Coursera—machine learning(Andrew Ng)第三周编程作业

plotData.m 

function plotData(X, y)%PLOTDATA Plots the data points X and y into a new figure %   PLOTDATA(x,y) plots the data points with + for the positive examples%   and o for the negative examples. X is assumed to be a Mx2 matrix.% Create New Figurefigure; hold on;% ====================== YOUR CODE HERE ======================% Instructions: Plot the positive and negative examples on a%               2D plot, using the option 'k+' for the positive%               examples and 'ko' for the negative examples.%% =========================================================================pos = find(y == 1); neg = find(y == 0);plot(X(pos, 1), X(pos, 2), 'k+', 'LineWidth', 2, 'MarkerSize', 7);plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);hold off;end

sigmoid.m 

function g = sigmoid(z)%SIGMOID Compute sigmoid function%   g = 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

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%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

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%temp = sigmoid(X * theta);p = temp > 0.5;% =========================================================================end

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[J, grad] = costFunction(theta, X, y);J = J + lambda / (2 * m) * (sum(theta .^ 2) - theta(1) ^ 2);grad = grad + lambda / m * theta;grad(1) = grad(1) - lambda / m * theta(1);% =============================================================end

希望进步能够快一点点。

By 信念