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							203 lines
						
					
					
						
							6.6 KiB
						
					
					
				| // This file is part of Eigen, a lightweight C++ template library | |
| // for linear algebra. | |
| // | |
| // Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com> | |
| // | |
| // This Source Code Form is subject to the terms of the Mozilla | |
| // Public License v. 2.0. If a copy of the MPL was not distributed | |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. | |
|  | |
| #include "main.h" | |
| #include <Eigen/LU> | |
| using namespace std; | |
| 
 | |
| template<typename MatrixType> void lu_non_invertible() | |
| { | |
|   typedef typename MatrixType::Index Index; | |
|   typedef typename MatrixType::RealScalar RealScalar; | |
|   /* this test covers the following files: | |
|      LU.h | |
|   */ | |
|   Index rows, cols, cols2; | |
|   if(MatrixType::RowsAtCompileTime==Dynamic) | |
|   { | |
|     rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); | |
|   } | |
|   else | |
|   { | |
|     rows = MatrixType::RowsAtCompileTime; | |
|   } | |
|   if(MatrixType::ColsAtCompileTime==Dynamic) | |
|   { | |
|     cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE); | |
|     cols2 = internal::random<int>(2,EIGEN_TEST_MAX_SIZE); | |
|   } | |
|   else | |
|   { | |
|     cols2 = cols = MatrixType::ColsAtCompileTime; | |
|   } | |
| 
 | |
|   enum { | |
|     RowsAtCompileTime = MatrixType::RowsAtCompileTime, | |
|     ColsAtCompileTime = MatrixType::ColsAtCompileTime | |
|   }; | |
|   typedef typename internal::kernel_retval_base<FullPivLU<MatrixType> >::ReturnType KernelMatrixType; | |
|   typedef typename internal::image_retval_base<FullPivLU<MatrixType> >::ReturnType ImageMatrixType; | |
|   typedef Matrix<typename MatrixType::Scalar, ColsAtCompileTime, ColsAtCompileTime> | |
|           CMatrixType; | |
|   typedef Matrix<typename MatrixType::Scalar, RowsAtCompileTime, RowsAtCompileTime> | |
|           RMatrixType; | |
| 
 | |
|   Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1); | |
| 
 | |
|   // The image of the zero matrix should consist of a single (zero) column vector | |
|   VERIFY((MatrixType::Zero(rows,cols).fullPivLu().image(MatrixType::Zero(rows,cols)).cols() == 1)); | |
| 
 | |
|   MatrixType m1(rows, cols), m3(rows, cols2); | |
|   CMatrixType m2(cols, cols2); | |
|   createRandomPIMatrixOfRank(rank, rows, cols, m1); | |
| 
 | |
|   FullPivLU<MatrixType> lu; | |
| 
 | |
|   // The special value 0.01 below works well in tests. Keep in mind that we're only computing the rank | |
|   // of singular values are either 0 or 1. | |
|   // So it's not clear at all that the epsilon should play any role there. | |
|   lu.setThreshold(RealScalar(0.01)); | |
|   lu.compute(m1); | |
| 
 | |
|   MatrixType u(rows,cols); | |
|   u = lu.matrixLU().template triangularView<Upper>(); | |
|   RMatrixType l = RMatrixType::Identity(rows,rows); | |
|   l.block(0,0,rows,(std::min)(rows,cols)).template triangularView<StrictlyLower>() | |
|     = lu.matrixLU().block(0,0,rows,(std::min)(rows,cols)); | |
| 
 | |
|   VERIFY_IS_APPROX(lu.permutationP() * m1 * lu.permutationQ(), l*u); | |
| 
 | |
|   KernelMatrixType m1kernel = lu.kernel(); | |
|   ImageMatrixType m1image = lu.image(m1); | |
| 
 | |
|   VERIFY_IS_APPROX(m1, lu.reconstructedMatrix()); | |
|   VERIFY(rank == lu.rank()); | |
|   VERIFY(cols - lu.rank() == lu.dimensionOfKernel()); | |
|   VERIFY(!lu.isInjective()); | |
|   VERIFY(!lu.isInvertible()); | |
|   VERIFY(!lu.isSurjective()); | |
|   VERIFY((m1 * m1kernel).isMuchSmallerThan(m1)); | |
|   VERIFY(m1image.fullPivLu().rank() == rank); | |
|   VERIFY_IS_APPROX(m1 * m1.adjoint() * m1image, m1image); | |
| 
 | |
|   m2 = CMatrixType::Random(cols,cols2); | |
|   m3 = m1*m2; | |
|   m2 = CMatrixType::Random(cols,cols2); | |
|   // test that the code, which does resize(), may be applied to an xpr | |
|   m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3); | |
|   VERIFY_IS_APPROX(m3, m1*m2); | |
| } | |
| 
 | |
| template<typename MatrixType> void lu_invertible() | |
| { | |
|   /* this test covers the following files: | |
|      LU.h | |
|   */ | |
|   typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; | |
|   int size = internal::random<int>(1,EIGEN_TEST_MAX_SIZE); | |
| 
 | |
|   MatrixType m1(size, size), m2(size, size), m3(size, size); | |
|   FullPivLU<MatrixType> lu; | |
|   lu.setThreshold(RealScalar(0.01)); | |
|   do { | |
|     m1 = MatrixType::Random(size,size); | |
|     lu.compute(m1); | |
|   } while(!lu.isInvertible()); | |
| 
 | |
|   VERIFY_IS_APPROX(m1, lu.reconstructedMatrix()); | |
|   VERIFY(0 == lu.dimensionOfKernel()); | |
|   VERIFY(lu.kernel().cols() == 1); // the kernel() should consist of a single (zero) column vector | |
|   VERIFY(size == lu.rank()); | |
|   VERIFY(lu.isInjective()); | |
|   VERIFY(lu.isSurjective()); | |
|   VERIFY(lu.isInvertible()); | |
|   VERIFY(lu.image(m1).fullPivLu().isInvertible()); | |
|   m3 = MatrixType::Random(size,size); | |
|   m2 = lu.solve(m3); | |
|   VERIFY_IS_APPROX(m3, m1*m2); | |
|   VERIFY_IS_APPROX(m2, lu.inverse()*m3); | |
| } | |
| 
 | |
| template<typename MatrixType> void lu_partial_piv() | |
| { | |
|   /* this test covers the following files: | |
|      PartialPivLU.h | |
|   */ | |
|   typedef typename MatrixType::Index Index; | |
|   Index rows = internal::random<Index>(1,4); | |
|   Index cols = rows; | |
| 
 | |
|   MatrixType m1(cols, rows); | |
|   m1.setRandom(); | |
|   PartialPivLU<MatrixType> plu(m1); | |
| 
 | |
|   VERIFY_IS_APPROX(m1, plu.reconstructedMatrix()); | |
| } | |
| 
 | |
| template<typename MatrixType> void lu_verify_assert() | |
| { | |
|   MatrixType tmp; | |
| 
 | |
|   FullPivLU<MatrixType> lu; | |
|   VERIFY_RAISES_ASSERT(lu.matrixLU()) | |
|   VERIFY_RAISES_ASSERT(lu.permutationP()) | |
|   VERIFY_RAISES_ASSERT(lu.permutationQ()) | |
|   VERIFY_RAISES_ASSERT(lu.kernel()) | |
|   VERIFY_RAISES_ASSERT(lu.image(tmp)) | |
|   VERIFY_RAISES_ASSERT(lu.solve(tmp)) | |
|   VERIFY_RAISES_ASSERT(lu.determinant()) | |
|   VERIFY_RAISES_ASSERT(lu.rank()) | |
|   VERIFY_RAISES_ASSERT(lu.dimensionOfKernel()) | |
|   VERIFY_RAISES_ASSERT(lu.isInjective()) | |
|   VERIFY_RAISES_ASSERT(lu.isSurjective()) | |
|   VERIFY_RAISES_ASSERT(lu.isInvertible()) | |
|   VERIFY_RAISES_ASSERT(lu.inverse()) | |
| 
 | |
|   PartialPivLU<MatrixType> plu; | |
|   VERIFY_RAISES_ASSERT(plu.matrixLU()) | |
|   VERIFY_RAISES_ASSERT(plu.permutationP()) | |
|   VERIFY_RAISES_ASSERT(plu.solve(tmp)) | |
|   VERIFY_RAISES_ASSERT(plu.determinant()) | |
|   VERIFY_RAISES_ASSERT(plu.inverse()) | |
| } | |
| 
 | |
| void test_lu() | |
| { | |
|   for(int i = 0; i < g_repeat; i++) { | |
|     CALL_SUBTEST_1( lu_non_invertible<Matrix3f>() ); | |
|     CALL_SUBTEST_1( lu_verify_assert<Matrix3f>() ); | |
| 
 | |
|     CALL_SUBTEST_2( (lu_non_invertible<Matrix<double, 4, 6> >()) ); | |
|     CALL_SUBTEST_2( (lu_verify_assert<Matrix<double, 4, 6> >()) ); | |
| 
 | |
|     CALL_SUBTEST_3( lu_non_invertible<MatrixXf>() ); | |
|     CALL_SUBTEST_3( lu_invertible<MatrixXf>() ); | |
|     CALL_SUBTEST_3( lu_verify_assert<MatrixXf>() ); | |
| 
 | |
|     CALL_SUBTEST_4( lu_non_invertible<MatrixXd>() ); | |
|     CALL_SUBTEST_4( lu_invertible<MatrixXd>() ); | |
|     CALL_SUBTEST_4( lu_partial_piv<MatrixXd>() ); | |
|     CALL_SUBTEST_4( lu_verify_assert<MatrixXd>() ); | |
| 
 | |
|     CALL_SUBTEST_5( lu_non_invertible<MatrixXcf>() ); | |
|     CALL_SUBTEST_5( lu_invertible<MatrixXcf>() ); | |
|     CALL_SUBTEST_5( lu_verify_assert<MatrixXcf>() ); | |
| 
 | |
|     CALL_SUBTEST_6( lu_non_invertible<MatrixXcd>() ); | |
|     CALL_SUBTEST_6( lu_invertible<MatrixXcd>() ); | |
|     CALL_SUBTEST_6( lu_partial_piv<MatrixXcd>() ); | |
|     CALL_SUBTEST_6( lu_verify_assert<MatrixXcd>() ); | |
| 
 | |
|     CALL_SUBTEST_7(( lu_non_invertible<Matrix<float,Dynamic,16> >() )); | |
| 
 | |
|     // Test problem size constructors | |
|     CALL_SUBTEST_9( PartialPivLU<MatrixXf>(10) ); | |
|     CALL_SUBTEST_9( FullPivLU<MatrixXf>(10, 20); ); | |
|   } | |
| }
 |