eigen/test/product_large.cpp

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 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 "product.h"

void test_product_large()
{
  for(int i = 0; i < g_repeat; i++) {
    CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
    CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
    CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
    CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
    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))) );
  }

#if defined EIGEN_TEST_PART_6
  {
    // test a specific issue in DiagonalProduct
    int N = 1000000;
    VectorXf v = VectorXf::Ones(N);
    MatrixXf m = MatrixXf::Ones(N,3);
    m = (v+v).asDiagonal() * m;
    VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
  }

  {
    // test deferred resizing in Matrix::operator=
    MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
    VERIFY_IS_APPROX((a = a * b), (c * b).eval());
  }

  {
    // check the functions to setup blocking sizes compile and do not segfault
    // FIXME check they do what they are supposed to do !!
    std::ptrdiff_t l1 = internal::random<int>(10000,20000);
    std::ptrdiff_t l2 = internal::random<int>(1000000,2000000);
    setCpuCacheSizes(l1,l2);
    VERIFY(l1==l1CacheSize());
    VERIFY(l2==l2CacheSize());
    std::ptrdiff_t k1 = internal::random<int>(10,100)*16;
    std::ptrdiff_t m1 = internal::random<int>(10,100)*16;
    std::ptrdiff_t n1 = internal::random<int>(10,100)*16;
    // only makes sure it compiles fine
    internal::computeProductBlockingSizes<float,float>(k1,m1,n1);
  }

  {
    // test regression in row-vector by matrix (bad Map type)
    MatrixXf mat1(10,32); mat1.setRandom();
    MatrixXf mat2(32,32); mat2.setRandom();
    MatrixXf r1 = mat1.row(2)*mat2.transpose();
    VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval());

    MatrixXf r2 = mat1.row(2)*mat2;
    VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval());
  }
#endif
}