ones(rows(a2), 1) a2];
z3 = Theta2*a2';
a3 = sigmoid(z3);
a3 = a3';
[max, imax] = max(a3, [], 2);
p = imax;
% =========================================================================
end

45
machine-learning-ex3/ex3/predictOneVsAll.m Normal file
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function p = predictOneVsAll(all_theta, X)
%PREDICT Predict the label for a trained one-vs-all classifier. The labels
%are in the range 1..K, where K = size(all_theta, 1).
% p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
% for each example in the matrix X. Note that X contains the examples in
% rows. all_theta is a matrix where the i-th row is a trained logistic
% regression theta vector for the i-th class. You should set p to a vector
% of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2
% for 4 examples)
m = size(X, 1);
num_labels = size(all_theta, 1);
% You need to return the following variables correctly
p = zeros(size(X, 1), 1);
% Add ones to the X data matrix
X = [ones(m, 1) X];
% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
% your learned logistic regression parameters (one-vs-all).
% You should set p to a vector of predictions (from 1 to
% num_labels).
%
% Hint: This code can be done all vectorized using the max function.
% In particular, the max function can also return the index of the
% max element, for more information see 'help max'. If your examples
% are in rows, then, you can use max(A, [], 2) to obtain the max
% for each row.
%
temp = sigmoid(X*all_theta');
for i = 1:size(X,1)
[tempMax, iTempMax] = max(temp(i,:));
p(i) = iTempMax;
endfor
% =========================================================================
end

6
machine-learning-ex3/ex3/sigmoid.m Normal file
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function g = sigmoid(z)
%SIGMOID Compute sigmoid functoon
% J = SIGMOID(z) computes the sigmoid of z.
g = 1.0 ./ (1.0 + exp(-z));
end

56
machine-learning-ex3/ex3/submit.m Normal file
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function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ...
{ ...
'2', ...
{ 'oneVsAll.m' }, ...
'One-vs-All Classifier Training', ...
}, ...
{ ...
'3', ...
{ 'predictOneVsAll.m' }, ...
'One-vs-All Classifier Prediction', ...
}, ...
{ ...
'4', ...
{ 'predict.m' }, ...
'Neural Network Prediction Function' ...
}, ...
};
conf.output = @output;
submitWithConfiguration(conf);
end
function out = output(partId, auxdata)
% Random Test Cases
X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
y = sin(X(:,1) + X(:,2)) > 0;
Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...
1 1 ; 1 2 ; 2 1 ; 2 2 ; ...
-1 1 ; -1 2 ; -2 1 ; -2 2 ; ...
1 -1 ; 1 -2 ; -2 -1 ; -2 -2 ];
ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]';
t1 = sin(reshape(1:2:24, 4, 3));
t2 = cos(reshape(1:2:40, 4, 5));
if partId == '1'
[J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);
out = sprintf('%0.5f ', J);
out = [out sprintf('%0.5f ', grad)];
elseif partId == '2'
out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));
elseif partId == '3'
out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));
elseif partId == '4'
out = sprintf('%0.5f ', predict(t1, t2, Xm));
end
end

15
machine-learning-ex3/ex3/token.mat Normal file
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# Created by Octave 4.4.1, Sun Aug 18 21:05:54 2019 GMT <unknown@LAPTOP-8PSOVU3K>
# name: email
# type: sq_string
# elements: 1
# length: 17
tsb1995@gmail.com
# name: token
# type: sq_string
# elements: 1
# length: 16
hVVScLtdMyJhvFGP

BIN
machine-learning-ex4/ex4.pdf Normal file
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machine-learning-ex4/ex4/checkNNGradients.m Normal file
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function checkNNGradients(lambda)
%CHECKNNGRADIENTS Creates a small neural network to check the
%backpropagation gradients
% CHECKNNGRADIENTS(lambda) Creates a small neural network to check the
% backpropagation gradients, it will output the analytical gradients
% produced by your backprop code and the numerical gradients (computed
% using computeNumericalGradient). These two gradient computations should
% result in very similar values.
%
if ~exist('lambda', 'var') || isempty(lambda)
lambda = 0;
end
input_layer_size = 3;
hidden_layer_size = 5;
num_labels = 3;
m = 5;
% We generate some 'random' test data
Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size);
Theta2 = debugInitializeWeights(num_labels, hidden_layer_size);
% Reusing debugInitializeWeights to generate X
X = debugInitializeWeights(m, input_layer_size - 1);
y = 1 + mod(1:m, num_labels)';
% Unroll parameters
nn_params = [Theta1(:) ; Theta2(:)];
% Short hand for cost function
costFunc = @(p) nnCostFunction(p, input_layer_size, hidden_layer_size, ...
num_labels, X, y, lambda);
[cost, grad] = costFunc(nn_params);
numgrad = computeNumericalGradient(costFunc, nn_params);
% Visually examine the two gradient computations. The two columns
% you get should be very similar.
disp([numgrad grad]);
fprintf(['The above two columns you get should be very similar.\n' ...
'(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']);
% Evaluate the norm of the difference between two solutions.
% If you have a correct implementation, and assuming you used EPSILON = 0.0001
% in computeNumericalGradient.m, then diff below should be less than 1e-9
diff = norm(numgrad-grad)/norm(numgrad+grad);
fprintf(['If your backpropagation implementation is correct, then \n' ...
'the relative difference will be small (less than 1e-9). \n' ...
'\nRelative Difference: %g\n'], diff);
end

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machine-learning-ex4/ex4/computeNumericalGradient.m Normal file
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function numgrad = computeNumericalGradient(J, theta)
%COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences"
%and gives us a numerical estimate of the gradient.
% numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical
% gradient of the function J around theta. Calling y = J(theta) should
% return the function value at theta.
% Notes: The following code implements numerical gradient checking, and
% returns the numerical gradient.It sets numgrad(i) to (a numerical
% approximation of) the partial derivative of J with respect to the
% i-th input argument, evaluated at theta. (i.e., numgrad(i) should
% be the (approximately) the partial derivative of J with respect
% to theta(i).)
%
numgrad = zeros(size(theta));
perturb = zeros(size(theta));
e = 1e-4;
for p = 1:numel(theta)
% Set perturbation vector
perturb(p) = e;
loss1 = J(theta - perturb);
loss2 = J(theta + perturb);
% Compute Numerical Gradient
numgrad(p) = (loss2 - loss1) / (2*e);
perturb(p) = 0;
end
end

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machine-learning-ex4/ex4/debugInitializeWeights.m Normal file
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function W = debugInitializeWeights(fan_out, fan_in)
%DEBUGINITIALIZEWEIGHTS Initialize the weights of a layer with fan_in
%incoming connections and fan_out outgoing connections using a fixed
%strategy, this will help you later in debugging
% W = DEBUGINITIALIZEWEIGHTS(fan_in, fan_out) initializes the weights
% of a layer with fan_in incoming connections and fan_out outgoing
% connections using a fix set of values
%
% Note that W should be set to a matrix of size(1 + fan_in, fan_out) as
% the first row of W handles the "bias" terms
%
% Set W to zeros
W = zeros(fan_out, 1 + fan_in);
% Initialize W using "sin", this ensures that W is always of the same
% values and will be useful for debugging
W = reshape(sin(1:numel(W)), size(W)) / 10;
% =========================================================================
end

59
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function [h, display_array] = displayData(X, example_width)
%DISPLAYDATA Display 2D data in a nice grid
% [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
% stored in X in a nice grid. It returns the figure handle h and the
% displayed array if requested.
% Set example_width automatically if not passed in
if ~exist('example_width', 'var') || isempty(example_width)
example_width = round(sqrt(size(X, 2)));
end
% Gray Image
colormap(gray);
% Compute rows, cols
[m n] = size(X);
example_height = (n / example_width);
% Compute number of items to display
display_rows = floor(sqrt(m));
display_cols = ceil(m / display_rows);
% Between images padding
pad = 1;
% Setup blank display
display_array = - ones(pad + display_rows * (example_height + pad), ...
pad + display_cols * (example_width + pad));
% Copy each example into a patch on the display array
curr_ex = 1;
for j = 1:display_rows
for i = 1:display_cols
if curr_ex > m,
break;
end
% Copy the patch
% Get the max value of the patch
max_val = max(abs(X(curr_ex, :)));
display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...
pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...
reshape(X(curr_ex, :), example_height, example_width) / max_val;
curr_ex = curr_ex + 1;
end
if curr_ex > m,
break;
end
end
% Display Image
h = imagesc(display_array, [-1 1]);
% Do not show axis
axis image off
drawnow;
end

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%% Machine Learning Online Class - Exercise 4 Neural Network Learning
% Instructions
% ------------
%
% This file contains code that helps you get started on the
% linear exercise. You will need to complete the following functions
% in this exericse:
%
% sigmoidGradient.m
% randInitializeWeights.m
% nnCostFunction.m
%
% For this exercise, you will not need to change any code in this file,
% or any other files other than those mentioned above.
%
%% Initialization
clear ; close all; clc
%% Setup the parameters you will use for this exercise
input_layer_size = 400; % 20x20 Input Images of Digits
hidden_layer_size = 25; % 25 hidden units
num_labels = 10; % 10 labels, from 1 to 10
% (note that we have mapped "0" to label 10)
%% =========== Part 1: Loading and Visualizing Data =============
% We start the exercise by first loading and visualizing the dataset.
% You will be working with a dataset that contains handwritten digits.
%
% Load Training Data
fprintf('Loading and Visualizing Data ...\n')
load('ex4data1.mat');
m = size(X, 1);
% Randomly select 100 data points to display
sel = randperm(size(X, 1));
sel = sel(1:100);
displayData(X(sel, :));
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ================ Part 2: Loading Parameters ================
% In this part of the exercise, we load some pre-initialized
% neural network parameters.
fprintf('\nLoading Saved Neural Network Parameters ...\n')
% Load the weights into variables Theta1 and Theta2
load('ex4weights.mat');
% Unroll parameters
nn_params = [Theta1(:) ; Theta2(:)];
%% ================ Part 3: Compute Cost (Feedforward) ================
% To the neural network, you should first start by implementing the
% feedforward part of the neural network that returns the cost only. You
% should complete the code in nnCostFunction.m to return cost. After
% implementing the feedforward to compute the cost, you can verify that
% your implementation is correct by verifying that you get the same cost
% as us for the fixed debugging parameters.
%
% We suggest implementing the feedforward cost *without* regularization
% first so that it will be easier for you to debug. Later, in part 4, you
% will get to implement the regularized cost.
%
fprintf('\nFeedforward Using Neural Network ...\n')
% Weight regularization parameter (we set this to 0 here).
lambda = 0;
J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
num_labels, X, y, lambda);
fprintf(['Cost at parameters (loaded from ex4weights): %f '...
'\n(this value should be about 0.287629)\n'], J);
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% =============== Part 4: Implement Regularization ===============
% Once your cost function implementation is correct, you should now
% continue to implement the regularization with the cost.
%
fprintf('\nChecking Cost Function (w/ Regularization) ... \n')
% Weight regularization parameter (we set this to 1 here).
lambda = 1;
J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
num_labels, X, y, lambda);
fprintf(['Cost at parameters (loaded from ex4weights): %f '...
'\n(this value should be about 0.383770)\n'], J);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ================ Part 5: Sigmoid Gradient ================
% Before you start implementing the neural network, you will first
% implement the gradient for the sigmoid function. You should complete the
% code in the sigmoidGradient.m file.
%
fprintf('\nEvaluating sigmoid gradient...\n')
g = sigmoidGradient([-1 -0.5 0 0.5 1]);
fprintf('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n ');
fprintf('%f ', g);
fprintf('\n\n');
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ================ Part 6: Initializing Pameters ================
% In this part of the exercise, you will be starting to implment a two
% layer neural network that classifies digits. You will start by
% implementing a function to initialize the weights of the neural network
% (randInitializeWeights.m)
fprintf('\nInitializing Neural Network Parameters ...\n')
initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);
% Unroll parameters
initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];
%% =============== Part 7: Implement Backpropagation ===============
% Once your cost matches up with ours, you should proceed to implement the
% backpropagation algorithm for the neural network. You should add to the
% code you've written in nnCostFunction.m to return the partial
% derivatives of the parameters.
%
fprintf('\nChecking Backpropagation... \n');
% Check gradients by running checkNNGradients
checkNNGradients;
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% =============== Part 8: Implement Regularization ===============
% Once your backpropagation implementation is correct, you should now
% continue to implement the regularization with the cost and gradient.
%
fprintf('\nChecking Backpropagation (w/ Regularization) ... \n')
% Check gradients by running checkNNGradients
lambda = 3;
checkNNGradients(lambda);
% Also output the costFunction debugging values
debug_J = nnCostFunction(nn_params, input_layer_size, ...
hidden_layer_size, num_labels, X, y, lambda);
fprintf(['\n\nCost at (fixed) debugging parameters (w/ lambda = %f): %f ' ...
'\n(for lambda = 3, this value should be about 0.576051)\n\n'], lambda, debug_J);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% =================== Part 8: Training NN ===================
% You have now implemented all the code necessary to train a neural
% network. To train your neural network, we will now use "fmincg", which
% is a function which works similarly to "fminunc". Recall that these
% advanced optimizers are able to train our cost functions efficiently as
% long as we provide them with the gradient computations.
%
fprintf('\nTraining Neural Network... \n')
% After you have completed the assignment, change the MaxIter to a larger
% value to see how more training helps.
options = optimset('MaxIter', 50);
% You should also try different values of lambda
lambda = 1;
% Create "short hand" for the cost function to be minimized
costFunction = @(p) nnCostFunction(p, ...
input_layer_size, ...
hidden_layer_size, ...
num_labels, X, y, lambda);
% Now, costFunction is a function that takes in only one argument (the
% neural network parameters)
[nn_params, cost] = fmincg(costFunction, initial_nn_params, options);
% Obtain Theta1 and Theta2 back from nn_params
Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
hidden_layer_size, (input_layer_size + 1));
Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
num_labels, (hidden_layer_size + 1));
fprintf('Program paused. Press enter to continue.\n');
pause;
%% ================= Part 9: Visualize Weights =================
% You can now "visualize" what the neural network is learning by
% displaying the hidden units to see what features they are capturing in
% the data.
fprintf('\nVisualizing Neural Network... \n')
displayData(Theta1(:, 2:end));
fprintf('\nProgram paused. Press enter to continue.\n');
pause;
%% ================= Part 10: Implement Predict =================
% After training the neural network, we would like to use it to predict
% the labels. You will now implement the "predict" function to use the
% neural network to predict the labels of the training set. This lets
% you compute the training set accuracy.
pred = predict(Theta1, Theta2, X);
fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);

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function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
% Minimize a continuous differentialble multivariate function. Starting point
% is given by "X" (D by 1), and the function named in the string "f", must
% return a function value and a vector of partial derivatives. The Polack-
% Ribiere flavour of conjugate gradients is used to compute search directions,
% and a line search using quadratic and cubic polynomial approximations and the
% Wolfe-Powell stopping criteria is used together with the slope ratio method
% for guessing initial step sizes. Additionally a bunch of checks are made to
% make sure that exploration is taking place and that extrapolation will not
% be unboundedly large. The "length" gives the length of the run: if it is
% positive, it gives the maximum number of line searches, if negative its
% absolute gives the maximum allowed number of function evaluations. You can
% (optionally) give "length" a second component, which will indicate the
% reduction in function value to be expected in the first line-search (defaults
% to 1.0). The function returns when either its length is up, or if no further
% progress can be made (ie, we are at a minimum, or so close that due to
% numerical problems, we cannot get any closer). If the function terminates
% within a few iterations, it could be an indication that the function value
% and derivatives are not consistent (ie, there may be a bug in the
% implementation of your "f" function). The function returns the found
% solution "X", a vector of function values "fX" indicating the progress made
% and "i" the number of iterations (line searches or function evaluations,
% depending on the sign of "length") used.
%
% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
%
% See also: checkgrad
%
% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13
%
%
% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen
%
% Permission is granted for anyone to copy, use, or modify these
% programs and accompanying documents for purposes of research or
% education, provided this copyright notice is retained, and note is
% made of any changes that have been made.
%
% These programs and documents are distributed without any warranty,
% express or implied. As the programs were written for research
% purposes only, they have not been tested to the degree that would be
% advisable in any important application. All use of these programs is
% entirely at the user's own risk.
%
% [ml-class] Changes Made:
% 1) Function name and argument specifications
% 2) Output display
%
% Read options
if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter')
length = options.MaxIter;
else
length = 100;
end
RHO = 0.01; % a bunch of constants for line searches
SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions
INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket
EXT = 3.0; % extrapolate maximum 3 times the current bracket
MAX = 20; % max 20 function evaluations per line search
RATIO = 100; % maximum allowed slope ratio
argstr = ['feval(f, X']; % compose string used to call function
for i = 1:(nargin - 3)
argstr = [argstr, ',P', int2str(i)];
end
argstr = [argstr, ')'];
if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end
S=['Iteration '];
i = 0; % zero the run length counter
ls_failed = 0; % no previous line search has failed
fX = [];
[f1 df1] = eval(argstr); % get function value and gradient
i = i + (length<0); % count epochs?!
s = -df1; % search direction is steepest
d1 = -s'*s; % this is the slope
z1 = red/(1-d1); % initial step is red/(|s|+1)
while i < abs(length) % while not finished
i = i + (length>0); % count iterations?!
X0 = X; f0 = f1; df0 = df1; % make a copy of current values
X = X + z1*s; % begin line search
[f2 df2] = eval(argstr);
i = i + (length<0); % count epochs?!
d2 = df2'*s;
f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1
if length>0, M = MAX; else M = min(MAX, -length-i); end
success = 0; limit = -1; % initialize quanteties
while 1
while ((f2 > f1+z1*RHO*d1) || (d2 > -SIG*d1)) && (M > 0)
limit = z1; % tighten the bracket
if f2 > f1
z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit
else
A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit
B = 3*(f3-f2)-z3*(d3+2*d2);
z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok!
end
if isnan(z2) || isinf(z2)
z2 = z3/2; % if we had a numerical problem then bisect
end
z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits
z1 = z1 + z2; % update the step
X = X + z2*s;
[f2 df2] = eval(argstr);
M = M - 1; i = i + (length<0); % count epochs?!
d2 = df2'*s;
z3 = z3-z2; % z3 is now relative to the location of z2
end
if f2 > f1+z1*RHO*d1 || d2 > -SIG*d1
break; % this is a failure
elseif d2 > SIG*d1
success = 1; break; % success
elseif M == 0
break; % failure
end
A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation
B = 3*(f3-f2)-z3*(d3+2*d2);
z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok!
if ~isreal(z2) || isnan(z2) || isinf(z2) || z2 < 0 % num prob or wrong sign?
if limit < -0.5 % if we have no upper limit
z2 = z1 * (EXT-1); % the extrapolate the maximum amount
else
z2 = (limit-z1)/2; % otherwise bisect
end
elseif (limit > -0.5) && (z2+z1 > limit) % extraplation beyond max?
z2 = (limit-z1)/2; % bisect
elseif (limit < -0.5) && (z2+z1 > z1*EXT) % extrapolation beyond limit
z2 = z1*(EXT-1.0); % set to extrapolation limit
elseif z2 < -z3*INT
z2 = -z3*INT;
elseif (limit > -0.5) && (z2 < (limit-z1)*(1.0-INT)) % too close to limit?
z2 = (limit-z1)*(1.0-INT);
end
f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2
z1 = z1 + z2; X = X + z2*s; % update current estimates
[f2 df2] = eval(argstr);
M = M - 1; i = i + (length<0); % count epochs?!
d2 = df2'*s;
end % end of line search
if success % if line search succeeded
f1 = f2; fX = [fX' f1]';
fprintf('%s %4i | Cost: %4.6e\r', S, i, f1);
s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction
tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
d2 = df1'*s;
if d2 > 0 % new slope must be negative
s = -df1; % otherwise use steepest direction
d2 = -s'*s;
end
z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO
d1 = d2;
ls_failed = 0; % this line search did not fail
else
X = X0; f1 = f0; df1 = df0; % restore point from before failed line search
if ls_failed || i > abs(length) % line search failed twice in a row
break; % or we ran out of time, so we give up
end
tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
s = -df1; % try steepest
d1 = -s'*s;
z1 = 1/(1-d1);
ls_failed = 1; % this line search failed
end
if exist('OCTAVE_VERSION')
fflush(stdout);
end
end
fprintf('\n');

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The author of "jsonlab" toolbox is Qianqian Fang. Qianqian
is currently an Assistant Professor at Massachusetts General Hospital,
Harvard Medical School.
Address: Martinos Center for Biomedical Imaging,
Massachusetts General Hospital,
Harvard Medical School
Bldg 149, 13th St, Charlestown, MA 02129, USA
URL: http://nmr.mgh.harvard.edu/~fangq/
Email: <fangq at nmr.mgh.harvard.edu> or <fangqq at gmail.com>
The script loadjson.m was built upon previous works by
- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
date: 2009/11/02
- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
date: 2009/03/22
- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565
date: 2008/07/03
This toolbox contains patches submitted by the following contributors:
- Blake Johnson <bjohnso at bbn.com>
part of revision 341
- Niclas Borlin <Niclas.Borlin at cs.umu.se>
various fixes in revision 394, including
- loadjson crashes for all-zero sparse matrix.
- loadjson crashes for empty sparse matrix.
- Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson.
- loadjson crashes for sparse real column vector.
- loadjson crashes for sparse complex column vector.
- Data is corrupted by savejson for sparse real row vector.
- savejson crashes for sparse complex row vector.
- Yul Kang <yul.kang.on at gmail.com>
patches for svn revision 415.
- savejson saves an empty cell array as [] instead of null
- loadjson differentiates an empty struct from an empty array

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============================================================================
JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave
----------------------------------------------------------------------------
JSONlab ChangeLog (key features marked by *):
== JSONlab 1.0 (codename: Optimus - Final), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2015/01/02 polish help info for all major functions, update examples, finalize 1.0
2014/12/19 fix a bug to strictly respect NoRowBracket in savejson
== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/11/22 show progress bar in loadjson ('ShowProgress')
2014/11/17 add Compact option in savejson to output compact JSON format ('Compact')
2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels
2014/09/18 start official github mirror: https://github.com/fangq/jsonlab
== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8
2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson
2014/08/04 escape special characters in a JSON string
2014/02/16 fix a bug when saving ubjson files
== JSONlab 0.9.9 (codename: Optimus - beta), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2014/01/22 use binary read and write in saveubjson and loadubjson
== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang)
== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson
== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin)
== JSONlab 0.9.0 (codename: Rodimus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson
2012/06/01 support JSONP in savejson
2012/05/25 fix the empty cell bug (reported by Cyril Davin)
2012/04/05 savejson can save to a file (suggested by Patrick Rapin)
== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS
2012/01/25 patch to handle root-less objects, contributed by Blake Johnson
== JSONlab 0.8.0 (codename: Sentiel), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab
2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer
2011/12/22 *accept sequence of 'param',value input in savejson and loadjson
2011/11/18 fix struct array bug reported by Mykel Kochenderfer
== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration
2011/10/20 loadjson supports JSON collections - concatenated JSON objects
== JSONlab 0.5.0 (codename: Nexus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
2011/10/16 package and release jsonlab 0.5.0
2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug
2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level
2011/10/10 create jsonlab project, start jsonlab website, add online documentation
2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support
2011/10/06 *savejson works for structs, cells and arrays
2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m

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Copyright 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are
permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list
of conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those of the
authors and should not be interpreted as representing official policies, either expressed
or implied, of the copyright holders.

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===============================================================================
= JSONLab =
= An open-source MATLAB/Octave JSON encoder and decoder =
===============================================================================
*Copyright (C) 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>
*License: BSD License, see License_BSD.txt for details
*Version: 1.0 (Optimus - Final)
-------------------------------------------------------------------------------
Table of Content:
I. Introduction
II. Installation
III.Using JSONLab
IV. Known Issues and TODOs
V. Contribution and feedback
-------------------------------------------------------------------------------
I. Introduction
JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable,
human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format
to represent complex and hierarchical data. It is as powerful as
[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely
used for data-exchange in applications, and is essential for the wild success
of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and
[http://en.wikipedia.org/wiki/Web_2.0 Web2.0].
UBJSON (Universal Binary JSON) is a binary JSON format, specifically
optimized for compact file size and better performance while keeping
the semantics as simple as the text-based JSON format. Using the UBJSON
format allows to wrap complex binary data in a flexible and extensible
structure, making it possible to process complex and large dataset
without accuracy loss due to text conversions.
We envision that both JSON and its binary version will serve as part of
the mainstream data-exchange formats for scientific research in the future.
It will provide the flexibility and generality achieved by other popular
general-purpose file specifications, such as
[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly
reduced complexity and enhanced performance.
JSONLab is a free and open-source implementation of a JSON/UBJSON encoder
and a decoder in the native MATLAB language. It can be used to convert a MATLAB
data structure (array, struct, cell, struct array and cell array) into
JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB
data structure. JSONLab supports both MATLAB and
[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-------------------------------------------------------------------------------
II. Installation
The installation of JSONLab is no different than any other simple
MATLAB toolbox. You only need to download/unzip the JSONLab package
to a folder, and add the folder's path to MATLAB/Octave's path list
by using the following command:
addpath('/path/to/jsonlab');
If you want to add this path permanently, you need to type "pathtool",
browse to the jsonlab root folder and add to the list, then click "Save".
Then, run "rehash" in MATLAB, and type "which loadjson", if you see an
output, that means JSONLab is installed for MATLAB/Octave.
-------------------------------------------------------------------------------
III.Using JSONLab
JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder,
and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and
two equivallent functions -- loadubjson and saveubjson for the binary
JSON. The detailed help info for the four functions can be found below:
=== loadjson.m ===
<pre>
data=loadjson(fname,opt)
or
data=loadjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09, including previous works from
Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
created on 2009/11/02
François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
created on 2009/03/22
Joel Feenstra:
http://www.mathworks.com/matlabcentral/fileexchange/20565
created on 2008/07/03
$Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a JSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.FastArrayParser [1|0 or integer]: if set to 1, use a
speed-optimized array parser when loading an
array object. The fast array parser may
collapse block arrays into a single large
array similar to rules defined in cell2mat; 0 to
use a legacy parser; if set to a larger-than-1
value, this option will specify the minimum
dimension to enable the fast array parser. For
example, if the input is a 3D array, setting
FastArrayParser to 1 will return a 3D array;
setting to 2 will return a cell array of 2D
arrays; setting to 3 will return to a 2D cell
array of 1D vectors; setting to 4 will return a
3D cell array.
opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
dat=loadjson(['examples' filesep 'example1.json'])
dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
</pre>
=== savejson.m ===
<pre>
json=savejson(rootname,obj,filename)
or
json=savejson(rootname,obj,opt)
json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
Object Notation) string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2011/09/09
$Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array).
filename: a string for the file name to save the output JSON data.
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.FloatFormat ['%.10g'|string]: format to show each numeric element
of a 1D/2D array;
opt.ArrayIndent [1|0]: if 1, output explicit data array with
precedent indentation; if 0, no indentation
opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [0|1]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
to represent +/-Inf. The matched pattern is '([-+]*)Inf'
and $1 represents the sign. For those who want to use
1e999 to represent Inf, they can set opt.Inf to '$11e999'
opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
to represent NaN
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSONP='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a string in the JSON format (see http://json.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
savejson('jmesh',jsonmesh)
savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
</pre>
=== loadubjson.m ===
<pre>
data=loadubjson(fname,opt)
or
data=loadubjson(fname,'param1',value1,'param2',value2,...)
parse a JSON (JavaScript Object Notation) file or string
authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/01
$Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
input:
fname: input file name, if fname contains "{}" or "[]", fname
will be interpreted as a UBJSON string
opt: a struct to store parsing options, opt can be replaced by
a list of ('param',value) pairs - the param string is equivallent
to a field in opt. opt can have the following
fields (first in [.|.] is the default)
opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
for each element of the JSON data, and group
arrays based on the cell2mat rules.
opt.IntEndian [B|L]: specify the endianness of the integer fields
in the UBJSON input data. B - Big-Endian format for
integers (as required in the UBJSON specification);
L - input integer fields are in Little-Endian order.
output:
dat: a cell array, where {...} blocks are converted into cell arrays,
and [...] are converted to arrays
examples:
obj=struct('string','value','array',[1 2 3]);
ubjdata=saveubjson('obj',obj);
dat=loadubjson(ubjdata)
dat=loadubjson(['examples' filesep 'example1.ubj'])
dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
</pre>
=== saveubjson.m ===
<pre>
json=saveubjson(rootname,obj,filename)
or
json=saveubjson(rootname,obj,opt)
json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
convert a MATLAB object (cell, struct or array) into a Universal
Binary JSON (UBJSON) binary string
author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
created on 2013/08/17
$Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
input:
rootname: the name of the root-object, when set to '', the root name
is ignored, however, when opt.ForceRootName is set to 1 (see below),
the MATLAB variable name will be used as the root name.
obj: a MATLAB object (array, cell, cell array, struct, struct array)
filename: a string for the file name to save the output UBJSON data
opt: a struct for additional options, ignore to use default values.
opt can have the following fields (first in [.|.] is the default)
opt.FileName [''|string]: a file name to save the output JSON data
opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
array in JSON array format; if sets to 1, an
array will be shown as a struct with fields
"_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
sparse arrays, the non-zero elements will be
saved to _ArrayData_ field in triplet-format i.e.
(ix,iy,val) and "_ArrayIsSparse_" will be added
with a value of 1; for a complex array, the
_ArrayData_ array will include two columns
(4 for sparse) to record the real and imaginary
parts, and also "_ArrayIsComplex_":1 is added.
opt.ParseLogical [1|0]: if this is set to 1, logical array elem
will use true/false rather than 1/0.
opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
numerical element will be shown without a square
bracket, unless it is the root object; if 0, square
brackets are forced for any numerical arrays.
opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
will use the name of the passed obj variable as the
root object name; if obj is an expression and
does not have a name, 'root' will be used; if this
is set to 0 and rootname is empty, the root level
will be merged down to the lower level.
opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
for example, if opt.JSON='foo', the JSON data is
wrapped inside a function call as 'foo(...);'
opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
back to the string form
opt can be replaced by a list of ('param',value) pairs. The param
string is equivallent to a field in opt and is case sensitive.
output:
json: a binary string in the UBJSON format (see http://ubjson.org)
examples:
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
saveubjson('jsonmesh',jsonmesh)
saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
</pre>
=== examples ===
Under the "examples" folder, you can find several scripts to demonstrate the
basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you
will see the conversions from MATLAB data structure to JSON text and backward.
In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet
and validate the loadjson/savejson functions for regression testing purposes.
Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson
and loadubjson pairs for various matlab data structures.
Please run these examples and understand how JSONLab works before you use
it to process your data.
-------------------------------------------------------------------------------
IV. Known Issues and TODOs
JSONLab has several known limitations. We are striving to make it more general
and robust. Hopefully in a few future releases, the limitations become less.
Here are the known issues:
# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays;
# When processing names containing multi-byte characters, Octave and MATLAB \
can give different field-names; you can use feature('DefaultCharacterSet','latin1') \
in MATLAB to get consistant results
# savejson can not handle class and dataset.
# saveubjson converts a logical array into a uint8 ([U]) array
# an unofficial N-D array count syntax is implemented in saveubjson. We are \
actively communicating with the UBJSON spec maintainer to investigate the \
possibility of making it upstream
# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \
files, however, it can parse all UBJSON files produced by saveubjson.
-------------------------------------------------------------------------------
V. Contribution and feedback
JSONLab is an open-source project. This means you can not only use it and modify
it as you wish, but also you can contribute your changes back to JSONLab so
that everyone else can enjoy the improvement. For anyone who want to contribute,
please download JSONLab source code from it's subversion repository by using the
following command:
svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab
You can make changes to the files as needed. Once you are satisfied with your
changes, and ready to share it with others, please cd the root directory of
JSONLab, and type
svn diff > yourname_featurename.patch
You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at
the email address shown in the beginning of this file. Qianqian will review
the changes and commit it to the subversion if they are satisfactory.
We appreciate any suggestions and feedbacks from you. Please use iso2mesh's
mailing list to report any questions you may have with JSONLab:
http://groups.google.com/group/iso2mesh-users?hl=en&pli=1
(Subscription to the mailing list is needed in order to post messages).

32
machine-learning-ex4/ex4/lib/jsonlab/jsonopt.m Normal file
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function val=jsonopt(key,default,varargin)
%
% val=jsonopt(key,default,optstruct)
%
% setting options based on a struct. The struct can be produced
% by varargin2struct from a list of 'param','value' pairs
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
%
% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $
%
% input:
% key: a string with which one look up a value from a struct
% default: if the key does not exist, return default
% optstruct: a struct where each sub-field is a key
%
% output:
% val: if key exists, val=optstruct.key; otherwise val=default
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
val=default;
if(nargin<=2) return; end
opt=varargin{1};
if(isstruct(opt) && isfield(opt,key))
val=getfield(opt,key);
end

566
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function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
% created on 2009/11/02
% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
% created on 2009/03/22
% Joel Feenstra:
% http://www.mathworks.com/matlabcentral/fileexchange/20565
% created on 2008/07/03
%
% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a JSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.FastArrayParser [1|0 or integer]: if set to 1, use a
% speed-optimized array parser when loading an
% array object. The fast array parser may
% collapse block arrays into a single large
% array similar to rules defined in cell2mat; 0 to
% use a legacy parser; if set to a larger-than-1
% value, this option will specify the minimum
% dimension to enable the fast array parser. For
% example, if the input is a 3D array, setting
% FastArrayParser to 1 will return a 3D array;
% setting to 2 will return a cell array of 2D
% arrays; setting to 3 will return to a 2D cell
% array of 1D vectors; setting to 4 will return a
% 3D cell array.
% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
% dat=loadjson(['examples' filesep 'example1.json'])
% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
if(jsonopt('ShowProgress',0,opt)==1)
opt.progressbar_=waitbar(0,'loading ...');
end
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
if(isfield(opt,'progressbar_'))
close(opt.progressbar_);
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=data(j).x0x5F_ArraySize_;
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
if next_char ~= '}'
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
parse_char(':');
val = parse_value(varargin{:});
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}'
break;
end
parse_char(',');
end
end
parse_char('}');
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim2=[];
arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
pbar=jsonopt('progressbar_',-1,varargin{:});
if next_char ~= ']'
if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
[endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
arraystr=['[' inStr(pos:endpos)];
arraystr=regexprep(arraystr,'"_NaN_"','NaN');
arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
arraystr(arraystr==sprintf('\n'))=[];
arraystr(arraystr==sprintf('\r'))=[];
%arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
astr=inStr((e1l+1):(e1r-1));
astr=regexprep(astr,'"_NaN_"','NaN');
astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
astr(astr==sprintf('\n'))=[];
astr(astr==sprintf('\r'))=[];
astr(astr==' ')='';
if(isempty(find(astr=='[', 1))) % array is 2D
dim2=length(sscanf(astr,'%f,',[1 inf]));
end
else % array is 1D
astr=arraystr(2:end-1);
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
if(nextidx>=length(astr)-1)
object=obj;
pos=endpos;
parse_char(']');
return;
end
end
if(~isempty(dim2))
astr=arraystr;
astr(astr=='[')='';
astr(astr==']')='';
astr(astr==' ')='';
[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
if(nextidx>=length(astr)-1)
object=reshape(obj,dim2,numel(obj)/dim2)';
pos=endpos;
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
return;
end
end
arraystr=regexprep(arraystr,'\]\s*,','];');
else
arraystr='[';
end
try
if(isoct && regexp(arraystr,'"','once'))
error('Octave eval can produce empty cells for JSON-like input');
end
object=eval(arraystr);
pos=endpos;
catch
while 1
newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
val = parse_value(newopt);
object{end+1} = val;
if next_char == ']'
break;
end
parse_char(',');
end
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
parse_char(']');
if(pbar>0)
waitbar(pos/length(inStr),pbar,'loading ...');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr len esc index_esc len_esc
% len, ns = length(inStr), keyboard
if inStr(pos) ~= '"'
error_pos('String starting with " expected at position %d');
else
pos = pos + 1;
end
str = '';
while pos <= len
while index_esc <= len_esc && esc(index_esc) < pos
index_esc = index_esc + 1;
end
if index_esc > len_esc
str = [str inStr(pos:len)];
pos = len + 1;
break;
else
str = [str inStr(pos:esc(index_esc)-1)];
pos = esc(index_esc);
end
nstr = length(str); switch inStr(pos)
case '"'
pos = pos + 1;
if(~isempty(str))
if(strcmp(str,'_Inf_'))
str=Inf;
elseif(strcmp(str,'-_Inf_'))
str=-Inf;
elseif(strcmp(str,'_NaN_'))
str=NaN;
end
end
return;
case '\'
if pos+1 > len
error_pos('End of file reached right after escape character');
end
pos = pos + 1;
switch inStr(pos)
case {'"' '\' '/'}
str(nstr+1) = inStr(pos);
pos = pos + 1;
case {'b' 'f' 'n' 'r' 't'}
str(nstr+1) = sprintf(['\' inStr(pos)]);
pos = pos + 1;
case 'u'
if pos+4 > len
error_pos('End of file reached in escaped unicode character');
end
str(nstr+(1:6)) = inStr(pos-1:pos+4);
pos = pos + 5;
end
otherwise % should never happen
str(nstr+1) = inStr(pos), keyboard
pos = pos + 1;
end
end
error_pos('End of file while expecting end of inStr');
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct
currstr=inStr(pos:end);
numstr=0;
if(isoct~=0)
numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
[num, one] = sscanf(currstr, '%f', 1);
delta=numstr+1;
else
[num, one, err, delta] = sscanf(currstr, '%f', 1);
if ~isempty(err)
error_pos('Error reading number at position %d');
end
end
pos = pos + delta-1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
pbar=jsonopt('progressbar_',-1,varargin{:});
if(pbar>0)
waitbar(pos/len,pbar,'loading ...');
end
switch(inStr(pos))
case '"'
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'-','0','1','2','3','4','5','6','7','8','9'}
val = parse_number(varargin{:});
return;
case 't'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
val = true;
pos = pos + 4;
return;
end
case 'f'
if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
val = false;
pos = pos + 5;
return;
end
case 'n'
if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
val = [];
pos = pos + 4;
return;
end
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

528
machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m Normal file
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@ -0,0 +1,528 @@
function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% fname: input file name, if fname contains "{}" or "[]", fname
% will be interpreted as a UBJSON string
% opt: a struct to store parsing options, opt can be replaced by
% a list of ('param',value) pairs - the param string is equivallent
% to a field in opt. opt can have the following
% fields (first in [.|.] is the default)
%
% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
% for each element of the JSON data, and group
% arrays based on the cell2mat rules.
% opt.IntEndian [B|L]: specify the endianness of the integer fields
% in the UBJSON input data. B - Big-Endian format for
% integers (as required in the UBJSON specification);
% L - input integer fields are in Little-Endian order.
%
% output:
% dat: a cell array, where {...} blocks are converted into cell arrays,
% and [...] are converted to arrays
%
% examples:
% obj=struct('string','value','array',[1 2 3]);
% ubjdata=saveubjson('obj',obj);
% dat=loadubjson(ubjdata)
% dat=loadubjson(['examples' filesep 'example1.ubj'])
% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian
if(regexp(fname,'[\{\}\]\[]','once'))
string=fname;
elseif(exist(fname,'file'))
fid = fopen(fname,'rb');
string = fread(fid,inf,'uint8=>char')';
fclose(fid);
else
error('input file does not exist');
end
pos = 1; len = length(string); inStr = string;
isoct=exist('OCTAVE_VERSION','builtin');
arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
jstr=regexprep(inStr,'\\\\',' ');
escquote=regexp(jstr,'\\"');
arraytoken=sort([arraytoken escquote]);
% String delimiters and escape chars identified to improve speed:
esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
index_esc = 1; len_esc = length(esc);
opt=varargin2struct(varargin{:});
fileendian=upper(jsonopt('IntEndian','B',opt));
[os,maxelem,systemendian]=computer;
jsoncount=1;
while pos <= len
switch(next_char)
case '{'
data{jsoncount} = parse_object(opt);
case '['
data{jsoncount} = parse_array(opt);
otherwise
error_pos('Outer level structure must be an object or an array');
end
jsoncount=jsoncount+1;
end % while
jsoncount=length(data);
if(jsoncount==1 && iscell(data))
data=data{1};
end
if(~isempty(data))
if(isstruct(data)) % data can be a struct array
data=jstruct2array(data);
elseif(iscell(data))
data=jcell2array(data);
end
end
%%
function newdata=parse_collection(id,data,obj)
if(jsoncount>0 && exist('data','var'))
if(~iscell(data))
newdata=cell(1);
newdata{1}=data;
data=newdata;
end
end
%%
function newdata=jcell2array(data)
len=length(data);
newdata=data;
for i=1:len
if(isstruct(data{i}))
newdata{i}=jstruct2array(data{i});
elseif(iscell(data{i}))
newdata{i}=jcell2array(data{i});
end
end
%%-------------------------------------------------------------------------
function newdata=jstruct2array(data)
fn=fieldnames(data);
newdata=data;
len=length(data);
for i=1:length(fn) % depth-first
for j=1:len
if(isstruct(getfield(data(j),fn{i})))
newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
end
end
end
if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
newdata=cell(len,1);
for j=1:len
ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
iscpx=0;
if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
if(data(j).x0x5F_ArrayIsComplex_)
iscpx=1;
end
end
if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
if(data(j).x0x5F_ArrayIsSparse_)
if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
dim=double(data(j).x0x5F_ArraySize_);
if(iscpx && size(ndata,2)==4-any(dim==1))
ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
end
if isempty(ndata)
% All-zeros sparse
ndata=sparse(dim(1),prod(dim(2:end)));
elseif dim(1)==1
% Sparse row vector
ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
elseif dim(2)==1
% Sparse column vector
ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
else
% Generic sparse array.
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
end
else
if(iscpx && size(ndata,2)==4)
ndata(:,3)=complex(ndata(:,3),ndata(:,4));
end
ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
end
end
elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
if(iscpx && size(ndata,2)==2)
ndata=complex(ndata(:,1),ndata(:,2));
end
ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
end
newdata{j}=ndata;
end
if(len==1)
newdata=newdata{1};
end
end
%%-------------------------------------------------------------------------
function object = parse_object(varargin)
parse_char('{');
object = [];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1); % TODO
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
count=double(parse_number());
end
if next_char ~= '}'
num=0;
while 1
str = parseStr(varargin{:});
if isempty(str)
error_pos('Name of value at position %d cannot be empty');
end
%parse_char(':');
val = parse_value(varargin{:});
num=num+1;
eval( sprintf( 'object.%s = val;', valid_field(str) ) );
if next_char == '}' || (count>=0 && num>=count)
break;
end
%parse_char(',');
end
end
if(count==-1)
parse_char('}');
end
%%-------------------------------------------------------------------------
function [cid,len]=elem_info(type)
id=strfind('iUIlLdD',type);
dataclass={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
if(id>0)
cid=dataclass{id};
len=bytelen(id);
else
error_pos('unsupported type at position %d');
end
%%-------------------------------------------------------------------------
function [data adv]=parse_block(type,count,varargin)
global pos inStr isoct fileendian systemendian
[cid,len]=elem_info(type);
datastr=inStr(pos:pos+len*count-1);
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
id=strfind('iUIlLdD',type);
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,cid));
end
data=typecast(newdata,cid);
adv=double(len*count);
%%-------------------------------------------------------------------------
function object = parse_array(varargin) % JSON array is written in row-major order
global pos inStr isoct
parse_char('[');
object = cell(0, 1);
dim=[];
type='';
count=-1;
if(next_char == '$')
type=inStr(pos+1);
pos=pos+2;
end
if(next_char == '#')
pos=pos+1;
if(next_char=='[')
dim=parse_array(varargin{:});
count=prod(double(dim));
else
count=double(parse_number());
end
end
if(~isempty(type))
if(count>=0)
[object adv]=parse_block(type,count,varargin{:});
if(~isempty(dim))
object=reshape(object,dim);
end
pos=pos+adv;
return;
else
endpos=matching_bracket(inStr,pos);
[cid,len]=elem_info(type);
count=(endpos-pos)/len;
[object adv]=parse_block(type,count,varargin{:});
pos=pos+adv;
parse_char(']');
return;
end
end
if next_char ~= ']'
while 1
val = parse_value(varargin{:});
object{end+1} = val;
if next_char == ']'
break;
end
%parse_char(',');
end
end
if(jsonopt('SimplifyCell',0,varargin{:})==1)
try
oldobj=object;
object=cell2mat(object')';
if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
object=oldobj;
elseif(size(object,1)>1 && ndims(object)==2)
object=object';
end
catch
end
end
if(count==-1)
parse_char(']');
end
%%-------------------------------------------------------------------------
function parse_char(c)
global pos inStr len
skip_whitespace;
if pos > len || inStr(pos) ~= c
error_pos(sprintf('Expected %c at position %%d', c));
else
pos = pos + 1;
skip_whitespace;
end
%%-------------------------------------------------------------------------
function c = next_char
global pos inStr len
skip_whitespace;
if pos > len
c = [];
else
c = inStr(pos);
end
%%-------------------------------------------------------------------------
function skip_whitespace
global pos inStr len
while pos <= len && isspace(inStr(pos))
pos = pos + 1;
end
%%-------------------------------------------------------------------------
function str = parseStr(varargin)
global pos inStr esc index_esc len_esc
% len, ns = length(inStr), keyboard
type=inStr(pos);
if type ~= 'S' && type ~= 'C' && type ~= 'H'
error_pos('String starting with S expected at position %d');
else
pos = pos + 1;
end
if(type == 'C')
str=inStr(pos);
pos=pos+1;
return;
end
bytelen=double(parse_number());
if(length(inStr)>=pos+bytelen-1)
str=inStr(pos:pos+bytelen-1);
pos=pos+bytelen;
else
error_pos('End of file while expecting end of inStr');
end
%%-------------------------------------------------------------------------
function num = parse_number(varargin)
global pos inStr len isoct fileendian systemendian
id=strfind('iUIlLdD',inStr(pos));
if(isempty(id))
error_pos('expecting a number at position %d');
end
type={'int8','uint8','int16','int32','int64','single','double'};
bytelen=[1,1,2,4,8,4,8];
datastr=inStr(pos+1:pos+bytelen(id));
if(isoct)
newdata=int8(datastr);
else
newdata=uint8(datastr);
end
if(id<=5 && fileendian~=systemendian)
newdata=swapbytes(typecast(newdata,type{id}));
end
num=typecast(newdata,type{id});
pos = pos + bytelen(id)+1;
%%-------------------------------------------------------------------------
function val = parse_value(varargin)
global pos inStr len
true = 1; false = 0;
switch(inStr(pos))
case {'S','C','H'}
val = parseStr(varargin{:});
return;
case '['
val = parse_array(varargin{:});
return;
case '{'
val = parse_object(varargin{:});
if isstruct(val)
if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
val=jstruct2array(val);
end
elseif isempty(val)
val = struct;
end
return;
case {'i','U','I','l','L','d','D'}
val = parse_number(varargin{:});
return;
case 'T'
val = true;
pos = pos + 1;
return;
case 'F'
val = false;
pos = pos + 1;
return;
case {'Z','N'}
val = [];
pos = pos + 1;
return;
end
error_pos('Value expected at position %d');
%%-------------------------------------------------------------------------
function error_pos(msg)
global pos inStr len
poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
if poShow(3) == poShow(2)
poShow(3:4) = poShow(2)+[0 -1]; % display nothing after
end
msg = [sprintf(msg, pos) ': ' ...
inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
error( ['JSONparser:invalidFormat: ' msg] );
%%-------------------------------------------------------------------------
function str = valid_field(str)
global isoct
% From MATLAB doc: field names must begin with a letter, which may be
% followed by any combination of letters, digits, and underscores.
% Invalid characters will be converted to underscores, and the prefix
% "x0x[Hex code]_" will be added if the first character is not a letter.
pos=regexp(str,'^[^A-Za-z]','once');
if(~isempty(pos))
if(~isoct)
str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
else
str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
end
end
if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end
if(~isoct)
str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
else
pos=regexp(str,'[^0-9A-Za-z_]');
if(isempty(pos)) return; end
str0=str;
pos0=[0 pos(:)' length(str)];
str='';
for i=1:length(pos)
str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
end
if(pos(end)~=length(str))
str=[str str0(pos0(end-1)+1:pos0(end))];
end
end
%str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
%%-------------------------------------------------------------------------
function endpos = matching_quote(str,pos)
len=length(str);
while(pos<len)
if(str(pos)=='"')
if(~(pos>1 && str(pos-1)=='\'))
endpos=pos;
return;
end
end
pos=pos+1;
end
error('unmatched quotation mark');
%%-------------------------------------------------------------------------
function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
global arraytoken
level=1;
maxlevel=level;
endpos=0;
bpos=arraytoken(arraytoken>=pos);
tokens=str(bpos);
len=length(tokens);
pos=1;
e1l=[];
e1r=[];
while(pos<=len)
c=tokens(pos);
if(c==']')
level=level-1;
if(isempty(e1r)) e1r=bpos(pos); end
if(level==0)
endpos=bpos(pos);
return
end
end
if(c=='[')
if(isempty(e1l)) e1l=bpos(pos); end
level=level+1;
maxlevel=max(maxlevel,level);
end
if(c=='"')
pos=matching_quote(tokens,pos+1);
end
pos=pos+1;
end
if(endpos==0)
error('unmatched "]"');
end

33
machine-learning-ex4/ex4/lib/jsonlab/mergestruct.m Normal file
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function s=mergestruct(s1,s2)
%
% s=mergestruct(s1,s2)
%
% merge two struct objects into one
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% date: 2012/12/22
%
% input:
% s1,s2: a struct object, s1 and s2 can not be arrays
%
% output:
% s: the merged struct object. fields in s1 and s2 will be combined in s.
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(~isstruct(s1) || ~isstruct(s2))
error('input parameters contain non-struct');
end
if(length(s1)>1 || length(s2)>1)
error('can not merge struct arrays');
end
fn=fieldnames(s2);
s=s1;
for i=1:length(fn)
s=setfield(s,fn{i},getfield(s2,fn{i}));
end

475
machine-learning-ex4/ex4/lib/jsonlab/savejson.m Normal file
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@ -0,0 +1,475 @@
function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09
%
% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
%
% input:
% rootname: the name of the root-object, when set to '', the root name
% is ignored, however, when opt.ForceRootName is set to 1 (see below),
% the MATLAB variable name will be used as the root name.
% obj: a MATLAB object (array, cell, cell array, struct, struct array).
% filename: a string for the file name to save the output JSON data.
% opt: a struct for additional options, ignore to use default values.
% opt can have the following fields (first in [.|.] is the default)
%
% opt.FileName [''|string]: a file name to save the output JSON data
% opt.FloatFormat ['%.10g'|string]: format to show each numeric element
% of a 1D/2D array;
% opt.ArrayIndent [1|0]: if 1, output explicit data array with
% precedent indentation; if 0, no indentation
% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
% array in JSON array format; if sets to 1, an
% array will be shown as a struct with fields
% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
% sparse arrays, the non-zero elements will be
% saved to _ArrayData_ field in triplet-format i.e.
% (ix,iy,val) and "_ArrayIsSparse_" will be added
% with a value of 1; for a complex array, the
% _ArrayData_ array will include two columns
% (4 for sparse) to record the real and imaginary
% parts, and also "_ArrayIsComplex_":1 is added.
% opt.ParseLogical [0|1]: if this is set to 1, logical array elem
% will use true/false rather than 1/0.
% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
% numerical element will be shown without a square
% bracket, unless it is the root object; if 0, square
% brackets are forced for any numerical arrays.
% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
% will use the name of the passed obj variable as the
% root object name; if obj is an expression and
% does not have a name, 'root' will be used; if this
% is set to 0 and rootname is empty, the root level
% will be merged down to the lower level.
% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
% to represent +/-Inf. The matched pattern is '([-+]*)Inf'
% and $1 represents the sign. For those who want to use
% 1e999 to represent Inf, they can set opt.Inf to '$11e999'
% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
% to represent NaN
% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
% for example, if opt.JSONP='foo', the JSON data is
% wrapped inside a function call as 'foo(...);'
% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson
% back to the string form
% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
%
% opt can be replaced by a list of ('param',value) pairs. The param
% string is equivallent to a field in opt and is case sensitive.
% output:
% json: a string in the JSON format (see http://json.org)
%
% examples:
% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
% 'MeshCreator','FangQ','MeshTitle','T6 Cube',...
% 'SpecialData',[nan, inf, -inf]);
% savejson('jmesh',jsonmesh)
% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
%
% license:
% BSD, see LICENSE_BSD.txt files for details
%
% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
%
if(nargin==1)
varname=inputname(1);
obj=rootname;
if(isempty(varname))
varname='root';
end
rootname=varname;
else
varname=inputname(2);
end
if(length(varargin)==1 && ischar(varargin{1}))
opt=struct('FileName',varargin{1});
else
opt=varargin2struct(varargin{:});
end
opt.IsOctave=exist('OCTAVE_VERSION','builtin');
rootisarray=0;
rootlevel=1;
forceroot=jsonopt('ForceRootName',0,opt);
if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
rootisarray=1;
rootlevel=0;
else
if(isempty(rootname))
rootname=varname;
end
end
if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
rootname='root';
end
whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
if(jsonopt('Compact',0,opt)==1)
whitespaces=struct('tab','','newline','','sep',',');
end
if(~isfield(opt,'whitespaces_'))
opt.whitespaces_=whitespaces;
end
nl=whitespaces.newline;
json=obj2json(rootname,obj,rootlevel,opt);
if(rootisarray)
json=sprintf('%s%s',json,nl);
else
json=sprintf('{%s%s%s}\n',nl,json,nl);
end
jsonp=jsonopt('JSONP','',opt);
if(~isempty(jsonp))
json=sprintf('%s(%s);%s',jsonp,json,nl);
end
% save to a file if FileName is set, suggested by Patrick Rapin
if(~isempty(jsonopt('FileName','',opt)))
if(jsonopt('SaveBinary',0,opt)==1)
fid = fopen(opt.FileName, 'wb');
fwrite(fid,json);
else
fid = fopen(opt.FileName, 'wt');
fwrite(fid,json,'char');
end
fclose(fid);
end
%%-------------------------------------------------------------------------
function txt=obj2json(name,item,level,varargin)
if(iscell(item))
txt=cell2json(name,item,level,varargin{:});
elseif(isstruct(item))
txt=struct2json(name,item,level,varargin{:});
elseif(ischar(item))
txt=str2json(name,item,level,varargin{:});
else
txt=mat2json(name,item,level,varargin{:});
end
%%-------------------------------------------------------------------------
function txt=cell2json(name,item,level,varargin)
txt='';
if(~iscell(item))
error('input is not a cell');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
nl=ws.newline;
if(len>1)
if(~isempty(name))
txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name='';
else
txt=sprintf('%s[%s',padding0,nl);
end
elseif(len==0)
if(~isempty(name))
txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name='';
else
txt=sprintf('%s[]',padding0);
end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
%if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=struct2json(name,item,level,varargin)
txt='';
if(~isstruct(item))
error('input is not a struct');
end
dim=size(item);
if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
item=reshape(item,dim(1),numel(item)/dim(1));
dim=size(item);
end
len=numel(item);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding0=repmat(ws.tab,1,level);
padding2=repmat(ws.tab,1,level+1);
padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
nl=ws.newline;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding0,nl); end
end
for j=1:dim(2)
if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
for i=1:dim(1)
names = fieldnames(item(i,j));
if(~isempty(name) && len==1)
txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl);
else
txt=sprintf('%s%s{%s',txt,padding1,nl);
end
if(~isempty(names))
for e=1:length(names)
txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
if(e<length(names)) txt=sprintf('%s%s',txt,','); end
txt=sprintf('%s%s',txt,nl);
end
end
txt=sprintf('%s%s}',txt,padding1);
if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
end
if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
%%-------------------------------------------------------------------------
function txt=str2json(name,item,level,varargin)
txt='';
if(~ischar(item))
error('input is not a string');
end
item=reshape(item, max(size(item),[1 0]));
len=size(item,1);
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(~isempty(name))
if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
else
if(len>1) txt=sprintf('%s[%s',padding1,nl); end
end
isoct=jsonopt('IsOctave',0,varargin{:});
for e=1:len
if(isoct)
val=regexprep(item(e,:),'\\','\\');
val=regexprep(val,'"','\"');
val=regexprep(val,'^"','\"');
else
val=regexprep(item(e,:),'\\','\\\\');
val=regexprep(val,'"','\\"');
val=regexprep(val,'^"','\\"');
end
val=escapejsonstring(val);
if(len==1)
obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
if(isempty(name)) obj=['"',val,'"']; end
txt=sprintf('%s%s%s%s',txt,padding1,obj);
else
txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
end
if(e==len) sep=''; end
txt=sprintf('%s%s',txt,sep);
end
if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
%%-------------------------------------------------------------------------
function txt=mat2json(name,item,level,varargin)
if(~isnumeric(item) && ~islogical(item))
error('input is not an array');
end
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
padding1=repmat(ws.tab,1,level);
padding0=repmat(ws.tab,1,level+1);
nl=ws.newline;
sep=ws.sep;
if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
if(isempty(name))
txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
else
txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
end
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
else
numtxt=matdata2json(item,level+1,varargin{:});
end
if(isempty(name))
txt=sprintf('%s%s',padding1,numtxt);
else
if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
else
txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
end
end
return;
end
dataformat='%s%s%s%s%s';
if(issparse(item))
[ix,iy]=find(item);
data=full(item(find(item)));
if(~isreal(item))
data=[real(data(:)),imag(data(:))];
if(size(item,1)==1)
% Kludge to have data's 'transposedness' match item's.
% (Necessary for complex row vector handling below.)
data=data';
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
end
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
if(size(item,1)==1)
% Row vector, store only column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([iy(:),data'],level+2,varargin{:}), nl);
elseif(size(item,2)==1)
% Column vector, store only row indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,data],level+2,varargin{:}), nl);
else
% General case, store row and column indices.
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([ix,iy,data],level+2,varargin{:}), nl);
end
else
if(isreal(item))
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json(item(:)',level+2,varargin{:}), nl);
else
txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
end
end
txt=sprintf('%s%s%s',txt,padding1,'}');
%%-------------------------------------------------------------------------
function txt=matdata2json(mat,level,varargin)
ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
ws=jsonopt('whitespaces_',ws,varargin{:});
tab=ws.tab;
nl=ws.newline;
if(size(mat,1)==1)
pre='';
post='';
level=level-1;
else
pre=sprintf('[%s',nl);
post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
end
if(isempty(mat))
txt='null';
return;
end
floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
%if(numel(mat)>1)
formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
%else
% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
%end
if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
formatstr=[repmat(tab,1,level) formatstr];
end
txt=sprintf(formatstr,mat');
txt(end-length(nl):end)=[];
if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
txt=regexprep(txt,'1','true');
txt=regexprep(txt,'0','false');
end
%txt=regexprep(mat2str(mat),'\s+',',');
%txt=regexprep(txt,';',sprintf('],\n['));
% if(nargin>=2 && size(mat,1)>1)
% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
% end
txt=[pre txt post];
if(any(isinf(mat(:))))
txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
end
if(any(isnan(mat(:))))
txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
end
%%-------------------------------------------------------------------------
function newname=checkname(name,varargin)
isunpack=jsonopt('UnpackHex',1,varargin{:});
newname=name;
if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
return
end
if(isunpack)
isoct=jsonopt('IsOctave',0,varargin{:});
if(~isoct)
newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
else
pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
if(isempty(pos)) return; end
str0=name;
pos0=[0 pend(:)' length(name)];
newname='';
for i=1:length(pos)
newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
end
if(pos(end)~=length(name))
newname=[newname str0(pos0(end-1)+1:pos0(end))];
end
end
end
%%-------------------------------------------------------------------------
function newstr=escapejsonstring(str)
newstr=str;
isoct=exist('OCTAVE_VERSION','builtin');
if(isoct)
vv=sscanf(OCTAVE_VERSION,'%f');
if(vv(1)>=3.8) isoct=0; end
end
if(isoct)
escapechars={'\a','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},escapechars{i});
end
else
escapechars={'\a','\b','\f','\n','\r','\t','\v'};
for i=1:length(escapechars);
newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
end
end

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