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 Benoit Jacob <jacob.benoit.1@gmail.com> 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 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void matrixSum(const MatrixType& m) 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int rows = m.rows(); 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int cols = m.cols(); 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols); 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1)); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar x = Scalar(0); 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < rows; i++) for(int j = 0; j < cols; j++) x += m1(i,j); 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.sum(), x); 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename VectorType> void vectorSum(const VectorType& w) 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename VectorType::Scalar Scalar; 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int size = w.size(); 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorType v = VectorType::Random(size); 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 1; i < size; i++) 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s = Scalar(0); 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int j = 0; j < i; j++) s += v[j]; 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(s, v.start(i).sum()); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < size-1; i++) 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s = Scalar(0); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int j = i; j < size; j++) s += v[j]; 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(s, v.end(size-i).sum()); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < size/2; i++) 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s = Scalar(0); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int j = i; j < size-i; j++) s += v[j]; 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(s, v.segment(i, size-2*i).sum()); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_sum() 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( matrixSum(Matrix<float, 1, 1>()) ); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( matrixSum(Matrix2f()) ); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( matrixSum(Matrix4d()) ); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( matrixSum(MatrixXcf(3, 3)) ); 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( matrixSum(MatrixXf(8, 12)) ); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( matrixSum(MatrixXi(8, 12)) ); 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( vectorSum(VectorXf(5)) ); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_7( vectorSum(VectorXd(10)) ); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( vectorSum(VectorXf(33)) ); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 72