mirror of
https://github.com/davisking/dlib.git
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Added a lot more tests for the kcentroid object.
--HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402959
This commit is contained in:
Davis King
parent
8791dcc9c2
commit
6bb0f8d05d
@ -39,6 +39,7 @@ set (tests
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hash_set.cpp
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hash_table.cpp
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image.cpp
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kcentroid.cpp
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lz77_buffer.cpp
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map.cpp
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matrix.cpp
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682
dlib/test/kcentroid.cpp
Executable file
682
dlib/test/kcentroid.cpp
Executable file
@ -0,0 +1,682 @@
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// Copyright (C) 2009 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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#include <dlib/matrix.h>
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#include <sstream>
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#include <string>
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#include <cstdlib>
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#include <ctime>
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#include <vector>
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#include <map>
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#include "../stl_checked.h"
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#include "../array.h"
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#include "../rand.h"
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#include "checkerboard.h"
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#include <dlib/statistics.h>
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#include "tester.h"
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#include <dlib/svm_threaded.h>
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namespace
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{
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using namespace test;
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using namespace dlib;
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using namespace std;
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logger dlog("test.kcentroid");
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// ----------------------------------------------------------------------------------------
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template <typename T>
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struct unopt_sparse_linear_kernel
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{
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typedef typename T::value_type::second_type scalar_type;
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typedef T sample_type;
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typedef dlib::memory_manager<char>::kernel_1a mem_manager_type;
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scalar_type operator() (
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const sample_type& a,
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const sample_type& b
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) const
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{
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return sparse_vector::dot_product(a,b);
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}
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bool operator== (
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const unopt_sparse_linear_kernel& k
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) const
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{
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return true;
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}
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};
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template <typename T>
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struct unopt_linear_kernel
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{
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typedef typename T::type scalar_type;
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typedef T sample_type;
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typedef typename T::mem_manager_type mem_manager_type;
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scalar_type operator() (
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const sample_type& a,
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const sample_type& b
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) const
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{
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return trans(a)*b;
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}
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bool operator== (
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const unopt_linear_kernel& k
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) const
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{
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return true;
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}
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};
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bool approx_equal(double a, double b)
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{
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return (std::abs(a-b) < 1000*(std::numeric_limits<double>::epsilon()));
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}
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bool approx_equal(double a, double b, double eps)
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{
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return (std::abs(a-b) < eps);
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}
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template <typename K>
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double dist (
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const K& k,
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const matrix<double,4,1>& a,
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const matrix<double,5,1>& b
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)
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/*!
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ensures
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- returns the distance between the a and b vectors in the
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feature space defined by the given kernel k.
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!*/
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{
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const double bias = std::sqrt(k.offset);
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return std::sqrt(length_squared(a-colm(b,0,4)) + std::pow(b(4)-bias,2.0));
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}
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template <typename K>
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double dist (
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const K& k,
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std::map<long,double> a,
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std::map<long,double> b
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)
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/*!
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ensures
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- returns the distance between the a and b vectors in the
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feature space defined by the given kernel k.
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!*/
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{
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double temp = 0;
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const double bias = std::sqrt(k.offset);
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temp += std::pow(a[0]-b[0],2.0);
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temp += std::pow(a[1]-b[1],2.0);
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temp += std::pow(a[2]-b[2],2.0);
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temp += std::pow(a[3]-b[3],2.0);
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temp += std::pow(bias-b[4],2.0);
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return std::sqrt(temp);
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}
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// ----------------------------------------------------------------------------------------
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template <typename kernel_type>
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void test_kcentroid_with_linear_kernel(
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)
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/*!
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requires
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- kernel_type::sample_type == a matrix<double,5,1>
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- kernel_type == a kernel that just computes a dot product
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between its inputs. I.e. a linear kernel
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ensures
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- tests the kcentroid object with the given kernel
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!*/
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{
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// Here we declare that our samples will be 2 dimensional column vectors.
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typedef typename kernel_type::sample_type sample_type;
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kcentroid<kernel_type> test(kernel_type(),0.001,20);
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sample_type temp, temp2;
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temp = 2,0,0,0,0;
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dlog << LDEBUG << test(temp) ;
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dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
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DLIB_CASSERT(approx_equal(test(temp), 2), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 0), "");
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// make test store the point(2,0,0,0,0)
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test.train(temp, 0, 1);
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dlog << LDEBUG << test(temp) ;
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dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
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DLIB_CASSERT(approx_equal(test(temp), 0), "");
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DLIB_CASSERT(approx_equal(test.get_distance_function()(temp), 0), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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temp = 0,2,0,0,0;
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dlog << LDEBUG << test(temp) ;
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DLIB_CASSERT(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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// make test store the point(0,2,0,0,0)
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test.train(temp, 0, 1);
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dlog << LDEBUG << test(temp) ;
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DLIB_CASSERT(approx_equal(test(temp), 0), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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temp = 2,0,0,0,0;
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DLIB_CASSERT(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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// make test store the point(1,1,0,0,0)
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test.train(temp, 0.5, 0.5);
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temp = 0;
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DLIB_CASSERT(approx_equal(test(temp), std::sqrt(2.0)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 2), "");
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// make test store the point(1,1,0,3,0)
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temp = 0,0,0,3,0;
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temp2 = 1,1,0,3,0;
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test.train(temp, 1, 1);
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temp = 0;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
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temp = 1,2,3,4,5;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2-temp)), "");
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DLIB_CASSERT(approx_equal(test.get_distance_function()(temp), length(temp2-temp)), "");
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// make test store the point(0,1,0,3,-1)
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temp = 1,0,0,0,1;
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test.train(temp, 1, -1);
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temp2 = 0,1,0,3,-1;
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temp = 0;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
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temp = 1,2,3,4,5;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2-temp)), "");
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// make test store the -1*point(0,1,0,3,-1)
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temp = 0,0,0,0,0;
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test.train(temp, -1, 0);
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temp2 = -temp2;
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temp = 0;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
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temp = 1,2,-3,4,5;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2-temp)), "");
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// make test store the point(0,0,0,0,0)
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temp = 0,0,0,0,0;
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test.train(temp, 0, 0);
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temp2 = 0;
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temp = 0;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
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temp = 1,2,-3,4,5;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2-temp)), "");
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// make test store the point(1,0,0,0,0)
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temp = 1,0,0,0,0;
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test.train(temp, 1, 1);
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temp2 = 1,0,0,0,0;
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temp = 0;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
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DLIB_CASSERT(approx_equal(test.inner_product(test), length_squared(temp2)), "");
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temp = 1,2,-3,4,5;
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DLIB_CASSERT(approx_equal(test(temp), length(temp2-temp)), "");
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DLIB_CASSERT(approx_equal(test(test), 0), "");
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DLIB_CASSERT(approx_equal(test.get_distance_function()(test.get_distance_function()), 0), "");
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}
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// ----------------------------------------------------------------------------------------
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template <typename kernel_type>
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void test_kcentroid_with_offset_linear_kernel(
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)
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/*!
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requires
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- kernel_type::sample_type == a matrix<double,4,1>
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- kernel_type == a kernel that just computes a dot product
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between its inputs + some constant. I.e. a linear kernel
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wrapped by offset_kernel
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ensures
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- tests the kcentroid object with the given kernel
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!*/
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{
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// Here we declare that our samples will be 2 dimensional column vectors.
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typedef typename kernel_type::sample_type sample_type;
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kernel_type k;
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kcentroid<kernel_type> test(k,0.001,20);
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sample_type temp, temp2, temp3;
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matrix<double,5,1> val, val2;
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const double b = std::sqrt(k.offset);
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temp = 2,0,0,0;
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temp2 = 0;
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val = 0;
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DLIB_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
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DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
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temp2 = 0;
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// make test store the point(0,0,0,0,b)
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val = 0,0,0,0,b;
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test.train(temp2, 0,1);
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temp = 2,0,0,0;
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dlog << LDEBUG << test(temp) ;
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dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
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DLIB_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
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DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
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DLIB_CASSERT(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val), 1e-6),
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test.get_distance_function()(temp2) - dist(k,temp2,val) << " compare to: " <<
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test(temp2) - dist(k,temp2,val)
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);
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
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// make test store the point(0,0,0,0,0)
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val = 0,0,0,0,0;
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test.train(temp2, 1,-1);
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DLIB_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
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DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
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DLIB_CASSERT(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val)),
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test.get_distance_function()(temp2) - dist(k,temp2,val) << " compare to: " <<
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test(temp2) - dist(k,temp2,val)
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);
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
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val2 = 0,1,0,0,b;
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val += val2;
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temp2 = 0,1,0,0;
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// make test store the point val
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test.train(temp2, 1,1);
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temp = 1,0,3,0;
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DLIB_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
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DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
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DLIB_CASSERT(approx_equal(test(test), 0), "");
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val2 = 0,1,2.6,8,b;
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val += val2;
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temp2 = 0,1,2.6,8;
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// make test store the point val
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test.train(temp2, 1,1);
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temp = 1,1,3,0;
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DLIB_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
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DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), test(temp2) - dist(k,temp2,val));
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
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DLIB_CASSERT(approx_equal(test.inner_product(test), length_squared(val)), "");
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DLIB_CASSERT(approx_equal(test(test), 0), "");
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DLIB_CASSERT(approx_equal(test.get_distance_function()(test.get_distance_function()), 0),
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test.get_distance_function()(test.get_distance_function()));
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}
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// ----------------------------------------------------------------------------------------
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template <typename kernel_type>
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void test_kcentroid_with_sparse_linear_kernel(
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)
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/*!
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requires
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- kernel_type::sample_type == a std::map<long,double>
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- kernel_type == a kernel that just computes a dot product
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between its inputs. I.e. a linear kernel
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ensures
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- tests the kcentroid object with the given kernel
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!*/
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{
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using namespace dlib::sparse_vector;
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// Here we declare that our samples will be 2 dimensional column vectors.
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typedef typename kernel_type::sample_type sample_type;
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kcentroid<kernel_type> test(kernel_type(),0.001,20);
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dlog << LDEBUG << "AAAA 1" ;
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sample_type temp, temp2;
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temp[0] = 2;
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dlog << LDEBUG << test(temp) ;
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dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
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DLIB_CASSERT(approx_equal(test(temp), 2), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 0), "");
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// make test store the point(2,0,0,0,0)
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test.train(temp, 0, 1);
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dlog << LDEBUG << test(temp) ;
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dlog << LDEBUG << "squared_norm(): " << test.squared_norm() ;
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DLIB_CASSERT(approx_equal(test(temp), 0), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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dlog << LDEBUG << "AAAA 2" ;
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temp.clear();
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temp[1] = 2;
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dlog << LDEBUG << test(temp) ;
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DLIB_CASSERT(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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// make test store the point(0,2,0,0,0)
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test.train(temp, 0, 1);
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dlog << LDEBUG << test(temp) ;
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DLIB_CASSERT(approx_equal(test(temp), 0), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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temp.clear();
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temp[0] = 2;
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DLIB_CASSERT(approx_equal(test(temp), std::sqrt(2*2 + 2*2.0)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 4), "");
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// make test store the point(1,1,0,0,0)
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test.train(temp, 0.5, 0.5);
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dlog << LDEBUG << "AAAA 3" ;
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temp.clear();
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DLIB_CASSERT(approx_equal(test(temp), std::sqrt(2.0)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), 2), "");
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DLIB_CASSERT(approx_equal(test(test), 0), "");
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DLIB_CASSERT(approx_equal(test.get_distance_function()(test.get_distance_function()), 0), "");
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dlog << LDEBUG << "AAAA 3.1" ;
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// make test store the point(1,1,0,3,0)
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temp.clear(); temp[3] = 3;
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temp2.clear();
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temp2[0] = 1;
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temp2[1] = 1;
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temp2[3] = 3;
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test.train(temp, 1, 1);
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dlog << LDEBUG << "AAAA 3.2" ;
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temp.clear();
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DLIB_CASSERT(approx_equal(test(temp), length(temp2)), "");
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DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
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dlog << LDEBUG << "AAAA 3.3" ;
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temp[0] = 1;
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temp[1] = 2;
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temp[2] = 3;
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temp[3] = 4;
|
||||
temp[4] = 5;
|
||||
dlog << LDEBUG << "AAAA 3.4" ;
|
||||
double junk = sparse_vector::distance(temp2,temp);
|
||||
dlog << LDEBUG << "AAAA 3.5" ;
|
||||
DLIB_CASSERT(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_CASSERT(approx_equal(test(temp), length(temp2)), "");
|
||||
DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
|
||||
temp[0] = 1;
|
||||
temp[1] = 2;
|
||||
temp[2] = 3;
|
||||
temp[3] = 4;
|
||||
temp[4] = 5;
|
||||
DLIB_CASSERT(approx_equal(test(temp), sparse_vector::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_CASSERT(approx_equal(test(temp), length(temp2)), "");
|
||||
DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
|
||||
temp[0] = 1;
|
||||
temp[1] = 2;
|
||||
temp[2] = -3;
|
||||
temp[3] = 4;
|
||||
temp[4] = 5;
|
||||
DLIB_CASSERT(approx_equal(test(temp), sparse_vector::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_CASSERT(approx_equal(test(temp), length(temp2)), "");
|
||||
DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
|
||||
temp[0] = 1;
|
||||
temp[1] = 2;
|
||||
temp[2] = -3;
|
||||
temp[3] = 4;
|
||||
temp[4] = 5;
|
||||
DLIB_CASSERT(approx_equal(test(temp), sparse_vector::distance(temp2,temp)), "");
|
||||
DLIB_CASSERT(approx_equal(test.get_distance_function()(temp), sparse_vector::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_CASSERT(approx_equal(test(temp), length(temp2)), "");
|
||||
DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(temp2)), "");
|
||||
DLIB_CASSERT(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_CASSERT(approx_equal(test(temp), sparse_vector::distance(temp2,temp)), "");
|
||||
DLIB_CASSERT(approx_equal(test.get_distance_function()(temp), sparse_vector::distance(temp2,temp)), "");
|
||||
DLIB_CASSERT(approx_equal(test(test), 0), "");
|
||||
DLIB_CASSERT(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<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
|
||||
!*/
|
||||
{
|
||||
using namespace sparse_vector;
|
||||
// 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<long,double> val, val2;
|
||||
|
||||
const double b = std::sqrt(k.offset);
|
||||
|
||||
temp.clear();
|
||||
temp[0] = 2;
|
||||
temp2.clear();
|
||||
val.clear();
|
||||
DLIB_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
|
||||
DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
|
||||
DLIB_CASSERT(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_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
|
||||
DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
|
||||
DLIB_CASSERT(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val), 1e-7),
|
||||
test.get_distance_function()(temp2) - dist(k,temp2,val)
|
||||
);
|
||||
DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
|
||||
DLIB_CASSERT(approx_equal(test(test), 0), "");
|
||||
DLIB_CASSERT(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_CASSERT(approx_equal(test(temp), dist(k,temp,val)), test(temp) - dist(k,temp,val));
|
||||
DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
|
||||
DLIB_CASSERT(approx_equal(test.get_distance_function()(temp2), dist(k,temp2,val)), "");
|
||||
DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
|
||||
DLIB_CASSERT(approx_equal(test(test), 0), "");
|
||||
DLIB_CASSERT(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_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
|
||||
DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), "");
|
||||
DLIB_CASSERT(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_CASSERT(approx_equal(test(temp), dist(k,temp,val)), "");
|
||||
DLIB_CASSERT(approx_equal(test(temp2), dist(k,temp2,val)), test(temp2) - dist(k,temp2,val));
|
||||
DLIB_CASSERT(approx_equal(test.squared_norm(), length_squared(val)), "");
|
||||
DLIB_CASSERT(approx_equal(test.inner_product(test), length_squared(val)), "");
|
||||
DLIB_CASSERT(approx_equal(test(test), 0), "");
|
||||
DLIB_CASSERT(approx_equal(test.get_distance_function()(test.get_distance_function()), 0), "");
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------------------------------
|
||||
|
||||
class kcentroid : public tester
|
||||
{
|
||||
public:
|
||||
kcentroid (
|
||||
) :
|
||||
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<long,double> > >();
|
||||
test_kcentroid_with_offset_sparse_linear_kernel<offset_kernel<sparse_linear_kernel<std::map<long,double> > > >();
|
||||
test_kcentroid_with_sparse_linear_kernel<unopt_sparse_linear_kernel<std::map<long,double> > >();
|
||||
test_kcentroid_with_offset_sparse_linear_kernel<offset_kernel<unopt_sparse_linear_kernel<std::map<long,double> > > >();
|
||||
}
|
||||
} a;
|
||||
|
||||
}
|
||||
|
||||
|
||||
@ -49,6 +49,7 @@ SRC += hash_map.cpp
|
||||
SRC += hash_set.cpp
|
||||
SRC += hash_table.cpp
|
||||
SRC += image.cpp
|
||||
SRC += kcentroid.cpp
|
||||
SRC += lz77_buffer.cpp
|
||||
SRC += map.cpp
|
||||
SRC += matrix.cpp
|
||||
|
||||
Loading…
Reference in New Issue
Block a user