machine-learning第二周 review中提交 Linear Regression 上机作业相关问题
环境：windows7
一、使用matlab
1、一开始使用matlab，但在提交时发现ex1/lib/makeValidFieldName.m 的18行应该为if(isoct)，否则程序都无法跑通。
2、然后又碰到怎么都无法提交，总是提示
Submission failed: unexpected error: Error using ==> urlread
Could not POST to URL.
问题解决详见https://www.coursera.org/learn/machine-learning/discussions/vgCyrQoMEeWv5yIAC00Eog
注意到Tom Mosher 有这么一句话：
As with most open-source projects, the Octave team doesn't really focus on Windows. I don't have any problems with the Octave 4.0.0 Command Line Interface, so I usually stick with that. Sometimes the GUI acts a little weird, so I don't use it much unless I need to copy-and-paste a result into a Forum thread.
这给了我灵感，也许matlab也是不怎么care windows，因此我转而下载octave （什么鬼逻辑，上文明明说octave 不care windows）
二、使用Octave4.0.0
1、提交时出现CA certificate 之类的错误，然后打了两个patch（不要再去修改上述makeValidFieldName.m的代码，用patch覆盖即可）：
https://drive.google.com/file/d/0B6lXyE7fgSlXZjlqQ3FIRExmTDA/view?usp=sharing
https://drive.google.com/file/d/0B6lXyE7fgSlXXy1nMXlpb3RyZ1E/view?usp=sharing
2、重启Octave。 终于Done！
==
== Part Name | Score | Feedback
== --------- | ----- | --------
== Warm-up Exercise | 10 / 10 | Nice work!
== Computing Cost (for One Variable) | 40 / 40 | Nice work!
== Gradient Descent (for One Variable) | 50 / 50 | Nice work!
== Feature Normalization | 0 / 0 | Nice work!
== Computing Cost (for Multiple Variables) | 0 / 0 | Nice work!
== Gradient Descent (for Multiple Variables) | 0 / 0 | Nice work!
== Normal Equations | 0 / 0 | Nice work!
== --------------------------------
== | 100 / 100 |
三、总结：
1、请使用Octave上机，即使你修复了程序bug，在matlab也无法提交。
2、有问题先找讨论组：https://www.coursera.org/learn/machine-learning/discussions，有众多大神贴心解答。
3、作业我前前后后整了一周多，明白只有真正自己做出来的人才知道收获多少，因此不要轻易去看“答案”哦
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.
J = (1/(2*m))* sum((X * theta - y).^2);
% =========================================================================
endfunction 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 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.
J = (1/(2*m))* (X * theta - y)' * (X * theta - y);
% =========================================================================
end
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 correctly
X_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.
%
%disp(X);
mu = mean(X);
%fprintf('%f %f \n', mu(1), mu(2));
sigma = std(X);
%fprintf('%f %f \n', sigma(1), sigma(2));
n = size(X, 2);
for iter = 1:n
X_norm(:,iter) =( X_norm(:,iter) - mu(iter) ) / sigma(iter) ;
end
% ============================================================
endfunction [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESENT(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.
%
% answer1:
%theta(1) = theta(1) - (alpha * (1/m)) * sum((X*theta - y) ) ;
%theta(2) = theta(2) - (alpha * (1/m)) * (X(:,2)' * (X*theta - y)) ;
% answer2:
delta = ((theta' * X' - y')*X)';
theta = theta - alpha / m * delta;
%fprintf('%f %f \n', theta(1), theta(2));
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCost(X, y, theta);
fprintf('%f \n', J_history(iter));
end
end
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 values
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);
fprintf('X =\n');
disp(X);
fprintf('y =\n');
disp(y);
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.
%
%answer1 :
%
%tmp_status = (X * theta - y);
%theta = theta - (alpha * (1/m)) * ( X' * tmp_status ) ;
%
%answer2 :
%
%tmp_status = (X * theta - y);
%theta(1) = theta(1) - (alpha * (1/m)) * sum(tmp_status ) ;
%theta(2) = theta(2) - (alpha * (1/m)) * (X(:,2)' * tmp_status) ;
%theta(3) = theta(3) - (alpha * (1/m)) * (X(:,3)' * tmp_status) ;
%
%answer3 :
delta = ((theta' * X' - y')*X)';
theta = theta - alpha / m * delta;
%fprintf('%f %f %f\n', theta(1), theta(2),theta(3));
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCostMulti(X, y, theta);
fprintf('%f \n', J_history(iter));
end
end
function [theta] = normalEqn(X, y)
%NORMALEQN Computes the closed-form solution to linear regression
% NORMALEQN(X,y) computes the closed-form solution to linear
% regression using the normal equations.
theta = zeros(size(X, 2), 1);
% ====================== YOUR CODE HERE ======================
% Instructions: Complete the code to compute the closed form solution
% to linear regression and put the result in theta.
%
% ---------------------- Sample Solution ----------------------
theta = (inv( X' * X )) * X' * y ;
% -------------------------------------------------------------
% ============================================================
end