资源简介
coursera机器学习课后习题答案全套,真实可用,Andrew NG,斯坦福大学课程
代码片段和文件信息
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 i = 1:m
J = J+(X(i:)*theta-y(i))^2;
end
J = J/(2*m);
% =========================================================================
end
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
目录 0 2015-08-11 18:52 coursera‘s machine learning answer\
目录 0 2015-08-11 18:51 coursera‘s machine learning answer\machine-learning-ex1\
目录 0 2015-08-11 18:51 coursera‘s machine learning answer\machine-learning-ex1\ex1\
文件 690 2015-04-16 08:26 coursera‘s machine learning answer\machine-learning-ex1\ex1\computeCost.m
文件 728 2015-04-18 14:56 coursera‘s machine learning answer\machine-learning-ex1\ex1\computeCostMulti.m
文件 3438 2015-04-18 09:55 coursera‘s machine learning answer\machine-learning-ex1\ex1\ex1.m
文件 4476 2015-04-18 15:21 coursera‘s machine learning answer\machine-learning-ex1\ex1\ex1_multi.m
文件 1358 2015-04-16 07:34 coursera‘s machine learning answer\machine-learning-ex1\ex1\ex1data1.txt
文件 657 2013-11-07 14:43 coursera‘s machine learning answer\machine-learning-ex1\ex1\ex1data2.txt
文件 1568 2015-04-18 10:38 coursera‘s machine learning answer\machine-learning-ex1\ex1\featureNormalize.m
文件 1126 2015-04-18 15:01 coursera‘s machine learning answer\machine-learning-ex1\ex1\gradientDescent.m
文件 1265 2015-04-18 15:05 coursera‘s machine learning answer\machine-learning-ex1\ex1\gradientDescentMulti.m
目录 0 2015-08-11 18:51 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\
目录 0 2015-08-11 18:51 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\
文件 1624 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\AUTHORS.txt
文件 3862 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\ChangeLog.txt
文件 1551 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\LICENSE_BSD.txt
文件 19369 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\README.txt
文件 881 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\jsonopt.m
文件 18732 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\loadjson.m
文件 15574 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\loadubjson.m
文件 771 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\mergestruct.m
文件 17462 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\savejson.m
文件 16123 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\saveubjson.m
文件 1094 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\jsonlab\varargin2struct.m
文件 1195 2015-02-26 23:12 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\makeValidFieldName.m
文件 3734 2015-04-02 16:05 coursera‘s machine learning answer\machine-learning-ex1\ex1\lib\submitWithConfiguration.m
文件 669 2015-04-18 15:31 coursera‘s machine learning answer\machine-learning-ex1\ex1\normalEqn.m
文件 977 2015-04-16 07:49 coursera‘s machine learning answer\machine-learning-ex1\ex1\plotData.m
文件 1882 2015-04-16 07:33 coursera‘s machine learning answer\machine-learning-ex1\ex1\submit.m
文件 258 2015-08-01 11:16 coursera‘s machine learning answer\machine-learning-ex1\ex1\token.mat
............此处省略249个文件信息
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