linr, logr

1 files changed, 36 insertions, 0 deletions

diff --git a/R_LogR/mlclass-ex2/costFunctionReg.m b/R_LogR/mlclass-ex2/costFunctionReg.m
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+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
+
+
+pred = sigmoid(X*theta);
+
+J = 1./m .* sum(-y .* log(pred) - (1-y) .* log(1-pred)) + lambda ./ 2 ./ m .* sum(theta.^2);
+
+
+reg = lambda .* theta ./ m;
+reg(1) = 0;
+grad = 1 / m .* (X' * (pred - y)) + reg;
+
+
+
+
+
+% =============================================================
+
+end