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Fix annoying warnings

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This commit is contained in:
Charles Schlosser
2023-07-07 20:19:58 +00:00
parent 63dcb429cd
commit 1a2bfca8f0
18 changed files with 207 additions and 105 deletions
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2
unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
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@@ -998,8 +998,8 @@ class StridedLinearBufferCopy {
enum {
Vectorizable = packet_traits<Scalar>::Vectorizable,
PacketSize = packet_traits<Scalar>::size,
HasHalfPacket = unpacket_traits<HalfPacket>::size < PacketSize,
HalfPacketSize = unpacket_traits<HalfPacket>::size,
HasHalfPacket = static_cast<int>(HalfPacketSize) < static_cast<int>(PacketSize)
};
public:

17
unsupported/Eigen/FFT
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@@ -181,15 +181,16 @@ class FFT
typedef typename impl_type::Scalar Scalar;
typedef typename impl_type::Complex Complex;
enum Flag {
Default=0, // goof proof
Unscaled=1,
HalfSpectrum=2,
// SomeOtherSpeedOptimization=4
Speedy=32767
};
using Flag = int;
static constexpr Flag Default = 0;
static constexpr Flag Unscaled = 1;
static constexpr Flag HalfSpectrum = 2;
static constexpr Flag Speedy = 32767;
FFT( const impl_type & impl=impl_type() , Flag flags=Default ) :m_impl(impl),m_flag(flags) { }
FFT( const impl_type & impl=impl_type() , Flag flags=Default ) :m_impl(impl),m_flag(flags)
{
eigen_assert((flags == Default || flags == Unscaled || flags == HalfSpectrum || flags == Speedy) && "invalid flags argument");
}
inline
bool HasFlag(Flag f) const { return (m_flag & (int)f) == f;}

2
unsupported/test/EulerAngles.cpp
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@@ -9,6 +9,8 @@
#include "main.h"
EIGEN_DISABLE_DEPRECATED_WARNING
#include <unsupported/Eigen/EulerAngles>
using namespace Eigen;

11
unsupported/test/NNLS.cpp
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@@ -54,7 +54,7 @@ void test_nnls_known_solution(const MatrixType &A, const VectorB &b, const Vecto
}
template <typename MatrixType>
void test_nnls_random_problem() {
void test_nnls_random_problem(const MatrixType&) {
//
// SETUP
//
@@ -448,12 +448,9 @@ EIGEN_DECLARE_TEST(NNLS) {
for (int i = 0; i < g_repeat; i++) {
// Essential NNLS properties, across different types.
CALL_SUBTEST_2(test_nnls_random_problem<MatrixXf>());
CALL_SUBTEST_3(test_nnls_random_problem<MatrixXd>());
{
using MatFixed = Matrix<double, 12, 5>;
CALL_SUBTEST_4(test_nnls_random_problem<MatFixed>());
}
CALL_SUBTEST_2(test_nnls_random_problem(MatrixXf()));
CALL_SUBTEST_3(test_nnls_random_problem(MatrixXd()));
CALL_SUBTEST_4(test_nnls_random_problem(Matrix<double, 12, 5>()));
CALL_SUBTEST_5(test_nnls_with_half_precision());
// Robustness tests:

2
unsupported/test/cxx11_tensor_casts.cpp
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@@ -30,7 +30,7 @@ static void test_type_cast() {
for (int i = 0; i < 101; ++i) {
for (int j = 0; j < 201; ++j) {
const ToType ref = static_cast<ToType>(ftensor(i, j));
const ToType ref = internal::cast<FromType, ToType>(ftensor(i, j));
VERIFY_IS_EQUAL(ttensor(i, j), ref);
}
}

2
unsupported/test/cxx11_tensor_reduction.cpp
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@@ -485,7 +485,7 @@ void test_sum_accuracy() {
// Test against probabilistic forward error bound. In reality, the error is much smaller
// when we use tree summation.
double err = Eigen::numext::abs(static_cast<double>(sum()) - expected_sum);
double tol = numext::sqrt(static_cast<double>(num_elements)) * NumTraits<ScalarType>::epsilon() * static_cast<ScalarType>(abs_sum);
double tol = numext::sqrt(static_cast<double>(num_elements)) * static_cast<double>(NumTraits<ScalarType>::epsilon()) * abs_sum;
VERIFY_LE(err, tol);
}
}