Coursera——Machine Learning （linear Regression) week2 编程作业答案

最新推荐文章于 2021-07-11 16:48:44 发布

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最新推荐文章于 2021-07-11 16:48:44 发布 · 471 阅读

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这篇博客涵盖了Coursera上Andrew Ng的机器学习课程第二周的编程作业。内容包括：实现5x5单位矩阵、绘制散点图、计算单变量和多变量的代价函数、使用梯度下降法求解θ、特征归一化以及利用正规方程直接求解θ的最优值。作业涉及Octave命令行操作，对理解线性回归概念和算法实践具有指导意义。

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1、warmUpExercise.m：输出一个5*5的单位矩阵（identity matrix）

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

A = [];
% ============= 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. 


A=eye(5);




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


end

在Octave中输入命令为：
这里写图片描述
2、plotData.m:绘制一个散点图

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; %打开一个新的图像窗口

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