首页 > 分享 > Meachine Learning 第二周作业

Meachine Learning 第二周作业

花匠小妙招
2024-11-21 18:25

花了6-8个小时才完成第二周的作业，不熟悉matlab，走了很多弯路，纪念一下。

function A = warmUpExercise()
%WARMUPEXERCISE Example function in octave
% A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix

A = eye(5);
% ============= YOUR CODE HERE ==============
% Instructions: Return the 5x5 identity matrix
%               In octave, we return values by defining which variables
%               represent the return values (at the top of the file)
%               and then set them accordingly.
% ===========================================
end

function plotData(x, y)
%PLOTDATA Plots the data points x and y into a new figure
% PLOTDATA(x,y) plots the data points and gives the figure axes labels of
% population and profit.

figure; % open a new figure window

% ====================== YOUR CODE HERE ======================
% Instructions: Plot the training data into a figure using the
%               "figure" and "plot" commands. Set the axes labels using
%               the "xlabel" and "ylabel" commands. Assume the
%               population and revenue data have been passed in
%               as the x and y arguments of this function.
%
% Hint: You can use the 'rx' option with plot to have the markers
%       appear as red crosses. Furthermore, you can make the
%       markers larger by using plot(..., 'rx', 'MarkerSize', 10);

plot(x,y,'rx','MarkerSize',10);
ylabel('Profit in $10000s');
xlabel('Population of City in $10000s');
% ============================================================

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 values
m = 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.

for k=1:m
L =(X(k,:)*theta-y(k))^2;
J = J+L;
end
J = J/(2*m);

% =========================================================================
end

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 values
m = length(y); % number of training examples
J_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.
    %
    J1=0;
    J2=0;
    for i = 1:m;
      J1 = J1 + X(i,:)*theta-y(i);
      J2 = J2 + (X(i,:)*theta-y(i))*X(i,2);
    end

            temp1 = theta(1) - alpha*J1/m;
    temp2 = theta(2) - alpha*J2/m;
    theta(1) = temp1;
    theta(2) = temp2;

% ============================================================

    % Save the cost J in every iteration
    J_history(iter) = computeCost(X, y, theta);
    fprintf('With theta =%f   %f n Cost computed = %fn',theta(1),theta(2) ,J_history(iter));

end