ublas/test/tensor/test_functions.cpp
2019-02-25 17:05:02 +01:00

454 lines
11 KiB
C++

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