cart-elc

Source code for CART-ELC
git clone git://git.laack.co/cart-elc.git
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special_packetmath.cpp (6372B)


      1 // This file is part of Eigen, a lightweight C++ template library
      2 // for linear algebra.
      3 //
      4 // Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
      5 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
      6 //
      7 // This Source Code Form is subject to the terms of the Mozilla
      8 // Public License v. 2.0. If a copy of the MPL was not distributed
      9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
     10 
     11 #include <limits>
     12 #include "packetmath_test_shared.h"
     13 #include "../Eigen/SpecialFunctions"
     14 
     15 template<typename Scalar,typename Packet> void packetmath_real()
     16 {
     17   using std::abs;
     18   typedef internal::packet_traits<Scalar> PacketTraits;
     19   const int PacketSize = internal::unpacket_traits<Packet>::size;
     20 
     21   const int size = PacketSize*4;
     22   EIGEN_ALIGN_MAX Scalar data1[PacketSize*4];
     23   EIGEN_ALIGN_MAX Scalar data2[PacketSize*4];
     24   EIGEN_ALIGN_MAX Scalar ref[PacketSize*4];
     25 
     26 #if EIGEN_HAS_C99_MATH
     27   {
     28     data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
     29     test::packet_helper<internal::packet_traits<Scalar>::HasLGamma,Packet> h;
     30     h.store(data2, internal::plgamma(h.load(data1)));
     31     VERIFY((numext::isnan)(data2[0]));
     32   }
     33   if (internal::packet_traits<Scalar>::HasErf) {
     34     data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
     35     test::packet_helper<internal::packet_traits<Scalar>::HasErf,Packet> h;
     36     h.store(data2, internal::perf(h.load(data1)));
     37     VERIFY((numext::isnan)(data2[0]));
     38   }
     39   {
     40     data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
     41     test::packet_helper<internal::packet_traits<Scalar>::HasErfc,Packet> h;
     42     h.store(data2, internal::perfc(h.load(data1)));
     43     VERIFY((numext::isnan)(data2[0]));
     44   }
     45   {
     46     for (int i=0; i<size; ++i) {
     47       data1[i] = internal::random<Scalar>(Scalar(0),Scalar(1));
     48     }
     49     CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasNdtri, numext::ndtri, internal::pndtri);
     50   }
     51 #endif  // EIGEN_HAS_C99_MATH
     52 
     53   // For bessel_i*e and bessel_j*, the valid range is negative reals.
     54   {
     55     const int max_exponent = numext::mini(std::numeric_limits<Scalar>::max_exponent10-1, 6);
     56     for (int i=0; i<size; ++i)
     57     {
     58       data1[i] = internal::random<Scalar>(Scalar(-1),Scalar(1)) * Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-max_exponent),Scalar(max_exponent))));
     59       data2[i] = internal::random<Scalar>(Scalar(-1),Scalar(1)) * Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-max_exponent),Scalar(max_exponent))));
     60     }
     61 
     62     CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i0e, internal::pbessel_i0e);
     63     CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i1e, internal::pbessel_i1e);
     64     CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_j0, internal::pbessel_j0);
     65     CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_j1, internal::pbessel_j1);
     66   }
     67 
     68   // Use a smaller data range for the bessel_i* as these can become very large.
     69   // Following #1693, we also restrict this range further to avoid inf's due to
     70   // differences in pexp and exp.
     71   for (int i=0; i<size; ++i) {
     72       data1[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) *
     73                   Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1),Scalar(2))));
     74       data2[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) *
     75                   Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1),Scalar(2))));
     76   }
     77   CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i0, internal::pbessel_i0);
     78   CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_i1, internal::pbessel_i1);
     79 
     80 
     81   // y_i, and k_i are valid for x > 0.
     82   {
     83     const int max_exponent = numext::mini(std::numeric_limits<Scalar>::max_exponent10-1, 5);
     84     for (int i=0; i<size; ++i)
     85     {
     86       data1[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) * Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-2),Scalar(max_exponent))));
     87       data2[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) * Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-2),Scalar(max_exponent))));
     88     }
     89   }
     90 
     91   // TODO(srvasude): Re-enable this test once properly investigated why the
     92   // scalar and vector paths differ.
     93   // CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_y0, internal::pbessel_y0);
     94   CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_y1, internal::pbessel_y1);
     95   CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k0e, internal::pbessel_k0e);
     96   CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k1e, internal::pbessel_k1e);
     97 
     98   // Following #1693, we restrict the range for exp to avoid zeroing out too
     99   // fast.
    100   for (int i=0; i<size; ++i) {
    101       data1[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) *
    102                   Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1),Scalar(2))));
    103       data2[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) *
    104                   Scalar(std::pow(Scalar(9), internal::random<Scalar>(Scalar(-1),Scalar(2))));
    105   }
    106   CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k0, internal::pbessel_k0);
    107   CHECK_CWISE1_IF(PacketTraits::HasBessel, numext::bessel_k1, internal::pbessel_k1);
    108 
    109 
    110   for (int i=0; i<size; ++i) {
    111       data1[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) *
    112                   Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-1),Scalar(2))));
    113       data2[i] = internal::random<Scalar>(Scalar(0.01),Scalar(1)) *
    114                   Scalar(std::pow(Scalar(10), internal::random<Scalar>(Scalar(-1),Scalar(2))));
    115   }
    116 
    117 #if EIGEN_HAS_C99_MATH && (EIGEN_COMP_CXXVER >= 11)
    118   CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLGamma, std::lgamma, internal::plgamma);
    119   CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErf, std::erf, internal::perf);
    120   CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasErfc, std::erfc, internal::perfc);
    121 #endif
    122 
    123 }
    124 
    125 namespace Eigen {
    126 namespace test {
    127 
    128 template<typename Scalar,typename PacketType, bool IsComplex, bool IsInteger>
    129 struct runall {
    130   static void run() {
    131     packetmath_real<Scalar,PacketType>();
    132   }
    133 };
    134 
    135 }
    136 }
    137 
    138 EIGEN_DECLARE_TEST(special_packetmath)
    139 {
    140   g_first_pass = true;
    141   for(int i = 0; i < g_repeat; i++) {
    142 
    143     CALL_SUBTEST_1( test::runner<float>::run() );
    144     CALL_SUBTEST_2( test::runner<double>::run() );
    145     CALL_SUBTEST_3( test::runner<Eigen::half>::run() );
    146     CALL_SUBTEST_4( test::runner<Eigen::bfloat16>::run() );
    147     g_first_pass = false;
    148   }
    149 }