mirror of https://github.com/davisking/dlib.git
Added the kkmeans object.
--HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402282
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Davis King
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#include "svm/svm.h"
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#include "svm/krls.h"
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#include "svm/kcentroid.h"
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#include "svm/kkmeans.h"
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#endif // DLIB_SVm_HEADER
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// Copyright (C) 2008 Davis E. King (davisking@users.sourceforge.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#ifndef DLIB_KKMEANs_
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#define DLIB_KKMEANs_
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#include <cmath>
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#include "../matrix/matrix_abstract.h"
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#include "../algs.h"
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#include "../serialize.h"
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#include "kernel_abstract.h"
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#include "../array.h"
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#include "kcentroid.h"
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#include "kkmeans_abstract.h"
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#include "../noncopyable.h"
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#include "../smart_pointers.h"
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namespace dlib
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{
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template <
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typename kernel_type
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>
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class kkmeans : public noncopyable
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{
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public:
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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kkmeans (
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const kcentroid<kernel_type>& kc_
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):
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kc(kc_)
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{
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set_number_of_centers(1);
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}
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~kkmeans()
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{
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}
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void set_kcentroid (
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const kcentroid<kernel_type>& kc_
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)
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{
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kc = kc_;
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set_number_of_centers(number_of_centers());
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}
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const kcentroid<kernel_type>& get_kcentroid (
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unsigned long i
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) const
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{
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return *centers[i];
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}
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void set_number_of_centers (
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unsigned long num
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)
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{
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// make sure requires clause is not broken
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DLIB_CASSERT(num > 0,
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"\tvoid kkmeans::set_number_of_centers"
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<< "\n\tYou can't set the number of centers to zero"
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<< "\n\tthis: " << this
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);
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centers.set_max_size(num);
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centers.set_size(num);
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for (unsigned long i = 0; i < centers.size(); ++i)
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{
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centers[i].reset(new kcentroid<kernel_type>(kc));
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}
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}
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unsigned long number_of_centers (
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) const
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{
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return centers.size();
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}
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template <typename matrix_type>
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void train (
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const matrix_type& samples,
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const matrix_type& initial_centers
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)
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{
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// clear out the old data and initialize the centers
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for (unsigned long i = 0; i < centers.size(); ++i)
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{
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centers[i]->clear_dictionary();
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centers[i]->train(initial_centers(i));
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}
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assignments.expand(samples.size());
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bool assignment_changed = true;
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// loop until the centers stabilize
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while (assignment_changed)
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{
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assignment_changed = false;
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// loop over all the samples and assign them to their closest centers
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for (long i = 0; i < samples.size(); ++i)
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{
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// find the best center
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unsigned long best_center = 0;
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scalar_type best_score = (*centers[0])(samples(i));
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for (unsigned long c = 1; c < centers.size(); ++c)
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{
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scalar_type temp = (*centers[c])(samples(i));
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if (temp < best_score)
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{
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best_score = temp;
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best_center = c;
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}
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}
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// if the current sample changed centers then make note of that
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if (assignments[i] != best_center)
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{
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assignments[i] = best_center;
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assignment_changed = true;
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}
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}
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if (assignment_changed)
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{
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// now clear out the old data
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for (unsigned long i = 0; i < centers.size(); ++i)
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centers[i]->clear_dictionary();
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// recalculate the cluster centers
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for (unsigned long i = 0; i < assignments.size(); ++i)
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centers[assignments[i]]->train(samples(i));
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}
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}
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}
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unsigned long operator() (
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const sample_type& sample
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) const
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{
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unsigned long label = 0;
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scalar_type best_score = (*centers[0])(sample);
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// figure out which center the given sample is closest too
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for (unsigned long i = 1; i < centers.size(); ++i)
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{
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scalar_type temp = (*centers[i])(sample);
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if (temp < best_score)
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{
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label = i;
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best_score = temp;
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}
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}
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return label;
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}
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void swap (
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kkmeans& item
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)
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{
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centers.swap(item.centers);
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kc.swap(item.kc);
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assignments.swap(item.assignments);
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}
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friend void serialize(const kkmeans& item, std::ostream& out)
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{
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serialize(item.centers, out);
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serialize(item.kc, out);
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serialize(item.assignments, out);
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}
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friend void deserialize(kkmeans& item, std::istream& in)
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{
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deserialize(item.centers, in);
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deserialize(item.kc, in);
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deserialize(item.assignments, in);
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}
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private:
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typename array<scoped_ptr<kcentroid<kernel_type> > >::expand_1b_c centers;
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kcentroid<kernel_type> kc;
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// temp variables
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array<unsigned long>::expand_1b_c assignments;
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};
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// ----------------------------------------------------------------------------------------
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template <typename kernel_type>
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void swap(kkmeans<kernel_type>& a, kkmeans<kernel_type>& b)
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{ a.swap(b); }
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_KKMEANs_
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@ -0,0 +1,172 @@
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// Copyright (C) 2008 Davis E. King (davisking@users.sourceforge.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#undef DLIB_KKMEANs_ABSTRACT_
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#ifdef DLIB_KKMEANs_ABSTRACT_
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#include <cmath>
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#include "../matrix/matrix_abstract.h"
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#include "../algs.h"
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#include "../serialize.h"
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#include "kernel_abstract.h"
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#include "kcentroid_abstract.h"
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#include "../noncopyable.h"
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namespace dlib
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{
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template <
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typename kernel_type : public noncopyable
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>
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class kkmeans
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{
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/*!
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REQUIREMENTS ON kernel_type
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is a kernel function object as defined in dlib/svm/kernel_abstract.h
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INITIAL VALUE
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- number_of_centers() == 1
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WHAT THIS OBJECT REPRESENTS
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This is an implementation of a kernelized k-means clustering algorithm.
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It performs k-means clustering by using the kcentroid object.
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!*/
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public:
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typedef typename kernel_type::scalar_type scalar_type;
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typedef typename kernel_type::sample_type sample_type;
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typedef typename kernel_type::mem_manager_type mem_manager_type;
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kkmeans (
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const kcentroid<kernel_type>& kc_
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);
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/*!
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ensures
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- #number_of_centers() == 1
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- #get_kcentroid(0) == a copy of kc_
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!*/
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~kkmeans(
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);
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/*!
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ensures
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- all resources associated with *this have been released
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!*/
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void set_kcentroid (
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const kcentroid<kernel_type>& kc_
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);
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/*!
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ensures
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- for all idx:
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- #get_kcentroid(idx) == a copy of kc_
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!*/
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const kcentroid<kernel_type>& get_kcentroid (
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unsigned long i
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) const;
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/*!
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ensures
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- returns a const reference to the ith kcentroid object contained in
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this object. Each kcentroid represents one of the centers found
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by the k-means clustering algorithm.
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!*/
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void set_number_of_centers (
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unsigned long num
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);
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/*!
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requires
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- num > 0
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ensures
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- #number_of_centers() == num
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!*/
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unsigned long number_of_centers (
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) const;
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/*!
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ensures
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- returns the number of centers used in this instance of the k-means clustering
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algorithm.
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!*/
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template <
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typename matrix_type
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>
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void train (
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const matrix_type& samples,
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const matrix_type& initial_centers
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);
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/*!
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requires
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- matrix_type::type == sample_type (i.e. matrix_type should contain sample_type objects)
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- initial_centers.nc() == 1 (i.e. must be a column vector)
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- samples.nc() == 1 (i.e. must be a column vector)
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- initial_centers.nr() == number_of_centers()
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ensures
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- performs k-means clustering of the given set of samples. The initial center points
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are taken from the initial_centers argument.
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- After this function finishes you can call the operator() function below
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to determine which centroid a given sample is closest to.
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!*/
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unsigned long operator() (
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const sample_type& sample
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) const;
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/*!
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ensures
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- returns a number idx such that:
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- idx < number_of_centers()
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- get_kcentroid(idx) == the centroid that is closest to the given
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sample.
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!*/
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void swap (
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kkmeans& item
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);
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/*!
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ensures
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- swaps *this and item
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!*/
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};
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// ----------------------------------------------------------------------------------------
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template <
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typename kernel_type
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>
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void swap(
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kkmeans<kernel_type>& a,
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kkmeans<kernel_type>& b
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) { a.swap(b); }
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/*!
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provides a global swap function
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!*/
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template <
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typename kernel_type
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>
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void serialize (
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const kkmeans<kernel_type>& item,
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std::ostream& out
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);
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/*!
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provides serialization support for kkmeans objects
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!*/
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template <
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typename kernel_type
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>
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void deserialize (
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kkmeans<kernel_type>& item,
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std::istream& in
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);
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/*!
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provides serialization support for kkmeans objects
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!*/
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_KKMEANs_ABSTRACT_
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