Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 154 insertions, 0 deletions

diff --git a/src/ml/dlib/examples/kkmeans_ex.cpp b/src/ml/dlib/examples/kkmeans_ex.cpp
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+++ b/src/ml/dlib/examples/kkmeans_ex.cpp
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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;
+
+}
+
+