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-rw-r--r--ml/dlib/dlib/test/kcentroid.cpp684
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diff --git a/ml/dlib/dlib/test/kcentroid.cpp b/ml/dlib/dlib/test/kcentroid.cpp
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--- /dev/null
+++ b/ml/dlib/dlib/test/kcentroid.cpp
@@ -0,0 +1,684 @@
+// Copyright (C) 2009 Davis E. King (davis@dlib.net)
+// License: Boost Software License See LICENSE.txt for the full license.
+
+
+#include <dlib/matrix.h>
+#include <sstream>
+#include <string>
+#include <cstdlib>
+#include <ctime>
+#include <vector>
+#include <map>
+#include "../stl_checked.h"
+#include "../array.h"
+#include "../rand.h"
+#include "checkerboard.h"
+#include <dlib/statistics.h>
+
+#include "tester.h"
+#include <dlib/svm_threaded.h>
+
+
+namespace
+{
+
+ using namespace test;
+ using namespace dlib;
+ using namespace std;
+
+ logger dlog("test.kcentroid");
+
+// ----------------------------------------------------------------------------------------
+
+ template <typename T>
+ struct unopt_sparse_linear_kernel
+ {
+ typedef typename T::value_type::second_type scalar_type;
+ typedef T sample_type;
+ typedef default_memory_manager mem_manager_type;
+
+ scalar_type operator() (
+ const sample_type& a,
+ const sample_type& b
+ ) const
+ {
+ return dot(a,b);
+ }
+
+ bool operator== (
+ const unopt_sparse_linear_kernel&
+ ) const
+ {
+ return true;
+ }
+ };
+
+ template <typename T>
+ struct unopt_linear_kernel
+ {
+ typedef typename T::type scalar_type;
+ typedef T sample_type;
+ typedef typename T::mem_manager_type mem_manager_type;
+
+ scalar_type operator() (
+ const sample_type& a,
+ const sample_type& b
+ ) const
+ {
+ return trans(a)*b;
+ }
+
+ bool operator== (
+ const unopt_linear_kernel&
+ ) const
+ {
+ return true;
+ }
+ };
+
+ bool approx_equal(double a, double b)
+ {
+ return (std::abs(a-b) < 1000*(std::numeric_limits<double>::epsilon()));
+ }
+
+ bool approx_equal(double a, double b, double eps)
+ {
+ return (std::abs(a-b) < eps);
+ }
+
+ template <typename K>
+ double dist (
+ const K& k,
+ const matrix<double,4,1>& a,
+ const matrix<double,5,1>& b
+ )
+ /*!
+ ensures
+ - returns the distance between the a and b vectors in the
+ feature space defined by the given kernel k.
+ !*/
+ {
+ const double bias = std::sqrt(k.offset);
+ return std::sqrt(length_squared(a-colm(b,0,4)) + std::pow(b(4)-bias,2.0));
+
+ }
+
+ template <typename K>
+ double dist (
+ const K& k,
+ std::map<unsigned long,double> a,
+ std::map<unsigned long,double> b
+ )
+ /*!
+ ensures
+ - returns the distance between the a and b vectors in the
+ feature space defined by the given kernel k.
+ !*/
+ {
+ double temp = 0;
+ const double bias = std::sqrt(k.offset);
+ temp += std::pow(a[0]-b[0],2.0);
+ temp += std::pow(a[1]-b[1],2.0);
+ temp += std::pow(a[2]-b[2],2.0);
+ temp += std::pow(a[3]-b[3],2.0);
+ temp += std::pow(bias-b[4],2.0);
+
+ return std::sqrt(temp);
+
+ }
+
+// ----------------------------------------------------------------------------------------
+
+ template <typename kernel_type>
+ void test_kcentroid_with_linear_kernel(
+ )
+ /*!
+ requires
+ - kernel_type::sample_type == a matrix<double,5,1>
+ - kernel_type == a kernel that just computes a dot product
+ between its inputs. I.e. a linear kernel
+ ensures
+ - tests the kcentroid object with the given kernel
+ !*/
+ {
+ // Here we declare that our samples will be 2 dimensional column vectors.
+ typedef typename kernel_type::sample_type sample_type;
+
+ kernel_type default_kernel;
+ kcentroid<kernel_type> test(default_kernel,0.001,20);
+
+ sample_type temp, temp2;
+
+ temp = 2,0,0,0,0;
+ dlog << LDEBUG << test(temp) ;
+ dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
+
+ DLIB_TEST(approx_equal(test(temp), 2));
+ DLIB_TEST(approx_equal(test.squared_norm(), 0));
+
+ // make test store the point(2,0,0,0,0)
+ test.train(temp, 0, 1);
+ dlog << LDEBUG << test(temp) ;
+ dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
+ DLIB_TEST(approx_equal(test(temp), 0));
+ DLIB_TEST(approx_equal(test.get_distance_function()(temp), 0));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ temp = 0,2,0,0,0;
+ dlog << LDEBUG << test(temp) ;
+ DLIB_TEST(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ // make test store the point(0,2,0,0,0)
+ test.train(temp, 0, 1);
+
+ dlog << LDEBUG << test(temp) ;
+ DLIB_TEST(approx_equal(test(temp), 0));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ temp = 2,0,0,0,0;
+ DLIB_TEST(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ // make test store the point(1,1,0,0,0)
+ test.train(temp, 0.5, 0.5);
+
+ temp = 0;
+ DLIB_TEST(approx_equal(test(temp), std::sqrt(2.0)));
+ DLIB_TEST(approx_equal(test.squared_norm(), 2));
+
+ // make test store the point(1,1,0,3,0)
+ temp = 0,0,0,3,0;
+ temp2 = 1,1,0,3,0;
+ test.train(temp, 1, 1);
+
+ temp = 0;
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ temp = 1,2,3,4,5;
+ DLIB_TEST(approx_equal(test(temp), length(temp2-temp)));
+ DLIB_TEST(approx_equal(test.get_distance_function()(temp), length(temp2-temp)));
+
+ // make test store the point(0,1,0,3,-1)
+ temp = 1,0,0,0,1;
+ test.train(temp, 1, -1);
+ temp2 = 0,1,0,3,-1;
+
+ temp = 0;
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ temp = 1,2,3,4,5;
+ DLIB_TEST(approx_equal(test(temp), length(temp2-temp)));
+
+
+ // make test store the -1*point(0,1,0,3,-1)
+ temp = 0,0,0,0,0;
+ test.train(temp, -1, 0);
+ temp2 = -temp2;
+
+ temp = 0;
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ temp = 1,2,-3,4,5;
+ DLIB_TEST(approx_equal(test(temp), length(temp2-temp)));
+
+
+
+ // make test store the point(0,0,0,0,0)
+ temp = 0,0,0,0,0;
+ test.train(temp, 0, 0);
+ temp2 = 0;
+
+ temp = 0;
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ temp = 1,2,-3,4,5;
+ DLIB_TEST(approx_equal(test(temp), length(temp2-temp)));
+
+
+
+ // make test store the point(1,0,0,0,0)
+ temp = 1,0,0,0,0;
+ test.train(temp, 1, 1);
+ temp2 = 1,0,0,0,0;
+
+ temp = 0;
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ DLIB_TEST(approx_equal(test.inner_product(test), length_squared(temp2)));
+ temp = 1,2,-3,4,5;
+ DLIB_TEST(approx_equal(test(temp), length(temp2-temp)));
+ DLIB_TEST(approx_equal(test(test), 0));
+ DLIB_TEST(approx_equal(test.get_distance_function()(test.get_distance_function()), 0));
+
+ }
+
+// ----------------------------------------------------------------------------------------
+
+ template <typename kernel_type>
+ void test_kcentroid_with_offset_linear_kernel(
+ )
+ /*!
+ requires
+ - kernel_type::sample_type == a matrix<double,4,1>
+ - kernel_type == a kernel that just computes a dot product
+ between its inputs + some constant. I.e. a linear kernel
+ wrapped by offset_kernel
+ ensures
+ - tests the kcentroid object with the given kernel
+ !*/
+ {
+ // Here we declare that our samples will be 2 dimensional column vectors.
+ typedef typename kernel_type::sample_type sample_type;
+
+ kernel_type k;
+ kcentroid<kernel_type> test(k,0.001,20);
+
+ sample_type temp, temp2, temp3;
+
+ matrix<double,5,1> val, val2;
+
+ const double b = std::sqrt(k.offset);
+
+ temp = 2,0,0,0;
+ temp2 = 0;
+ val = 0;
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST(approx_equal(test(temp2), dist(k,temp2,val)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+
+
+ temp2 = 0;
+
+ // make test store the point(0,0,0,0,b)
+ val = 0,0,0,0,b;
+ test.train(temp2, 0,1);
+
+ temp = 2,0,0,0;
+ dlog << LDEBUG << test(temp) ;
+ dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
+
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST(approx_equal(test(temp2), dist(k,temp2,val)));
+ DLIB_TEST_MSG(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val), 1e-6),
+ test.get_distance_function()(temp2) - dist(k,temp2,val) << " compare to: " <<
+ test(temp2) - dist(k,temp2,val)
+ );
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+
+
+ // make test store the point(0,0,0,0,0)
+ val = 0,0,0,0,0;
+ test.train(temp2, 1,-1);
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST(approx_equal(test(temp2), dist(k,temp2,val)));
+ DLIB_TEST_MSG(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val)),
+ test.get_distance_function()(temp2) - dist(k,temp2,val) << " compare to: " <<
+ test(temp2) - dist(k,temp2,val)
+ );
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+
+
+
+ val2 = 0,1,0,0,b;
+ val += val2;
+ temp2 = 0,1,0,0;
+ // make test store the point val
+ test.train(temp2, 1,1);
+
+ temp = 1,0,3,0;
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST_MSG(approx_equal(test(temp2), dist(k,temp2,val), 1e-7),
+ test(temp2) - dist(k,temp2,val));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+ DLIB_TEST_MSG(approx_equal(test(test), 0, 1e-7), test(test));
+
+
+ val2 = 0,1,2.6,8,b;
+ val += val2;
+ temp2 = 0,1,2.6,8;
+ // make test store the point val
+ test.train(temp2, 1,1);
+
+ temp = 1,1,3,0;
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST_MSG(approx_equal(test(temp2), dist(k,temp2,val)), test(temp2) - dist(k,temp2,val));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+ DLIB_TEST(approx_equal(test.inner_product(test), length_squared(val)));
+ DLIB_TEST(approx_equal(test(test), 0));
+ DLIB_TEST_MSG(approx_equal(test.get_distance_function()(test.get_distance_function()), 0, 1e-6),
+ test.get_distance_function()(test.get_distance_function()));
+ }
+
+// ----------------------------------------------------------------------------------------
+
+ template <typename kernel_type>
+ void test_kcentroid_with_sparse_linear_kernel(
+ )
+ /*!
+ requires
+ - kernel_type::sample_type == a std::map<unsigned long,double>
+ - kernel_type == a kernel that just computes a dot product
+ between its inputs. I.e. a linear kernel
+ ensures
+ - tests the kcentroid object with the given kernel
+ !*/
+ {
+ // Here we declare that our samples will be 2 dimensional column vectors.
+ typedef typename kernel_type::sample_type sample_type;
+
+ kernel_type default_kernel;
+ kcentroid<kernel_type> test(default_kernel,0.001,20);
+
+ dlog << LDEBUG << "AAAA 1" ;
+
+ sample_type temp, temp2;
+
+ temp[0] = 2;
+ dlog << LDEBUG << test(temp) ;
+ dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
+
+ DLIB_TEST(approx_equal(test(temp), 2));
+ DLIB_TEST(approx_equal(test.squared_norm(), 0));
+
+ // make test store the point(2,0,0,0,0)
+ test.train(temp, 0, 1);
+ dlog << LDEBUG << test(temp) ;
+ dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
+ DLIB_TEST(approx_equal(test(temp), 0));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ dlog << LDEBUG << "AAAA 2" ;
+ temp.clear();
+ temp[1] = 2;
+ dlog << LDEBUG << test(temp) ;
+ DLIB_TEST(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ // make test store the point(0,2,0,0,0)
+ test.train(temp, 0, 1);
+
+ dlog << LDEBUG << test(temp) ;
+ DLIB_TEST(approx_equal(test(temp), 0));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ temp.clear();
+ temp[0] = 2;
+ DLIB_TEST(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)));
+ DLIB_TEST(approx_equal(test.squared_norm(), 4));
+
+ // make test store the point(1,1,0,0,0)
+ test.train(temp, 0.5, 0.5);
+
+ dlog << LDEBUG << "AAAA 3" ;
+ temp.clear();
+ DLIB_TEST(approx_equal(test(temp), std::sqrt(2.0)));
+ DLIB_TEST(approx_equal(test.squared_norm(), 2));
+ DLIB_TEST(approx_equal(test(test), 0));
+ DLIB_TEST(approx_equal(test.get_distance_function()(test.get_distance_function()), 0));
+
+ dlog << LDEBUG << "AAAA 3.1" ;
+ // make test store the point(1,1,0,3,0)
+ temp.clear(); temp[3] = 3;
+ temp2.clear();
+ temp2[0] = 1;
+ temp2[1] = 1;
+ temp2[3] = 3;
+ test.train(temp, 1, 1);
+
+ dlog << LDEBUG << "AAAA 3.2" ;
+ temp.clear();
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ dlog << LDEBUG << "AAAA 3.3" ;
+ temp[0] = 1;
+ temp[1] = 2;
+ temp[2] = 3;
+ temp[3] = 4;
+ temp[4] = 5;
+ dlog << LDEBUG << "AAAA 3.4" ;
+ double junk = dlib::distance(temp2,temp);
+ dlog << LDEBUG << "AAAA 3.5" ;
+ DLIB_TEST(approx_equal(test(temp), junk) );
+
+ dlog << LDEBUG << "AAAA 4" ;
+ // make test store the point(0,1,0,3,-1)
+ temp.clear();
+ temp[0] = 1;
+ temp[4] = 1;
+ test.train(temp, 1, -1);
+ temp2.clear();
+ temp2[1] = 1;
+ temp2[3] = 3;
+ temp2[4] = -1;
+
+ temp.clear();
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ temp[0] = 1;
+ temp[1] = 2;
+ temp[2] = 3;
+ temp[3] = 4;
+ temp[4] = 5;
+ DLIB_TEST(approx_equal(test(temp), dlib::distance(temp2,temp)));
+
+
+ // make test store the -1*point(0,1,0,3,-1)
+ temp.clear();
+ test.train(temp, -1, 0);
+ temp2[0] = -temp2[0];
+ temp2[1] = -temp2[1];
+ temp2[2] = -temp2[2];
+ temp2[3] = -temp2[3];
+ temp2[4] = -temp2[4];
+
+ dlog << LDEBUG << "AAAA 5" ;
+ temp.clear();
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ temp[0] = 1;
+ temp[1] = 2;
+ temp[2] = -3;
+ temp[3] = 4;
+ temp[4] = 5;
+ DLIB_TEST(approx_equal(test(temp), dlib::distance(temp2,temp)));
+
+
+
+ // make test store the point(0,0,0,0,0)
+ temp.clear();
+ test.train(temp, 0, 0);
+ temp2.clear();
+
+ temp.clear();
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ temp[0] = 1;
+ temp[1] = 2;
+ temp[2] = -3;
+ temp[3] = 4;
+ temp[4] = 5;
+ DLIB_TEST(approx_equal(test(temp), dlib::distance(temp2,temp)));
+ DLIB_TEST(approx_equal(test.get_distance_function()(temp), dlib::distance(temp2,temp)));
+
+
+ dlog << LDEBUG << "AAAA 6" ;
+
+ // make test store the point(1,0,0,0,0)
+ temp.clear();
+ temp[0] = 1;
+ test.train(temp, 1, 1);
+ temp2.clear();
+ temp2[0] = 1;
+
+ temp.clear();
+ DLIB_TEST(approx_equal(test(temp), length(temp2)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(temp2)));
+ DLIB_TEST(approx_equal(test.inner_product(test), length_squared(temp2)));
+ temp[0] = 1;
+ temp[1] = 2;
+ temp[2] = -3;
+ temp[3] = 4;
+ temp[4] = 5;
+ DLIB_TEST(approx_equal(test(temp), dlib::distance(temp2,temp)));
+ DLIB_TEST(approx_equal(test.get_distance_function()(temp), dlib::distance(temp2,temp)));
+ DLIB_TEST(approx_equal(test(test), 0));
+ DLIB_TEST(approx_equal(test.get_distance_function()(test.get_distance_function()), 0));
+
+ dlog << LDEBUG << "AAAA 7" ;
+ }
+
+// ----------------------------------------------------------------------------------------
+
+ template <typename kernel_type>
+ void test_kcentroid_with_offset_sparse_linear_kernel(
+ )
+ /*!
+ requires
+ - kernel_type::sample_type == a std::map<unsigned long,double>
+ - kernel_type == a kernel that just computes a dot product
+ between its inputs + some constant. I.e. a linear kernel
+ wrapped by offset_kernel
+ ensures
+ - tests the kcentroid object with the given kernel
+ !*/
+ {
+ // Here we declare that our samples will be 2 dimensional column vectors.
+ typedef typename kernel_type::sample_type sample_type;
+
+ kernel_type k;
+ kcentroid<kernel_type> test(k,0.001,20);
+
+ sample_type temp, temp2, temp3;
+
+ std::map<unsigned long,double> val, val2;
+
+ const double b = std::sqrt(k.offset);
+
+ temp.clear();
+ temp[0] = 2;
+ temp2.clear();
+ val.clear();
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST(approx_equal(test(temp2), dist(k,temp2,val)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+
+
+ temp2.clear();
+
+ // make test store the point(0,0,0,0,b)
+ val.clear();
+ val[4] = b;
+ test.train(temp2, 0,1);
+
+ temp.clear();
+ temp[0] = 2;
+ dlog << LDEBUG << test(temp) ;
+ dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
+
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST(approx_equal(test(temp2), dist(k,temp2,val)));
+ DLIB_TEST_MSG(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val), 1e-7),
+ test.get_distance_function()(temp2) - dist(k,temp2,val)
+ );
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+ DLIB_TEST(approx_equal(test(test), 0));
+ DLIB_TEST(approx_equal(test.get_distance_function()(test.get_distance_function()), 0, 1e-6));
+
+ // make test store the point(0,0,0,0,0)
+ val.clear();
+ test.train(temp2, 1,-1);
+
+ temp.clear();
+ temp[0] = 2;
+ dlog << LDEBUG << test(temp) ;
+ dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
+
+ DLIB_TEST_MSG(approx_equal(test(temp), dist(k,temp,val)), test(temp) - dist(k,temp,val));
+ DLIB_TEST(approx_equal(test(temp2), dist(k,temp2,val)));
+ DLIB_TEST(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val)));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+ DLIB_TEST(approx_equal(test(test), 0));
+ DLIB_TEST(approx_equal(test.get_distance_function()(test.get_distance_function()), 0));
+
+ val2.clear();
+ val2[0] = 0;
+ val2[1] = 1;
+ val2[2] = 0;
+ val2[3] = 0;
+ val2[4] = b;
+ for (unsigned int i = 0; i < 5; ++i) val[i] += val2[i];
+ temp2.clear();
+ temp2[1] = 1;
+ // make test store the point val
+ test.train(temp2, 1,1);
+
+ temp.clear();
+ temp[0] = 1;
+ temp[2] = 3;
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST_MSG(approx_equal(test(temp2), dist(k,temp2,val), 1e-7),
+ test(temp2) - dist(k,temp2,val));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+
+
+ val2.clear();
+ val2[0] = 0;
+ val2[1] = 1;
+ val2[2] = 2.6;
+ val2[3] = 8;
+ val2[4] = b;
+ for (unsigned int i = 0; i < 5; ++i) val[i] += val2[i];
+
+ temp2.clear();
+ temp2[0] = 0;
+ temp2[1] = 1;
+ temp2[2] = 2.6;
+ temp2[3] = 8;
+ // make test store the point val
+ test.train(temp2, 1,1);
+
+ temp.clear();
+ temp[0] = 1;
+ temp[1] = 1;
+ temp[2] = 3;
+ temp[3] = 0;
+ DLIB_TEST(approx_equal(test(temp), dist(k,temp,val)));
+ DLIB_TEST_MSG(approx_equal(test(temp2), dist(k,temp2,val)), test(temp2) - dist(k,temp2,val));
+ DLIB_TEST(approx_equal(test.squared_norm(), length_squared(val)));
+ DLIB_TEST(approx_equal(test.inner_product(test), length_squared(val)));
+ DLIB_TEST_MSG(approx_equal(test(test), 0, 1e-6), test(test));
+ DLIB_TEST(approx_equal(test.get_distance_function()(test.get_distance_function()), 0));
+ }
+
+// ----------------------------------------------------------------------------------------
+
+ class kcentroid_tester : public tester
+ {
+ public:
+ kcentroid_tester (
+ ) :
+ tester ("test_kcentroid",
+ "Runs tests on the kcentroid components.")
+ {}
+
+ void perform_test (
+ )
+ {
+ // The idea here is to exercize all the various overloads of the kcentroid object. We also want
+ // to exercize the non-overloaded default version. That is why we have these unopt_* linear
+ // kernels
+ test_kcentroid_with_linear_kernel<linear_kernel<matrix<double,5,1> > >();
+ test_kcentroid_with_offset_linear_kernel<offset_kernel<linear_kernel<matrix<double,4,1> > > >();
+ test_kcentroid_with_linear_kernel<unopt_linear_kernel<matrix<double,5,1> > >();
+ test_kcentroid_with_offset_linear_kernel<offset_kernel<unopt_linear_kernel<matrix<double,4,1> > > >();
+ test_kcentroid_with_sparse_linear_kernel<sparse_linear_kernel<std::map<unsigned long,double> > >();
+ test_kcentroid_with_offset_sparse_linear_kernel<offset_kernel<sparse_linear_kernel<std::map<unsigned long,double> > > >();
+ test_kcentroid_with_sparse_linear_kernel<unopt_sparse_linear_kernel<std::map<unsigned long,double> > >();
+ test_kcentroid_with_offset_sparse_linear_kernel<offset_kernel<unopt_sparse_linear_kernel<std::map<unsigned long,double> > > >();
+ }
+ } a;
+
+}
+
+
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