dlib C++ Library - kkmeans_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 kkmeans object 
 and spectral_cluster() routine from the dlib C++ Library.
 The kkmeans object is an implementation of a kernelized k-means clustering 
 algorithm. It is implemented by using the kcentroid object to represent 
 each center found by the usual k-means clustering algorithm. 
 So this object allows you to perform non-linear clustering in the same way 
 a svm classifier finds non-linear decision surfaces. 
 
 This example will make points from 3 classes and perform kernelized k-means 
 clustering on those points. It will also do the same thing using spectral 
 clustering.
 The classes are as follows:
 - points very close to the origin
 - points on the circle of radius 10 around the origin
 - points that are on a circle of radius 4 but not around the origin at all
*/
#include <iostream>
#include <vector>
#include <dlib/clustering.h>
#include <dlib/rand.h>
using namespace std;
using namespace dlib;
int main()
{
 // Here we declare that our samples will be 2 dimensional column vectors. 
 // (Note that if you don't know the dimensionality of your vectors at compile time
 // you can change the 2 to a 0 and then set the size at runtime)
 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. It is the object used to 
 // represent each of the centers used for clustering. The kcentroid has 3 parameters 
 // you need to set. 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 attempt to use more dictionary vectors 
 // (and thus run slower and use more memory). The third argument, however, is the 
 // maximum number of dictionary vectors a kcentroid is allowed to use. So you can use
 // it to control the runtime complexity. 
 kcentroid<kernel_type> kc(kernel_type(0.1),0.01, 8);
 // Now we make an instance of the kkmeans object and tell it to use kcentroid objects
 // that are configured with the parameters from the kc object we defined above.
 kkmeans<kernel_type> test(kc);
 std::vector<sample_type> samples;
 std::vector<sample_type> initial_centers;
 sample_type m;
 dlib::rand rnd;
 // we will make 50 points from each class
 const long num = 50;
 // make some samples near the origin
 double radius = 0.5;
 for (long i = 0; i < num; ++i)
 {
 double sign = 1;
 if (rnd.get_random_double() < 0.5)
 sign = -1;
 m(0) = 2*radius*rnd.get_random_double()-radius;
 m(1) = sign*sqrt(radius*radius - m(0)*m(0));
 // add this sample to our set of samples we will run k-means 
 samples.push_back(m);
 }
 // make some samples in a circle around the origin but far away
 radius = 10.0;
 for (long i = 0; i < num; ++i)
 {
 double sign = 1;
 if (rnd.get_random_double() < 0.5)
 sign = -1;
 m(0) = 2*radius*rnd.get_random_double()-radius;
 m(1) = sign*sqrt(radius*radius - m(0)*m(0));
 // add this sample to our set of samples we will run k-means 
 samples.push_back(m);
 }
 // make some samples in a circle around the point (25,25) 
 radius = 4.0;
 for (long i = 0; i < num; ++i)
 {
 double sign = 1;
 if (rnd.get_random_double() < 0.5)
 sign = -1;
 m(0) = 2*radius*rnd.get_random_double()-radius;
 m(1) = sign*sqrt(radius*radius - m(0)*m(0));
 // translate this point away from the origin
 m(0) += 25;
 m(1) += 25;
 // add this sample to our set of samples we will run k-means 
 samples.push_back(m);
 }
 // tell the kkmeans object we made that we want to run k-means with k set to 3. 
 // (i.e. we want 3 clusters)
 test.set_number_of_centers(3);
 // You need to pick some initial centers for the k-means algorithm. So here
 // we will use the dlib::pick_initial_centers() function which tries to find
 // n points that are far apart (basically). 
 pick_initial_centers(3, initial_centers, samples, test.get_kernel());
 // now run the k-means algorithm on our set of samples. 
 test.train(samples,initial_centers);
 // now loop over all our samples and print out their predicted class. In this example
 // all points are correctly identified.
 for (unsigned long i = 0; i < samples.size()/3; ++i)
 {
 cout << test(samples[i]) << " ";
 cout << test(samples[i+num]) << " ";
 cout << test(samples[i+2*num]) << "\n";
 }
 // Now print out how many dictionary vectors each center used. Note that 
 // the maximum number of 8 was reached. If you went back to the kcentroid 
 // constructor and changed the 8 to some bigger number you would see that these
 // numbers would go up. However, 8 is all we need to correctly cluster this dataset.
 cout << "num dictionary vectors for center 0: " << test.get_kcentroid(0).dictionary_size() << endl;
 cout << "num dictionary vectors for center 1: " << test.get_kcentroid(1).dictionary_size() << endl;
 cout << "num dictionary vectors for center 2: " << test.get_kcentroid(2).dictionary_size() << endl;
 // Finally, we can also solve the same kind of non-linear clustering problem with
 // spectral_cluster(). The output is a vector that indicates which cluster each sample
 // belongs to. Just like with kkmeans, it assigns each point to the correct cluster.
 std::vector<unsigned long> assignments = spectral_cluster(kernel_type(0.1), samples, 3);
 cout << mat(assignments) << endl;
}