吳恩達機器學習 - 邏輯迴歸的正則化

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						<p>題目連結：<a href="http://s3.amazonaws.com/spark-public/ml/exercises/on-demand/machine-learning-ex2.zip" rel="nofollow" target="_blank">點選開啟連結</a></p>
 

先貼筆記

程式碼：

costFunction.m(求代價和各方向梯度)(注意：Θ0Θ0單獨計算)：

function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
%   theta as
 
 the parameter for regularized logistic regression and the
%   gradient of the cost w.r.t. to the parameters. 

% Initialize some useful values
m = length(y); % number of training examples

% You need to return the following variables correctly 
J = 0;
grad = zeros(size(theta));

% ====================== YOUR CODE HERE ======================
% Instructions: Compute the
 
 cost of a particular choice of theta.
%               You should set J to the cost.
%               Compute the partial derivatives and set grad to the partial
%               derivatives of the cost w.r.t. each parameter in theta

[~, n] = size(X);
%以下計算一定要記得不正則化theta_0
J = (-y'*log(sigmoid(X*theta))-(1-y')*log(1-sigmoid(X*theta)))/m + ...
lambda/(2.0*m)*(theta(2:n)'*theta(2:n));

grad(1) = X(:,1)'*(sigmoid(X*theta)-y)./m;
grad(2:n) = X(:,2:n)'*(sigmoid(X*theta)-y)./m + lambda/m*theta(2:n);


% =============================================================

end

  

   
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然後展示下不同λ畫出的不同圖案

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