// 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;intmain(){// 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 constlongnum = 50; // make some samples near the origindoubleradius = 0.5; for (longi = 0; i < num; ++i){doublesign = 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 (longi = 0; i < num; ++i){doublesign = 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 (longi = 0; i < num; ++i){doublesign = 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 (unsignedlongi = 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<unsignedlong> assignments = spectral_cluster(kernel_type(0.1), samples, 3); cout << mat(assignments) << endl;}