// 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.doublesinc(doublex){if (x == 0) return 1; return sin(x)/x;}intmain(){// 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 (doublex = -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}