jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
90 lines
2.7 KiB
Matlab
90 lines
2.7 KiB
Matlab
%
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%This exercise uses a data from the UCI repository:
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% Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
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% http://archive.ics.uci.edu/ml
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% Irvine, CA: University of California, School of Information and Computer Science.
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%
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%Data created by:
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% Harrison, D. and Rubinfeld, D.L.
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% ''Hedonic prices and the demand for clean air''
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% J. Environ. Economics & Management, vol.5, 81-102, 1978.
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%
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addpath ../common
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addpath ../common/minFunc_2012/minFunc
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addpath ../common/minFunc_2012/minFunc/compiled
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% Load housing data from file.
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data = load('housing.data');
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data=data'; % put examples in columns
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% Include a row of 1s as an additional intercept feature.
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data = [ ones(1,size(data,2)); data ];
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% Shuffle examples.
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data = data(:, randperm(size(data,2)));
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% Split into train and test sets
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% The last row of 'data' is the median home price.
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train.X = data(1:end-1,1:400);
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train.y = data(end,1:400);
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test.X = data(1:end-1,401:end);
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test.y = data(end,401:end);
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m=size(train.X,2);
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n=size(train.X,1);
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% Initialize the coefficient vector theta to random values.
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theta = rand(n,1);
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% Run the minFunc optimizer with linear_regression.m as the objective.
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%
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% TODO: Implement the linear regression objective and gradient computations
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% in linear_regression.m
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%
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tic;
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options = struct('MaxIter', 200);
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theta = minFunc(@linear_regression, theta, options, train.X, train.y);
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fprintf('Optimization took %f seconds.\n', toc);
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% Run minFunc with linear_regression_vec.m as the objective.
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%
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% TODO: Implement linear regression in linear_regression_vec.m
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% using MATLAB's vectorization features to speed up your code.
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% Compare the running time for your linear_regression.m and
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% linear_regression_vec.m implementations.
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%
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% Uncomment the lines below to run your vectorized code.
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%Re-initialize parameters
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%theta = rand(n,1);
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%tic;
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%theta = minFunc(@linear_regression_vec, theta, options, train.X, train.y);
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%fprintf('Optimization took %f seconds.\n', toc);
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% Plot predicted prices and actual prices from training set.
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actual_prices = train.y;
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predicted_prices = theta'*train.X;
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% Print out root-mean-squared (RMS) training error.
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train_rms=sqrt(mean((predicted_prices - actual_prices).^2));
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fprintf('RMS training error: %f\n', train_rms);
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% Print out test RMS error
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actual_prices = test.y;
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predicted_prices = theta'*test.X;
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test_rms=sqrt(mean((predicted_prices - actual_prices).^2));
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fprintf('RMS testing error: %f\n', test_rms);
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% Plot predictions on test data.
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plot_prices=true;
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if (plot_prices)
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[actual_prices,I] = sort(actual_prices);
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predicted_prices=predicted_prices(I);
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plot(actual_prices, 'rx');
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hold on;
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plot(predicted_prices,'bx');
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legend('Actual Price', 'Predicted Price');
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xlabel('House #');
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ylabel('House price ($1000s)');
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end |