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-rw-r--r--ml/dlib/dlib/test/cca.cpp460
1 files changed, 0 insertions, 460 deletions
diff --git a/ml/dlib/dlib/test/cca.cpp b/ml/dlib/dlib/test/cca.cpp
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--- a/ml/dlib/dlib/test/cca.cpp
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@@ -1,460 +0,0 @@
-// Copyright (C) 2013 Davis E. King (davis@dlib.net)
-// License: Boost Software License See LICENSE.txt for the full license.
-
-#include <dlib/statistics.h>
-#include <dlib/sparse_vector.h>
-#include <dlib/timing.h>
-#include <map>
-
-#include "tester.h"
-
-namespace
-{
- using namespace test;
- using namespace dlib;
- using namespace std;
-
- logger dlog("test.cca");
-
- dlib::rand rnd;
-// ----------------------------------------------------------------------------------------
-
- /*
- std::vector<std::map<unsigned long, double> > make_really_big_test_matrix (
- )
- {
- std::vector<std::map<unsigned long,double> > temp(30000);
- for (unsigned long i = 0; i < temp.size(); ++i)
- {
- for (int k = 0; k < 30; ++k)
- temp[i][rnd.get_random_32bit_number()%10000] = 1;
- }
- return temp;
- }
- */
-
- template <typename T>
- std::vector<std::map<unsigned long, T> > mat_to_sparse (
- const matrix<T>& A
- )
- {
- std::vector<std::map<unsigned long,T> > temp(A.nr());
- for (long r = 0; r < A.nr(); ++r)
- {
- for (long c = 0; c < A.nc(); ++c)
- {
- temp[r][c] = A(r,c);
- }
- }
- return temp;
- }
-
-// ----------------------------------------------------------------------------------------
-
- template <typename EXP>
- matrix<typename EXP::type> rm_zeros (
- const matrix_exp<EXP>& m
- )
- {
- // Do this to avoid trying to correlate super small numbers that are really just
- // zero. Doing this avoids some potential false alarms in the unit tests below.
- return round_zeros(m, max(abs(m))*1e-14);
- }
-
-// ----------------------------------------------------------------------------------------
-
- /*
- void check_correlation (
- matrix<double> L,
- matrix<double> R,
- const matrix<double>& Ltrans,
- const matrix<double>& Rtrans,
- const matrix<double,0,1>& correlations
- )
- {
- // apply the transforms
- L = L*Ltrans;
- R = R*Rtrans;
-
- // compute the real correlation values. Store them in A.
- matrix<double> A = compute_correlations(L, R);
-
- for (long i = 0; i < correlations.size(); ++i)
- {
- // compare what the measured correlation values are (in A) to the
- // predicted values.
- cout << "error: "<< A(i) - correlations(i);
- }
- }
- */
-
-// ----------------------------------------------------------------------------------------
-
- void test_cca3()
- {
- print_spinner();
- const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
- const unsigned long m = rank + rnd.get_random_32bit_number()%15;
- const unsigned long n = rank + rnd.get_random_32bit_number()%15;
- const unsigned long n2 = rank + rnd.get_random_32bit_number()%15;
- const unsigned long rank2 = rank + rnd.get_random_32bit_number()%5;
-
- dlog << LINFO << "m: " << m;
- dlog << LINFO << "n: " << n;
- dlog << LINFO << "n2: " << n2;
- dlog << LINFO << "rank: " << rank;
- dlog << LINFO << "rank2: " << rank2;
-
-
- matrix<double> L = randm(m,rank, rnd)*randm(rank,n, rnd);
- //matrix<double> R = randm(m,rank, rnd)*randm(rank,n2, rnd);
- matrix<double> R = L*randm(n,n2, rnd);
- //matrix<double> L = randm(m,n, rnd);
- //matrix<double> R = randm(m,n2, rnd);
-
- matrix<double> Ltrans, Rtrans;
- matrix<double,0,1> correlations;
-
- {
- correlations = cca(L, R, Ltrans, Rtrans, min(m,n), max(n,n2));
- DLIB_TEST(Ltrans.nc() == Rtrans.nc());
- dlog << LINFO << "correlations: "<< trans(correlations);
-
- const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
- dlog << LINFO << "correlation error: "<< corr_error;
- DLIB_TEST_MSG(corr_error < 1e-13, Ltrans << "\n\n" << Rtrans);
-
- const double trans_error = max(abs(L*Ltrans - R*Rtrans));
- dlog << LINFO << "trans_error: "<< trans_error;
- DLIB_TEST_MSG(trans_error < 1e-9, trans_error);
- }
- {
- correlations = cca(mat_to_sparse(L), mat_to_sparse(R), Ltrans, Rtrans, min(m,n), max(n,n2)+6, 4);
- DLIB_TEST(Ltrans.nc() == Rtrans.nc());
- dlog << LINFO << "correlations: "<< trans(correlations);
- dlog << LINFO << "computed cors: " << trans(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)));
-
- const double trans_error = max(abs(L*Ltrans - R*Rtrans));
- dlog << LINFO << "trans_error: "<< trans_error;
- const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
- dlog << LINFO << "correlation error: "<< corr_error;
- DLIB_TEST_MSG(corr_error < 1e-13, Ltrans << "\n\n" << Rtrans);
-
- DLIB_TEST(trans_error < 2e-9);
- }
-
- dlog << LINFO << "*****************************************************";
- }
-
- void test_cca2()
- {
- print_spinner();
- const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
- const unsigned long m = rank + rnd.get_random_32bit_number()%15;
- const unsigned long n = rank + rnd.get_random_32bit_number()%15;
- const unsigned long n2 = rank + rnd.get_random_32bit_number()%15;
-
- dlog << LINFO << "m: " << m;
- dlog << LINFO << "n: " << n;
- dlog << LINFO << "n2: " << n2;
- dlog << LINFO << "rank: " << rank;
-
-
- matrix<double> L = randm(m,n, rnd);
- matrix<double> R = randm(m,n2, rnd);
-
- matrix<double> Ltrans, Rtrans;
- matrix<double,0,1> correlations;
-
- {
- correlations = cca(L, R, Ltrans, Rtrans, min(n,n2), max(n,n2)-min(n,n2));
- DLIB_TEST(Ltrans.nc() == Rtrans.nc());
- dlog << LINFO << "correlations: "<< trans(correlations);
-
- if (Ltrans.nc() > 1)
- {
- // The CCA projection directions are supposed to be uncorrelated for
- // non-matching pairs of projections.
- const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
- dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
- DLIB_TEST(std::abs(corr_rot1_error) < 1e-10);
- }
- // Matching projection directions should be correlated with the amount of
- // correlation indicated by the return value of cca().
- const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
- dlog << LINFO << "correlation error: "<< corr_error;
- DLIB_TEST(corr_error < 1e-13);
- }
- {
- correlations = cca(mat_to_sparse(L), mat_to_sparse(R), Ltrans, Rtrans, min(n,n2), max(n,n2)-min(n,n2));
- DLIB_TEST(Ltrans.nc() == Rtrans.nc());
- dlog << LINFO << "correlations: "<< trans(correlations);
-
- if (Ltrans.nc() > 1)
- {
- // The CCA projection directions are supposed to be uncorrelated for
- // non-matching pairs of projections.
- const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
- dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
- DLIB_TEST(std::abs(corr_rot1_error) < 1e-10);
- }
- // Matching projection directions should be correlated with the amount of
- // correlation indicated by the return value of cca().
- const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
- dlog << LINFO << "correlation error: "<< corr_error;
- DLIB_TEST(corr_error < 1e-13);
- }
-
- dlog << LINFO << "*****************************************************";
- }
-
- void test_cca1()
- {
- print_spinner();
- const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
- const unsigned long m = rank + rnd.get_random_32bit_number()%15;
- const unsigned long n = rank + rnd.get_random_32bit_number()%15;
-
- dlog << LINFO << "m: " << m;
- dlog << LINFO << "n: " << n;
- dlog << LINFO << "rank: " << rank;
-
- matrix<double> T = randm(n,n, rnd);
-
- matrix<double> L = randm(m,rank, rnd)*randm(rank,n, rnd);
- //matrix<double> L = randm(m,n, rnd);
- matrix<double> R = L*T;
-
- matrix<double> Ltrans, Rtrans;
- matrix<double,0,1> correlations;
-
- {
- correlations = cca(L, R, Ltrans, Rtrans, rank);
- DLIB_TEST(Ltrans.nc() == Rtrans.nc());
- if (Ltrans.nc() > 1)
- {
- // The CCA projection directions are supposed to be uncorrelated for
- // non-matching pairs of projections.
- const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
- dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
- DLIB_TEST(std::abs(corr_rot1_error) < 2e-9);
- }
- // Matching projection directions should be correlated with the amount of
- // correlation indicated by the return value of cca().
- const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
- dlog << LINFO << "correlation error: "<< corr_error;
- DLIB_TEST(corr_error < 1e-13);
-
- const double trans_error = max(abs(L*Ltrans - R*Rtrans));
- dlog << LINFO << "trans_error: "<< trans_error;
- DLIB_TEST(trans_error < 2e-9);
-
- dlog << LINFO << "correlations: "<< trans(correlations);
- }
- {
- correlations = cca(mat_to_sparse(L), mat_to_sparse(R), Ltrans, Rtrans, rank);
- DLIB_TEST(Ltrans.nc() == Rtrans.nc());
- if (Ltrans.nc() > 1)
- {
- // The CCA projection directions are supposed to be uncorrelated for
- // non-matching pairs of projections.
- const double corr_rot1_error = max(abs(compute_correlations(rm_zeros(L*rotate<0,1>(Ltrans)), rm_zeros(R*Rtrans))));
- dlog << LINFO << "corr_rot1_error: "<< corr_rot1_error;
- DLIB_TEST(std::abs(corr_rot1_error) < 2e-9);
- }
- // Matching projection directions should be correlated with the amount of
- // correlation indicated by the return value of cca().
- const double corr_error = max(abs(compute_correlations(rm_zeros(L*Ltrans), rm_zeros(R*Rtrans)) - correlations));
- dlog << LINFO << "correlation error: "<< corr_error;
- DLIB_TEST(corr_error < 1e-13);
-
- const double trans_error = max(abs(L*Ltrans - R*Rtrans));
- dlog << LINFO << "trans_error: "<< trans_error;
- DLIB_TEST(trans_error < 2e-9);
-
- dlog << LINFO << "correlations: "<< trans(correlations);
- }
-
- dlog << LINFO << "*****************************************************";
- }
-
-// ----------------------------------------------------------------------------------------
-
- void test_svd_fast(
- long rank,
- long m,
- long n
- )
- {
- print_spinner();
- matrix<double> A = randm(m,rank,rnd)*randm(rank,n,rnd);
- matrix<double> u,v;
- matrix<double,0,1> w;
-
- dlog << LINFO << "rank: "<< rank;
- dlog << LINFO << "m: "<< m;
- dlog << LINFO << "n: "<< n;
-
- svd_fast(A, u, w, v, rank, 2);
- DLIB_TEST(u.nr() == m);
- DLIB_TEST(u.nc() == rank);
- DLIB_TEST(w.nr() == rank);
- DLIB_TEST(w.nc() == 1);
- DLIB_TEST(v.nr() == n);
- DLIB_TEST(v.nc() == rank);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
-
- DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-13);
- svd_fast(mat_to_sparse(A), u, w, v, rank, 2);
- DLIB_TEST(u.nr() == m);
- DLIB_TEST(u.nc() == rank);
- DLIB_TEST(w.nr() == rank);
- DLIB_TEST(w.nc() == 1);
- DLIB_TEST(v.nr() == n);
- DLIB_TEST(v.nc() == rank);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-13);
-
- svd_fast(A, u, w, v, rank, 0);
- DLIB_TEST(u.nr() == m);
- DLIB_TEST(u.nc() == rank);
- DLIB_TEST(w.nr() == rank);
- DLIB_TEST(w.nc() == 1);
- DLIB_TEST(v.nr() == n);
- DLIB_TEST(v.nc() == rank);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST_MSG(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-9,max(abs(tmp(A - u*diagm(w)*trans(v)))));
- svd_fast(mat_to_sparse(A), u, w, v, rank, 0);
- DLIB_TEST(u.nr() == m);
- DLIB_TEST(u.nc() == rank);
- DLIB_TEST(w.nr() == rank);
- DLIB_TEST(w.nc() == 1);
- DLIB_TEST(v.nr() == n);
- DLIB_TEST(v.nc() == rank);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-10);
-
- svd_fast(A, u, w, v, rank+5, 0);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-11);
- svd_fast(mat_to_sparse(A), u, w, v, rank+5, 0);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-11);
- svd_fast(A, u, w, v, rank+5, 1);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-12);
- svd_fast(mat_to_sparse(A), u, w, v, rank+5, 1);
- DLIB_TEST(max(abs(trans(u)*u - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(trans(v)*v - identity_matrix<double>(u.nc()))) < 1e-13);
- DLIB_TEST(max(abs(tmp(A - u*diagm(w)*trans(v)))) < 1e-12);
- }
-
- void test_svd_fast()
- {
- for (int iter = 0; iter < 1000; ++iter)
- {
- const unsigned long rank = rnd.get_random_32bit_number()%10 + 1;
- const unsigned long m = rank + rnd.get_random_32bit_number()%10;
- const unsigned long n = rank + rnd.get_random_32bit_number()%10;
-
- test_svd_fast(rank, m, n);
-
- }
- test_svd_fast(1, 1, 1);
- test_svd_fast(1, 2, 2);
- test_svd_fast(1, 1, 2);
- test_svd_fast(1, 2, 1);
- }
-
-// ----------------------------------------------------------------------------------------
-
- /*
- typedef std::vector<std::pair<unsigned int, float>> sv;
- sv rand_sparse_vector()
- {
- static dlib::rand rnd;
- sv v;
- for (int i = 0; i < 50; ++i)
- v.push_back(make_pair(rnd.get_integer(400000), rnd.get_random_gaussian()*100));
-
- make_sparse_vector_inplace(v);
- return v;
- }
-
- sv rand_basis_combo(const std::vector<sv>& basis)
- {
- static dlib::rand rnd;
- sv result;
-
- for (int i = 0; i < 5; ++i)
- {
- sv temp = basis[rnd.get_integer(basis.size())];
- scale_by(temp, rnd.get_random_gaussian());
- result = add(result,temp);
- }
- return result;
- }
-
- void big_sparse_speed_test()
- {
- cout << "making A" << endl;
- std::vector<sv> basis;
- for (int i = 0; i < 100; ++i)
- basis.emplace_back(rand_sparse_vector());
-
- std::vector<sv> A;
- for (int i = 0; i < 500000; ++i)
- A.emplace_back(rand_basis_combo(basis));
-
- cout << "done making A" << endl;
-
- matrix<float> u,v;
- matrix<float,0,1> w;
- {
- timing::block aosijdf(0,"call it");
- svd_fast(A, u,w,v, 100, 5);
- }
-
- timing::print();
- }
- */
-
-// ----------------------------------------------------------------------------------------
-
- class test_cca : public tester
- {
- public:
- test_cca (
- ) :
- tester ("test_cca",
- "Runs tests on the cca() and svd_fast() routines.")
- {}
-
- void perform_test (
- )
- {
- //big_sparse_speed_test();
- for (int i = 0; i < 200; ++i)
- {
- test_cca1();
- test_cca2();
- test_cca3();
- }
- test_svd_fast();
- }
- } a;
-
-
-
-}
-
-
-
-
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