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https://github.com/OPM/opm-simulators.git
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231 lines
6.6 KiB
C++
231 lines
6.6 KiB
C++
/*
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Copyright 2015 SINTEF ICT, Applied Mathematics.
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This file is part of the Open Porous Media project (OPM).
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OPM is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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OPM is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with OPM. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <config.h>
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#if HAVE_DYNAMIC_BOOST_TEST
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#define BOOST_TEST_DYN_LINK
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#endif
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#define BOOST_TEST_MODULE AutoDiffHelpersTest
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#include <opm/autodiff/AutoDiffHelpers.hpp>
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#include <boost/test/unit_test.hpp>
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using namespace Opm;
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namespace {
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template <typename Scalar>
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bool
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operator ==(const Eigen::SparseMatrix<Scalar>& A,
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const Eigen::SparseMatrix<Scalar>& B)
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{
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// Two SparseMatrices are equal if
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// 0) They have the same ordering (enforced by equal types)
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// 1) They have the same outer and inner dimensions
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// 2) They have the same number of non-zero elements
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// 3) They have the same sparsity structure
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// 4) The non-zero elements are equal
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// 1) Outer and inner dimensions
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bool eq = (A.outerSize() == B.outerSize());
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eq = eq && (A.innerSize() == B.innerSize());
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// 2) Equal number of non-zero elements
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eq = eq && (A.nonZeros() == B.nonZeros());
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for (typename Eigen::SparseMatrix<Scalar>::Index
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k0 = 0, kend = A.outerSize(); eq && (k0 < kend); ++k0) {
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for (typename Eigen::SparseMatrix<Scalar>::InnerIterator
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iA(A, k0), iB(B, k0); eq && (iA && iB); ++iA, ++iB) {
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// 3) Sparsity structure
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eq = (iA.row() == iB.row()) && (iA.col() == iB.col());
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// 4) Equal non-zero elements
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eq = eq && (iA.value() == iB.value());
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}
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}
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return eq;
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// Note: Investigate implementing this operator as
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// return A.cwiseNotEqual(B).count() == 0;
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}
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}
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BOOST_AUTO_TEST_CASE(vertcatCollapseJacsTest)
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{
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typedef AutoDiffBlock<double> ADB;
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typedef ADB::V V;
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typedef ADB::M M;
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typedef Eigen::SparseMatrix<double> S;
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// We will build a system with the block structure
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// { 2, 0, 1 } (total of three columns) and { 1, 2, 1 } (row) sizes.
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//
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// value jacobians
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// 10 1 2 | 3
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// ----------------------------------
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// 11 0 0 | 0 (empty jacobian)
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// 12 0 0 | 0
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// ----------------------------------
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// 13 4 5 | 6
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std::vector<ADB> v;
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{
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// First block.
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V val(1);
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val << 10;
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std::vector<M> jacs;
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jacs.reserve(3);
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S s1(1, 2);
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S s2(1, 0);
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S s3(1, 1);
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s1.insert(0, 0) = 1.0;
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s1.insert(0, 1) = 2.0;
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s3.insert(0, 0) = 3.0;
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jacs.push_back(M(s1));
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jacs.push_back(M(s2));
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jacs.push_back(M(s3));
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v.push_back(ADB::function(val, jacs));
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}
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{
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// Second block (with empty jacobian).
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V val(2);
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val << 11, 12;
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v.push_back(ADB::constant(val));
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}
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{
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// Third block.
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V val(1);
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val << 13;
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std::vector<M> jacs(0);
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jacs.reserve(3);
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S s1(1, 2);
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S s2(1, 0);
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S s3(1, 1);
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s1.insert(0, 0) = 4.0;
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s1.insert(0, 1) = 5.0;
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s3.insert(0, 0) = 6.0;
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jacs.push_back(M(s1));
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jacs.push_back(M(s2));
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jacs.push_back(M(s3));
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v.push_back(ADB::function(val, jacs));
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}
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std::vector<int> expected_block_pattern{ 2, 0, 1 };
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BOOST_CHECK(v[0].blockPattern() == expected_block_pattern);
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// Call vertcatCollapseJacs().
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const ADB x = vertcatCollapseJacs(v);
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// Build expected results.
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V expected_val(4);
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expected_val << 10, 11, 12, 13;
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S expected_jac_s(4, 3);
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expected_jac_s.insert(0, 0) = 1.0;
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expected_jac_s.insert(0, 1) = 2.0;
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expected_jac_s.insert(0, 2) = 3.0;
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expected_jac_s.insert(3, 0) = 4.0;
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expected_jac_s.insert(3, 1) = 5.0;
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expected_jac_s.insert(3, 2) = 6.0;
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M expected_jac(expected_jac_s);
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// Compare.
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BOOST_CHECK((x.value() == expected_val).all());
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S derivative;
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x.derivative()[0].toSparse(derivative);
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BOOST_CHECK(derivative == expected_jac_s);
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}
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BOOST_AUTO_TEST_CASE(supersetTest)
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{
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typedef AutoDiffBlock<double> ADB;
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{
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ADB subset = ADB::constant(ADB::V::Ones(3));
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const int full_size = 32;
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std::vector<int> indices = {1, 3, 5};
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AutoDiffBlock<double> n_vals = superset(subset, indices, full_size);
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BOOST_CHECK_EQUAL(n_vals.size(), full_size);
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for (int i=0; i<n_vals.size(); ++i) {
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if (find(indices.begin(), indices.end(), i) != indices.end()) {
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BOOST_CHECK_EQUAL(n_vals.value()[i], 1);
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}
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else {
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BOOST_CHECK_EQUAL(n_vals.value()[i], 0);
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}
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}
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}
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}
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BOOST_AUTO_TEST_CASE(supersetEmptyTest)
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{
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typedef AutoDiffBlock<double> ADB;
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{
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ADB subset = ADB::constant(ADB::V::Ones(0));
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const int full_size = 32;
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std::vector<int> indices = {};
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AutoDiffBlock<double> n_vals = superset(subset, indices, full_size);
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BOOST_CHECK_EQUAL(n_vals.size(), full_size);
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for (int i=0; i<n_vals.size(); ++i) {
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BOOST_CHECK_EQUAL(n_vals.value()[i], 0);
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}
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}
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}
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BOOST_AUTO_TEST_CASE(subsetTest)
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{
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typedef AutoDiffBlock<double> ADB;
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{
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ADB superset = ADB::constant(ADB::V::Ones(32));
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std::vector<int> indices = {1, 3, 5};
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AutoDiffBlock<double> n_vals = subset(superset, indices);
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BOOST_CHECK_EQUAL(n_vals.size(), 3);
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for (int i=0; i<n_vals.size(); ++i) {
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if (find(indices.begin(), indices.end(), i) != indices.end()) {
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BOOST_CHECK_EQUAL(n_vals.value()[i], 1);
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}
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}
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}
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}
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BOOST_AUTO_TEST_CASE(subsetEmptyTest)
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{
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typedef AutoDiffBlock<double> ADB;
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{
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ADB superset = ADB::constant(ADB::V::Ones(32));
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std::vector<int> indices = {};
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AutoDiffBlock<double> n_vals = subset(superset, indices);
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BOOST_CHECK_EQUAL(n_vals.size(), 0);
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}
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}
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