estimateGaussian.m

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,
  %for j = 1:n,
    %mu(i) = (1/m) * sum(X(:,j));
    %sigma2(i) = (1/m) * sum((X(:,j) - mu(i)).^2);
  %endfor  
%endfor
mu = (1/m) * sum(X(:,[1:n]));
disp(mu);
sigma2 = (1/m) * sum((X - mu).^2);
disp(sigma2);
% =============================================================


end