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