402 lines
16 KiB
C++
402 lines
16 KiB
C++
// Copyright (C) 2013 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#include <dlib/statistics.h>
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#include <dlib/sparse_vector.h>
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#include <map>
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#include "tester.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.cca");
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dlib::rand rnd;
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// ----------------------------------------------------------------------------------------
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std::vector<std::map<unsigned long, double> > make_really_big_test_matrix (
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)
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{
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std::vector<std::map<unsigned long,double> > temp(30000);
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for (unsigned long i = 0; i < temp.size(); ++i)
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{
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for (int k = 0; k < 30; ++k)
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temp[i][rnd.get_random_32bit_number()%10000] = 1;
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}
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return temp;
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}
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template <typename T>
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std::vector<std::map<unsigned long, T> > mat_to_sparse (
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const matrix<T>& A
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)
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{
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std::vector<std::map<unsigned long,T> > temp(A.nr());
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for (long r = 0; r < A.nr(); ++r)
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{
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for (long c = 0; c < A.nc(); ++c)
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{
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temp[r][c] = A(r,c);
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}
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}
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return temp;
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}
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// ----------------------------------------------------------------------------------------
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template <typename EXP>
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matrix<typename EXP::type> rm_zeros (
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const matrix_exp<EXP>& m
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)
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{
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// Do this to avoid trying to correlate super small numbers that are really just
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// zero. Doing this avoids some potential false alarms in the unit tests below.
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return round_zeros(m, max(abs(m))*1e-14);
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}
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// ----------------------------------------------------------------------------------------
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void check_correlation (
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matrix<double> L,
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matrix<double> R,
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const matrix<double>& Ltrans,
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const matrix<double>& Rtrans,
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const matrix<double,0,1>& correlations
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)
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{
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// apply the transforms
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L = L*Ltrans;
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R = R*Rtrans;
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// compute the real correlation values. Store them in A.
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matrix<double> A = compute_correlations(L, R);
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for (long i = 0; i < correlations.size(); ++i)
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{
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// compare what the measured correlation values are (in A) to the
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// predicted values.
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cout << "error: "<< A(i) - correlations(i);
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}
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}
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// ----------------------------------------------------------------------------------------
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void test_cca3()
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{
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print_spinner();
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const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
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const unsigned long m = rank + rnd.get_random_32bit_number()%15;
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const unsigned long n = rank + rnd.get_random_32bit_number()%15;
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const unsigned long n2 = rank + rnd.get_random_32bit_number()%15;
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const unsigned long rank2 = rank + rnd.get_random_32bit_number()%5;
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dlog << LINFO << "m: " << m;
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dlog << LINFO << "n: " << n;
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dlog << LINFO << "n2: " << n2;
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dlog << LINFO << "rank: " << rank;
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dlog << LINFO << "rank2: " << rank2;
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matrix<double> L = randm(m,rank, rnd)*randm(rank,n, rnd);
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//matrix<double> R = randm(m,rank, rnd)*randm(rank,n2, rnd);
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matrix<double> R = L*randm(n,n2);
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//matrix<double> L = randm(m,n, rnd);
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//matrix<double> R = randm(m,n2, rnd);
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matrix<double> Ltrans, Rtrans;
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matrix<double,0,1> correlations;
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{
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correlations = cca(L, R, Ltrans, Rtrans, min(m,n), max(n,n2));
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DLIB_TEST(Ltrans.nc() == Rtrans.nc());
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dlog << LINFO << "correlations: "<< trans(correlations);
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const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
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dlog << LINFO << "correlation error: "<< corr_error;
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DLIB_TEST_MSG(corr_error < 1e-13, Ltrans << "\n\n" << Rtrans);
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const double trans_error = max(abs(L*Ltrans - R*Rtrans));
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dlog << LINFO << "trans_error: "<< trans_error;
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DLIB_TEST(trans_error < 1e-10);
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}
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{
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correlations = cca(mat_to_sparse(L), mat_to_sparse(R), Ltrans, Rtrans, min(m,n), max(n,n2)+6, 4);
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DLIB_TEST(Ltrans.nc() == Rtrans.nc());
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dlog << LINFO << "correlations: "<< trans(correlations);
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dlog << LINFO << "computed cors: " << trans(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)));
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const double trans_error = max(abs(L*Ltrans - R*Rtrans));
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dlog << LINFO << "trans_error: "<< trans_error;
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const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
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dlog << LINFO << "correlation error: "<< corr_error;
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DLIB_TEST_MSG(corr_error < 1e-13, Ltrans << "\n\n" << Rtrans);
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DLIB_TEST(trans_error < 1e-10);
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}
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dlog << LINFO << "*****************************************************";
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}
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void test_cca2()
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{
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print_spinner();
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const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
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const unsigned long m = rank + rnd.get_random_32bit_number()%15;
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const unsigned long n = rank + rnd.get_random_32bit_number()%15;
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const unsigned long n2 = rank + rnd.get_random_32bit_number()%15;
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dlog << LINFO << "m: " << m;
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dlog << LINFO << "n: " << n;
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dlog << LINFO << "n2: " << n2;
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dlog << LINFO << "rank: " << rank;
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matrix<double> L = randm(m,n, rnd);
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matrix<double> R = randm(m,n2, rnd);
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matrix<double> Ltrans, Rtrans;
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matrix<double,0,1> correlations;
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{
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correlations = cca(L, R, Ltrans, Rtrans, min(n,n2), max(n,n2)-min(n,n2));
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DLIB_TEST(Ltrans.nc() == Rtrans.nc());
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dlog << LINFO << "correlations: "<< trans(correlations);
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if (Ltrans.nc() > 1)
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{
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// The CCA projection directions are supposed to be uncorrelated for
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// non-matching pairs of projections.
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const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
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dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
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DLIB_TEST(std::abs(corr_rot1_error) < 1e-10);
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}
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// Matching projection directions should be correlated with the amount of
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// correlation indicated by the return value of cca().
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const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
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dlog << LINFO << "correlation error: "<< corr_error;
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DLIB_TEST(corr_error < 1e-13);
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}
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{
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correlations = cca(mat_to_sparse(L), mat_to_sparse(R), Ltrans, Rtrans, min(n,n2), max(n,n2)-min(n,n2));
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DLIB_TEST(Ltrans.nc() == Rtrans.nc());
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dlog << LINFO << "correlations: "<< trans(correlations);
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if (Ltrans.nc() > 1)
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{
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// The CCA projection directions are supposed to be uncorrelated for
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// non-matching pairs of projections.
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const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
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dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
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DLIB_TEST(std::abs(corr_rot1_error) < 1e-10);
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}
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// Matching projection directions should be correlated with the amount of
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// correlation indicated by the return value of cca().
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const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
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dlog << LINFO << "correlation error: "<< corr_error;
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DLIB_TEST(corr_error < 1e-13);
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}
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dlog << LINFO << "*****************************************************";
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}
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void test_cca1()
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{
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print_spinner();
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const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
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const unsigned long m = rank + rnd.get_random_32bit_number()%15;
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const unsigned long n = rank + rnd.get_random_32bit_number()%15;
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dlog << LINFO << "m: " << m;
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dlog << LINFO << "n: " << n;
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dlog << LINFO << "rank: " << rank;
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matrix<double> T = randm(n,n, rnd);
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matrix<double> L = randm(m,rank, rnd)*randm(rank,n, rnd);
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//matrix<double> L = randm(m,n, rnd);
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matrix<double> R = L*T;
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matrix<double> Ltrans, Rtrans;
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matrix<double,0,1> correlations;
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{
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correlations = cca(L, R, Ltrans, Rtrans, rank);
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DLIB_TEST(Ltrans.nc() == Rtrans.nc());
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if (Ltrans.nc() > 1)
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{
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// The CCA projection directions are supposed to be uncorrelated for
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// non-matching pairs of projections.
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const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
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dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
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DLIB_TEST(std::abs(corr_rot1_error) < 1e-10);
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}
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// Matching projection directions should be correlated with the amount of
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// correlation indicated by the return value of cca().
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const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
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dlog << LINFO << "correlation error: "<< corr_error;
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DLIB_TEST(corr_error < 1e-13);
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const double trans_error = max(abs(L*Ltrans - R*Rtrans));
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dlog << LINFO << "trans_error: "<< trans_error;
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DLIB_TEST(trans_error < 1e-10);
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dlog << LINFO << "correlations: "<< trans(correlations);
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}
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{
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correlations = cca(mat_to_sparse(L), mat_to_sparse(R), Ltrans, Rtrans, rank);
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DLIB_TEST(Ltrans.nc() == Rtrans.nc());
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if (Ltrans.nc() > 1)
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{
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// The CCA projection directions are supposed to be uncorrelated for
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// non-matching pairs of projections.
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const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
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dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
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DLIB_TEST(std::abs(corr_rot1_error) < 1e-10);
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}
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// Matching projection directions should be correlated with the amount of
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// correlation indicated by the return value of cca().
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const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
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dlog << LINFO << "correlation error: "<< corr_error;
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DLIB_TEST(corr_error < 1e-13);
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const double trans_error = max(abs(L*Ltrans - R*Rtrans));
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dlog << LINFO << "trans_error: "<< trans_error;
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DLIB_TEST(trans_error < 1e-9);
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dlog << LINFO << "correlations: "<< trans(correlations);
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}
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dlog << LINFO << "*****************************************************";
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}
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// ----------------------------------------------------------------------------------------
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void test_svd_fast(
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long rank,
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long m,
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long n
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)
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{
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print_spinner();
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matrix<double> A = randm(m,rank,rnd)*randm(rank,n,rnd);
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matrix<double> u,v;
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matrix<double,0,1> w;
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dlog << LINFO << "rank: "<< rank;
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dlog << LINFO << "m: "<< m;
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dlog << LINFO << "n: "<< n;
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svd_fast(A, u, w, v, rank, 2);
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DLIB_TEST(u.nr() == m);
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DLIB_TEST(u.nc() == rank);
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DLIB_TEST(w.nr() == rank);
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DLIB_TEST(w.nc() == 1);
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DLIB_TEST(v.nr() == n);
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DLIB_TEST(v.nc() == rank);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-13);
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svd_fast(mat_to_sparse(A), u, w, v, rank, 2);
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DLIB_TEST(u.nr() == m);
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DLIB_TEST(u.nc() == rank);
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DLIB_TEST(w.nr() == rank);
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DLIB_TEST(w.nc() == 1);
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DLIB_TEST(v.nr() == n);
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DLIB_TEST(v.nc() == rank);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-13);
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svd_fast(A, u, w, v, rank, 0);
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DLIB_TEST(u.nr() == m);
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DLIB_TEST(u.nc() == rank);
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DLIB_TEST(w.nr() == rank);
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DLIB_TEST(w.nc() == 1);
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DLIB_TEST(v.nr() == n);
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DLIB_TEST(v.nc() == rank);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST_MSG(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-9,max(abs(tmp(A - u*diagm(w)*trans(v)))));
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svd_fast(mat_to_sparse(A), u, w, v, rank, 0);
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DLIB_TEST(u.nr() == m);
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DLIB_TEST(u.nc() == rank);
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DLIB_TEST(w.nr() == rank);
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DLIB_TEST(w.nc() == 1);
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DLIB_TEST(v.nr() == n);
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DLIB_TEST(v.nc() == rank);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-10);
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svd_fast(A, u, w, v, rank+5, 0);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-11);
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svd_fast(mat_to_sparse(A), u, w, v, rank+5, 0);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-11);
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svd_fast(A, u, w, v, rank+5, 1);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-12);
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svd_fast(mat_to_sparse(A), u, w, v, rank+5, 1);
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DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
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DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-12);
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}
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void test_svd_fast()
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{
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for (int iter = 0; iter < 1000; ++iter)
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{
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const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
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const unsigned long m = rank + rnd.get_random_32bit_number()%10;
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const unsigned long n = rank + rnd.get_random_32bit_number()%10;
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test_svd_fast(rank, m, n);
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}
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test_svd_fast(1, 1, 1);
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test_svd_fast(1, 2, 2);
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test_svd_fast(1, 1, 2);
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test_svd_fast(1, 2, 1);
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}
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// ----------------------------------------------------------------------------------------
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class test_cca : public tester
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{
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public:
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test_cca (
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) :
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tester ("test_cca",
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"Runs tests on the cca() and svd_fast() routines.")
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{}
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void perform_test (
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)
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{
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for (int i = 0; i < 200; ++i)
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{
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test_cca1();
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test_cca2();
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test_cca3();
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}
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test_svd_fast();
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}
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} a;
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}
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