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Merge upstream changes

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This commit is contained in:
Igor Babuschkin
2016-08-05 14:34:57 +01:00
parent 9537e8b118 fe4b927e9c
commit 0425118e2a
168 changed files with 20927 additions and 2324 deletions
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13
unsupported/test/CMakeLists.txt
View File

@@ -109,6 +109,7 @@ ei_add_test(gmres)
ei_add_test(minres)
ei_add_test(levenberg_marquardt)
ei_add_test(kronecker_product)
ei_add_test(special_functions)
# TODO: The following test names are prefixed with the cxx11 string, since historically
# the tests depended on c++11. This isn't the case anymore so we ought to rename them.
@@ -116,7 +117,6 @@ ei_add_test(kronecker_product)
# when using visual studio. We should make the check more strict to enable the tests for
# newer versions of MSVC.
if (NOT CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
ei_add_test(cxx11_float16)
ei_add_test(cxx11_tensor_dimension)
ei_add_test(cxx11_tensor_map)
ei_add_test(cxx11_tensor_assign)
@@ -191,10 +191,12 @@ if(CUDA_FOUND AND EIGEN_TEST_CUDA)
# Make sure to compile without the -pedantic, -Wundef, -Wnon-virtual-dtor
# and -fno-check-new flags since they trigger thousands of compilation warnings
# in the CUDA runtime
# Also remove -ansi that is incompatible with std=c++11.
string(REPLACE "-pedantic" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE "-Wundef" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE "-Wnon-virtual-dtor" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE "-fno-check-new" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE "-ansi" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
message(STATUS "Flags used to compile cuda code: " ${CMAKE_CXX_FLAGS})
@@ -210,7 +212,14 @@ if(CUDA_FOUND AND EIGEN_TEST_CUDA)
set(EIGEN_CUDA_RELAXED_CONSTEXPR "--relaxed-constexpr")
endif()
set(CUDA_NVCC_FLAGS "-std=c++11 ${EIGEN_CUDA_RELAXED_CONSTEXPR} -arch compute_${EIGEN_CUDA_COMPUTE_ARCH} -Xcudafe \"--display_error_number\"")
if( (NOT EIGEN_TEST_CXX11) OR (CMAKE_VERSION VERSION_LESS 3.3))
set(EIGEN_CUDA_CXX11_FLAG "-std=c++11")
else()
# otherwise the flag has already been added because of the above set(CMAKE_CXX_STANDARD 11)
set(EIGEN_CUDA_CXX11_FLAG "")
endif()
set(CUDA_NVCC_FLAGS "${EIGEN_CUDA_CXX11_FLAG} ${EIGEN_CUDA_RELAXED_CONSTEXPR} -arch compute_${EIGEN_CUDA_COMPUTE_ARCH} -Xcudafe \"--display_error_number\" ${CUDA_NVCC_FLAGS}")
cuda_include_directories("${CMAKE_CURRENT_BINARY_DIR}" "${CUDA_TOOLKIT_ROOT_DIR}/include")
set(EIGEN_ADD_TEST_FILENAME_EXTENSION "cu")

32
unsupported/test/FFTW.cpp
View File

@@ -18,11 +18,11 @@ using namespace Eigen;
template < typename T>
complex<long double> promote(complex<T> x) { return complex<long double>(x.real(),x.imag()); }
complex<long double> promote(complex<T> x) { return complex<long double>((long double)x.real(),(long double)x.imag()); }
complex<long double> promote(float x) { return complex<long double>( x); }
complex<long double> promote(double x) { return complex<long double>( x); }
complex<long double> promote(long double x) { return complex<long double>( x); }
complex<long double> promote(float x) { return complex<long double>((long double)x); }
complex<long double> promote(double x) { return complex<long double>((long double)x); }
complex<long double> promote(long double x) { return complex<long double>((long double)x); }
template <typename VT1,typename VT2>
@@ -33,7 +33,7 @@ complex<long double> promote(long double x) { return complex<long double>( x);
long double pi = acos((long double)-1 );
for (size_t k0=0;k0<(size_t)fftbuf.size();++k0) {
complex<long double> acc = 0;
long double phinc = -2.*k0* pi / timebuf.size();
long double phinc = (long double)(-2.)*k0* pi / timebuf.size();
for (size_t k1=0;k1<(size_t)timebuf.size();++k1) {
acc += promote( timebuf[k1] ) * exp( complex<long double>(0,k1*phinc) );
}
@@ -54,8 +54,8 @@ complex<long double> promote(long double x) { return complex<long double>( x);
long double difpower=0;
size_t n = (min)( buf1.size(),buf2.size() );
for (size_t k=0;k<n;++k) {
totalpower += (numext::abs2( buf1[k] ) + numext::abs2(buf2[k]) )/2;
difpower += numext::abs2(buf1[k] - buf2[k]);
totalpower += (long double)((numext::abs2( buf1[k] ) + numext::abs2(buf2[k]) )/2);
difpower += (long double)(numext::abs2(buf1[k] - buf2[k]));
}
return sqrt(difpower/totalpower);
}
@@ -93,19 +93,19 @@ void test_scalar_generic(int nfft)
fft.SetFlag(fft.HalfSpectrum );
fft.fwd( freqBuf,tbuf);
VERIFY((size_t)freqBuf.size() == (size_t)( (nfft>>1)+1) );
VERIFY( fft_rmse(freqBuf,tbuf) < test_precision<T>() );// gross check
VERIFY( T(fft_rmse(freqBuf,tbuf)) < test_precision<T>() );// gross check
fft.ClearFlag(fft.HalfSpectrum );
fft.fwd( freqBuf,tbuf);
VERIFY( (size_t)freqBuf.size() == (size_t)nfft);
VERIFY( fft_rmse(freqBuf,tbuf) < test_precision<T>() );// gross check
VERIFY( T(fft_rmse(freqBuf,tbuf)) < test_precision<T>() );// gross check
if (nfft&1)
return; // odd FFTs get the wrong size inverse FFT
ScalarVector tbuf2;
fft.inv( tbuf2 , freqBuf);
VERIFY( dif_rmse(tbuf,tbuf2) < test_precision<T>() );// gross check
VERIFY( T(dif_rmse(tbuf,tbuf2)) < test_precision<T>() );// gross check
// verify that the Unscaled flag takes effect
@@ -121,12 +121,12 @@ void test_scalar_generic(int nfft)
//for (size_t i=0;i<(size_t) tbuf.size();++i)
// cout << "freqBuf=" << freqBuf[i] << " in2=" << tbuf3[i] << " - in=" << tbuf[i] << " => " << (tbuf3[i] - tbuf[i] ) << endl;
VERIFY( dif_rmse(tbuf,tbuf3) < test_precision<T>() );// gross check
VERIFY( T(dif_rmse(tbuf,tbuf3)) < test_precision<T>() );// gross check
// verify that ClearFlag works
fft.ClearFlag(fft.Unscaled);
fft.inv( tbuf2 , freqBuf);
VERIFY( dif_rmse(tbuf,tbuf2) < test_precision<T>() );// gross check
VERIFY( T(dif_rmse(tbuf,tbuf2)) < test_precision<T>() );// gross check
}
template <typename T>
@@ -152,10 +152,10 @@ void test_complex_generic(int nfft)
inbuf[k]= Complex( (T)(rand()/(double)RAND_MAX - .5), (T)(rand()/(double)RAND_MAX - .5) );
fft.fwd( outbuf , inbuf);
VERIFY( fft_rmse(outbuf,inbuf) < test_precision<T>() );// gross check
VERIFY( T(fft_rmse(outbuf,inbuf)) < test_precision<T>() );// gross check
fft.inv( buf3 , outbuf);
VERIFY( dif_rmse(inbuf,buf3) < test_precision<T>() );// gross check
VERIFY( T(dif_rmse(inbuf,buf3)) < test_precision<T>() );// gross check
// verify that the Unscaled flag takes effect
ComplexVector buf4;
@@ -163,12 +163,12 @@ void test_complex_generic(int nfft)
fft.inv( buf4 , outbuf);
for (int k=0;k<nfft;++k)
buf4[k] *= T(1./nfft);
VERIFY( dif_rmse(inbuf,buf4) < test_precision<T>() );// gross check
VERIFY( T(dif_rmse(inbuf,buf4)) < test_precision<T>() );// gross check
// verify that ClearFlag works
fft.ClearFlag(fft.Unscaled);
fft.inv( buf3 , outbuf);
VERIFY( dif_rmse(inbuf,buf3) < test_precision<T>() );// gross check
VERIFY( T(dif_rmse(inbuf,buf3)) < test_precision<T>() );// gross check
}
template <typename T>

32
unsupported/test/autodiff.cpp
View File

@@ -205,6 +205,10 @@ void test_autodiff_hessian()
VERIFY_IS_APPROX(y.value().derivatives()(1), s4*std::cos(s1*s3+s2*s4));
VERIFY_IS_APPROX(y.derivatives()(0).derivatives(), -std::sin(s1*s3+s2*s4)*Vector2d(s3*s3,s4*s3));
VERIFY_IS_APPROX(y.derivatives()(1).derivatives(), -std::sin(s1*s3+s2*s4)*Vector2d(s3*s4,s4*s4));
ADD z = x(0)*x(1);
VERIFY_IS_APPROX(z.derivatives()(0).derivatives(), Vector2d(0,1));
VERIFY_IS_APPROX(z.derivatives()(1).derivatives(), Vector2d(1,0));
}
double bug_1222() {
@@ -234,6 +238,32 @@ double bug_1223() {
return t.value() + t2.value();
}
// regression test for some compilation issues with specializations of ScalarBinaryOpTraits
void bug_1260() {
Matrix4d A;
Vector4d v;
A*v;
}
// check a compilation issue with numext::max
double bug_1261() {
typedef AutoDiffScalar<Matrix2d> AD;
typedef Matrix<AD,2,1> VectorAD;
VectorAD v;
const AD maxVal = v.maxCoeff();
const AD minVal = v.minCoeff();
return maxVal.value() + minVal.value();
}
double bug_1264() {
typedef AutoDiffScalar<Vector2d> AD;
const AD s;
const Matrix<AD, 3, 1> v1;
const Matrix<AD, 3, 1> v2 = (s + 3.0) * v1;
return v2(0).value();
}
void test_autodiff()
{
for(int i = 0; i < g_repeat; i++) {
@@ -245,5 +275,7 @@ void test_autodiff()
bug_1222();
bug_1223();
bug_1260();
bug_1261();
}

203
unsupported/test/cxx11_float16.cpp
View File

@@ -1,203 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_float16
#include "main.h"
#include <Eigen/src/Core/arch/CUDA/Half.h>
using Eigen::half;
void test_conversion()
{
// Conversion from float.
VERIFY_IS_EQUAL(half(1.0f).x, 0x3c00);
VERIFY_IS_EQUAL(half(0.5f).x, 0x3800);
VERIFY_IS_EQUAL(half(0.33333f).x, 0x3555);
VERIFY_IS_EQUAL(half(0.0f).x, 0x0000);
VERIFY_IS_EQUAL(half(-0.0f).x, 0x8000);
VERIFY_IS_EQUAL(half(65504.0f).x, 0x7bff);
VERIFY_IS_EQUAL(half(65536.0f).x, 0x7c00); // Becomes infinity.
// Denormals.
VERIFY_IS_EQUAL(half(-5.96046e-08f).x, 0x8001);
VERIFY_IS_EQUAL(half(5.96046e-08f).x, 0x0001);
VERIFY_IS_EQUAL(half(1.19209e-07f).x, 0x0002);
// Verify round-to-nearest-even behavior.
float val1 = float(half(__half(0x3c00)));
float val2 = float(half(__half(0x3c01)));
float val3 = float(half(__half(0x3c02)));
VERIFY_IS_EQUAL(half(0.5f * (val1 + val2)).x, 0x3c00);
VERIFY_IS_EQUAL(half(0.5f * (val2 + val3)).x, 0x3c02);
// Conversion from int.
VERIFY_IS_EQUAL(half(-1).x, 0xbc00);
VERIFY_IS_EQUAL(half(0).x, 0x0000);
VERIFY_IS_EQUAL(half(1).x, 0x3c00);
VERIFY_IS_EQUAL(half(2).x, 0x4000);
VERIFY_IS_EQUAL(half(3).x, 0x4200);
// Conversion from bool.
VERIFY_IS_EQUAL(half(false).x, 0x0000);
VERIFY_IS_EQUAL(half(true).x, 0x3c00);
// Conversion to float.
VERIFY_IS_EQUAL(float(half(__half(0x0000))), 0.0f);
VERIFY_IS_EQUAL(float(half(__half(0x3c00))), 1.0f);
// Denormals.
VERIFY_IS_APPROX(float(half(__half(0x8001))), -5.96046e-08f);
VERIFY_IS_APPROX(float(half(__half(0x0001))), 5.96046e-08f);
VERIFY_IS_APPROX(float(half(__half(0x0002))), 1.19209e-07f);
// NaNs and infinities.
VERIFY(!(numext::isinf)(float(half(65504.0f)))); // Largest finite number.
VERIFY(!(numext::isnan)(float(half(0.0f))));
VERIFY((numext::isinf)(float(half(__half(0xfc00)))));
VERIFY((numext::isnan)(float(half(__half(0xfc01)))));
VERIFY((numext::isinf)(float(half(__half(0x7c00)))));
VERIFY((numext::isnan)(float(half(__half(0x7c01)))));
#if !EIGEN_COMP_MSVC
// Visual Studio errors out on divisions by 0
VERIFY((numext::isnan)(float(half(0.0 / 0.0))));
VERIFY((numext::isinf)(float(half(1.0 / 0.0))));
VERIFY((numext::isinf)(float(half(-1.0 / 0.0))));
#endif
// Exactly same checks as above, just directly on the half representation.
VERIFY(!(numext::isinf)(half(__half(0x7bff))));
VERIFY(!(numext::isnan)(half(__half(0x0000))));
VERIFY((numext::isinf)(half(__half(0xfc00))));
VERIFY((numext::isnan)(half(__half(0xfc01))));
VERIFY((numext::isinf)(half(__half(0x7c00))));
VERIFY((numext::isnan)(half(__half(0x7c01))));
#if !EIGEN_COMP_MSVC
// Visual Studio errors out on divisions by 0
VERIFY((numext::isnan)(half(0.0 / 0.0)));
VERIFY((numext::isinf)(half(1.0 / 0.0)));
VERIFY((numext::isinf)(half(-1.0 / 0.0)));
#endif
}
void test_numtraits()
{
std::cout << "expsilin = " << NumTraits<half>::epsilon() << std::endl;
std::cout << "highest = " << NumTraits<half>::highest() << std::endl;
std::cout << "lowest = " << NumTraits<half>::lowest() << std::endl;
std::cout << "inifinty = " << NumTraits<half>::infinity() << std::endl;
std::cout << "nan = " << NumTraits<half>::quiet_NaN() << std::endl;
}
void test_arithmetic()
{
VERIFY_IS_EQUAL(float(half(2) + half(2)), 4);
VERIFY_IS_EQUAL(float(half(2) + half(-2)), 0);
VERIFY_IS_APPROX(float(half(0.33333f) + half(0.66667f)), 1.0f);
VERIFY_IS_EQUAL(float(half(2.0f) * half(-5.5f)), -11.0f);
VERIFY_IS_APPROX(float(half(1.0f) / half(3.0f)), 0.33333f);
VERIFY_IS_EQUAL(float(-half(4096.0f)), -4096.0f);
VERIFY_IS_EQUAL(float(-half(-4096.0f)), 4096.0f);
}
void test_comparison()
{
VERIFY(half(1.0f) > half(0.5f));
VERIFY(half(0.5f) < half(1.0f));
VERIFY(!(half(1.0f) < half(0.5f)));
VERIFY(!(half(0.5f) > half(1.0f)));
VERIFY(!(half(4.0f) > half(4.0f)));
VERIFY(!(half(4.0f) < half(4.0f)));
VERIFY(!(half(0.0f) < half(-0.0f)));
VERIFY(!(half(-0.0f) < half(0.0f)));
VERIFY(!(half(0.0f) > half(-0.0f)));
VERIFY(!(half(-0.0f) > half(0.0f)));
VERIFY(half(0.2f) > half(-1.0f));
VERIFY(half(-1.0f) < half(0.2f));
VERIFY(half(-16.0f) < half(-15.0f));
VERIFY(half(1.0f) == half(1.0f));
VERIFY(half(1.0f) != half(2.0f));
// Comparisons with NaNs and infinities.
#if !EIGEN_COMP_MSVC
// Visual Studio errors out on divisions by 0
VERIFY(!(half(0.0 / 0.0) == half(0.0 / 0.0)));
VERIFY(half(0.0 / 0.0) != half(0.0 / 0.0));
VERIFY(!(half(1.0) == half(0.0 / 0.0)));
VERIFY(!(half(1.0) < half(0.0 / 0.0)));
VERIFY(!(half(1.0) > half(0.0 / 0.0)));
VERIFY(half(1.0) != half(0.0 / 0.0));
VERIFY(half(1.0) < half(1.0 / 0.0));
VERIFY(half(1.0) > half(-1.0 / 0.0));
#endif
}
void test_basic_functions()
{
VERIFY_IS_EQUAL(float(numext::abs(half(3.5f))), 3.5f);
VERIFY_IS_EQUAL(float(numext::abs(half(-3.5f))), 3.5f);
VERIFY_IS_EQUAL(float(numext::floor(half(3.5f))), 3.0f);
VERIFY_IS_EQUAL(float(numext::floor(half(-3.5f))), -4.0f);
VERIFY_IS_EQUAL(float(numext::ceil(half(3.5f))), 4.0f);
VERIFY_IS_EQUAL(float(numext::ceil(half(-3.5f))), -3.0f);
VERIFY_IS_APPROX(float(numext::sqrt(half(0.0f))), 0.0f);
VERIFY_IS_APPROX(float(numext::sqrt(half(4.0f))), 2.0f);
VERIFY_IS_APPROX(float(numext::pow(half(0.0f), half(1.0f))), 0.0f);
VERIFY_IS_APPROX(float(numext::pow(half(2.0f), half(2.0f))), 4.0f);
VERIFY_IS_EQUAL(float(numext::exp(half(0.0f))), 1.0f);
VERIFY_IS_APPROX(float(numext::exp(half(EIGEN_PI))), float(20.0 + EIGEN_PI));
VERIFY_IS_EQUAL(float(numext::log(half(1.0f))), 0.0f);
VERIFY_IS_APPROX(float(numext::log(half(10.0f))), 2.30273f);
}
void test_trigonometric_functions()
{
VERIFY_IS_APPROX(numext::cos(half(0.0f)), half(cosf(0.0f)));
VERIFY_IS_APPROX(numext::cos(half(EIGEN_PI)), half(cosf(EIGEN_PI)));
//VERIFY_IS_APPROX(numext::cos(half(EIGEN_PI/2)), half(cosf(EIGEN_PI/2)));
//VERIFY_IS_APPROX(numext::cos(half(3*EIGEN_PI/2)), half(cosf(3*EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::cos(half(3.5f)), half(cosf(3.5f)));
VERIFY_IS_APPROX(numext::sin(half(0.0f)), half(sinf(0.0f)));
// VERIFY_IS_APPROX(numext::sin(half(EIGEN_PI)), half(sinf(EIGEN_PI)));
VERIFY_IS_APPROX(numext::sin(half(EIGEN_PI/2)), half(sinf(EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::sin(half(3*EIGEN_PI/2)), half(sinf(3*EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::sin(half(3.5f)), half(sinf(3.5f)));
VERIFY_IS_APPROX(numext::tan(half(0.0f)), half(tanf(0.0f)));
// VERIFY_IS_APPROX(numext::tan(half(EIGEN_PI)), half(tanf(EIGEN_PI)));
// VERIFY_IS_APPROX(numext::tan(half(EIGEN_PI/2)), half(tanf(EIGEN_PI/2)));
//VERIFY_IS_APPROX(numext::tan(half(3*EIGEN_PI/2)), half(tanf(3*EIGEN_PI/2)));
VERIFY_IS_APPROX(numext::tan(half(3.5f)), half(tanf(3.5f)));
}
void test_cxx11_float16()
{
CALL_SUBTEST(test_conversion());
CALL_SUBTEST(test_numtraits());
CALL_SUBTEST(test_arithmetic());
CALL_SUBTEST(test_comparison());
CALL_SUBTEST(test_basic_functions());
CALL_SUBTEST(test_trigonometric_functions());
}

16
unsupported/test/cxx11_tensor_dimension.cpp
View File

@@ -21,7 +21,7 @@ static void test_dynamic_size()
VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2);
VERIFY_IS_EQUAL((int)Eigen::internal::array_get<1>(dimensions), 3);
VERIFY_IS_EQUAL((int)Eigen::internal::array_get<2>(dimensions), 7);
VERIFY_IS_EQUAL(dimensions.TotalSize(), 2*3*7);
VERIFY_IS_EQUAL((int)dimensions.TotalSize(), 2*3*7);
VERIFY_IS_EQUAL((int)dimensions[0], 2);
VERIFY_IS_EQUAL((int)dimensions[1], 3);
VERIFY_IS_EQUAL((int)dimensions[2], 7);
@@ -34,12 +34,12 @@ static void test_fixed_size()
VERIFY_IS_EQUAL((int)Eigen::internal::array_get<0>(dimensions), 2);
VERIFY_IS_EQUAL((int)Eigen::internal::array_get<1>(dimensions), 3);
VERIFY_IS_EQUAL((int)Eigen::internal::array_get<2>(dimensions), 7);
VERIFY_IS_EQUAL(dimensions.TotalSize(), 2*3*7);
VERIFY_IS_EQUAL((int)dimensions.TotalSize(), 2*3*7);
}
static void test_match()
{
Eigen::DSizes<int, 3> dyn(2,3,7);
Eigen::DSizes<unsigned int, 3> dyn((unsigned int)2,(unsigned int)3,(unsigned int)7);
Eigen::Sizes<2,3,7> stat;
VERIFY_IS_EQUAL(Eigen::dimensions_match(dyn, stat), true);
@@ -51,13 +51,13 @@ static void test_match()
static void test_rank_zero()
{
Eigen::Sizes<> scalar;
VERIFY_IS_EQUAL(scalar.TotalSize(), 1);
VERIFY_IS_EQUAL(scalar.rank(), 0);
VERIFY_IS_EQUAL(internal::array_prod(scalar), 1);
VERIFY_IS_EQUAL((int)scalar.TotalSize(), 1);
VERIFY_IS_EQUAL((int)scalar.rank(), 0);
VERIFY_IS_EQUAL((int)internal::array_prod(scalar), 1);
Eigen::DSizes<ptrdiff_t, 0> dscalar;
VERIFY_IS_EQUAL(dscalar.TotalSize(), 1);
VERIFY_IS_EQUAL(dscalar.rank(), 0u);
VERIFY_IS_EQUAL((int)dscalar.TotalSize(), 1);
VERIFY_IS_EQUAL((int)dscalar.rank(), 0);
}
void test_cxx11_tensor_dimension()

42
unsupported/test/cxx11_tensor_morphing.cpp
View File

@@ -13,6 +13,7 @@
using Eigen::Tensor;
template<typename>
static void test_simple_reshape()
{
Tensor<float, 5> tensor1(2,3,1,7,1);
@@ -40,7 +41,7 @@ static void test_simple_reshape()
}
}
template<typename>
static void test_reshape_in_expr() {
MatrixXf m1(2,3*5*7*11);
MatrixXf m2(3*5*7*11,13);
@@ -65,7 +66,7 @@ static void test_reshape_in_expr() {
}
}
template<typename>
static void test_reshape_as_lvalue()
{
Tensor<float, 3> tensor(2,3,7);
@@ -114,6 +115,7 @@ static void test_simple_slice()
}
}
template<typename=void>
static void test_const_slice()
{
const float b[1] = {42};
@@ -459,25 +461,25 @@ static void test_composition()
void test_cxx11_tensor_morphing()
{
CALL_SUBTEST(test_simple_reshape());
CALL_SUBTEST(test_reshape_in_expr());
CALL_SUBTEST(test_reshape_as_lvalue());
CALL_SUBTEST_1(test_simple_reshape<void>());
CALL_SUBTEST_1(test_reshape_in_expr<void>());
CALL_SUBTEST_1(test_reshape_as_lvalue<void>());
CALL_SUBTEST(test_simple_slice<ColMajor>());
CALL_SUBTEST(test_simple_slice<RowMajor>());
CALL_SUBTEST(test_const_slice());
CALL_SUBTEST(test_slice_in_expr<ColMajor>());
CALL_SUBTEST(test_slice_in_expr<RowMajor>());
CALL_SUBTEST(test_slice_as_lvalue<ColMajor>());
CALL_SUBTEST(test_slice_as_lvalue<RowMajor>());
CALL_SUBTEST(test_slice_raw_data<ColMajor>());
CALL_SUBTEST(test_slice_raw_data<RowMajor>());
CALL_SUBTEST_1(test_simple_slice<ColMajor>());
CALL_SUBTEST_1(test_simple_slice<RowMajor>());
CALL_SUBTEST_1(test_const_slice());
CALL_SUBTEST_2(test_slice_in_expr<ColMajor>());
CALL_SUBTEST_3(test_slice_in_expr<RowMajor>());
CALL_SUBTEST_4(test_slice_as_lvalue<ColMajor>());
CALL_SUBTEST_4(test_slice_as_lvalue<RowMajor>());
CALL_SUBTEST_5(test_slice_raw_data<ColMajor>());
CALL_SUBTEST_5(test_slice_raw_data<RowMajor>());
CALL_SUBTEST(test_strided_slice_write<ColMajor>());
CALL_SUBTEST(test_strided_slice<ColMajor>());
CALL_SUBTEST(test_strided_slice_write<RowMajor>());
CALL_SUBTEST(test_strided_slice<RowMajor>());
CALL_SUBTEST_6(test_strided_slice_write<ColMajor>());
CALL_SUBTEST_6(test_strided_slice<ColMajor>());
CALL_SUBTEST_6(test_strided_slice_write<RowMajor>());
CALL_SUBTEST_6(test_strided_slice<RowMajor>());
CALL_SUBTEST(test_composition<ColMajor>());
CALL_SUBTEST(test_composition<RowMajor>());
CALL_SUBTEST_7(test_composition<ColMajor>());
CALL_SUBTEST_7(test_composition<RowMajor>());
}

87
unsupported/test/cxx11_tensor_of_float16_cuda.cu
View File

@@ -19,8 +19,47 @@
using Eigen::Tensor;
template<typename>
void test_cuda_numext() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
int num_elem = 101;
float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
bool* d_res_half = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(
d_float, num_elem);
Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_half(
d_res_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_float(
d_res_float, num_elem);
gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>());
gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>());
Tensor<bool, 1> half_prec(num_elem);
Tensor<bool, 1> full_prec(num_elem);
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(bool));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(bool));
gpu_device.synchronize();
for (int i = 0; i < num_elem; ++i) {
std::cout << "Checking numext " << i << std::endl;
VERIFY_IS_EQUAL(full_prec(i), half_prec(i));
}
gpu_device.deallocate(d_float);
gpu_device.deallocate(d_res_half);
gpu_device.deallocate(d_res_float);
}
#ifdef EIGEN_HAS_CUDA_FP16
template<typename>
void test_cuda_conversion() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
@@ -55,7 +94,7 @@ void test_cuda_conversion() {
gpu_device.deallocate(d_conv);
}
template<typename>
void test_cuda_unary() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
@@ -92,7 +131,7 @@ void test_cuda_unary() {
gpu_device.deallocate(d_res_float);
}
template<typename>
void test_cuda_elementwise() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
@@ -134,6 +173,7 @@ void test_cuda_elementwise() {
gpu_device.deallocate(d_res_float);
}
template<typename>
void test_cuda_trancendental() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
@@ -186,7 +226,10 @@ void test_cuda_trancendental() {
}
for (int i = 0; i < num_elem; ++i) {
std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i) << " half = " << half_prec2(i) << std::endl;
VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
if(std::abs(input2(i)-1.f)<0.05f) // log lacks accurary nearby 1
VERIFY_IS_APPROX(full_prec2(i)+Eigen::half(0.1f), half_prec2(i)+Eigen::half(0.1f));
else
VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
}
gpu_device.deallocate(d_float1);
gpu_device.deallocate(d_float2);
@@ -196,7 +239,7 @@ void test_cuda_trancendental() {
gpu_device.deallocate(d_res2_float);
}
template<typename>
void test_cuda_contractions() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
@@ -247,7 +290,7 @@ void test_cuda_contractions() {
gpu_device.deallocate(d_res_float);
}
template<typename>
void test_cuda_reductions(int size1, int size2, int redux) {
std::cout << "Reducing " << size1 << " by " << size2
@@ -296,17 +339,19 @@ void test_cuda_reductions(int size1, int size2, int redux) {
gpu_device.deallocate(d_res_float);
}
template<typename>
void test_cuda_reductions() {
test_cuda_reductions(13, 13, 0);
test_cuda_reductions(13, 13, 1);
test_cuda_reductions<void>(13, 13, 0);
test_cuda_reductions<void>(13, 13, 1);
test_cuda_reductions(35, 36, 0);
test_cuda_reductions(35, 36, 1);
test_cuda_reductions<void>(35, 36, 0);
test_cuda_reductions<void>(35, 36, 1);
test_cuda_reductions(36, 35, 0);
test_cuda_reductions(36, 35, 1);
test_cuda_reductions<void>(36, 35, 0);
test_cuda_reductions<void>(36, 35, 1);
}
template<typename>
void test_cuda_full_reductions() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
@@ -355,7 +400,7 @@ void test_cuda_full_reductions() {
gpu_device.deallocate(d_res_float);
}
template<typename>
void test_cuda_forced_evals() {
Eigen::CudaStreamDevice stream;
@@ -408,15 +453,17 @@ void test_cuda_forced_evals() {
void test_cxx11_tensor_of_float16_cuda()
{
CALL_SUBTEST_1(test_cuda_numext<void>());
#ifdef EIGEN_HAS_CUDA_FP16
CALL_SUBTEST_1(test_cuda_conversion());
CALL_SUBTEST_1(test_cuda_unary());
CALL_SUBTEST_1(test_cuda_elementwise());
CALL_SUBTEST_1(test_cuda_trancendental());
CALL_SUBTEST_2(test_cuda_contractions());
CALL_SUBTEST_3(test_cuda_reductions());
CALL_SUBTEST_4(test_cuda_full_reductions());
CALL_SUBTEST_5(test_cuda_forced_evals());
CALL_SUBTEST_1(test_cuda_conversion<void>());
CALL_SUBTEST_1(test_cuda_unary<void>());
CALL_SUBTEST_1(test_cuda_elementwise<void>());
CALL_SUBTEST_1(test_cuda_trancendental<void>());
CALL_SUBTEST_2(test_cuda_contractions<void>());
CALL_SUBTEST_3(test_cuda_reductions<void>());
CALL_SUBTEST_4(test_cuda_full_reductions<void>());
CALL_SUBTEST_5(test_cuda_forced_evals<void>());
#else
std::cout << "Half floats are not supported by this version of cuda: skipping the test" << std::endl;
#endif

29
unsupported/test/cxx11_tensor_reduction.cpp
View File

@@ -239,6 +239,33 @@ static void test_simple_reductions() {
}
}
template <int DataLayout>
static void test_reductions_in_expr() {
Tensor<float, 4, DataLayout> tensor(2, 3, 5, 7);
tensor.setRandom();
array<ptrdiff_t, 2> reduction_axis2;
reduction_axis2[0] = 1;
reduction_axis2[1] = 3;
Tensor<float, 2, DataLayout> result(2, 5);
result = result.constant(1.0f) - tensor.sum(reduction_axis2);
VERIFY_IS_EQUAL(result.dimension(0), 2);
VERIFY_IS_EQUAL(result.dimension(1), 5);
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 5; ++j) {
float sum = 0.0f;
for (int k = 0; k < 3; ++k) {
for (int l = 0; l < 7; ++l) {
sum += tensor(i, k, j, l);
}
}
VERIFY_IS_APPROX(result(i, j), 1.0f - sum);
}
}
}
template <int DataLayout>
static void test_full_reductions() {
Tensor<float, 2, DataLayout> tensor(2, 3);
@@ -462,6 +489,8 @@ void test_cxx11_tensor_reduction() {
CALL_SUBTEST(test_trivial_reductions<RowMajor>());
CALL_SUBTEST(test_simple_reductions<ColMajor>());
CALL_SUBTEST(test_simple_reductions<RowMajor>());
CALL_SUBTEST(test_reductions_in_expr<ColMajor>());
CALL_SUBTEST(test_reductions_in_expr<RowMajor>());
CALL_SUBTEST(test_full_reductions<ColMajor>());
CALL_SUBTEST(test_full_reductions<RowMajor>());
CALL_SUBTEST(test_user_defined_reductions<ColMajor>());

1
unsupported/test/mpreal_support.cpp
View File

@@ -17,6 +17,7 @@ void test_mpreal_support()
std::cerr << "dummy_precision = " << NumTraits<mpreal>::dummy_precision() << "\n";
std::cerr << "highest = " << NumTraits<mpreal>::highest() << "\n";
std::cerr << "lowest = " << NumTraits<mpreal>::lowest() << "\n";
std::cerr << "digits10 = " << NumTraits<mpreal>::digits10() << "\n";
for(int i = 0; i < g_repeat; i++) {
int s = Eigen::internal::random<int>(1,100);

345
unsupported/test/special_functions.cpp Normal file
View File

@@ -0,0 +1,345 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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 "main.h"
#include "../Eigen/SpecialFunctions"
template<typename X, typename Y>
void verify_component_wise(const X& x, const Y& y)
{
for(Index i=0; i<x.size(); ++i)
{
if((numext::isfinite)(y(i)))
VERIFY_IS_APPROX( x(i), y(i) );
else if((numext::isnan)(y(i)))
VERIFY((numext::isnan)(x(i)));
else
VERIFY_IS_EQUAL( x(i), y(i) );
}
}
template<typename ArrayType> void array_special_functions()
{
using std::abs;
using std::sqrt;
typedef typename ArrayType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
Scalar plusinf = std::numeric_limits<Scalar>::infinity();
Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
Index rows = internal::random<Index>(1,30);
Index cols = 1;
// API
{
ArrayType m1 = ArrayType::Random(rows,cols);
#if EIGEN_HAS_C99_MATH
VERIFY_IS_APPROX(m1.lgamma(), lgamma(m1));
VERIFY_IS_APPROX(m1.digamma(), digamma(m1));
VERIFY_IS_APPROX(m1.erf(), erf(m1));
VERIFY_IS_APPROX(m1.erfc(), erfc(m1));
#endif // EIGEN_HAS_C99_MATH
}
#if EIGEN_HAS_C99_MATH
// check special functions (comparing against numpy implementation)
if (!NumTraits<Scalar>::IsComplex)
{
{
ArrayType m1 = ArrayType::Random(rows,cols);
ArrayType m2 = ArrayType::Random(rows,cols);
// Test various propreties of igamma & igammac. These are normalized
// gamma integrals where
// igammac(a, x) = Gamma(a, x) / Gamma(a)
// igamma(a, x) = gamma(a, x) / Gamma(a)
// where Gamma and gamma are considered the standard unnormalized
// upper and lower incomplete gamma functions, respectively.
ArrayType a = m1.abs() + 2;
ArrayType x = m2.abs() + 2;
ArrayType zero = ArrayType::Zero(rows, cols);
ArrayType one = ArrayType::Constant(rows, cols, Scalar(1.0));
ArrayType a_m1 = a - one;
ArrayType Gamma_a_x = Eigen::igammac(a, x) * a.lgamma().exp();
ArrayType Gamma_a_m1_x = Eigen::igammac(a_m1, x) * a_m1.lgamma().exp();
ArrayType gamma_a_x = Eigen::igamma(a, x) * a.lgamma().exp();
ArrayType gamma_a_m1_x = Eigen::igamma(a_m1, x) * a_m1.lgamma().exp();
// Gamma(a, 0) == Gamma(a)
VERIFY_IS_APPROX(Eigen::igammac(a, zero), one);
// Gamma(a, x) + gamma(a, x) == Gamma(a)
VERIFY_IS_APPROX(Gamma_a_x + gamma_a_x, a.lgamma().exp());
// Gamma(a, x) == (a - 1) * Gamma(a-1, x) + x^(a-1) * exp(-x)
VERIFY_IS_APPROX(Gamma_a_x, (a - 1) * Gamma_a_m1_x + x.pow(a-1) * (-x).exp());
// gamma(a, x) == (a - 1) * gamma(a-1, x) - x^(a-1) * exp(-x)
VERIFY_IS_APPROX(gamma_a_x, (a - 1) * gamma_a_m1_x - x.pow(a-1) * (-x).exp());
}
{
// Check exact values of igamma and igammac against a third party calculation.
Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
Scalar x_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
// location i*6+j corresponds to a_s[i], x_s[j].
Scalar igamma_s[][6] = {{0.0, nan, nan, nan, nan, nan},
{0.0, 0.6321205588285578, 0.7768698398515702,
0.9816843611112658, 9.999500016666262e-05, 1.0},
{0.0, 0.4275932955291202, 0.608374823728911,
0.9539882943107686, 7.522076445089201e-07, 1.0},
{0.0, 0.01898815687615381, 0.06564245437845008,
0.5665298796332909, 4.166333347221828e-18, 1.0},
{0.0, 0.9999780593618628, 0.9999899967080838,
0.9999996219837988, 0.9991370418689945, 1.0},
{0.0, 0.0, 0.0, 0.0, 0.0, 0.5042041932513908}};
Scalar igammac_s[][6] = {{nan, nan, nan, nan, nan, nan},
{1.0, 0.36787944117144233, 0.22313016014842982,
0.018315638888734182, 0.9999000049998333, 0.0},
{1.0, 0.5724067044708798, 0.3916251762710878,
0.04601170568923136, 0.9999992477923555, 0.0},
{1.0, 0.9810118431238462, 0.9343575456215499,
0.4334701203667089, 1.0, 0.0},
{1.0, 2.1940638138146658e-05, 1.0003291916285e-05,
3.7801620118431334e-07, 0.0008629581310054535,
0.0},
{1.0, 1.0, 1.0, 1.0, 1.0, 0.49579580674813944}};
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 6; ++j) {
if ((std::isnan)(igamma_s[i][j])) {
VERIFY((std::isnan)(numext::igamma(a_s[i], x_s[j])));
} else {
VERIFY_IS_APPROX(numext::igamma(a_s[i], x_s[j]), igamma_s[i][j]);
}
if ((std::isnan)(igammac_s[i][j])) {
VERIFY((std::isnan)(numext::igammac(a_s[i], x_s[j])));
} else {
VERIFY_IS_APPROX(numext::igammac(a_s[i], x_s[j]), igammac_s[i][j]);
}
}
}
}
}
#endif // EIGEN_HAS_C99_MATH
// Check the zeta function against scipy.special.zeta
{
ArrayType x(7), q(7), res(7), ref(7);
x << 1.5, 4, 10.5, 10000.5, 3, 1, 0.9;
q << 2, 1.5, 3, 1.0001, -2.5, 1.2345, 1.2345;
ref << 1.61237534869, 0.234848505667, 1.03086757337e-5, 0.367879440865, 0.054102025820864097, plusinf, nan;
CALL_SUBTEST( verify_component_wise(ref, ref); );
CALL_SUBTEST( res = x.zeta(q); verify_component_wise(res, ref); );
CALL_SUBTEST( res = zeta(x,q); verify_component_wise(res, ref); );
}
// digamma
{
ArrayType x(7), res(7), ref(7);
x << 1, 1.5, 4, -10.5, 10000.5, 0, -1;
ref << -0.5772156649015329, 0.03648997397857645, 1.2561176684318, 2.398239129535781, 9.210340372392849, plusinf, plusinf;
CALL_SUBTEST( verify_component_wise(ref, ref); );
CALL_SUBTEST( res = x.digamma(); verify_component_wise(res, ref); );
CALL_SUBTEST( res = digamma(x); verify_component_wise(res, ref); );
}
#if EIGEN_HAS_C99_MATH
{
ArrayType n(11), x(11), res(11), ref(11);
n << 1, 1, 1, 1.5, 17, 31, 28, 8, 42, 147, 170;
x << 2, 3, 25.5, 1.5, 4.7, 11.8, 17.7, 30.2, 15.8, 54.1, 64;
ref << 0.644934066848, 0.394934066848, 0.0399946696496, nan, 293.334565435, 0.445487887616, -2.47810300902e-07, -8.29668781082e-09, -0.434562276666, 0.567742190178, -0.0108615497927;
CALL_SUBTEST( verify_component_wise(ref, ref); );
if(sizeof(RealScalar)>=8) { // double
// Reason for commented line: http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1232
// CALL_SUBTEST( res = x.polygamma(n); verify_component_wise(res, ref); );
CALL_SUBTEST( res = polygamma(n,x); verify_component_wise(res, ref); );
}
else {
// CALL_SUBTEST( res = x.polygamma(n); verify_component_wise(res.head(8), ref.head(8)); );
CALL_SUBTEST( res = polygamma(n,x); verify_component_wise(res.head(8), ref.head(8)); );
}
}
#endif
#if EIGEN_HAS_C99_MATH
{
// Inputs and ground truth generated with scipy via:
// a = np.logspace(-3, 3, 5) - 1e-3
// b = np.logspace(-3, 3, 5) - 1e-3
// x = np.linspace(-0.1, 1.1, 5)
// (full_a, full_b, full_x) = np.vectorize(lambda a, b, x: (a, b, x))(*np.ix_(a, b, x))
// full_a = full_a.flatten().tolist() # same for full_b, full_x
// v = scipy.special.betainc(full_a, full_b, full_x).flatten().tolist()
//
// Note in Eigen, we call betainc with arguments in the order (x, a, b).
ArrayType a(125);
ArrayType b(125);
ArrayType x(125);
ArrayType v(125);
ArrayType res(125);
a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
999.999, 999.999, 999.999;
b << 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999,
0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999,
999.999, 999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.999, 0.999, 0.999, 0.999,
0.999, 31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999,
999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999,
999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999,
999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379,
0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999,
31.62177660168379, 31.62177660168379, 31.62177660168379,
31.62177660168379, 31.62177660168379, 999.999, 999.999, 999.999,
999.999, 999.999;
x << -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5,
0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2,
0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1,
0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1,
-0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8,
1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5,
0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2,
0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1,
0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5,
0.8, 1.1;
v << nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, 0.47972119876364683, 0.5, 0.5202788012363533, nan, nan,
0.9518683957740043, 0.9789663010413743, 0.9931729188073435, nan, nan,
0.999995949033062, 0.9999999999993698, 0.9999999999999999, nan, nan,
0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan,
nan, nan, nan, nan, nan, 0.006827081192655869, 0.0210336989586256,
0.04813160422599567, nan, nan, 0.20014344256217678, 0.5000000000000001,
0.7998565574378232, nan, nan, 0.9991401428435834, 0.999999999698403,
0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999,
0.9999999999999999, nan, nan, nan, nan, nan, nan, nan,
1.0646600232370887e-25, 6.301722877826246e-13, 4.050966937974938e-06,
nan, nan, 7.864342668429763e-23, 3.015969667594166e-10,
0.0008598571564165444, nan, nan, 6.031987710123844e-08,
0.5000000000000007, 0.9999999396801229, nan, nan, 0.9999999999999999,
0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan, nan,
nan, 0.0, 7.029920380986636e-306, 2.2450728208591345e-101, nan, nan,
0.0, 9.275871147869727e-302, 1.2232913026152827e-97, nan, nan, 0.0,
3.0891393081932924e-252, 2.9303043666183996e-60, nan, nan,
2.248913486879199e-196, 0.5000000000004947, 0.9999999999999999, nan;
CALL_SUBTEST(res = betainc(a, b, x);
verify_component_wise(res, v););
}
// Test various properties of betainc
{
ArrayType m1 = ArrayType::Random(32);
ArrayType m2 = ArrayType::Random(32);
ArrayType m3 = ArrayType::Random(32);
ArrayType one = ArrayType::Constant(32, Scalar(1.0));
const Scalar eps = std::numeric_limits<Scalar>::epsilon();
ArrayType a = (m1 * 4.0).exp();
ArrayType b = (m2 * 4.0).exp();
ArrayType x = m3.abs();
// betainc(a, 1, x) == x**a
CALL_SUBTEST(
ArrayType test = betainc(a, one, x);
ArrayType expected = x.pow(a);
verify_component_wise(test, expected););
// betainc(1, b, x) == 1 - (1 - x)**b
CALL_SUBTEST(
ArrayType test = betainc(one, b, x);
ArrayType expected = one - (one - x).pow(b);
verify_component_wise(test, expected););
// betainc(a, b, x) == 1 - betainc(b, a, 1-x)
CALL_SUBTEST(
ArrayType test = betainc(a, b, x) + betainc(b, a, one - x);
ArrayType expected = one;
verify_component_wise(test, expected););
// betainc(a+1, b, x) = betainc(a, b, x) - x**a * (1 - x)**b / (a * beta(a, b))
CALL_SUBTEST(
ArrayType num = x.pow(a) * (one - x).pow(b);
ArrayType denom = a * (a.lgamma() + b.lgamma() - (a + b).lgamma()).exp();
// Add eps to rhs and lhs so that component-wise test doesn't result in
// nans when both outputs are zeros.
ArrayType expected = betainc(a, b, x) - num / denom + eps;
ArrayType test = betainc(a + one, b, x) + eps;
if (sizeof(Scalar) >= 8) { // double
verify_component_wise(test, expected);
} else {
// Reason for limited test: http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1232
verify_component_wise(test.head(8), expected.head(8));
});
// betainc(a, b+1, x) = betainc(a, b, x) + x**a * (1 - x)**b / (b * beta(a, b))
CALL_SUBTEST(
// Add eps to rhs and lhs so that component-wise test doesn't result in
// nans when both outputs are zeros.
ArrayType num = x.pow(a) * (one - x).pow(b);
ArrayType denom = b * (a.lgamma() + b.lgamma() - (a + b).lgamma()).exp();
ArrayType expected = betainc(a, b, x) + num / denom + eps;
ArrayType test = betainc(a, b + one, x) + eps;
verify_component_wise(test, expected););
}
#endif
}
void test_special_functions()
{
CALL_SUBTEST_1(array_special_functions<ArrayXf>());
CALL_SUBTEST_2(array_special_functions<ArrayXd>());
}