You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
40 lines
1.2 KiB
Matlab
40 lines
1.2 KiB
Matlab
10 years ago
|
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));
|
||
|
|
|
||
|
|
% ====================== 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
|