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97 lines
3.5 KiB
C++
97 lines
3.5 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 epsilon-insensitive support vector
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regression object from the dlib C++ Library.
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In this example we will draw some points from the sinc() function and do a
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non-linear regression on them.
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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 the svr_trainer
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// object.
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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 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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std::vector<sample_type> samples;
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std::vector<double> targets;
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// The first thing we do is pick a few training points from the sinc() function.
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sample_type m;
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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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targets.push_back(sinc(x));
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}
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// Now setup a SVR trainer object. It has three parameters, the kernel and
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// two parameters specific to SVR.
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svr_trainer<kernel_type> trainer;
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trainer.set_kernel(kernel_type(0.1));
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// This parameter is the usual regularization parameter. It determines the trade-off
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// between trying to reduce the training error or allowing more errors but hopefully
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// improving the generalization of the resulting function. Larger values encourage exact
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// fitting while smaller values of C may encourage better generalization.
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trainer.set_c(10);
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// Epsilon-insensitive regression means we do regression but stop trying to fit a data
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// point once it is "close enough" to its target value. This parameter is the value that
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// controls what we mean by "close enough". In this case, I'm saying I'm happy if the
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// resulting regression function gets within 0.001 of the target value.
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trainer.set_epsilon_insensitivity(0.001);
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// Now do the training and save the results
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decision_function<kernel_type> df = trainer.train(samples, targets);
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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 SVR.
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m(0) = 2.5; cout << sinc(m(0)) << " " << df(m) << endl;
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m(0) = 0.1; cout << sinc(m(0)) << " " << df(m) << endl;
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m(0) = -4; cout << sinc(m(0)) << " " << df(m) << endl;
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m(0) = 5.0; cout << sinc(m(0)) << " " << df(m) << endl;
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// The output is as follows:
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// 0.239389 0.23905
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// 0.998334 0.997331
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// -0.189201 -0.187636
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// -0.191785 -0.218924
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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 SVR estimate.
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// We can also do 5-fold cross-validation and find the mean squared error and R-squared
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// values. Note that we need to randomly shuffle the samples first. See the svm_ex.cpp
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// for a discussion of why this is important.
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randomize_samples(samples, targets);
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cout << "MSE and R-Squared: "<< cross_validate_regression_trainer(trainer, samples, targets, 5) << endl;
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// The output is:
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// MSE and R-Squared: 1.65984e-05 0.999901
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}
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