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Require recent GCC and MSCV and removed EIGEN_HAS_CXX14 and some other feature test macros

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This commit is contained in:
Erik Schultheis
2021-12-01 00:48:34 +00:00
committed by Rasmus Munk Larsen
parent 085c2fc5d5
commit ec2fd0f7ed
39 changed files with 52 additions and 702 deletions
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4
unsupported/test/autodiff.cpp
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@@ -106,7 +106,6 @@ struct TestFunc1
};
#if EIGEN_HAS_VARIADIC_TEMPLATES
/* Test functor for the C++11 features. */
template <typename Scalar>
struct integratorFunctor
@@ -186,7 +185,6 @@ template<typename Func> void forward_jacobian_cpp11(const Func& f)
VERIFY_IS_APPROX(y, yref);
VERIFY_IS_APPROX(j, jref);
}
#endif
template<typename Func> void forward_jacobian(const Func& f)
{
@@ -247,9 +245,7 @@ void test_autodiff_jacobian()
CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,2>()) ));
CALL_SUBTEST(( forward_jacobian(TestFunc1<double,3,3>()) ));
CALL_SUBTEST(( forward_jacobian(TestFunc1<double>(3,3)) ));
#if EIGEN_HAS_VARIADIC_TEMPLATES
CALL_SUBTEST(( forward_jacobian_cpp11(integratorFunctor<double>(10)) ));
#endif
}

2
unsupported/test/cxx11_tensor_assign.cpp
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@@ -280,7 +280,6 @@ static void test_compound_assign()
}
static void test_std_initializers_tensor() {
#if EIGEN_HAS_VARIADIC_TEMPLATES
Tensor<int, 1> a(3);
a.setValues({0, 1, 2});
VERIFY_IS_EQUAL(a(0), 0);
@@ -349,7 +348,6 @@ static void test_std_initializers_tensor() {
VERIFY_IS_EQUAL(c(2, 1, 1), 25);
VERIFY_IS_EQUAL(c(2, 1, 2), 26);
VERIFY_IS_EQUAL(c(2, 1, 3), 27);
#endif // EIGEN_HAS_VARIADIC_TEMPLATES
}
EIGEN_DECLARE_TEST(cxx11_tensor_assign)

16
unsupported/test/cxx11_tensor_broadcasting.cpp
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@@ -91,15 +91,7 @@ static void test_vectorized_broadcasting()
}
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
tensor.resize(11,3,5);
#else
array<Index, 3> new_dims;
new_dims[0] = 11;
new_dims[1] = 3;
new_dims[2] = 5;
tensor.resize(new_dims);
#endif
tensor.setRandom();
broadcast = tensor.broadcast(broadcasts);
@@ -148,15 +140,7 @@ static void test_static_broadcasting()
}
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
tensor.resize(11,3,5);
#else
array<Index, 3> new_dims;
new_dims[0] = 11;
new_dims[1] = 3;
new_dims[2] = 5;
tensor.resize(new_dims);
#endif
tensor.setRandom();
broadcast = tensor.broadcast(broadcasts);