1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
|
// 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/test/unit_test.hpp>
#include "utility.hpp"
BOOST_AUTO_TEST_SUITE ( test_einstein_notation, * boost::unit_test::depends_on("test_multi_index") )
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>;
BOOST_AUTO_TEST_CASE_TEMPLATE( test_einstein_multiplication, value, test_types )
{
using namespace boost::numeric::ublas;
using value_type = typename value::first_type;
using layout_type = typename value::second_type;
using tensor_type = tensor<value_type,layout_type>;
using namespace boost::numeric::ublas::index;
{
auto A = tensor_type{5,3};
auto B = tensor_type{3,4};
// auto C = tensor_type{4,5,6};
for(auto j = 0u; j < A.extents().at(1); ++j)
for(auto i = 0u; i < A.extents().at(0); ++i)
A.at( i,j ) = value_type(i+1);
for(auto j = 0u; j < B.extents().at(1); ++j)
for(auto i = 0u; i < B.extents().at(0); ++i)
B.at( i,j ) = value_type(i+1);
auto AB = A(_,_e) * B(_e,_);
// std::cout << "A = " << A << std::endl;
// std::cout << "B = " << B << std::endl;
// std::cout << "AB = " << AB << std::endl;
for(auto j = 0u; j < AB.extents().at(1); ++j)
for(auto i = 0u; i < AB.extents().at(0); ++i)
BOOST_CHECK_EQUAL( AB.at( i,j ) , value_type(A.at( i,0 ) * ( B.extents().at(0) * (B.extents().at(0)+1) / 2 )) );
}
{
auto A = tensor_type{4,5,3};
auto B = tensor_type{3,4,2};
for(auto k = 0u; k < A.extents().at(2); ++k)
for(auto j = 0u; j < A.extents().at(1); ++j)
for(auto i = 0u; i < A.extents().at(0); ++i)
A.at( i,j,k ) = value_type(i+1);
for(auto k = 0u; k < B.extents().at(2); ++k)
for(auto j = 0u; j < B.extents().at(1); ++j)
for(auto i = 0u; i < B.extents().at(0); ++i)
B.at( i,j,k ) = value_type(i+1);
auto AB = A(_d,_,_f) * B(_f,_d,_);
// std::cout << "A = " << A << std::endl;
// std::cout << "B = " << B << std::endl;
// std::cout << "AB = " << AB << std::endl;
// n*(n+1)/2;
auto const nf = ( B.extents().at(0) * (B.extents().at(0)+1) / 2 );
auto const nd = ( A.extents().at(0) * (A.extents().at(0)+1) / 2 );
for(auto j = 0u; j < AB.extents().at(1); ++j)
for(auto i = 0u; i < AB.extents().at(0); ++i)
BOOST_CHECK_EQUAL( AB.at( i,j ) , value_type(nf * nd) );
}
{
auto A = tensor_type{4,3};
auto B = tensor_type{3,4,2};
for(auto j = 0u; j < A.extents().at(1); ++j)
for(auto i = 0u; i < A.extents().at(0); ++i)
A.at( i,j ) = value_type(i+1);
for(auto k = 0u; k < B.extents().at(2); ++k)
for(auto j = 0u; j < B.extents().at(1); ++j)
for(auto i = 0u; i < B.extents().at(0); ++i)
B.at( i,j,k ) = value_type(i+1);
auto AB = A(_d,_f) * B(_f,_d,_);
// n*(n+1)/2;
auto const nf = ( B.extents().at(0) * (B.extents().at(0)+1) / 2 );
auto const nd = ( A.extents().at(0) * (A.extents().at(0)+1) / 2 );
for(auto i = 0u; i < AB.extents().at(0); ++i)
BOOST_CHECK_EQUAL ( AB.at( i ) , value_type(nf * nd) );
}
}
BOOST_AUTO_TEST_SUITE_END()
|
