From 10173be55d29f5b1d3cfe800bb4a89c73320b45a Mon Sep 17 00:00:00 2001 From: Akash Date: Wed, 4 Sep 2019 23:55:39 +0530 Subject: [PATCH] featureNormalize bug removed --- .../ex1/featureNormalize.m | 23 ++++++++----------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/Machine Learning/Week 2/machine-learning-ex1/ex1/featureNormalize.m b/Machine Learning/Week 2/machine-learning-ex1/ex1/featureNormalize.m index 00fc97a..dec10a3 100644 --- a/Machine Learning/Week 2/machine-learning-ex1/ex1/featureNormalize.m +++ b/Machine Learning/Week 2/machine-learning-ex1/ex1/featureNormalize.m @@ -1,4 +1,4 @@ -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 @@ -6,10 +6,10 @@ % 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, @@ -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 \ No newline at end of file +end