Coursera机器学习 week3 逻辑回归 编程作业代码

这是Coursera上 Week3 的 “逻辑回归” 的编程作业代码。经过测验，全部通过。

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下面是 sigmoid.m 的代码：

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)); % Ff operator './' is replaced with '/', this expression will calculate the                        % inverse matrix of (1 + exp(-z)).% =============================================================end

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下面是 costFunction.m 的代码：

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%J = 1 / m * sum(((-y) .* log(sigmoid(X * theta)) - (1 - y) .* log(1 - sigmoid(X * theta))));grad = 1 / m * X' * (sigmoid(X * theta) - y);% =============================================================end

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下面是 predict.m 的代码：

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%temp = sigmoid(X * theta);p = temp > 0.5; % If a element is positive, make it 1, or keep it 0% =========================================================================end

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下面是 costFunctionReg.m 的代码：

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[J, grad] = costFunction(theta,X,y);J = J  + lambda / (2*m) * (sum(theta.^2) - theta(1)^2); % no need to regularize theta 1grad = grad + lambda / m * theta;grad(1) = grad(1) - lambda / m * theta(1); % no need to regularize theta 1% =============================================================end