ground/gcs/src/libs/eigen/test/product_large.cpp

   1 // This file is part of Eigen, a lightweight C++ template library
   2 // for linear algebra.
   3 //
   4 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
   5 //
   6 // This Source Code Form is subject to the terms of the Mozilla
   7 // Public License v. 2.0. If a copy of the MPL was not distributed
   8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
   9
  10 #include "product.h"
  11
  12 template<typename T>
  13 void test_aliasing()
  14 {
  15   int rows = internal::random<int>(1,12);
  16   int cols = internal::random<int>(1,12);
  17   typedef Matrix<T,Dynamic,Dynamic> MatrixType;
  18   typedef Matrix<T,Dynamic,1> VectorType;
  19   VectorType x(cols); x.setRandom();
  20   VectorType z(x);
  21   VectorType y(rows); y.setZero();
  22   MatrixType A(rows,cols); A.setRandom();
  23   // CwiseBinaryOp
  24   VERIFY_IS_APPROX(x = y + A*x, A*z);
  25   x = z;
  26   // CwiseUnaryOp
  27   VERIFY_IS_APPROX(x = T(1.)*(A*x), A*z);
  28   x = z;
  29   VERIFY_IS_APPROX(x = y+(-(A*x)), -A*z);
  30   x = z;
  31 }
  32
  33 void test_product_large()
  34 {
  35   for(int i = 0; i < g_repeat; i++) {
  36     CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
  37     CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
  38     CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
  39     CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
  40     CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
  41
  42     CALL_SUBTEST_1( test_aliasing<float>() );
  43   }
  44
  45 #if defined EIGEN_TEST_PART_6
  46   {
  47     // test a specific issue in DiagonalProduct
  48     int N = 1000000;
  49     VectorXf v = VectorXf::Ones(N);
  50     MatrixXf m = MatrixXf::Ones(N,3);
  51     m = (v+v).asDiagonal() * m;
  52     VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
  53   }
  54
  55   {
  56     // test deferred resizing in Matrix::operator=
  57     MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
  58     VERIFY_IS_APPROX((a = a * b), (c * b).eval());
  59   }
  60
  61   {
  62     // check the functions to setup blocking sizes compile and do not segfault
  63     // FIXME check they do what they are supposed to do !!
  64     std::ptrdiff_t l1 = internal::random<int>(10000,20000);
  65     std::ptrdiff_t l2 = internal::random<int>(1000000,2000000);
  66     setCpuCacheSizes(l1,l2);
  67     VERIFY(l1==l1CacheSize());
  68     VERIFY(l2==l2CacheSize());
  69     std::ptrdiff_t k1 = internal::random<int>(10,100)*16;
  70     std::ptrdiff_t m1 = internal::random<int>(10,100)*16;
  71     std::ptrdiff_t n1 = internal::random<int>(10,100)*16;
  72     // only makes sure it compiles fine
  73     internal::computeProductBlockingSizes<float,float>(k1,m1,n1);
  74   }
  75
  76   {
  77     // test regression in row-vector by matrix (bad Map type)
  78     MatrixXf mat1(10,32); mat1.setRandom();
  79     MatrixXf mat2(32,32); mat2.setRandom();
  80     MatrixXf r1 = mat1.row(2)*mat2.transpose();
  81     VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval());
  82
  83     MatrixXf r2 = mat1.row(2)*mat2;
  84     VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval());
  85   }
  86 #endif
  87 }