mirror of https://github.com/davisking/dlib.git
116 lines
5.5 KiB
C++
116 lines
5.5 KiB
C++
/*
|
|
This is an example illustrating the use of the kcentroid object
|
|
from the dlib C++ Library.
|
|
|
|
The kcentroid object is an implementation of an algorithm that recursively
|
|
computes the centroid (i.e. average) of a set of points. The interesting
|
|
thing about dlib::kcentroid is that it does so in a kernel induced feature
|
|
space. This means that you can use it as a non-linear one-class classifier.
|
|
So you might use it to perform online novelty detection.
|
|
|
|
This example will train an instance of it on points from the sinc function.
|
|
|
|
*/
|
|
|
|
#include <iostream>
|
|
#include <vector>
|
|
|
|
#include "dlib/svm.h"
|
|
#include "dlib/statistics.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 2 dimensional column vectors.
|
|
typedef matrix<double,2,1> 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 kcentroid 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 learning 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 0.01.
|
|
kcentroid<kernel_type> test(kernel_type(0.1),0.01);
|
|
|
|
// now we train our object on a few samples of the sinc function.
|
|
sample_type m;
|
|
for (double x = -15; x <= 8; x += 1)
|
|
{
|
|
m(0) = x;
|
|
m(1) = sinc(x);
|
|
test.train(m);
|
|
}
|
|
|
|
running_stats<double> rs;
|
|
|
|
// Now lets output the distance from the centroid to some points that are from the sinc function.
|
|
// These numbers should all be similar. We will also calculate the statistics of these numbers
|
|
// by accumulating them into the running_stats object called rs. This will let us easily
|
|
// find the mean and standard deviation of the distances for use below.
|
|
cout << "Points that are on the sinc function:\n";
|
|
m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
|
|
m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
|
|
m(0) = -0; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
|
|
m(0) = -0.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
|
|
m(0) = -4.1; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
|
|
m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
|
|
m(0) = -0.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
|
|
|
|
cout << endl;
|
|
// Lets output the distance from the centroid to some points that are NOT from the sinc function.
|
|
// These numbers should all be significantly bigger than previous set of numbers. We will also
|
|
// use the rs.scale() function to find out how many standard deviations they are away from the
|
|
// mean of the test points from the sinc function. So in this case our criterion for "significantly bigger"
|
|
// is > 3 or 4 standard deviations away from the above points that actually are on the sinc function.
|
|
cout << "Points that are NOT on the sinc function:\n";
|
|
m(0) = -1.5; m(1) = sinc(m(0))+4; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
|
|
m(0) = -1.5; m(1) = sinc(m(0))+3; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
|
|
m(0) = -0; m(1) = -sinc(m(0)); cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
|
|
m(0) = -0.5; m(1) = -sinc(m(0)); cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
|
|
m(0) = -4.1; m(1) = sinc(m(0))+2; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
|
|
m(0) = -1.5; m(1) = sinc(m(0))+0.9; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
|
|
m(0) = -0.5; m(1) = sinc(m(0))+1; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
|
|
|
|
// The output is as follows:
|
|
/*
|
|
Points that are on the sinc function:
|
|
0.869861
|
|
0.869861
|
|
0.873182
|
|
0.872628
|
|
0.870352
|
|
0.869861
|
|
0.872628
|
|
|
|
Points that are NOT on the sinc function:
|
|
1.06306 is 125.137 standard deviations from sinc.
|
|
1.0215 is 98.0313 standard deviations from sinc.
|
|
0.92136 is 32.717 standard deviations from sinc.
|
|
0.918282 is 30.7096 standard deviations from sinc.
|
|
0.930931 is 38.9595 standard deviations from sinc.
|
|
0.897916 is 17.4264 standard deviations from sinc.
|
|
0.913855 is 27.822 standard deviations from sinc.
|
|
*/
|
|
|
|
// So we can see that in this example the kcentroid object correctly indicates that
|
|
// the non-sinc points are definitely not points from the sinc function.
|
|
}
|
|
|
|
|