斯坦福機器學習編程作業1

本文僅爲本人記錄學習之用。

1.computeCost.m

function J = computeCost(X, y, theta)%COMPUTECOST Compute cost for linear regression%   J = COMPUTECOST(X, y, theta) computes the cost of using theta as the%   parameter for linear regression to fit the data points in X and y% Initialize some useful valuesm = length(y); % number of training examples% You need to return the following variables correctly J = 0;% ====================== YOUR CODE HERE ======================% Instructions: Compute the cost of a particular choice of theta%               You should set J to the cost.J=sum((X*theta-y).^2)/(2*m);% =========================================================================end

2.gradientDescent.m

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)%GRADIENTDESCENT Performs gradient descent to learn theta%   theta = GRADIENTDESCENT(X, y, theta, alpha, num_iters) updates theta by %   taking num_iters gradient steps with learning rate alpha% Initialize some useful valuesm = length(y); % number of training examplesJ_history = zeros(num_iters, 1);for iter = 1:num_iters    % ====================== YOUR CODE HERE ======================    % Instructions: Perform a single gradient step on the parameter vector    %               theta.     %    % Hint: While debugging, it can be useful to print out the values    %       of the cost function (computeCost) and gradient here.    %    theta=theta-alpha/m*X'*(X*theta-y);    % ============================================================    % Save the cost J in every iteration        J_history(iter) = computeCost(X, y, theta);endend

3.Feature Normalize.m

function [X_norm, mu, sigma] = featureNormalize(X)%FEATURENORMALIZE Normalizes the features in X %   FEATURENORMALIZE(X) returns a normalized version of X where%   the mean value of each feature is 0 and the standard deviation%   is 1. This is often a good preprocessing step to do when%   working with learning algorithms.% You need to set these values correctlyX_norm = X;mu = zeros(1, size(X, 2));sigma = zeros(1, size(X, 2));% ====================== YOUR CODE HERE ======================% Instructions: First, for each feature dimension, compute the mean%               of the feature and subtract it from the dataset,%               storing the mean value in mu. Next, compute the %               standard deviation of each feature and divide%               each feature by it's standard deviation, storing%               the standard deviation in sigma. %%               Note that X is a matrix where each column is a %               feature and each row is an example. You need %               to perform the normalization separately for %               each feature. %% Hint: You might find the 'mean' and 'std' functions useful.%       M(1)=max(X(:,1))-min(X(:,1));M(2)=max(X(:,2))-min(X(:,2));meanX=mean(X);X=(X-meanX);X(:,1)=X(:,1)./M(1);X(:,2)=X(:,2)./M(2);X_norm=X;mu=meanX';sigma=std(X);% ============================================================end

ComputeCostMulti.m

function J = computeCostMulti(X, y, theta)%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables%   J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the%   parameter for linear regression to fit the data points in X and y% Initialize some useful valuesm = length(y); % number of training examples% You need to return the following variables correctly J = 0;% ====================== YOUR CODE HERE ======================% Instructions: Compute the cost of a particular choice of theta%               You should set J to the cost.J=sum(((X*theta)-y).^2)/(2*m);% =========================================================================end

grandientDescentMuluti.m

function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)%GRADIENTDESCENTMULTI Performs gradient descent to learn theta%   theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by%   taking num_iters gradient steps with learning rate alpha% Initialize some useful valuesm = length(y); % number of training examplesJ_history = zeros(num_iters, 1);for iter = 1:num_iters    % ====================== YOUR CODE HERE ======================    % Instructions: Perform a single gradient step on the parameter vector    %               theta.     %    % Hint: While debugging, it can be useful to print out the values    %       of the cost function (computeCostMulti) and gradient here.    %    theta=theta-alpha/m*(X'*(X*theta-y));    % ============================================================    % Save the cost J in every iteration        J_history(iter) = computeCostMulti(X, y, theta);endend