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-// 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;
-
-}
-
-