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Diffstat (limited to 'third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc')
-rw-r--r-- | third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc | 193 |
1 files changed, 0 insertions, 193 deletions
diff --git a/third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc b/third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc deleted file mode 100644 index c49d44fb4796..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc +++ /dev/null @@ -1,193 +0,0 @@ -// 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/internal/distribution_test_util.h" - -#include "gtest/gtest.h" - -namespace { - -TEST(TestUtil, InverseErf) { - const struct { - const double z; - const double value; - } kErfInvTable[] = { - {0.0000001, 8.86227e-8}, - {0.00001, 8.86227e-6}, - {0.5, 0.4769362762044}, - {0.6, 0.5951160814499}, - {0.99999, 3.1234132743}, - {0.9999999, 3.7665625816}, - {0.999999944, 3.8403850690566985}, // = log((1-x) * (1+x)) =~ 16.004 - {0.999999999, 4.3200053849134452}, - }; - - for (const auto& data : kErfInvTable) { - auto value = absl::random_internal::erfinv(data.z); - - // Log using the Wolfram-alpha function name & parameters. - EXPECT_NEAR(value, data.value, 1e-8) - << " InverseErf[" << data.z << "] (expected=" << data.value << ") -> " - << value; - } -} - -const struct { - const double p; - const double q; - const double x; - const double alpha; -} kBetaTable[] = { - {0.5, 0.5, 0.01, 0.06376856085851985}, - {0.5, 0.5, 0.1, 0.2048327646991335}, - {0.5, 0.5, 1, 1}, - {1, 0.5, 0, 0}, - {1, 0.5, 0.01, 0.005012562893380045}, - {1, 0.5, 0.1, 0.0513167019494862}, - {1, 0.5, 0.5, 0.2928932188134525}, - {1, 1, 0.5, 0.5}, - {2, 2, 0.1, 0.028}, - {2, 2, 0.2, 0.104}, - {2, 2, 0.3, 0.216}, - {2, 2, 0.4, 0.352}, - {2, 2, 0.5, 0.5}, - {2, 2, 0.6, 0.648}, - {2, 2, 0.7, 0.784}, - {2, 2, 0.8, 0.896}, - {2, 2, 0.9, 0.972}, - {5.5, 5, 0.5, 0.4361908850559777}, - {10, 0.5, 0.9, 0.1516409096346979}, - {10, 5, 0.5, 0.08978271484375}, - {10, 5, 1, 1}, - {10, 10, 0.5, 0.5}, - {20, 5, 0.8, 0.4598773297575791}, - {20, 10, 0.6, 0.2146816102371739}, - {20, 10, 0.8, 0.9507364826957875}, - {20, 20, 0.5, 0.5}, - {20, 20, 0.6, 0.8979413687105918}, - {30, 10, 0.7, 0.2241297491808366}, - {30, 10, 0.8, 0.7586405487192086}, - {40, 20, 0.7, 0.7001783247477069}, - {1, 0.5, 0.1, 0.0513167019494862}, - {1, 0.5, 0.2, 0.1055728090000841}, - {1, 0.5, 0.3, 0.1633399734659245}, - {1, 0.5, 0.4, 0.2254033307585166}, - {1, 2, 0.2, 0.36}, - {1, 3, 0.2, 0.488}, - {1, 4, 0.2, 0.5904}, - {1, 5, 0.2, 0.67232}, - {2, 2, 0.3, 0.216}, - {3, 2, 0.3, 0.0837}, - {4, 2, 0.3, 0.03078}, - {5, 2, 0.3, 0.010935}, - - // These values test small & large points along the range of the Beta - // function. - // - // When selecting test points, remember that if BetaIncomplete(x, p, q) - // returns the same value to within the limits of precision over a large - // domain of the input, x, then BetaIncompleteInv(alpha, p, q) may return an - // essentially arbitrary value where BetaIncomplete(x, p, q) =~ alpha. - - // BetaRegularized[x, 0.00001, 0.00001], - // For x in {~0.001 ... ~0.999}, => ~0.5 - {1e-5, 1e-5, 1e-5, 0.4999424388184638311}, - {1e-5, 1e-5, (1.0 - 1e-8), 0.5000920948389232964}, - - // BetaRegularized[x, 0.00001, 10000]. - // For x in {~epsilon ... 1.0}, => ~1 - {1e-5, 1e5, 1e-6, 0.9999817708130066936}, - {1e-5, 1e5, (1.0 - 1e-7), 1.0}, - - // BetaRegularized[x, 10000, 0.00001]. - // For x in {0 .. 1-epsilon}, => ~0 - {1e5, 1e-5, 1e-6, 0}, - {1e5, 1e-5, (1.0 - 1e-6), 1.8229186993306369e-5}, -}; - -TEST(BetaTest, BetaIncomplete) { - for (const auto& data : kBetaTable) { - auto value = absl::random_internal::BetaIncomplete(data.x, data.p, data.q); - - // Log using the Wolfram-alpha function name & parameters. - EXPECT_NEAR(value, data.alpha, 1e-12) - << " BetaRegularized[" << data.x << ", " << data.p << ", " << data.q - << "] (expected=" << data.alpha << ") -> " << value; - } -} - -TEST(BetaTest, BetaIncompleteInv) { - for (const auto& data : kBetaTable) { - auto value = - absl::random_internal::BetaIncompleteInv(data.p, data.q, data.alpha); - - // Log using the Wolfram-alpha function name & parameters. - EXPECT_NEAR(value, data.x, 1e-6) - << " InverseBetaRegularized[" << data.alpha << ", " << data.p << ", " - << data.q << "] (expected=" << data.x << ") -> " << value; - } -} - -TEST(MaxErrorTolerance, MaxErrorTolerance) { - std::vector<std::pair<double, double>> cases = { - {0.0000001, 8.86227e-8 * 1.41421356237}, - {0.00001, 8.86227e-6 * 1.41421356237}, - {0.5, 0.4769362762044 * 1.41421356237}, - {0.6, 0.5951160814499 * 1.41421356237}, - {0.99999, 3.1234132743 * 1.41421356237}, - {0.9999999, 3.7665625816 * 1.41421356237}, - {0.999999944, 3.8403850690566985 * 1.41421356237}, - {0.999999999, 4.3200053849134452 * 1.41421356237}}; - for (auto entry : cases) { - EXPECT_NEAR(absl::random_internal::MaxErrorTolerance(entry.first), - entry.second, 1e-8); - } -} - -TEST(ZScore, WithSameMean) { - absl::random_internal::DistributionMoments m; - m.n = 100; - m.mean = 5; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(5, m), 0, 1e-12); - - m.n = 1; - m.mean = 0; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(0, m), 0, 1e-12); - - m.n = 10000; - m.mean = -5; - m.variance = 100; - EXPECT_NEAR(absl::random_internal::ZScore(-5, m), 0, 1e-12); -} - -TEST(ZScore, DifferentMean) { - absl::random_internal::DistributionMoments m; - m.n = 100; - m.mean = 5; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(4, m), 10, 1e-12); - - m.n = 1; - m.mean = 0; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(-1, m), 1, 1e-12); - - m.n = 10000; - m.mean = -5; - m.variance = 100; - EXPECT_NEAR(absl::random_internal::ZScore(-4, m), -10, 1e-12); -} -} // namespace |