Abseil Common Libraries (C++) (grcp 依赖)
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85 lines
2.9 KiB
85 lines
2.9 KiB
// Copyright 2017 The Abseil Authors. |
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// |
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// Licensed under the Apache License, Version 2.0 (the "License"); |
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// you may not use this file except in compliance with the License. |
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// You may obtain a copy of the License at |
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// |
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// https://www.apache.org/licenses/LICENSE-2.0 |
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// |
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// Unless required by applicable law or agreed to in writing, software |
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// distributed under the License is distributed on an "AS IS" BASIS, |
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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// See the License for the specific language governing permissions and |
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// limitations under the License. |
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#ifndef ABSL_RANDOM_INTERNAL_CHI_SQUARE_H_ |
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#define ABSL_RANDOM_INTERNAL_CHI_SQUARE_H_ |
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// The chi-square statistic. |
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// |
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// Useful for evaluating if `D` independent random variables are behaving as |
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// expected, or if two distributions are similar. (`D` is the degrees of |
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// freedom). |
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// |
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// Each bucket should have an expected count of 10 or more for the chi square to |
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// be meaningful. |
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#include <cassert> |
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namespace absl { |
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namespace random_internal { |
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constexpr const char kChiSquared[] = "chi-squared"; |
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// Returns the measured chi square value, using a single expected value. This |
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// assumes that the values in [begin, end) are uniformly distributed. |
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template <typename Iterator> |
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double ChiSquareWithExpected(Iterator begin, Iterator end, double expected) { |
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// Compute the sum and the number of buckets. |
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assert(expected >= 10); // require at least 10 samples per bucket. |
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double chi_square = 0; |
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for (auto it = begin; it != end; it++) { |
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double d = static_cast<double>(*it) - expected; |
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chi_square += d * d; |
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} |
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chi_square = chi_square / expected; |
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return chi_square; |
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} |
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// Returns the measured chi square value, taking the actual value of each bucket |
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// from the first set of iterators, and the expected value of each bucket from |
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// the second set of iterators. |
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template <typename Iterator, typename Expected> |
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double ChiSquare(Iterator it, Iterator end, Expected eit, Expected eend) { |
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double chi_square = 0; |
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for (; it != end && eit != eend; ++it, ++eit) { |
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if (*it > 0) { |
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assert(*eit > 0); |
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} |
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double e = static_cast<double>(*eit); |
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double d = static_cast<double>(*it - *eit); |
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if (d != 0) { |
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assert(e > 0); |
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chi_square += (d * d) / e; |
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} |
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} |
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assert(it == end && eit == eend); |
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return chi_square; |
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} |
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// ====================================================================== |
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// The following methods can be used for an arbitrary significance level. |
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// |
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// Calculates critical chi-square values to produce the given p-value using a |
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// bisection search for a value within epsilon, relying on the monotonicity of |
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// ChiSquarePValue(). |
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double ChiSquareValue(int dof, double p); |
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// Calculates the p-value (probability) of a given chi-square value. |
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double ChiSquarePValue(double chi_square, int dof); |
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} // namespace random_internal |
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} // namespace absl |
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#endif // ABSL_RANDOM_INTERNAL_CHI_SQUARE_H_
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