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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
- mu = mean(X);
- sigma = std(X);
- X_norm = (X - ones(length(X),1)*mu ) ./ (ones(length(X),1)*sigma);
- % ====================== 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.
- %
- % ============================================================
- end
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