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Austin Schuh36244a12019-09-21 17:52:38 -07001// Copyright 2017 The Abseil Authors.
2//
3// Licensed under the Apache License, Version 2.0 (the "License");
4// you may not use this file except in compliance with the License.
5// You may obtain a copy of the License at
6//
7// https://www.apache.org/licenses/LICENSE-2.0
8//
9// Unless required by applicable law or agreed to in writing, software
10// distributed under the License is distributed on an "AS IS" BASIS,
11// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12// See the License for the specific language governing permissions and
13// limitations under the License.
14
15#ifndef ABSL_RANDOM_INTERNAL_DISTRIBUTION_TEST_UTIL_H_
16#define ABSL_RANDOM_INTERNAL_DISTRIBUTION_TEST_UTIL_H_
17
18#include <cstddef>
19#include <iostream>
20#include <vector>
21
22#include "absl/strings/string_view.h"
23#include "absl/types/span.h"
24
25// NOTE: The functions in this file are test only, and are should not be used in
26// non-test code.
27
28namespace absl {
Austin Schuhb4691e92020-12-31 12:37:18 -080029ABSL_NAMESPACE_BEGIN
Austin Schuh36244a12019-09-21 17:52:38 -070030namespace random_internal {
31
32// http://webspace.ship.edu/pgmarr/Geo441/Lectures/Lec%205%20-%20Normality%20Testing.pdf
33
34// Compute the 1st to 4th standard moments:
35// mean, variance, skewness, and kurtosis.
36// http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
37struct DistributionMoments {
38 size_t n = 0;
39 double mean = 0.0;
40 double variance = 0.0;
41 double skewness = 0.0;
42 double kurtosis = 0.0;
43};
44DistributionMoments ComputeDistributionMoments(
45 absl::Span<const double> data_points);
46
47std::ostream& operator<<(std::ostream& os, const DistributionMoments& moments);
48
49// Computes the Z-score for a set of data with the given distribution moments
50// compared against `expected_mean`.
51double ZScore(double expected_mean, const DistributionMoments& moments);
52
53// Returns the probability of success required for a single trial to ensure that
54// after `num_trials` trials, the probability of at least one failure is no more
55// than `p_fail`.
56double RequiredSuccessProbability(double p_fail, int num_trials);
57
58// Computes the maximum distance from the mean tolerable, for Z-Tests that are
59// expected to pass with `acceptance_probability`. Will terminate if the
60// resulting tolerance is zero (due to passing in 0.0 for
61// `acceptance_probability` or rounding errors).
62//
63// For example,
64// MaxErrorTolerance(0.001) = 0.0
65// MaxErrorTolerance(0.5) = ~0.47
66// MaxErrorTolerance(1.0) = inf
67double MaxErrorTolerance(double acceptance_probability);
68
69// Approximation to inverse of the Error Function in double precision.
70// (http://people.maths.ox.ac.uk/gilesm/files/gems_erfinv.pdf)
71double erfinv(double x);
72
73// Beta(p, q) = Gamma(p) * Gamma(q) / Gamma(p+q)
74double beta(double p, double q);
75
76// The inverse of the normal survival function.
77double InverseNormalSurvival(double x);
78
79// Returns whether actual is "near" expected, based on the bound.
80bool Near(absl::string_view msg, double actual, double expected, double bound);
81
82// Implements the incomplete regularized beta function, AS63, BETAIN.
83// https://www.jstor.org/stable/2346797
84//
85// BetaIncomplete(x, p, q), where
86// `x` is the value of the upper limit
87// `p` is beta parameter p, `q` is beta parameter q.
88//
89// NOTE: This is a test-only function which is only accurate to within, at most,
90// 1e-13 of the actual value.
91//
92double BetaIncomplete(double x, double p, double q);
93
94// Implements the inverse of the incomplete regularized beta function, AS109,
95// XINBTA.
96// https://www.jstor.org/stable/2346798
97// https://www.jstor.org/stable/2346887
98//
99// BetaIncompleteInv(p, q, beta, alhpa)
100// `p` is beta parameter p, `q` is beta parameter q.
101// `alpha` is the value of the lower tail area.
102//
103// NOTE: This is a test-only function and, when successful, is only accurate to
104// within ~1e-6 of the actual value; there are some cases where it diverges from
105// the actual value by much more than that. The function uses Newton's method,
106// and thus the runtime is highly variable.
107double BetaIncompleteInv(double p, double q, double alpha);
108
109} // namespace random_internal
Austin Schuhb4691e92020-12-31 12:37:18 -0800110ABSL_NAMESPACE_END
Austin Schuh36244a12019-09-21 17:52:38 -0700111} // namespace absl
112
113#endif // ABSL_RANDOM_INTERNAL_DISTRIBUTION_TEST_UTIL_H_