1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. Eigen itself is part of the KDE project. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h" 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/Array> 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void array(const MatrixType& m) 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test covers the following files: 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Array.cpp 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename NumTraits<Scalar>::Real RealScalar; 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int rows = m.rows(); 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int cols = m.cols(); 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = MatrixType::Random(rows, cols), 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(rows, cols); 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = ei_random<Scalar>(), 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath s2 = ei_random<Scalar>(); 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // scalar addition 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.cwise() + s1, s1 + m1.cwise()); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.cwise() + s1, MatrixType::Constant(rows,cols,s1) + m1); 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1*Scalar(2)).cwise() - s2, (m1+m1) - MatrixType::Constant(rows,cols,s2) ); 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1; 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3.cwise() += s2; 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, m1.cwise() + s2); 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1; 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3.cwise() -= s1; 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, m1.cwise() - s1); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // reductions 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.colwise().sum().sum(), m1.sum()); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.rowwise().sum().sum(), m1.sum()); 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (!ei_isApprox(m1.sum(), (m1+m2).sum())) 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_NOT_APPROX(((m1+m2).rowwise().sum()).sum(), m1.sum()); 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar>())); 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void comparisons(const MatrixType& m) 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename NumTraits<Scalar>::Real RealScalar; 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int rows = m.rows(); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int cols = m.cols(); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int r = ei_random<int>(0, rows-1), 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath c = ei_random<int>(0, cols-1); 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = MatrixType::Random(rows, cols), 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(rows, cols); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(((m1.cwise() + Scalar(1)).cwise() > m1).all()); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(((m1.cwise() - Scalar(1)).cwise() < m1).all()); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (rows*cols>1) 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m1; 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(r,c) += 1; 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(! (m1.cwise() < m3).all() ); 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(! (m1.cwise() > m3).all() ); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // comparisons to scalar 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY( (m1.cwise() != (m1(r,c)+1) ).any() ); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY( (m1.cwise() > (m1(r,c)-1) ).any() ); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY( (m1.cwise() < (m1(r,c)+1) ).any() ); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY( (m1.cwise() == m1(r,c) ).any() ); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test Select 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( (m1.cwise()<m2).select(m1,m2), m1.cwise().min(m2) ); 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( (m1.cwise()>m2).select(m1,m2), m1.cwise().max(m2) ); 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar mid = (m1.cwise().abs().minCoeff() + m1.cwise().abs().maxCoeff())/Scalar(2); 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int j=0; j<cols; ++j) 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=0; i<rows; ++i) 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(i,j) = ei_abs(m1(i,j))<mid ? 0 : m1(i,j); 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<MatrixType::Constant(rows,cols,mid)) 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath .select(MatrixType::Zero(rows,cols),m1), m3); 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // shorter versions: 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<MatrixType::Constant(rows,cols,mid)) 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath .select(0,m1), m3); 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( (m1.cwise().abs().cwise()>=MatrixType::Constant(rows,cols,mid)) 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath .select(m1,0), m3); 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // even shorter version: 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<mid).select(0,m1), m3); 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // count 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).count() == rows*cols); 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).colwise().count().template cast<int>(), RowVectorXi::Constant(cols,rows)); 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(((m1.cwise().abs().cwise()+1).cwise()>RealScalar(0.1)).rowwise().count().template cast<int>(), VectorXi::Constant(rows, cols)); 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename VectorType> void lpNorm(const VectorType& v) 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorType u = VectorType::Random(v.size()); 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwise().abs().maxCoeff()); 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwise().abs().sum()); 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(u.template lpNorm<2>(), ei_sqrt(u.cwise().abs().cwise().square().sum())); 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(ei_pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.cwise().abs().cwise().pow(5).sum()); 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_array() 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( array(Matrix<float, 1, 1>()) ); 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( array(Matrix2f()) ); 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( array(Matrix4d()) ); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( array(MatrixXcf(3, 3)) ); 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( array(MatrixXf(8, 12)) ); 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( array(MatrixXi(8, 12)) ); 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) ); 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( comparisons(Matrix2f()) ); 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( comparisons(Matrix4d()) ); 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( comparisons(MatrixXf(8, 12)) ); 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( comparisons(MatrixXi(8, 12)) ); 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) ); 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( lpNorm(Vector2f()) ); 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( lpNorm(Vector3d()) ); 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( lpNorm(Vector4f()) ); 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( lpNorm(VectorXf(16)) ); 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_7( lpNorm(VectorXcd(10)) ); 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 143