mirror of https://github.com/davisking/dlib.git
74 lines
2.5 KiB
C++
74 lines
2.5 KiB
C++
/*
|
|
This is an example illustrating the use of the krls object
|
|
from the dlib C++ Library.
|
|
|
|
The krls object allows you to perform online regression. This
|
|
example will train an instance of it on the sinc function.
|
|
|
|
*/
|
|
|
|
#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 krls
|
|
// 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. The reason for
|
|
// using a matrix here is that in general you can use N dimensional vectors as inputs to the
|
|
// krls object. But here we only have 1 dimension to make the example simple.
|
|
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;
|
|
|
|
// Here we declare an instance of the krls object. The first argument to the constructor
|
|
// is the kernel we wish to use. The second is a parameter that determines the numerical
|
|
// accuracy with which the object will perform part of the regression algorithm. Generally
|
|
// smaller values give better results but cause the algorithm to run slower. You just have
|
|
// to play with it to decide what balance of speed and accuracy is right for your problem.
|
|
// Here we have set it to 0.001.
|
|
krls<kernel_type> test(kernel_type(0.1),0.001);
|
|
|
|
// now we train our object on a few samples of the sinc function.
|
|
sample_type m;
|
|
for (double x = -10; x <= 4; x += 1)
|
|
{
|
|
m(0) = x;
|
|
test.train(m, sinc(x));
|
|
}
|
|
|
|
// now we output the value of the sinc function for a few test points as well as the
|
|
// value predicted by krls object.
|
|
m(0) = 2.5; cout << sinc(m(0)) << " " << test(m) << endl;
|
|
m(0) = 0.1; cout << sinc(m(0)) << " " << test(m) << endl;
|
|
m(0) = -4; cout << sinc(m(0)) << " " << test(m) << endl;
|
|
m(0) = 5.0; cout << sinc(m(0)) << " " << test(m) << endl;
|
|
|
|
// The output is as follows:
|
|
// 0.239389 0.238808
|
|
// 0.998334 0.997779
|
|
// -0.189201 -0.189754
|
|
// -0.191785 -0.1979
|
|
|
|
// The first column is the true value of the sinc function and the second
|
|
// column is the output from the krls estimate.
|
|
|
|
}
|
|
|
|
|