# mlclass / exercise-8 / octave / estimateGaussian.m

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36``` ```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. % % ============================================================= end ```