【机器学习】UFLDL练习1

%
%This exercise uses a data from the UCI repository:
% Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
% http://archive.ics.uci.edu/ml
% Irvine, CA: University of California, School of Information and Computer Science.
%
%Data created by:
% Harrison, D. and Rubinfeld, D.L.
% ''Hedonic prices and the demand for clean air''
% J. Environ. Economics & Management, vol.5, 81-102, 1978.
%
addpath ../common
addpath ../common/minFunc_2012/minFunc
addpath ../common/minFunc_2012/minFunc/compiled

% Load housing data from file.
data = load('housing.data');
data=data'; % put examples in columns

% Include a row of 1s as an additional intercept feature.
data = [ ones(1,size(data,2)); data ];

% Shuffle examples.
data = data(:, randperm(size(data,2)));

% Split into train and test sets
% The last row of 'data' is the median home price.
train.X = data(1:end-1,1:400);
train.y = data(end,1:400);

test.X = data(1:end-1,401:end);
test.y = data(end,401:end);

m=size(train.X,2);
n=size(train.X,1);

% Initialize the coefficient vector theta to random values.
theta = rand(n,1);

% Run the minFunc optimizer with linear_regression.m as the objective.
%
% TODO: Implement the linear regression objective and gradient computations
% in linear_regression.m
%
tic;
options = struct('MaxIter', 200);
theta = minFunc(@linear_regression, theta, options, train.X, train.y);
fprintf('Optimization took %f seconds.\n', toc);

% Run minFunc with linear_regression_vec.m as the objective.
%
% TODO: Implement linear regression in linear_regression_vec.m
% using MATLAB's vectorization features to speed up your code.
% Compare the running time for your linear_regression.m and
% linear_regression_vec.m implementations.
%
% Uncomment the lines below to run your vectorized code.
%Re-initialize parameters
%theta = rand(n,1);
%tic;
%theta = minFunc(@linear_regression_vec, theta, options, train.X, train.y);
%fprintf('Optimization took %f seconds.\n', toc);

% Plot predicted prices and actual prices from training set.
actual_prices = train.y;
predicted_prices = theta'*train.X;

% Print out root-mean-squared (RMS) training error.
train_rms=sqrt(mean((predicted_prices - actual_prices).^2));
fprintf('RMS training error: %f\n', train_rms);

% Print out test RMS error
actual_prices = test.y;
predicted_prices = theta'*test.X;
test_rms=sqrt(mean((predicted_prices - actual_prices).^2));
fprintf('RMS testing error: %f\n', test_rms);


% Plot predictions on test data.
plot_prices=true;
if (plot_prices)
  [actual_prices,I] = sort(actual_prices);
  predicted_prices=predicted_prices(I);
  plot(actual_prices, 'rx');
  hold on;
  plot(predicted_prices,'bx');
  legend('Actual Price', 'Predicted Price');
  xlabel('House #');
  ylabel('House price ($1000s)');
end

function [f,g] = linear_regression(theta, X,y)
  %
  % Arguments:
  %   theta - A vector containing the parameter values to optimize.
  %   X - The examples stored in a matrix.
  %       X(i,j) is the i'th coordinate of the j'th example.
  %   y - The target value for each example.  y(j) is the target for example j.
  %

  m=size(X,2);
  n=size(X,1);

  f=0;
  g=zeros(size(theta));

  %
  % TODO:  Compute the linear regression objective by looping over the examples in X.
  %        Store the objective function value in 'f'.
  %
  % TODO:  Compute the gradient of the objective with respect to theta by looping over
  %        the examples in X and adding up the gradient for each example.  Store the
  %        computed gradient in 'g'.

%%% YOUR CODE HERE %%%

  % Compute 'f' as the objective function with looping.
  for i = 1:m
     f = f + ( theta' * X(:,i) - y(i) ) ^2; 
  end
  f = f / 2;

  % Compute 'g' as the gradient of the objective w.r.t theta by looping
  for j = 1:n
      for i = 1:m
          g(j) = g(j) + X(j,i) * (theta' * X(:,i) - y(i) );
      end
  end