dlib C++ Library - discriminant_pca.cpp

// Copyright (C) 2009 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include "tester.h"
#include <dlib/svm.h>
#include <dlib/rand.h>
#include <dlib/string.h>
#include <vector>
#include <sstream>
#include <ctime>
namespace 
{
 using namespace test;
 using namespace dlib;
 using namespace std;
 dlib::logger dlog("test.discriminant_pca");
 using dlib::equal;
 class discriminant_pca_tester : public tester
 {
 /*!
 WHAT THIS OBJECT REPRESENTS
 This object represents a unit test. When it is constructed
 it adds itself into the testing framework.
 !*/
 public:
 discriminant_pca_tester (
 ) :
 tester (
 "test_discriminant_pca", // the command line argument name for this test
 "Run tests on the discriminant_pca object.", // the command line argument description
 0 // the number of command line arguments for this test
 )
 {
 thetime = 1407805946;// time(0);
 }
 time_t thetime;
 dlib::rand rnd;
 template <typename dpca_type>
 void test1()
 {
 dpca_type dpca, dpca2, dpca3;
 DLIB_TEST(dpca.in_vector_size() == 0);
 DLIB_TEST(dpca.between_class_weight() == 1);
 DLIB_TEST(dpca.within_class_weight() == 1);
 // generate a bunch of 4 dimensional vectors and compute the normal PCA transformation matrix
 // and just make sure it is a unitary matrix as it should be.
 for (int i = 0; i < 5000; ++i)
 {
 dpca.add_to_total_variance(randm(4,1,rnd));
 DLIB_TEST(dpca.in_vector_size() == 4);
 }
 matrix<double> mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(4)));
 mat = dpca.dpca_matrix(0.9);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(mat.nr())));
 matrix<double> eig;
 dpca.dpca_matrix(mat, eig, 1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(4)));
 // check that all eigen values are grater than 0
 DLIB_TEST(min(eig > 0) == 1);
 DLIB_TEST(eig.size() == mat.nr());
 DLIB_TEST(is_col_vector(eig));
 // check that the eigenvalues are sorted
 double last = eig(0);
 for (long i = 1; i < eig.size(); ++i)
 {
 DLIB_TEST(last >= eig(i));
 }
 {
 matrix<double> mat = dpca.dpca_matrix_of_size(4);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(4)));
 }
 {
 matrix<double> mat = dpca.dpca_matrix_of_size(3);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(3)));
 }
 dpca.set_within_class_weight(5);
 dpca.set_between_class_weight(6);
 DLIB_TEST(dpca.in_vector_size() == 4);
 DLIB_TEST(dpca.within_class_weight() == 5);
 DLIB_TEST(dpca.between_class_weight() == 6);
 ostringstream sout;
 serialize(dpca, sout);
 istringstream sin(sout.str());
 deserialize(dpca2, sin);
 // now make sure the serialization worked
 DLIB_TEST(dpca.in_vector_size() == 4);
 DLIB_TEST(dpca.within_class_weight() == 5);
 DLIB_TEST(dpca.between_class_weight() == 6);
 DLIB_TEST(dpca2.in_vector_size() == 4);
 DLIB_TEST(dpca2.within_class_weight() == 5);
 DLIB_TEST(dpca2.between_class_weight() == 6);
 DLIB_TEST(equal(dpca.dpca_matrix(), dpca2.dpca_matrix(), 1e-10));
 DLIB_TEST(equal(mat, dpca2.dpca_matrix(1), 1e-10));
 DLIB_TEST(equal(dpca.dpca_matrix(1), mat, 1e-10));
 // now test swap
 dpca2.swap(dpca3);
 DLIB_TEST(dpca2.in_vector_size() == 0);
 DLIB_TEST(dpca2.between_class_weight() == 1);
 DLIB_TEST(dpca2.within_class_weight() == 1);
 DLIB_TEST(dpca3.in_vector_size() == 4);
 DLIB_TEST(dpca3.within_class_weight() == 5);
 DLIB_TEST(dpca3.between_class_weight() == 6);
 DLIB_TEST(equal(mat, dpca3.dpca_matrix(1), 1e-10));
 DLIB_TEST((dpca3 + dpca3).in_vector_size() == 4);
 DLIB_TEST((dpca3 + dpca3).within_class_weight() == 5);
 DLIB_TEST((dpca3 + dpca3).between_class_weight() == 6);
 dpca.clear();
 DLIB_TEST(dpca.in_vector_size() == 0);
 DLIB_TEST(dpca.between_class_weight() == 1);
 DLIB_TEST(dpca.within_class_weight() == 1);
 }
 template <typename dpca_type>
 void test2()
 {
 dpca_type dpca, dpca2, dpca3;
 typename dpca_type::column_matrix samp1(4), samp2(4);
 for (int i = 0; i < 5000; ++i)
 {
 dpca.add_to_total_variance(randm(4,1,rnd));
 DLIB_TEST(dpca.in_vector_size() == 4);
 // do this to subtract out the variance along the 3rd axis 
 samp1 = 0,0,0,0;
 samp2 = 0,0,1,0;
 dpca.add_to_within_class_variance(samp1, samp2);
 }
 matrix<double> mat;
 dpca.set_within_class_weight(0);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(4)));
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 4);
 dpca.set_within_class_weight(1000);
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 3);
 // the 3rd column of the transformation matrix should be all zero since
 // we killed all the variation long the 3rd axis
 DLIB_TEST(sum(abs(colm(dpca.dpca_matrix(1),2))) < 1e-5);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(3)));
 }
 template <typename dpca_type>
 void test3()
 {
 dpca_type dpca, dpca2, dpca3;
 typename dpca_type::column_matrix samp1(4), samp2(4);
 for (int i = 0; i < 5000; ++i)
 {
 dpca.add_to_total_variance(randm(4,1,rnd));
 DLIB_TEST(dpca.in_vector_size() == 4);
 // do this to subtract out the variance along the 3rd axis 
 samp1 = 0,0,0,0;
 samp2 = 0,0,1,0;
 dpca.add_to_within_class_variance(samp1, samp2);
 // do this to subtract out the variance along the 1st axis 
 samp1 = 0,0,0,0;
 samp2 = 1,0,0,0;
 dpca.add_to_within_class_variance(samp1, samp2);
 }
 matrix<double> mat;
 dpca.set_within_class_weight(0);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(4)));
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 4);
 dpca.set_within_class_weight(10000);
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 2);
 // the 1st and 3rd columns of the transformation matrix should be all zero since
 // we killed all the variation long the 1st and 3rd axes
 DLIB_TEST(sum(abs(colm(dpca.dpca_matrix(1),2))) < 1e-5);
 DLIB_TEST(sum(abs(colm(dpca.dpca_matrix(1),0))) < 1e-5);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(2)));
 }
 template <typename dpca_type>
 void test4()
 {
 dpca_type dpca, dpca2, dpca3;
 dpca_type add_dpca1, add_dpca2, add_dpca3, add_dpca4, sum_dpca;
 typename dpca_type::column_matrix samp1(4), samp2(4), samp;
 for (int i = 0; i < 5000; ++i)
 {
 samp = randm(4,1,rnd);
 dpca.add_to_total_variance(samp);
 add_dpca4.add_to_total_variance(samp);
 DLIB_TEST(dpca.in_vector_size() == 4);
 // do this to subtract out the variance along the 3rd axis 
 samp1 = 0,0,0,0;
 samp2 = 0,0,1,0;
 dpca.add_to_within_class_variance(samp1, samp2);
 add_dpca1.add_to_within_class_variance(samp1, samp2);
 // do this to subtract out the variance along the 1st axis 
 samp1 = 0,0,0,0;
 samp2 = 1,0,0,0;
 dpca.add_to_within_class_variance(samp1, samp2);
 add_dpca2.add_to_within_class_variance(samp1, samp2);
 // do this to add the variance along the 3rd axis back in
 samp1 = 0,0,0,0;
 samp2 = 0,0,1,0;
 dpca.add_to_between_class_variance(samp1, samp2);
 add_dpca3.add_to_between_class_variance(samp1, samp2);
 }
 matrix<double> mat, mat2;
 sum_dpca += dpca_type() + dpca_type() + add_dpca1 + dpca_type() + add_dpca2 + add_dpca3 + add_dpca4;
 dpca.set_within_class_weight(0);
 dpca.set_between_class_weight(0);
 sum_dpca.set_within_class_weight(0);
 sum_dpca.set_between_class_weight(0);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat, sum_dpca.dpca_matrix(1), 1e-10));
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(4)));
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 4);
 dpca.set_within_class_weight(10000);
 sum_dpca.set_within_class_weight(10000);
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 2);
 // the 1st and 3rd columns of the transformation matrix should be all zero since
 // we killed all the variation long the 1st and 3rd axes
 DLIB_TEST(sum(abs(colm(dpca.dpca_matrix(1),2))) < 1e-4);
 DLIB_TEST(sum(abs(colm(dpca.dpca_matrix(1),0))) < 1e-4);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(2)));
 DLIB_TEST_MSG(equal(mat, mat2=sum_dpca.dpca_matrix(1), 1e-9), max(abs(mat - mat2)));
 // now add the variance back in using the between class weight
 dpca.set_within_class_weight(0);
 dpca.set_between_class_weight(1);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(4)));
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 4);
 dpca.set_within_class_weight (10000);
 dpca.set_between_class_weight(100000);
 sum_dpca.set_within_class_weight (10000);
 sum_dpca.set_between_class_weight(100000);
 DLIB_TEST(dpca.dpca_matrix(1).nr() == 3);
 // the first column should be all zeros
 DLIB_TEST(sum(abs(colm(dpca.dpca_matrix(1),0))) < 1e-5);
 mat = dpca.dpca_matrix(1);
 DLIB_TEST(equal(mat*trans(mat), identity_matrix<double>(3)));
 DLIB_TEST(equal(mat, sum_dpca.dpca_matrix(1)));
 }
 template <typename dpca_type>
 void test5()
 {
 dpca_type dpca, dpca2;
 typename dpca_type::column_matrix samp1(4), samp2(4);
 samp1 = 0,0,0,0;
 samp2 = 0,0,1,0;
 for (int i = 0; i < 5000; ++i)
 {
 dpca.add_to_between_class_variance(samp1, samp2);
 dpca2.add_to_total_variance(samp1);
 dpca2.add_to_total_variance(samp2);
 }
 matrix<double> mat, eig;
 dpca.dpca_matrix(mat, eig, 1);
 // make sure the eigenvalues come out the way they should for this simple data set
 DLIB_TEST(eig.size() == 1);
 DLIB_TEST_MSG(abs(eig(0) - 1) < 1e-10, abs(eig(0) - 1));
 dpca2.dpca_matrix(mat, eig, 1);
 // make sure the eigenvalues come out the way they should for this simple data set
 DLIB_TEST(eig.size() == 1);
 DLIB_TEST(abs(eig(0) - 0.25) < 1e-10);
 }
 void perform_test (
 )
 {
 ++thetime;
 typedef matrix<double,0,1> sample_type;
 typedef discriminant_pca<sample_type> dpca_type;
 dlog << LINFO << "time seed: " << thetime;
 rnd.set_seed(cast_to_string(thetime));
 test5<dpca_type>();
 for (int i = 0; i < 10; ++i)
 {
 print_spinner();
 test1<dpca_type>();
 print_spinner();
 test2<dpca_type>();
 print_spinner();
 test3<dpca_type>();
 print_spinner();
 test4<dpca_type>();
 }
 }
 };
 // Create an instance of this object. Doing this causes this test
 // to be automatically inserted into the testing framework whenever this cpp file
 // is linked into the project. Note that since we are inside an unnamed-namespace 
 // we won't get any linker errors about the symbol a being defined multiple times. 
 discriminant_pca_tester a;
}