先贴笔记
代码:
costFunction.m(求代价和各方向梯度)(注意: 单独计算):
function [J,grad] = costFunctionReg(theta,X,y,lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
% J = COSTFUNCTIONREG(theta,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);
%以下计算一定要记得不正则化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
然后展示下不同λ画出的不同图案
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