454 lines
11 KiB
C++
454 lines
11 KiB
C++
// Copyright (c) 2018-2019 Cem Bassoy
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//
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// Distributed under the Boost Software License, Version 1.0. (See
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// accompanying file LICENSE_1_0.txt or copy at
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// http://www.boost.org/LICENSE_1_0.txt)
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//
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// The authors gratefully acknowledge the support of
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// Fraunhofer and Google in producing this work
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// which started as a Google Summer of Code project.
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//
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// And we acknowledge the support from all contributors.
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#include <iostream>
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#include <algorithm>
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#include <boost/numeric/ublas/tensor.hpp>
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#include <boost/numeric/ublas/matrix.hpp>
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#include <boost/numeric/ublas/vector.hpp>
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#include <boost/test/unit_test.hpp>
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#include "utility.hpp"
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BOOST_AUTO_TEST_SUITE ( test_tensor_functions, * boost::unit_test::depends_on("test_tensor_contraction") )
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using test_types = zip<int,long,float,double,std::complex<float>>::with_t<boost::numeric::ublas::first_order, boost::numeric::ublas::last_order>;
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//using test_types = zip<int>::with_t<boost::numeric::ublas::first_order>;
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struct fixture
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{
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using extents_type = boost::numeric::ublas::shape;
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fixture()
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: extents {
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extents_type{1,1}, // 1
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extents_type{1,2}, // 2
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extents_type{2,1}, // 3
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extents_type{2,3}, // 4
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extents_type{2,3,1}, // 5
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extents_type{4,1,3}, // 6
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extents_type{1,2,3}, // 7
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extents_type{4,2,3}, // 8
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extents_type{4,2,3,5}} // 9
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{
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}
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std::vector<extents_type> extents;
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};
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_prod_vector, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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using vector_type = typename tensor_type::vector_type;
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for(auto const& n : extents){
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auto a = tensor_type(n, value_type{2});
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for(auto m = 0u; m < n.size(); ++m){
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auto b = vector_type (n[m], value_type{1} );
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auto c = ublas::prod(a, b, m+1);
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for(auto i = 0u; i < c.size(); ++i)
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BOOST_CHECK_EQUAL( c[i] , value_type(n[m]) * a[i] );
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}
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}
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_prod_matrix, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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using matrix_type = typename tensor_type::matrix_type;
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for(auto const& n : extents) {
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auto a = tensor_type(n, value_type{2});
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for(auto m = 0u; m < n.size(); ++m){
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auto b = matrix_type ( n[m], n[m], value_type{1} );
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auto c = ublas::prod(a, b, m+1);
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for(auto i = 0u; i < c.size(); ++i)
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BOOST_CHECK_EQUAL( c[i] , value_type(n[m]) * a[i] );
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}
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}
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_prod_tensor_1, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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// left-hand and right-hand side have the
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// the same number of elements
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for(auto const& na : extents) {
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auto a = tensor_type( na, value_type{2} );
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auto b = tensor_type( na, value_type{3} );
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auto const pa = a.rank();
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// the number of contractions is changed.
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for( auto q = 0ul; q <= pa; ++q) { // pa
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auto phi = std::vector<std::size_t> ( q );
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std::iota(phi.begin(), phi.end(), 1ul);
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auto c = ublas::prod(a, b, phi);
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auto acc = value_type(1);
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for(auto i = 0ul; i < q; ++i)
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acc *= a.extents().at(phi.at(i)-1);
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for(auto i = 0ul; i < c.size(); ++i)
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BOOST_CHECK_EQUAL( c[i] , acc * a[0] * b[0] );
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}
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}
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_prod_tensor_2, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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auto compute_factorial = [](auto const& p){
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auto f = 1ul;
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for(auto i = 1u; i <= p; ++i)
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f *= i;
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return f;
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};
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auto permute_extents = [](auto const& pi, auto const& na){
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auto nb = na;
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assert(pi.size() == na.size());
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for(auto j = 0u; j < pi.size(); ++j)
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nb[pi[j]-1] = na[j];
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return nb;
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};
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// left-hand and right-hand side have the
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// the same number of elements
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for(auto const& na : extents) {
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auto a = tensor_type( na, value_type{2} );
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auto const pa = a.rank();
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auto pi = std::vector<std::size_t>(pa);
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auto fac = compute_factorial(pa);
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std::iota( pi.begin(), pi.end(), 1 );
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for(auto f = 0ul; f < fac; ++f)
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{
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auto nb = permute_extents( pi, na );
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auto b = tensor_type( nb, value_type{3} );
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// the number of contractions is changed.
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for( auto q = 0ul; q <= pa; ++q) { // pa
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auto phia = std::vector<std::size_t> ( q ); // concatenation for a
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auto phib = std::vector<std::size_t> ( q ); // concatenation for b
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std::iota(phia.begin(), phia.end(), 1ul);
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std::transform( phia.begin(), phia.end(), phib.begin(),
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[&pi] ( std::size_t i ) { return pi.at(i-1); } );
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auto c = ublas::prod(a, b, phia, phib);
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auto acc = value_type(1);
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for(auto i = 0ul; i < q; ++i)
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acc *= a.extents().at(phia.at(i)-1);
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for(auto i = 0ul; i < c.size(); ++i)
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BOOST_CHECK_EQUAL( c[i] , acc * a[0] * b[0] );
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}
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std::next_permutation(pi.begin(), pi.end());
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}
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}
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_inner_prod, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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for(auto const& n : extents) {
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auto a = tensor_type(n, value_type(2));
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auto b = tensor_type(n, value_type(1));
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auto c = ublas::inner_prod(a, b);
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auto r = std::inner_product(a.begin(),a.end(), b.begin(),value_type(0));
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BOOST_CHECK_EQUAL( c , r );
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}
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_norm, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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for(auto const& n : extents) {
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auto a = tensor_type(n);
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auto one = value_type(1);
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auto v = one;
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for(auto& aa: a)
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aa = v, v += one;
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auto c = ublas::inner_prod(a, a);
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auto r = std::inner_product(a.begin(),a.end(), a.begin(),value_type(0));
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auto r2 = ublas::norm( (a+a) / 2 );
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BOOST_CHECK_EQUAL( c , r );
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BOOST_CHECK_EQUAL( std::sqrt( c ) , r2 );
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}
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}
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BOOST_FIXTURE_TEST_CASE( test_tensor_real_imag_conj, fixture )
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{
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using namespace boost::numeric;
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using value_type = float;
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using complex_type = std::complex<value_type>;
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using layout_type = ublas::first_order;
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using tensor_complex_type = ublas::tensor<complex_type,layout_type>;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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for(auto const& n : extents) {
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auto a = tensor_type(n);
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auto r0 = tensor_type(n);
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auto r00 = tensor_complex_type(n);
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auto one = value_type(1);
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auto v = one;
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for(auto& aa: a)
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aa = v, v += one;
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tensor_type b = (a+a) / value_type( 2 );
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tensor_type r1 = ublas::real( (a+a) / value_type( 2 ) );
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std::transform( b.begin(), b.end(), r0.begin(), [](auto const& l){ return std::real( l ); } );
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BOOST_CHECK( r0 == r1 );
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tensor_type r2 = ublas::imag( (a+a) / value_type( 2 ) );
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std::transform( b.begin(), b.end(), r0.begin(), [](auto const& l){ return std::imag( l ); } );
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BOOST_CHECK( r0 == r2 );
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tensor_complex_type r3 = ublas::conj( (a+a) / value_type( 2 ) );
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std::transform( b.begin(), b.end(), r00.begin(), [](auto const& l){ return std::conj( l ); } );
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BOOST_CHECK( r00 == r3 );
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}
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for(auto const& n : extents) {
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auto a = tensor_complex_type(n);
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auto r00 = tensor_complex_type(n);
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auto r0 = tensor_type(n);
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auto one = complex_type(1,1);
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auto v = one;
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for(auto& aa: a)
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aa = v, v = v + one;
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tensor_complex_type b = (a+a) / complex_type( 2,2 );
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tensor_type r1 = ublas::real( (a+a) / complex_type( 2,2 ) );
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std::transform( b.begin(), b.end(), r0.begin(), [](auto const& l){ return std::real( l ); } );
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BOOST_CHECK( r0 == r1 );
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tensor_type r2 = ublas::imag( (a+a) / complex_type( 2,2 ) );
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std::transform( b.begin(), b.end(), r0.begin(), [](auto const& l){ return std::imag( l ); } );
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BOOST_CHECK( r0 == r2 );
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tensor_complex_type r3 = ublas::conj( (a+a) / complex_type( 2,2 ) );
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std::transform( b.begin(), b.end(), r00.begin(), [](auto const& l){ return std::conj( l ); } );
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BOOST_CHECK( r00 == r3 );
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}
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_outer_prod, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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for(auto const& n1 : extents) {
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auto a = tensor_type(n1, value_type(2));
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for(auto const& n2 : extents) {
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auto b = tensor_type(n2, value_type(1));
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auto c = ublas::outer_prod(a, b);
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for(auto const& cc : c)
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BOOST_CHECK_EQUAL( cc , a[0]*b[0] );
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}
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}
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}
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template<class V>
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void init(std::vector<V>& a)
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{
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auto v = V(1);
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for(auto i = 0u; i < a.size(); ++i, ++v){
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a[i] = v;
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}
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}
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template<class V>
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void init(std::vector<std::complex<V>>& a)
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{
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auto v = std::complex<V>(1,1);
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for(auto i = 0u; i < a.size(); ++i){
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a[i] = v;
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v.real(v.real()+1);
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v.imag(v.imag()+1);
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}
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_trans, value, test_types, fixture )
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{
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using namespace boost::numeric;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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using tensor_type = ublas::tensor<value_type,layout_type>;
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auto fak = [](auto const& p){
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auto f = 1ul;
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for(auto i = 1u; i <= p; ++i)
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f *= i;
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return f;
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};
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auto inverse = [](auto const& pi){
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auto pi_inv = pi;
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for(auto j = 0u; j < pi.size(); ++j)
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pi_inv[pi[j]-1] = j+1;
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return pi_inv;
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};
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for(auto const& n : extents)
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{
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auto const p = n.size();
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auto const s = n.product();
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auto aref = tensor_type(n);
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auto v = value_type{};
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for(auto i = 0u; i < s; ++i, v+=1)
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aref[i] = v;
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auto a = aref;
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auto pi = std::vector<std::size_t>(p);
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std::iota(pi.begin(), pi.end(), 1);
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a = ublas::trans( a, pi );
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BOOST_CHECK( a == aref );
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auto const pfak = fak(p);
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auto i = 0u;
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for(; i < pfak-1; ++i) {
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std::next_permutation(pi.begin(), pi.end());
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a = ublas::trans( a, pi );
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}
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std::next_permutation(pi.begin(), pi.end());
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for(; i > 0; --i) {
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std::prev_permutation(pi.begin(), pi.end());
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auto pi_inv = inverse(pi);
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a = ublas::trans( a, pi_inv );
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}
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BOOST_CHECK( a == aref );
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}
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}
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BOOST_AUTO_TEST_SUITE_END()
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