[[TableOfContents]]

=== Multiple Features ===
=== Gradient Descent for Multiple Variables ===
=== Feature Scaling ===
=== Learning Rate ===
=== Polynomial Regression ===
=== Normal Equation ===
=== Octave로 Linear Regression With Multiple Varables 구현하기 ===
==== Feature Normalize ====
{{{
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));
n_of_feature = size(X_norm, 2);
for i = 1:n_of_feature
	mu(i) = mean(X_norm(:, i));
	sigma(i) = std(X_norm(:, i));
	X_norm(:, i) = (X_norm(:, i ) - mu(i)) / sigma(i);
end
}}}

 * mean : 평균 구하는 함수.
 * std : 표준 편차 구하는 함수.
 * 표준 편차를 이용해서 데이터를 정규화 시킴.