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							81 lines
						
					
					
						
							2.7 KiB
						
					
					
				
			
		
		
		
			
			
			
				
					
				
				
					
				
			
		
		
	
	
							81 lines
						
					
					
						
							2.7 KiB
						
					
					
				
								// This file is part of Eigen, a lightweight C++ template library
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								// for linear algebra.
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								//
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								// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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								// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
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								//
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								// This Source Code Form is subject to the terms of the Mozilla
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								// Public License v. 2.0. If a copy of the MPL was not distributed
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								// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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								#include "main.h"
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								#include <StormEigen/SVD>
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								template<typename MatrixType, typename JacobiScalar>
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								void jacobi(const MatrixType& m = MatrixType())
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								{
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								  typedef typename MatrixType::Index Index;
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								  Index rows = m.rows();
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								  Index cols = m.cols();
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								  enum {
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								    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
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								    ColsAtCompileTime = MatrixType::ColsAtCompileTime
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								  };
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								  typedef Matrix<JacobiScalar, 2, 1> JacobiVector;
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								  const MatrixType a(MatrixType::Random(rows, cols));
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								  JacobiVector v = JacobiVector::Random().normalized();
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								  JacobiScalar c = v.x(), s = v.y();
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								  JacobiRotation<JacobiScalar> rot(c, s);
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								  {
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								    Index p = internal::random<Index>(0, rows-1);
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								    Index q;
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								    do {
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								      q = internal::random<Index>(0, rows-1);
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								    } while (q == p);
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								    MatrixType b = a;
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								    b.applyOnTheLeft(p, q, rot);
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								    VERIFY_IS_APPROX(b.row(p), c * a.row(p) + numext::conj(s) * a.row(q));
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								    VERIFY_IS_APPROX(b.row(q), -s * a.row(p) + numext::conj(c) * a.row(q));
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								  }
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								  {
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								    Index p = internal::random<Index>(0, cols-1);
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								    Index q;
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								    do {
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								      q = internal::random<Index>(0, cols-1);
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								    } while (q == p);
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								    MatrixType b = a;
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								    b.applyOnTheRight(p, q, rot);
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								    VERIFY_IS_APPROX(b.col(p), c * a.col(p) - s * a.col(q));
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								    VERIFY_IS_APPROX(b.col(q), numext::conj(s) * a.col(p) + numext::conj(c) * a.col(q));
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								  }
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								}
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								void test_jacobi()
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								{
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								  for(int i = 0; i < g_repeat; i++) {
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								    CALL_SUBTEST_1(( jacobi<Matrix3f, float>() ));
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								    CALL_SUBTEST_2(( jacobi<Matrix4d, double>() ));
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								    CALL_SUBTEST_3(( jacobi<Matrix4cf, float>() ));
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								    CALL_SUBTEST_3(( jacobi<Matrix4cf, std::complex<float> >() ));
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								    int r = internal::random<int>(2, internal::random<int>(1,STORMEIGEN_TEST_MAX_SIZE)/2),
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								        c = internal::random<int>(2, internal::random<int>(1,STORMEIGEN_TEST_MAX_SIZE)/2);
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								    CALL_SUBTEST_4(( jacobi<MatrixXf, float>(MatrixXf(r,c)) ));
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								    CALL_SUBTEST_5(( jacobi<MatrixXcd, double>(MatrixXcd(r,c)) ));
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								    CALL_SUBTEST_5(( jacobi<MatrixXcd, std::complex<double> >(MatrixXcd(r,c)) ));
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								    // complex<float> is really important to test as it is the only way to cover conjugation issues in certain unaligned paths
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								    CALL_SUBTEST_6(( jacobi<MatrixXcf, float>(MatrixXcf(r,c)) ));
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								    CALL_SUBTEST_6(( jacobi<MatrixXcf, std::complex<float> >(MatrixXcf(r,c)) ));
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								    TEST_SET_BUT_UNUSED_VARIABLE(r);
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								    TEST_SET_BUT_UNUSED_VARIABLE(c);
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								  }
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								}
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