FastDeploy/third_party/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"
#include <Eigen/LU>

template <typename T>
void test_aliasing() {
  int rows = internal::random<int>(1, 12);
  int cols = internal::random<int>(1, 12);
  typedef Matrix<T, Dynamic, Dynamic> MatrixType;
  typedef Matrix<T, Dynamic, 1> VectorType;
  VectorType x(cols);
  x.setRandom();
  VectorType z(x);
  VectorType y(rows);
  y.setZero();
  MatrixType A(rows, cols);
  A.setRandom();
  // CwiseBinaryOp
  VERIFY_IS_APPROX(
      x = y + A * x,
      A * z);  // OK because "y + A*x" is marked as "assume-aliasing"
  x = z;
  // CwiseUnaryOp
  VERIFY_IS_APPROX(x = T(1.) * (A * x), A * z);  // OK because 1*(A*x) is
                                                 // replaced by (1*A*x) which is
                                                 // a Product<> expression
  x = z;
  // VERIFY_IS_APPROX(x = y-A*x, -A*z);   // Not OK in 3.3 because x is resized
  // before A*x gets evaluated
  x = z;
}

template <int>
void product_large_regressions() {
  {
    // 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>(100000, 200000);
    std::ptrdiff_t l3 = internal::random<int>(1000000, 2000000);
    setCpuCacheSizes(l1, l2, l3);
    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, std::ptrdiff_t>(k1, m1,
                                                                        n1, 1);
  }

  {
    // 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());
  }

  {
    Eigen::MatrixXd A(10, 10), B, C;
    A.setRandom();
    C = A;
    for (int k = 0; k < 79; ++k) C = C * A;
    B.noalias() =
        (((A * A) * (A * A)) * ((A * A) * (A * A)) * ((A * A) * (A * A)) *
         ((A * A) * (A * A)) * ((A * A) * (A * A)) * ((A * A) * (A * A)) *
         ((A * A) * (A * A)) * ((A * A) * (A * A)) * ((A * A) * (A * A)) *
         ((A * A) * (A * A))) *
        (((A * A) * (A * A)) * ((A * A) * (A * A)) * ((A * A) * (A * A)) *
         ((A * A) * (A * A)) * ((A * A) * (A * A)) * ((A * A) * (A * A)) *
         ((A * A) * (A * A)) * ((A * A) * (A * A)) * ((A * A) * (A * A)) *
         ((A * A) * (A * A)));
    VERIFY_IS_APPROX(B, C);
  }
}

template <int>
void bug_1622() {
  typedef Matrix<double, 2, -1, 0, 2, -1> Mat2X;
  Mat2X x(2, 2);
  x.setRandom();
  MatrixXd y(2, 2);
  y.setRandom();
  const Mat2X K1 = x * y.inverse();
  const Matrix2d K2 = x * y.inverse();
  VERIFY_IS_APPROX(K1, K2);
}

EIGEN_DECLARE_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_2(product(
        MatrixXd(internal::random<int>(1, 10), internal::random<int>(1, 10))));

    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))));

    CALL_SUBTEST_1(test_aliasing<float>());

    CALL_SUBTEST_6(bug_1622<1>());
  }

  CALL_SUBTEST_6(product_large_regressions<0>());

// Regression test for bug 714:
#if defined EIGEN_HAS_OPENMP
  omp_set_dynamic(1);
  for (int i = 0; i < g_repeat; i++) {
    CALL_SUBTEST_6(product(Matrix<float, Dynamic, Dynamic>(
        internal::random<int>(1, EIGEN_TEST_MAX_SIZE),
        internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
  }
#endif
}