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vectorization of featureNormalization #6

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vectorization of featureNormalization #6

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23 changes: 9 additions & 14 deletions Machine Learning/Week 2/machine-learning-ex1/ex1/featureNormalize.m
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Original file line number Diff line number Diff line change
@@ -1,15 +1,15 @@
function [X_norm, mu, sigma] = featureNormalize(X)
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));
x_norm=x;

%x_norm(:,2) = (x(:,2)-mu(:,2))/sigma(:,2);
%x_norm(:,1) = (x(:,1)-mu(:,1))/sigma(:,1);
% ====================== YOUR CODE HERE ======================
% Instructions: First, for each feature dimension, compute the mean
% of the feature and subtract it from the dataset,
Expand All @@ -26,21 +26,16 @@
% Hint: You might find the 'mean' and 'std' functions useful.
%

n = size(X, 2);

for i = 1:n
mu = mean(x);
sigma = std(x);
x_norm =(x.- mean(x,1))./std(x,0,1);


avg = mean(X(:, i));
deviation = std(X(:, i));

X_norm(:, i) = X_norm(:, i) - avg;
X_norm(:, i) = X_norm(:, i) / deviation;

mu(i) = avg;
sigma(i) = deviation;

end

% ============================================================

end
end