From e9324d926a9189e222741fce6e676f0944661a72 Mon Sep 17 00:00:00 2001 From: Abseil Team Date: Fri, 21 Jun 2019 13:11:42 -0700 Subject: Export of internal Abseil changes. -- 7a6ff16a85beb730c172d5d25cf1b5e1be885c56 by Laramie Leavitt : Internal change. PiperOrigin-RevId: 254454546 -- ff8f9bafaefc26d451f576ea4a06d150aed63f6f by Andy Soffer : Internal changes PiperOrigin-RevId: 254451562 -- deefc5b651b479ce36f0b4ef203e119c0c8936f2 by CJ Johnson : Account for subtracting unsigned values from the size of InlinedVector PiperOrigin-RevId: 254450625 -- 3c677316a27bcadc17e41957c809ca472d5fef14 by Andy Soffer : Add C++17's std::make_from_tuple to absl/utility/utility.h PiperOrigin-RevId: 254411573 -- 4ee3536a918830eeec402a28fc31a62c7c90b940 by CJ Johnson : Adds benchmark for the rest of the InlinedVector public API PiperOrigin-RevId: 254408378 -- e5a21a00700ee83498ff1efbf649169756463ee4 by CJ Johnson : Updates the definition of InlinedVector::shrink_to_fit() to be exception safe and adds exception safety tests for it. PiperOrigin-RevId: 254401387 -- 2ea82e72b86d82d78b4e4712a63a55981b53c64b by Laramie Leavitt : Use absl::InsecureBitGen in place of std::mt19937 in tests absl/random/...distribution_test.cc PiperOrigin-RevId: 254289444 -- fa099e02c413a7ffda732415e8105cad26a90337 by Andy Soffer : Internal changes PiperOrigin-RevId: 254286334 -- ce34b7f36933b30cfa35b9c9a5697a792b5666e4 by Andy Soffer : Internal changes PiperOrigin-RevId: 254273059 -- 6f9c473da7c2090c2e85a37c5f00622e8a912a89 by Jorg Brown : Change absl::container_internal::CompressedTuple to instantiate its internal Storage class with the name of the type it's holding, rather than the name of the Tuple. This is not an externally-visible change, other than less compiler memory is used and less debug information is generated. PiperOrigin-RevId: 254269285 -- 8bd3c186bf2fc0c55d8a2dd6f28a5327502c9fba by Andy Soffer : Adding short-hand IntervalClosed for IntervalClosedClosed and IntervalOpen for IntervalOpenOpen. PiperOrigin-RevId: 254252419 -- ea957f99b6a04fccd42aa05605605f3b44b1ecfd by Abseil Team : Do not directly use __SIZEOF_INT128__. In order to avoid linker errors when building with clang-cl (__fixunsdfti, __udivti3 and __fixunssfti are undefined), this CL uses ABSL_HAVE_INTRINSIC_INT128 which is not defined for clang-cl. PiperOrigin-RevId: 254250739 -- 89ab385cd26b34d64130bce856253aaba96d2345 by Andy Soffer : Internal changes PiperOrigin-RevId: 254242321 -- cffc793d93eca6d6bdf7de733847b6ab4a255ae9 by CJ Johnson : Adds benchmark for InlinedVector::reserve(size_type) PiperOrigin-RevId: 254199226 -- c90c7a9fa3c8f0c9d5114036979548b055ea2f2a by Gennadiy Rozental : Import of CCTZ from GitHub. PiperOrigin-RevId: 254072387 -- c4c388beae016c9570ab54ffa1d52660e4a85b7b by Laramie Leavitt : Internal cleanup. PiperOrigin-RevId: 254062381 -- d3c992e221cc74e5372d0c8fa410170b6a43c062 by Tom Manshreck : Update distributions.h to Abseil standards PiperOrigin-RevId: 254054946 -- d15ad0035c34ef11b14fadc5a4a2d3ec415f5518 by CJ Johnson : Removes functions with only one caller from the implementation details of InlinedVector by manually inlining the definitions PiperOrigin-RevId: 254005427 -- 2f37e807efc3a8ef1f4b539bdd379917d4151520 by Andy Soffer : Initial release of Abseil Random PiperOrigin-RevId: 253999861 -- 24ed1694b6430791d781ed533a8f8ccf6cac5856 by CJ Johnson : Updates the definition of InlinedVector::assign(...)/InlinedVector::operator=(...) to new, exception-safe implementations with exception safety tests to boot PiperOrigin-RevId: 253993691 -- 5613d95f5a7e34a535cfaeadce801441e990843e by CJ Johnson : Adds benchmarks for InlinedVector::shrink_to_fit() PiperOrigin-RevId: 253989647 -- 2a96ddfdac40bbb8cb6a7f1aeab90917067c6e63 by Abseil Team : Initial release of Abseil Random PiperOrigin-RevId: 253927497 -- bf1aff8fc9ffa921ad74643e9525ecf25b0d8dc1 by Andy Soffer : Initial release of Abseil Random PiperOrigin-RevId: 253920512 -- bfc03f4a3dcda3cf3a4b84bdb84cda24e3394f41 by Laramie Leavitt : Internal change. PiperOrigin-RevId: 253886486 -- 05036cfcc078ca7c5f581a00dfb0daed568cbb69 by Eric Fiselier : Don't include `winsock2.h` because it drags in `windows.h` and friends, and they define awful macros like OPAQUE, ERROR, and more. This has the potential to break abseil users. Instead we only forward declare `timeval` and require Windows users include `winsock2.h` themselves. This is both inconsistent and poor QoI, but so including 'windows.h' is bad too. PiperOrigin-RevId: 253852615 GitOrigin-RevId: 7a6ff16a85beb730c172d5d25cf1b5e1be885c56 Change-Id: Icd6aff87da26f29ec8915da856f051129987cef6 --- absl/random/bernoulli_distribution_test.cc | 213 +++++++++++++++++++++++++++++ 1 file changed, 213 insertions(+) create mode 100644 absl/random/bernoulli_distribution_test.cc (limited to 'absl/random/bernoulli_distribution_test.cc') diff --git a/absl/random/bernoulli_distribution_test.cc b/absl/random/bernoulli_distribution_test.cc new file mode 100644 index 00000000..f2c3b99c --- /dev/null +++ b/absl/random/bernoulli_distribution_test.cc @@ -0,0 +1,213 @@ +// Copyright 2017 The Abseil Authors. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "absl/random/bernoulli_distribution.h" + +#include +#include +#include +#include +#include + +#include "gtest/gtest.h" +#include "absl/random/internal/sequence_urbg.h" +#include "absl/random/random.h" + +namespace { + +class BernoulliTest : public testing::TestWithParam> { +}; + +TEST_P(BernoulliTest, Serialize) { + const double d = GetParam().first; + absl::bernoulli_distribution before(d); + + { + absl::bernoulli_distribution via_param{ + absl::bernoulli_distribution::param_type(d)}; + EXPECT_EQ(via_param, before); + } + + std::stringstream ss; + ss << before; + absl::bernoulli_distribution after(0.6789); + + EXPECT_NE(before.p(), after.p()); + EXPECT_NE(before.param(), after.param()); + EXPECT_NE(before, after); + + ss >> after; + + EXPECT_EQ(before.p(), after.p()); + EXPECT_EQ(before.param(), after.param()); + EXPECT_EQ(before, after); +} + +TEST_P(BernoulliTest, Accuracy) { + // Sadly, the claim to fame for this implementation is precise accuracy, which + // is very, very hard to measure, the improvements come as trials approach the + // limit of double accuracy; thus the outcome differs from the + // std::bernoulli_distribution with a probability of approximately 1 in 2^-53. + const std::pair para = GetParam(); + size_t trials = para.second; + double p = para.first; + + absl::InsecureBitGen rng; + + size_t yes = 0; + absl::bernoulli_distribution dist(p); + for (size_t i = 0; i < trials; ++i) { + if (dist(rng)) yes++; + } + + // Compute the distribution parameters for a binomial test, using a normal + // approximation for the confidence interval, as there are a sufficiently + // large number of trials that the central limit theorem applies. + const double stddev_p = std::sqrt((p * (1.0 - p)) / trials); + const double expected = trials * p; + const double stddev = trials * stddev_p; + + // 5 sigma, approved by Richard Feynman + EXPECT_NEAR(yes, expected, 5 * stddev) + << "@" << p << ", " + << std::abs(static_cast(yes) - expected) / stddev << " stddev"; +} + +// There must be many more trials to make the mean approximately normal for `p` +// closes to 0 or 1. +INSTANTIATE_TEST_SUITE_P( + All, BernoulliTest, + ::testing::Values( + // Typical values. + std::make_pair(0, 30000), std::make_pair(1e-3, 30000000), + std::make_pair(0.1, 3000000), std::make_pair(0.5, 3000000), + std::make_pair(0.9, 30000000), std::make_pair(0.999, 30000000), + std::make_pair(1, 30000), + // Boundary cases. + std::make_pair(std::nextafter(1.0, 0.0), 1), // ~1 - epsilon + std::make_pair(std::numeric_limits::epsilon(), 1), + std::make_pair(std::nextafter(std::numeric_limits::min(), + 1.0), // min + epsilon + 1), + std::make_pair(std::numeric_limits::min(), // smallest normal + 1), + std::make_pair( + std::numeric_limits::denorm_min(), // smallest denorm + 1), + std::make_pair(std::numeric_limits::min() / 2, 1), // denorm + std::make_pair(std::nextafter(std::numeric_limits::min(), + 0.0), // denorm_max + 1))); + +// NOTE: absl::bernoulli_distribution is not guaranteed to be stable. +TEST(BernoulliTest, StabilityTest) { + // absl::bernoulli_distribution stability relies on FastUniformBits and + // integer arithmetic. + absl::random_internal::sequence_urbg urbg({ + 0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, + 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, + 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, + 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull, + 0x4864f22c059bf29eull, 0x247856d8b862665cull, 0xe46e86e9a1337e10ull, + 0xd8c8541f3519b133ull, 0xe75b5162c567b9e4ull, 0xf732e5ded7009c5bull, + 0xb170b98353121eacull, 0x1ec2e8986d2362caull, 0x814c8e35fe9a961aull, + 0x0c3cd59c9b638a02ull, 0xcb3bb6478a07715cull, 0x1224e62c978bbc7full, + 0x671ef2cb04e81f6eull, 0x3c1cbd811eaf1808ull, 0x1bbc23cfa8fac721ull, + 0xa4c2cda65e596a51ull, 0xb77216fad37adf91ull, 0x836d794457c08849ull, + 0xe083df03475f49d7ull, 0xbc9feb512e6b0d6cull, 0xb12d74fdd718c8c5ull, + 0x12ff09653bfbe4caull, 0x8dd03a105bc4ee7eull, 0x5738341045ba0d85ull, + 0xe3fd722dc65ad09eull, 0x5a14fd21ea2a5705ull, 0x14e6ea4d6edb0c73ull, + 0x275b0dc7e0a18acfull, 0x36cebe0d2653682eull, 0x0361e9b23861596bull, + }); + + // Generate a std::string of '0' and '1' for the distribution output. + auto generate = [&urbg](absl::bernoulli_distribution& dist) { + std::string output; + output.reserve(36); + urbg.reset(); + for (int i = 0; i < 35; i++) { + output.append(dist(urbg) ? "1" : "0"); + } + return output; + }; + + const double kP = 0.0331289862362; + { + absl::bernoulli_distribution dist(kP); + auto v = generate(dist); + EXPECT_EQ(35, urbg.invocations()); + EXPECT_EQ(v, "00000000000010000000000010000000000") << dist; + } + { + absl::bernoulli_distribution dist(kP * 10.0); + auto v = generate(dist); + EXPECT_EQ(35, urbg.invocations()); + EXPECT_EQ(v, "00000100010010010010000011000011010") << dist; + } + { + absl::bernoulli_distribution dist(kP * 20.0); + auto v = generate(dist); + EXPECT_EQ(35, urbg.invocations()); + EXPECT_EQ(v, "00011110010110110011011111110111011") << dist; + } + { + absl::bernoulli_distribution dist(1.0 - kP); + auto v = generate(dist); + EXPECT_EQ(35, urbg.invocations()); + EXPECT_EQ(v, "11111111111111111111011111111111111") << dist; + } +} + +TEST(BernoulliTest, StabilityTest2) { + absl::random_internal::sequence_urbg urbg( + {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, + 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, + 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, + 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); + + // Generate a std::string of '0' and '1' for the distribution output. + auto generate = [&urbg](absl::bernoulli_distribution& dist) { + std::string output; + output.reserve(13); + urbg.reset(); + for (int i = 0; i < 12; i++) { + output.append(dist(urbg) ? "1" : "0"); + } + return output; + }; + + constexpr double b0 = 1.0 / 13.0 / 0.2; + constexpr double b1 = 2.0 / 13.0 / 0.2; + constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1)); + { + absl::bernoulli_distribution dist(b0); + auto v = generate(dist); + EXPECT_EQ(12, urbg.invocations()); + EXPECT_EQ(v, "000011100101") << dist; + } + { + absl::bernoulli_distribution dist(b1); + auto v = generate(dist); + EXPECT_EQ(12, urbg.invocations()); + EXPECT_EQ(v, "001111101101") << dist; + } + { + absl::bernoulli_distribution dist(b3); + auto v = generate(dist); + EXPECT_EQ(12, urbg.invocations()); + EXPECT_EQ(v, "001111101111") << dist; + } +} + +} // namespace -- cgit v1.2.3