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Diffstat (limited to 'absl/random/internal/distribution_test_util_test.cc')
| -rw-r--r-- | absl/random/internal/distribution_test_util_test.cc | 193 |
1 files changed, 193 insertions, 0 deletions
diff --git a/absl/random/internal/distribution_test_util_test.cc b/absl/random/internal/distribution_test_util_test.cc new file mode 100644 index 000000000000..c49d44fb4796 --- /dev/null +++ b/absl/random/internal/distribution_test_util_test.cc @@ -0,0 +1,193 @@ +// 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 |