dlib C++ Library - svr_ex.cpp

// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
 This is an example illustrating the use of the epsilon-insensitive support vector 
 regression object from the dlib C++ Library.
 In this example we will draw some points from the sinc() function and do a
 non-linear regression on them.
*/
#include <iostream>
#include <vector>
#include <dlib/svm.h>
using namespace std;
using namespace dlib;
// Here is the sinc function we will be trying to learn with the svr_trainer 
// object.
double sinc(double x)
{
 if (x == 0)
 return 1;
 return sin(x)/x;
}
int main()
{
 // Here we declare that our samples will be 1 dimensional column vectors. 
 typedef matrix<double,1,1> sample_type;
 // Now we are making a typedef for the kind of kernel we want to use. I picked the
 // radial basis kernel because it only has one parameter and generally gives good
 // results without much fiddling.
 typedef radial_basis_kernel<sample_type> kernel_type;
 std::vector<sample_type> samples;
 std::vector<double> targets;
 // The first thing we do is pick a few training points from the sinc() function.
 sample_type m;
 for (double x = -10; x <= 4; x += 1)
 {
 m(0) = x;
 samples.push_back(m);
 targets.push_back(sinc(x));
 }
 // Now setup a SVR trainer object. It has three parameters, the kernel and
 // two parameters specific to SVR. 
 svr_trainer<kernel_type> trainer;
 trainer.set_kernel(kernel_type(0.1));
 // This parameter is the usual regularization parameter. It determines the trade-off 
 // between trying to reduce the training error or allowing more errors but hopefully 
 // improving the generalization of the resulting function. Larger values encourage exact 
 // fitting while smaller values of C may encourage better generalization.
 trainer.set_c(10);
 // Epsilon-insensitive regression means we do regression but stop trying to fit a data 
 // point once it is "close enough" to its target value. This parameter is the value that 
 // controls what we mean by "close enough". In this case, I'm saying I'm happy if the
 // resulting regression function gets within 0.001 of the target value.
 trainer.set_epsilon_insensitivity(0.001);
 // Now do the training and save the results
 decision_function<kernel_type> df = trainer.train(samples, targets);
 // now we output the value of the sinc function for a few test points as well as the 
 // value predicted by SVR.
 m(0) = 2.5; cout << sinc(m(0)) << " " << df(m) << endl;
 m(0) = 0.1; cout << sinc(m(0)) << " " << df(m) << endl;
 m(0) = -4; cout << sinc(m(0)) << " " << df(m) << endl;
 m(0) = 5.0; cout << sinc(m(0)) << " " << df(m) << endl;
 // The output is as follows:
 // 0.239389 0.23905
 // 0.998334 0.997331
 // -0.189201 -0.187636
 // -0.191785 -0.218924
 // The first column is the true value of the sinc function and the second
 // column is the output from the SVR estimate. 
 // We can also do 5-fold cross-validation and find the mean squared error and R-squared
 // values. Note that we need to randomly shuffle the samples first. See the svm_ex.cpp 
 // for a discussion of why this is important. 
 randomize_samples(samples, targets);
 cout << "MSE and R-Squared: "<< cross_validate_regression_trainer(trainer, samples, targets, 5) << endl;
 // The output is: 
 // MSE and R-Squared: 1.65984e-05 0.999901
}