Programming Exercise 2: Logistic Regression Machine Learning

大家好，今天总结Coursera网课上Andrew Ng MachineLearning 第二次作业
(1)sigmoid.m

function g = sigmoid(z)%SIGMOID Compute sigmoid functoon%   J = SIGMOID(z) computes the sigmoid of z.% You need to return the following variables correctly g = zeros(size(z));% ====================== YOUR CODE HERE ======================% Instructions: Compute the sigmoid of each value of z (z can be a matrix,%               vector or scalar).g=1./(1+exp(-z));% =============================================================end

(2)costFunction.m

function [J, grad] = costFunction(theta, X, y)%COSTFUNCTION Compute cost and gradient for logistic regression%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the%   parameter for logistic regression and the gradient of the cost%   w.r.t. to the parameters.% Initialize some useful valuesm = 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%% Note: grad should have the same dimensions as theta%h=sigmoid(X*theta);J=-1/m*sum((log(h)'*y+log(1-h)'*(1-y)));grad=1/m*(X'*(h-y));% =============================================================end

(3)predict.m

function p = predict(theta, X)%PREDICT Predict whether the label is 0 or 1 using learned logistic %regression parameters theta%   p = PREDICT(theta, X) computes the predictions for X using a %   threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)m = size(X, 1); % Number of training examples% You need to return the following variables correctlyp = zeros(m, 1);% ====================== YOUR CODE HERE ======================% Instructions: Complete the following code to make predictions using%               your learned logistic regression parameters. %               You should set p to a vector of 0's and 1's%p(find(sigmoid(X*theta)>=0.5),1)=1;% =========================================================================end

(4) costFunctionReg.m

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 valuesm = 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[n1,n2]=size(theta);h=sigmoid(X*theta);theta2=theta(2:n1,1)J=-1/m*sum((log(h)'*y+log(1-h)'*(1-y)))+lambda/(2*m)*sum(theta2.^2);grad_orig=1/m*(X'*(h-y));grad=1/m*(X'*(h-y))+lambda/m*theta;grad(1)=grad_orig(1);% =============================================================end