Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex8-008/mlclass-ex8/estimateGaussian.m

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+function [mu sigma2] = estimateGaussian(X)
+%ESTIMATEGAUSSIAN This function estimates the parameters of a 
+%Gaussian distribution using the data in X
+%   [mu sigma2] = estimateGaussian(X), 
+%   The input X is the dataset with each n-dimensional data point in one row
+%   The output is an n-dimensional vector mu, the mean of the data set
+%   and the variances sigma^2, an n x 1 vector
+% 
+
+% Useful variables
+[m, n] = size(X);
+
+% You should return these values correctly
+mu = zeros(n, 1);
+sigma2 = zeros(n, 1);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the mean of the data and the variances
+%               In particular, mu(i) should contain the mean of
+%               the data for the i-th feature and sigma2(i)
+%               should contain variance of the i-th feature.
+%
+
+for i=1:m
+	mu += X(i,:)';
+	sigma2 += X(i,:)' .^2;
+end
+
+mu /=m;
+sigma2 = sigma2/m - mu .^2;
+
+
+% =============================================================
+
+
+end