Machine Learning -- ex1 作业分析

先看一下作业的要求：

前四个函数是必须要写的

warmUpExercise.m 视频中给来练习的函数。不多解释

plotData.m 要求如下：

题目的大体意思是将ex1data1.txt中x,y的值导入 并且画出图来

照着PDF 的代码稍加修改或抄上

function plotData(x, y)%PLOTDATA Plots the data points x and y into a new figure %画出(x,y)的图像%   PLOTDATA(x,y) plots the data points and gives the figure axes labels of%   population and profit.%给出轴的名称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);data = load('ex1data1.txt');x = data(:,1);y = data(:,2);plot(x,y,'rx','MarkerSize',10);xlabel('Population of City in 10,000s');ylabel('Profit in $10,1000s');

得到如下的图像：

第二个函数：

computeCost.m // 是让我们计算代价的

看了一下：

function J = computeCost(X, y, theta)

那么X是什么？？？？？

在这呢

x原本是行向量 又在前面加了一个行向量组成了 X（theta0，theta1）

PDF中给出了代价方程 所以我们就直接照着方程写代码

J = sum(((X * theta) - y).^2)/(2*m)

解释一下上面这行代码：

  X * theta：X是一个 m * 2 的矩阵 theta 是一个 2 * 1 的向量 所以得到一个 m * 1 的向量

完整代码如下：

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

梯度下降函数：

gradientDescent.m

也就是不断的更新theta 并计算出J

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);%生成了一个（迭代数 * 1）的零矩阵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.    %    temp1 = theta(1) - alpha * (1/m) * sum((X * theta) - y);    temp2 = theta(2) - alpha * (1/m) * sum(((X * theta) - y).*X(:,2));    theta(1) = temp1;    theta(2) = temp2;    %X(:,2) X的第二列 也就是x    % ============================================================    % Save the cost J in every iteration        J_history(iter) = computeCost(X, y, theta);endend

ok 之后给出其他的函数