Adding upstream version 14.2.21.upstream/14.2.21upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 67 insertions, 0 deletions

diff --git a/src/boost/libs/math/test/test_autodiff_7.cpp b/src/boost/libs/math/test/test_autodiff_7.cpp
new file mode 100644
index 00000000..c41d806a
--- /dev/null
+++ b/src/boost/libs/math/test/test_autodiff_7.cpp
@@ -0,0 +1,67 @@
+//           Copyright Matthew Pulver 2018 - 2019.
+// Distributed under the Boost Software License, Version 1.0.
+//      (See accompanying file LICENSE_1_0.txt or copy at
+//           https://www.boost.org/LICENSE_1_0.txt)
+
+#include "test_autodiff.hpp"
+
+BOOST_AUTO_TEST_SUITE(test_autodiff_7)
+
+BOOST_AUTO_TEST_CASE_TEMPLATE(expm1_hpp, T, all_float_types) {
+  using boost::math::differentiation::detail::log;
+  using boost::multiprecision::log;
+  using std::log;
+  using test_constants = test_constants_t<T>;
+  static constexpr auto m = test_constants::order;
+  test_detail::RandomSample<T> x_sampler{-log(T(2000)), log(T(2000))};
+  for (auto i : boost::irange(test_constants::n_samples)) {
+    std::ignore = i;
+    auto x = x_sampler.next();
+    BOOST_CHECK_CLOSE(boost::math::expm1(make_fvar<T, m>(x)).derivative(0u),
+                      boost::math::expm1(x),
+                      50 * test_constants::pct_epsilon());
+  }
+}
+
+BOOST_AUTO_TEST_CASE_TEMPLATE(fpclassify_hpp, T, all_float_types) {
+  using boost::math::fpclassify;
+  using boost::math::isfinite;
+  using boost::math::isinf;
+  using boost::math::isnan;
+  using boost::math::isnormal;
+  using boost::multiprecision::fpclassify;
+  using boost::multiprecision::isfinite;
+  using boost::multiprecision::isinf;
+  using boost::multiprecision::isnan;
+  using boost::multiprecision::isnormal;
+
+  using test_constants = test_constants_t<T>;
+  static constexpr auto m = test_constants::order;
+  test_detail::RandomSample<T> x_sampler{-1000, 1000};
+  for (auto i : boost::irange(test_constants::n_samples)) {
+    std::ignore = i;
+
+    BOOST_CHECK_EQUAL(fpclassify(make_fvar<T, m>(0)), FP_ZERO);
+    BOOST_CHECK_EQUAL(fpclassify(make_fvar<T, m>(10)), FP_NORMAL);
+    BOOST_CHECK_EQUAL(
+        fpclassify(make_fvar<T, m>(std::numeric_limits<T>::infinity())),
+        FP_INFINITE);
+    BOOST_CHECK_EQUAL(
+        fpclassify(make_fvar<T, m>(std::numeric_limits<T>::quiet_NaN())),
+        FP_NAN);
+    if (std::numeric_limits<T>::has_denorm != std::denorm_absent) {
+      BOOST_CHECK_EQUAL(
+          fpclassify(make_fvar<T, m>(std::numeric_limits<T>::denorm_min())),
+          FP_SUBNORMAL);
+    }
+
+    BOOST_CHECK(isfinite(make_fvar<T, m>(0)));
+    BOOST_CHECK(isnormal(make_fvar<T, m>((std::numeric_limits<T>::min)())));
+    BOOST_CHECK(
+        !isnormal(make_fvar<T, m>(std::numeric_limits<T>::denorm_min())));
+    BOOST_CHECK(isinf(make_fvar<T, m>(std::numeric_limits<T>::infinity())));
+    BOOST_CHECK(isnan(make_fvar<T, m>(std::numeric_limits<T>::quiet_NaN())));
+  }
+}
+
+BOOST_AUTO_TEST_SUITE_END()