mirror of
https://github.com/ethereum/solidity
synced 2023-10-03 13:03:40 +00:00
114 lines
4.1 KiB
C++
114 lines
4.1 KiB
C++
/*
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This file is part of solidity.
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solidity is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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solidity is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with solidity. If not, see <http://www.gnu.org/licenses/>.
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*/
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/**
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* Miscellaneous utilities for use in yul-phaser's test cases.
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*
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* - Generic code that's only used in these particular tests.
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* - Convenience functions and wrappers to make tests more concise.
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* - Mocks and dummy objects/functions used in multiple test suites.
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*
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* Note that the code included here may be not as generic, robust and/or complete as it could be
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* since it's not meant for production use. If some utility seems useful enough to be moved to
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* the normal code base, you should review its implementation before doing so.
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*/
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#pragma once
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#include <tools/yulPhaser/Chromosome.h>
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#include <tools/yulPhaser/FitnessMetrics.h>
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#include <tools/yulPhaser/Population.h>
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#include <cassert>
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#include <map>
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#include <string>
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#include <vector>
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namespace solidity::phaser::test
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{
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/**
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* Fitness metric that only takes into account the number of optimisation steps in the chromosome.
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* Recommended for use in tests because it's much faster than ProgramSize metric and it's very
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* easy to guess the result at a glance.
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*/
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class ChromosomeLengthMetric: public FitnessMetric
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{
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public:
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using FitnessMetric::FitnessMetric;
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size_t evaluate(Chromosome const& _chromosome) const override { return _chromosome.length(); }
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};
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// CHROMOSOME AND POPULATION HELPERS
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/// Returns a vector containing lengths of all chromosomes in the population (in the same order).
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std::vector<size_t> chromosomeLengths(Population const& _population);
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/// Assigns indices from 0 to N to all optimisation steps available in the OptimiserSuite.
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/// This is a convenience helper to make it easier to test their distribution with tools made for
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/// integers.
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std::map<std::string, size_t> enumerateOptmisationSteps();
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// STRING UTILITIES
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/// Returns the input string with all the whitespace characters (spaces, line endings, etc.) removed.
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std::string stripWhitespace(std::string const& input);
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// STATISTICAL UTILITIES
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/// Calculates the mean value of a series of samples given in a vector.
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///
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/// Supports any integer and real type as a convenience but the precision of the result is limited
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/// to the precision of type @a double as all the values are internally converted to it.
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///
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/// This is a very simple, naive implementation that's more than enough for tests where we usually
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/// deal with relatively short sequences of small, positive integers. It's not numerically stable
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/// in more complicated cases. Don't use in production.
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template <typename T>
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double mean(std::vector<T> const& _samples)
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{
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assert(_samples.size() > 0);
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double sum = 0;
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for (T const& sample: _samples)
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sum += static_cast<double>(sample);
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return sum / _samples.size();
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}
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/// Calculates the sum of squared differences between @a _expectedValue and the values of a series
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/// of samples given in a vector.
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///
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/// Supports any integer and real type as a convenience but the precision of the result is limited
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/// to the precision of type @a double as all the values are internally converted to it.
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///
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/// This is a very simple, naive implementation that's more than enough for tests where we usually
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/// deal with relatively short sequences of small, positive integers. It's not numerically stable
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/// in more complicated cases. Don't use in production.
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template <typename T>
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double meanSquaredError(std::vector<T> const& _samples, double _expectedValue)
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{
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assert(_samples.size() > 0);
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double sumOfSquaredDifferences = 0;
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for (T const& sample: _samples)
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sumOfSquaredDifferences += (sample - _expectedValue) * (sample - _expectedValue);
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return sumOfSquaredDifferences / _samples.size();
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}
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}
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