mirror of https://github.com/davisking/dlib.git
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
97 lines
3.5 KiB
97 lines
3.5 KiB
// 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
|
|
}
|
|
|
|
|