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with large datasets. --HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403815
107 lines
3.6 KiB
C++
107 lines
3.6 KiB
C++
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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/*
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This is an example illustrating the use of the kernel ridge regression
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object from the dlib C++ Library.
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This example will train on data from the sinc function.
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*/
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#include <iostream>
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#include <vector>
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#include "dlib/svm.h"
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using namespace std;
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using namespace dlib;
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// Here is the sinc function we will be trying to learn with kernel ridge regression
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double sinc(double x)
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{
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if (x == 0)
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return 1;
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return sin(x)/x;
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}
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int main()
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{
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// Here we declare that our samples will be 1 dimensional column vectors.
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typedef matrix<double,1,1> sample_type;
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// Now sample some points from the sinc() function
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sample_type m;
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std::vector<sample_type> samples;
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std::vector<double> labels;
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for (double x = -10; x <= 4; x += 1)
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{
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m(0) = x;
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samples.push_back(m);
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labels.push_back(sinc(x));
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}
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// Now we are making a typedef for the kind of kernel we want to use. I picked the
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// radial basis kernel because it only has one parameter and generally gives good
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// results without much fiddling.
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typedef radial_basis_kernel<sample_type> kernel_type;
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// Here we declare an instance of the krr_trainer object. This is the
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// object that we will later use to do the training.
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krr_trainer<kernel_type> trainer;
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// Here we set the kernel we want to use for training. The radial_basis_kernel
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// has a parameter called gamma that we need to determine. As a rule of thumb, a good
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// gamma to try is 1.0/(mean squared distance between your sample points). So
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// below we are using a similar value computed from at most 2000 randomly selected
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// samples.
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const double gamma = 3.0/compute_mean_squared_distance(randomly_subsample(samples, 2000));
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cout << "using gamma of " << gamma << endl;
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trainer.set_kernel(kernel_type(gamma));
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// now train a function based on our sample points
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decision_function<kernel_type> test = trainer.train(samples, labels);
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// now we output the value of the sinc function for a few test points as well as the
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// value predicted by our regression.
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m(0) = 2.5; cout << sinc(m(0)) << " " << test(m) << endl;
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m(0) = 0.1; cout << sinc(m(0)) << " " << test(m) << endl;
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m(0) = -4; cout << sinc(m(0)) << " " << test(m) << endl;
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m(0) = 5.0; cout << sinc(m(0)) << " " << test(m) << endl;
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// The output is as follows:
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//using gamma of 0.075
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// 0.239389 0.239389
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// 0.998334 0.998362
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// -0.189201 -0.189254
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// -0.191785 -0.186618
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// The first column is the true value of the sinc function and the second
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// column is the output from the krr estimate.
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// Note that the krr_trainer has the ability to tell us the leave-one-out cross-validation
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// accuracy. The train() function has an optional 3rd argument and if we give it a double
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// it will give us back the LOO error.
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double loo_error;
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trainer.train(samples, labels, loo_error);
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cout << "mean squared LOO error: " << loo_error << endl;
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// Which outputs the following:
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// mean squared LOO error: 8.29563e-07
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// Another thing that is worth knowing is that just about everything in dlib is serializable.
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// So for example, you can save the test object to disk and recall it later like so:
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ofstream fout("saved_function.dat",ios::binary);
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serialize(test,fout);
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fout.close();
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// now lets open that file back up and load the function object it contains
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ifstream fin("saved_function.dat",ios::binary);
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deserialize(test, fin);
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}
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