/* Copyright 2014 SINTEF ICT, Applied Mathematics. This file is part of the Open Porous Media project (OPM). OPM is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. OPM is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with OPM. If not, see . */ #include #if HAVE_DYNAMIC_BOOST_TEST #define BOOST_TEST_DYN_LINK #endif #define BOOST_TEST_MODULE AutoDiffMatrixTest #include #include #include typedef Eigen::SparseMatrix Sp; typedef Opm::AutoDiffMatrix Mat; using namespace Opm; bool operator ==(const Eigen::SparseMatrix& A, const Eigen::SparseMatrix& B) { // Two SparseMatrices are equal if // 0) They have the same ordering (enforced by equal types) // 1) They have the same outer and inner dimensions // 2) They have the same number of non-zero elements // 3) They have the same sparsity structure // 4) The non-zero elements are equal // 1) Outer and inner dimensions bool eq = (A.outerSize() == B.outerSize()); eq = eq && (A.innerSize() == B.innerSize()); // 2) Equal number of non-zero elements eq = eq && (A.nonZeros() == B.nonZeros()); for (typename Eigen::SparseMatrix::Index k0 = 0, kend = A.outerSize(); eq && (k0 < kend); ++k0) { for (typename Eigen::SparseMatrix::InnerIterator iA(A, k0), iB(B, k0); eq && (iA && iB); ++iA, ++iB) { // 3) Sparsity structure eq = (iA.row() == iB.row()) && (iA.col() == iB.col()); // 4) Equal non-zero elements eq = eq && (iA.value() == iB.value()); } } return eq; // Note: Investigate implementing this operator as // return A.cwiseNotEqual(B).count() == 0; } BOOST_AUTO_TEST_CASE(Initialization) { // Setup. Mat z = Mat(3, 3); Mat i = Mat::createIdentity(3); Eigen::Array d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Eigen::Matrix s1(3,2); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0; Sp s2(s1.sparseView()); Mat s = Mat(s2); } BOOST_AUTO_TEST_CASE(EigenConversion) { // Setup. Mat z = Mat(3, 3); Mat i = Mat::createIdentity(3); Eigen::Array d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Eigen::Matrix s1(3,2); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0; Sp s2(s1.sparseView()); Mat s = Mat(s2); // Convert to Eigen::SparseMatrix Sp x; z.toSparse(x); Sp z1(3,3); BOOST_CHECK(x == z1); i.toSparse(x); Sp i1(Eigen::Matrix::Identity(3,3).sparseView()); BOOST_CHECK(x == i1); d.toSparse(x); Sp d2 = spdiag(d1); BOOST_CHECK(x == d2); s.toSparse(x); BOOST_CHECK(x == s2); } BOOST_AUTO_TEST_CASE(AdditionOps) { // Setup. Mat z = Mat(3, 3); Sp zs(3,3); Mat i = Mat::createIdentity(3); Sp is(Eigen::Matrix::Identity(3,3).sparseView()); Eigen::Array d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Sp ds = spdiag(d1); Eigen::Matrix s1(3,3); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0; Sp ss(s1.sparseView()); Mat s = Mat(ss); // Convert to Eigen::SparseMatrix Sp x; z.toSparse(x); BOOST_CHECK(x == zs); i.toSparse(x); BOOST_CHECK(x == is); d.toSparse(x); BOOST_CHECK(x == ds); s.toSparse(x); BOOST_CHECK(x == ss); // Adding zero. auto zpz = z + z; zpz.toSparse(x); BOOST_CHECK(x == zs); auto ipz = i + z; ipz.toSparse(x); BOOST_CHECK(x == is); auto dpz = d + z; dpz.toSparse(x); BOOST_CHECK(x == ds); auto spz = s + z; spz.toSparse(x); BOOST_CHECK(x == ss); } BOOST_AUTO_TEST_CASE(MultOps) { // Setup. Mat z = Mat(3, 3); Sp zs(3,3); Mat i = Mat::createIdentity(3); Sp is(Eigen::Matrix::Identity(3,3).sparseView()); Eigen::Array d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Sp ds = spdiag(d1); Eigen::Matrix s1(3,3); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0; Sp ss(s1.sparseView()); Mat s = Mat(ss); // Convert to Eigen::SparseMatrix Sp x; z.toSparse(x); BOOST_CHECK(x == zs); i.toSparse(x); BOOST_CHECK(x == is); d.toSparse(x); BOOST_CHECK(x == ds); s.toSparse(x); BOOST_CHECK(x == ss); //Multiply by zero matrix auto ztz = z * z; ztz.toSparse(x); BOOST_CHECK(x == zs*zs); auto itz = i * z; itz.toSparse(x); BOOST_CHECK(x == is*zs); auto dtz = d * z; dtz.toSparse(x); BOOST_CHECK(x == ds*zs); auto stz = s * z; stz.toSparse(x); BOOST_CHECK(x == ss*zs); //Multiply by identity matrix auto zti = z * i; zti.toSparse(x); BOOST_CHECK(x == zs*is); auto iti = i * i; iti.toSparse(x); BOOST_CHECK(x == is*is); auto dti = d * i; dti.toSparse(x); BOOST_CHECK(x == ds*is); auto sti = s * i; sti.toSparse(x); BOOST_CHECK(x == ss*is); // Multiply by diagonal matrix. auto ztd = z * d; ztd.toSparse(x); BOOST_CHECK(x == zs*ds); auto itd = i * d; itd.toSparse(x); BOOST_CHECK(x == is*ds); auto dtd = d * d; dtd.toSparse(x); BOOST_CHECK(x == ds*ds); auto std = s * d; std.toSparse(x); BOOST_CHECK(x == ss*ds); // Multiply by sparse matrix. auto zts = z * s; zts.toSparse(x); BOOST_CHECK(x == zs*ss); auto its = i * s; its.toSparse(x); BOOST_CHECK(x == is*ss); auto dts = d * s; dts.toSparse(x); BOOST_CHECK(x == ds*ss); auto sts = s * s; sts.toSparse(x); BOOST_CHECK(x == ss*ss); } BOOST_AUTO_TEST_CASE(MultOpsDouble) { // Setup. Mat z = Mat(3, 3); Sp zs(3,3); Mat i = Mat::createIdentity(3); Sp is(Eigen::Matrix::Identity(3,3).sparseView()); Eigen::Array d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Sp ds = spdiag(d1); Eigen::Matrix s1(3,3); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0; Sp ss(s1.sparseView()); Mat s = Mat(ss); static double factor = 5.3; Sp x; auto zd = z*factor; zd.toSparse(x); BOOST_CHECK(x == zs*factor); auto id = i*factor; id.toSparse(x); BOOST_CHECK(x == is*factor); auto dd = d*factor; dd.toSparse(x); BOOST_CHECK(x == ds*factor); auto sd = s*factor; sd.toSparse(x); BOOST_CHECK(x == ss*factor); } BOOST_AUTO_TEST_CASE(DivOpsDouble) { // Setup. Mat z = Mat(3, 3); Sp zs(3,3); Mat i = Mat::createIdentity(3); Sp is(Eigen::Matrix::Identity(3,3).sparseView()); Eigen::Array d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Sp ds = spdiag(d1); Eigen::Matrix s1(3,3); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0; Sp ss(s1.sparseView()); Mat s = Mat(ss); static double factor = 5.3; Sp x; auto zd = z/factor; zd.toSparse(x); BOOST_CHECK(x == zs/factor); auto id = i/factor; id.toSparse(x); BOOST_CHECK(x == is/factor); auto dd = d/factor; dd.toSparse(x); BOOST_CHECK(x == ds/factor); auto sd = s/factor; sd.toSparse(x); BOOST_CHECK(x == ss/factor); } BOOST_AUTO_TEST_CASE(MultVectorXd) { Mat z = Mat(3, 3); Sp zs(3,3); Mat i = Mat::createIdentity(3); Sp is(Eigen::Matrix::Identity(3,3).sparseView()); Eigen::Array d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Sp ds = spdiag(d1); Eigen::Matrix s1(3,3); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0; Sp ss(s1.sparseView()); Mat s = Mat(ss); Eigen::VectorXd vec(3); vec << 1.0, 2.0, 3.0; Sp x; Eigen::VectorXd zd = z*vec; BOOST_CHECK(zd == zs*vec); Eigen::VectorXd id = i*vec; BOOST_CHECK(id == is*vec); Eigen::VectorXd dd = d*vec; BOOST_CHECK(dd == ds*vec); Eigen::VectorXd sd = s*vec; BOOST_CHECK(sd == ss*vec); } BOOST_AUTO_TEST_CASE(Coeff) { Eigen::Matrix s1(3,2); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0; Sp s2(s1.sparseView()); Mat s = Mat(s2); for (int row=0; row d1(3); d1 << 0.2, 1.2, 13.4; Mat d = Mat(d1.matrix().asDiagonal()); Sp ds = spdiag(d1); Eigen::Matrix s1(3,3); s1 << 1.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0; Sp ss(s1.sparseView()); Mat s = Mat(ss); BOOST_CHECK_EQUAL(z.nonZeros(), 0); BOOST_CHECK_EQUAL(i.nonZeros(), 3); BOOST_CHECK_EQUAL(d.nonZeros(), 3); BOOST_CHECK_EQUAL(s.nonZeros(), 4); }