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527 lines
25 KiB
C++
527 lines
25 KiB
C++
// test_inverse_chi_squared.cpp
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// Copyright Paul A. Bristow 2010.
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// Copyright John Maddock 2010.
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// Use, modification and distribution are subject to the
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// Boost Software License, Version 1.0.
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// (See accompanying file LICENSE_1_0.txt
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// or copy at http://www.boost.org/LICENSE_1_0.txt)
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#ifdef _MSC_VER
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# pragma warning (disable : 4310) // cast truncates constant value.
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#endif
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// http://www.wolframalpha.com/input/?i=inverse+chisquare+distribution
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#include <boost/math/tools/test.hpp>
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#include <boost/math/concepts/real_concept.hpp> // for real_concept
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using ::boost::math::concepts::real_concept;
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//#include <boost/math/tools/test.hpp>
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#define BOOST_TEST_MAIN
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#include <boost/test/unit_test.hpp> // for test_main
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#include <boost/test/floating_point_comparison.hpp> // for BOOST_CHECK_CLOSE_FRACTION
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#include "test_out_of_range.hpp"
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#include <boost/math/distributions/inverse_chi_squared.hpp> // for inverse_chisquared_distribution
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using boost::math::inverse_chi_squared_distribution;
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using boost::math::cdf;
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using boost::math::pdf;
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// Use Inverse Gamma distribution to check their relationship:
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// inverse_chi_squared<>(v) == inverse_gamma<>(v / 2., 0.5)
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#include <boost/math/distributions/inverse_gamma.hpp> // for inverse_gamma_distribution
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using boost::math::inverse_gamma_distribution;
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using boost::math::inverse_gamma;
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// using ::boost::math::cdf;
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// using ::boost::math::pdf;
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#include <boost/math/special_functions/gamma.hpp>
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using boost::math::tgamma; // for naive pdf.
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#include <iostream>
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using std::cout;
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using std::endl;
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#include <limits>
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using std::numeric_limits; // for epsilon.
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template <class RealType>
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RealType naive_pdf(RealType df, RealType scale, RealType x)
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{ // Formula from Wikipedia
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using namespace std; // For ADL of std functions.
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using boost::math::tgamma;
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RealType result = pow(scale * df/2, df/2) * exp(-df * scale/(2 * x));
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result /= tgamma(df/2) * pow(x, 1 + df/2);
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return result;
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}
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// Test using a spot value from some other reference source,
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// in this case test values from output from R provided by Thomas Mang,
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// and Wolfram Mathematica by Mark Coleman.
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template <class RealType>
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void test_spot(
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RealType degrees_of_freedom, // degrees_of_freedom,
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RealType scale, // scale,
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RealType x, // random variate x,
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RealType pd, // expected pdf,
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RealType P, // expected CDF,
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RealType Q, // expected complement of CDF,
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RealType tol) // test tolerance.
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{
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boost::math::inverse_chi_squared_distribution<RealType> dist(degrees_of_freedom, scale);
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BOOST_CHECK_CLOSE_FRACTION
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( // Compare to expected PDF.
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pdf(dist, x), // calculated.
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pd, // expected
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tol);
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BOOST_CHECK_CLOSE_FRACTION( // Compare to naive pdf formula (probably less accurate).
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pdf(dist, x), naive_pdf(dist.degrees_of_freedom(), dist.scale(), x), tol);
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BOOST_CHECK_CLOSE_FRACTION( // Compare to expected CDF.
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cdf(dist, x), P, tol);
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if((P < 0.999) && (Q < 0.999))
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{ // We can only check this if P is not too close to 1,
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// so that we can guarantee Q is accurate:
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BOOST_CHECK_CLOSE_FRACTION(
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cdf(complement(dist, x)), Q, tol); // 1 - cdf
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BOOST_CHECK_CLOSE_FRACTION(
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quantile(dist, P), x, tol); // quantile(cdf) = x
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BOOST_CHECK_CLOSE_FRACTION(
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quantile(complement(dist, Q)), x, tol); // quantile(complement(1 - cdf)) = x
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}
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} // test_spot
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template <class RealType> // Any floating-point type RealType.
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void test_spots(RealType)
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{
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// Basic sanity checks, some test data is to six decimal places only,
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// so set tolerance to 0.000001 (expressed as a percentage = 0.0001%).
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RealType tolerance = 0.000001f;
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cout << "Tolerance = " << tolerance * 100 << "%." << endl;
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// This test values from output from geoR (17 decimal digits) guided by Thomas Mang.
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test_spot(static_cast<RealType>(2), static_cast<RealType>(1./2.),
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// degrees_of_freedom, default scale = 1/df.
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static_cast<RealType>(1.L), // x.
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static_cast<RealType>(0.30326532985631671L), // pdf.
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static_cast<RealType>(0.60653065971263365L), // cdf.
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static_cast<RealType>(1 - 0.606530659712633657L), // cdf complement.
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tolerance // tol
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);
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// Tests from Mark Coleman & Georgi Boshnakov using Wolfram Mathematica.
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test_spot(static_cast<RealType>(10), static_cast<RealType>(0.1L), // degrees_of_freedom, scale
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static_cast<RealType>(0.2), // x
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static_cast<RealType>(1.6700235722635659824529759616528281217001163943570L), // pdf
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static_cast<RealType>(0.89117801891415124234834646836872197623907651175353L), // cdf
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static_cast<RealType>(1 - 0.89117801891415127L), // cdf complement
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tolerance // tol
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);
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test_spot(static_cast<RealType>(10), static_cast<RealType>(0.1L), // degrees_of_freedom, scale
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static_cast<RealType>(0.5), // x
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static_cast<RealType>(0.03065662009762021L), // pdf
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static_cast<RealType>(0.99634015317265628765454354418728984933240514654437L), // cdf
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static_cast<RealType>(1 - 0.99634015317265628765454354418728984933240514654437L), // cdf complement
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tolerance // tol
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);
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test_spot(static_cast<RealType>(10), static_cast<RealType>(2), // degrees_of_freedom, scale
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static_cast<RealType>(0.5), // x
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static_cast<RealType>(0.00054964096598361569L), // pdf
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static_cast<RealType>(0.000016944743930067383903707995865261004246785511612700L), // cdf
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static_cast<RealType>(1 - 0.000016944743930067383903707995865261004246785511612700L), // cdf complement
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tolerance // tol
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);
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// Check some bad parameters to the distribution cause expected exception to be thrown.
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#ifndef BOOST_NO_EXCEPTIONS
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType> ichsqbad1(-1), std::domain_error); // negative degrees_of_freedom.
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType> ichsqbad2(1, -1), std::domain_error); // negative scale.
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType> ichsqbad3(-1, -1), std::domain_error); // negative scale and degrees_of_freedom.
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#else
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType>(-1), std::domain_error); // negative degrees_of_freedom.
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType>(1, -1), std::domain_error); // negative scale.
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType>(-1, -1), std::domain_error); // negative scale and degrees_of_freedom.
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#endif
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check_out_of_range<boost::math::inverse_chi_squared_distribution<RealType> >(1, 1);
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inverse_chi_squared_distribution<RealType> ichsq;
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if(std::numeric_limits<RealType>::has_infinity)
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{
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BOOST_MATH_CHECK_THROW(pdf(ichsq, +std::numeric_limits<RealType>::infinity()), std::domain_error); // x = + infinity, pdf = 0
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BOOST_MATH_CHECK_THROW(pdf(ichsq, -std::numeric_limits<RealType>::infinity()), std::domain_error); // x = - infinity, pdf = 0
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BOOST_MATH_CHECK_THROW(cdf(ichsq, +std::numeric_limits<RealType>::infinity()),std::domain_error ); // x = + infinity, cdf = 1
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BOOST_MATH_CHECK_THROW(cdf(ichsq, -std::numeric_limits<RealType>::infinity()), std::domain_error); // x = - infinity, cdf = 0
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BOOST_MATH_CHECK_THROW(cdf(complement(ichsq, +std::numeric_limits<RealType>::infinity())), std::domain_error); // x = + infinity, c cdf = 0
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BOOST_MATH_CHECK_THROW(cdf(complement(ichsq, -std::numeric_limits<RealType>::infinity())), std::domain_error); // x = - infinity, c cdf = 1
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#ifndef BOOST_NO_EXCEPTIONS
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType> nbad1(std::numeric_limits<RealType>::infinity(), static_cast<RealType>(1)), std::domain_error); // +infinite mean
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType> nbad1(-std::numeric_limits<RealType>::infinity(), static_cast<RealType>(1)), std::domain_error); // -infinite mean
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType> nbad1(static_cast<RealType>(0), std::numeric_limits<RealType>::infinity()), std::domain_error); // infinite sd
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#else
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType>(std::numeric_limits<RealType>::infinity(), static_cast<RealType>(1)), std::domain_error); // +infinite mean
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType>(-std::numeric_limits<RealType>::infinity(), static_cast<RealType>(1)), std::domain_error); // -infinite mean
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BOOST_MATH_CHECK_THROW(boost::math::inverse_chi_squared_distribution<RealType>(static_cast<RealType>(0), std::numeric_limits<RealType>::infinity()), std::domain_error); // infinite sd
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#endif
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}
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if (std::numeric_limits<RealType>::has_quiet_NaN)
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{ // If no longer allow x or p to be NaN, then these tests should throw.
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BOOST_MATH_CHECK_THROW(pdf(ichsq, +std::numeric_limits<RealType>::quiet_NaN()), std::domain_error); // x = NaN
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BOOST_MATH_CHECK_THROW(cdf(ichsq, +std::numeric_limits<RealType>::quiet_NaN()), std::domain_error); // x = NaN
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BOOST_MATH_CHECK_THROW(cdf(complement(ichsq, +std::numeric_limits<RealType>::quiet_NaN())), std::domain_error); // x = + infinity
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BOOST_MATH_CHECK_THROW(quantile(ichsq, std::numeric_limits<RealType>::quiet_NaN()), std::domain_error); // p = + quiet_NaN
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BOOST_MATH_CHECK_THROW(quantile(complement(ichsq, std::numeric_limits<RealType>::quiet_NaN())), std::domain_error); // p = + quiet_NaN
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}
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// Spot check for pdf using 'naive pdf' function
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for(RealType x = 0.5; x < 5; x += 0.5)
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{
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BOOST_CHECK_CLOSE_FRACTION(
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pdf(inverse_chi_squared_distribution<RealType>(5, 6), x),
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naive_pdf(RealType(5), RealType(6), x),
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tolerance);
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} // Spot checks for parameters:
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RealType tol_2eps = boost::math::tools::epsilon<RealType>() * 2; // 2 eps as a fraction.
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inverse_chi_squared_distribution<RealType> dist51(5, 1);
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inverse_chi_squared_distribution<RealType> dist52(5, 2);
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inverse_chi_squared_distribution<RealType> dist31(3, 1);
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inverse_chi_squared_distribution<RealType> dist111(11, 1);
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// 11 mean 0.10000000000000001, variance 0.0011111111111111111, sd 0.033333333333333333
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using namespace std; // ADL of std names.
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using namespace boost::math;
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inverse_chi_squared_distribution<RealType> dist10(10);
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// mean, variance etc
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BOOST_CHECK_CLOSE_FRACTION(mean(dist10), static_cast<RealType>(0.125), tol_2eps);
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BOOST_CHECK_CLOSE_FRACTION(variance(dist10), static_cast<RealType>(0.0052083333333333333333333333333333333333333333333333L), tol_2eps);
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BOOST_CHECK_CLOSE_FRACTION(mode(dist10), static_cast<RealType>(0.08333333333333333333333333333333333333333333333L), tol_2eps);
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BOOST_CHECK_CLOSE_FRACTION(median(dist10), static_cast<RealType>(0.10704554778227709530244586234274024205738435512468L), tol_2eps);
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BOOST_CHECK_CLOSE_FRACTION(cdf(dist10, median(dist10)), static_cast<RealType>(0.5L), 4 * tol_2eps);
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BOOST_CHECK_CLOSE_FRACTION(skewness(dist10), static_cast<RealType>(3.4641016151377545870548926830117447338856105076208L), tol_2eps);
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BOOST_CHECK_CLOSE_FRACTION(kurtosis(dist10), static_cast<RealType>(45), tol_2eps);
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BOOST_CHECK_CLOSE_FRACTION(kurtosis_excess(dist10), static_cast<RealType>(45-3), tol_2eps);
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tol_2eps = boost::math::tools::epsilon<RealType>() * 2; // 2 eps as a percentage.
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// Special and limit cases:
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RealType mx = (std::numeric_limits<RealType>::max)();
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RealType mi = (std::numeric_limits<RealType>::min)();
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BOOST_CHECK_EQUAL(
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pdf(inverse_chi_squared_distribution<RealType>(1),
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static_cast<RealType>(mx)), // max()
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static_cast<RealType>(0)
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);
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BOOST_CHECK_EQUAL(
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pdf(inverse_chi_squared_distribution<RealType>(1),
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static_cast<RealType>(mi)), // min()
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static_cast<RealType>(0)
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);
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BOOST_CHECK_EQUAL(
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pdf(inverse_chi_squared_distribution<RealType>(1), static_cast<RealType>(0)), static_cast<RealType>(0));
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BOOST_CHECK_EQUAL(
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pdf(inverse_chi_squared_distribution<RealType>(3), static_cast<RealType>(0))
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, static_cast<RealType>(0.0f));
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BOOST_CHECK_EQUAL(
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cdf(inverse_chi_squared_distribution<RealType>(1), static_cast<RealType>(0))
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, static_cast<RealType>(0.0f));
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BOOST_CHECK_EQUAL(
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cdf(inverse_chi_squared_distribution<RealType>(2), static_cast<RealType>(0))
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, static_cast<RealType>(0.0f));
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BOOST_CHECK_EQUAL(
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cdf(inverse_chi_squared_distribution<RealType>(3L), static_cast<RealType>(0L))
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, static_cast<RealType>(0));
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BOOST_CHECK_EQUAL(
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cdf(complement(inverse_chi_squared_distribution<RealType>(1), static_cast<RealType>(0)))
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, static_cast<RealType>(1));
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BOOST_CHECK_EQUAL(
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cdf(complement(inverse_chi_squared_distribution<RealType>(2), static_cast<RealType>(0)))
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, static_cast<RealType>(1));
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BOOST_CHECK_EQUAL(
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cdf(complement(inverse_chi_squared_distribution<RealType>(3), static_cast<RealType>(0)))
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, static_cast<RealType>(1));
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BOOST_MATH_CHECK_THROW(
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pdf(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(-1)), // degrees_of_freedom negative.
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static_cast<RealType>(1)), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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pdf(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(8)),
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static_cast<RealType>(-1)), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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cdf(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(-1)),
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static_cast<RealType>(1)), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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cdf(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(8)),
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static_cast<RealType>(-1)), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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cdf(complement(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(-1)),
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static_cast<RealType>(1))), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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cdf(complement(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(8)),
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static_cast<RealType>(-1))), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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quantile(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(-1)),
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static_cast<RealType>(0.5)), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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quantile(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(8)),
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static_cast<RealType>(-1)), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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quantile(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(8)),
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static_cast<RealType>(1.1)), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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quantile(complement(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(-1)),
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static_cast<RealType>(0.5))), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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quantile(complement(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(8)),
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static_cast<RealType>(-1))), std::domain_error
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);
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BOOST_MATH_CHECK_THROW(
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quantile(complement(
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inverse_chi_squared_distribution<RealType>(static_cast<RealType>(8)),
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static_cast<RealType>(1.1))), std::domain_error
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);
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} // template <class RealType>void test_spots(RealType)
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BOOST_AUTO_TEST_CASE( test_main )
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{
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BOOST_MATH_CONTROL_FP;
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double tol_few_eps = numeric_limits<double>::epsilon() * 4;
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// Check that can generate inverse_chi_squared distribution using the two convenience methods:
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// inverse_chi_squared_distribution; // with default parameters, degrees_of_freedom = 1, scale - 1
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using boost::math::inverse_chi_squared;
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// Some constructor tests using default double.
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double tol4eps = boost::math::tools::epsilon<double>() * 4; // 4 eps as a fraction.
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inverse_chi_squared ichsqdef; // Using typedef and both default parameters.
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BOOST_CHECK_EQUAL(ichsqdef.degrees_of_freedom(), 1.); // df == 1
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BOOST_CHECK_EQUAL(ichsqdef.scale(), 1); // scale == 1./df
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BOOST_CHECK_CLOSE_FRACTION(pdf(ichsqdef, 1), 0.24197072451914330, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(pdf(ichsqdef, 9), 0.013977156581221969, tol4eps);
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inverse_chi_squared_distribution<double> ichisq102(10., 2); // Both parameters specified.
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BOOST_CHECK_EQUAL(ichisq102.degrees_of_freedom(), 10.); // Check both parameters stored OK.
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BOOST_CHECK_EQUAL(ichisq102.scale(), 2.); // Check both parameters stored OK.
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inverse_chi_squared_distribution<double> ichisq10(10.); // Only df parameter specified (unscaled).
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BOOST_CHECK_EQUAL(ichisq10.degrees_of_freedom(), 10.); // Check parameter stored.
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BOOST_CHECK_EQUAL(ichisq10.scale(), 0.1); // Check default scale = 1/df = 1/10 = 0.1
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BOOST_CHECK_CLOSE_FRACTION(pdf(ichisq10, 1), 0.00078975346316749169, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(pdf(ichisq10, 10), 0.0000000012385799798186384, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(mode(ichisq10), 0.0833333333333333333333333333333333333333, tol4eps);
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// nu * xi / nu + 2 = 10 * 0.1 / (10 + 2) = 1/12 = 0.0833333...
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// mode is not defined in Mathematica.
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// See Discussion section http://en.wikipedia.org/wiki/Talk:Scaled-inverse-chi-square_distribution
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// for origin of this formula.
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inverse_chi_squared_distribution<double> ichisq5(5.); // // Only df parameter specified.
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BOOST_CHECK_EQUAL(ichisq5.degrees_of_freedom(), 5.); // check parameter stored.
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BOOST_CHECK_EQUAL(ichisq5.scale(), 1./5.); // check default is 1/df
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BOOST_CHECK_CLOSE_FRACTION(pdf(ichisq5, 0.2), 3.0510380337346841, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(cdf(ichisq5, 0.5), 0.84914503608460956, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(cdf(complement(ichisq5, 0.5)), 1 - 0.84914503608460956, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(quantile(ichisq5, 0.84914503608460956), 0.5, tol4eps*100);
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BOOST_CHECK_CLOSE_FRACTION(quantile(complement(ichisq5, 1. - 0.84914503608460956)), 0.5, tol4eps*100);
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// Check mean, etc spot values.
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inverse_chi_squared_distribution<double> ichisq81(8., 1.); // degrees_of_freedom = 5, scale = 1
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BOOST_CHECK_CLOSE_FRACTION(mean(ichisq81),1.33333333333333333333333333333333333333333, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(variance(ichisq81), 0.888888888888888888888888888888888888888888888, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(skewness(ichisq81), 2 * std::sqrt(8.), tol4eps);
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inverse_chi_squared_distribution<double> ichisq21(2., 1.);
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BOOST_CHECK_CLOSE_FRACTION(mode(ichisq21), 0.5, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(median(ichisq21), 1.4426950408889634, tol4eps);
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inverse_chi_squared ichsq4(4.); // Using typedef and degrees_of_freedom parameter (and default scale = 1/df).
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BOOST_CHECK_EQUAL(ichsq4.degrees_of_freedom(), 4.); // df == 4.
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BOOST_CHECK_EQUAL(ichsq4.scale(), 0.25); // scale == 1 /df == 1/4.
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inverse_chi_squared ichsq32(3, 2);
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BOOST_CHECK_EQUAL(ichsq32.degrees_of_freedom(), 3.); // df == 3.
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BOOST_CHECK_EQUAL(ichsq32.scale(), 2); // scale == 2
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inverse_chi_squared ichsq11(1, 1); // Using explicit degrees_of_freedom parameter, and default scale = 1).
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BOOST_CHECK_CLOSE_FRACTION(mode(ichsq11), 0.3333333333333333333333333333333333333333, tol4eps);
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// (1 * 1)/ (1 + 2) = 1/3 using Wikipedia nu * xi /(nu + 2)
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BOOST_CHECK_EQUAL(ichsq11.degrees_of_freedom(), 1.); // df == 1 (default).
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BOOST_CHECK_EQUAL(ichsq11.scale(), 1.); // scale == 1.
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/*
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// Used to find some 'exact' values for testing mean, variance ...
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// First with scale fixed at unity (Wikipedia definition 1)
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cout << "df scale mean variance sd median" << endl;
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for (int degrees_of_freedom = 8; degrees_of_freedom < 30; degrees_of_freedom++)
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{
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inverse_chi_squared ichisq(degrees_of_freedom, 1);
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cout.precision(17);
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cout << degrees_of_freedom << " " << 1 << " " << mean(ichisq) << ' '
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<< variance(ichisq) << ' ' << standard_deviation(ichisq)
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<< ' ' << median(ichisq) << endl;
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}
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// Default scale = 1 / df
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cout << "|\n" << "df scale mean variance sd median" << endl;
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for (int degrees_of_freedom = 8; degrees_of_freedom < 30; degrees_of_freedom++)
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{
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|
inverse_chi_squared ichisq(degrees_of_freedom);
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|
cout.precision(17);
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cout << degrees_of_freedom << " " << 1./degrees_of_freedom << " " << mean(ichisq) << ' '
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<< variance(ichisq) << ' ' << standard_deviation(ichisq)
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|
<< ' ' << median(ichisq) << endl;
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|
}
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|
*/
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inverse_chi_squared_distribution<> ichisq14(14, 1); // Using default RealType double.
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BOOST_CHECK_CLOSE_FRACTION(mean(ichisq14), 1.166666666666666666666666666666666666666666666, tol4eps);
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BOOST_CHECK_CLOSE_FRACTION(variance(ichisq14), 0.272222222222222222222222222222222222222222222, tol4eps);
|
|
|
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inverse_chi_squared_distribution<> ichisq121(12); // Using default RealType double.
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BOOST_CHECK_CLOSE_FRACTION(mean(ichisq121), 0.1, tol4eps);
|
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BOOST_CHECK_CLOSE_FRACTION(variance(ichisq121), 0.0025, tol4eps);
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|
BOOST_CHECK_CLOSE_FRACTION(standard_deviation(ichisq121), 0.05, tol4eps);
|
|
|
|
// and "using boost::math::inverse_chi_squared_distribution;".
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|
inverse_chi_squared_distribution<> ichsq23(2., 3.); // Using default RealType double.
|
|
BOOST_CHECK_EQUAL(ichsq23.degrees_of_freedom(), 2.); //
|
|
BOOST_CHECK_EQUAL(ichsq23.scale(), 3.); //
|
|
BOOST_MATH_CHECK_THROW(mean(ichsq23), std::domain_error); // Degrees of freedom (nu) must be > 2
|
|
BOOST_MATH_CHECK_THROW(variance(ichsq23), std::domain_error); // Degrees of freedom (nu) must be > 4
|
|
BOOST_MATH_CHECK_THROW(skewness(ichsq23), std::domain_error); // Degrees of freedom (nu) must be > 6
|
|
BOOST_MATH_CHECK_THROW(kurtosis_excess(ichsq23), std::domain_error); // Degrees of freedom (nu) must be > 8
|
|
|
|
{ // Check relationship between inverse gamma and inverse chi_squared distributions.
|
|
using boost::math::inverse_gamma_distribution;
|
|
|
|
double df = 2.;
|
|
double scale = 1.;
|
|
double alpha = df/2; // aka inv_gamma shape
|
|
double beta = scale /2; // inv_gamma scale.
|
|
|
|
inverse_gamma_distribution<> ig(alpha, beta);
|
|
|
|
inverse_chi_squared_distribution<> ichsq(df, 1./df); // == default scale.
|
|
BOOST_CHECK_EQUAL(pdf(ichsq, 0), 0); // Special case of zero x.
|
|
|
|
double x = 0.5;
|
|
BOOST_CHECK_EQUAL(pdf(ig, x), pdf(ichsq, x)); // inv_gamma compared to inv_chisq
|
|
BOOST_CHECK_EQUAL(cdf(ichsq, 0), 0); // Special case of zero.
|
|
BOOST_CHECK_EQUAL(cdf(ig, x), cdf(ichsq, x)); // invgamma == invchisq
|
|
|
|
// Test pdf by comparing using naive_pdf with relation to inverse gamma distribution
|
|
// wikipedia http://en.wikipedia.org/wiki/Scaled-inverse-chi-square_distribution related distributions.
|
|
// So if naive_pdf is correct, inverse_chi_squared_distribution should agree.
|
|
df = 1.; scale = 1.;
|
|
BOOST_CHECK_CLOSE_FRACTION(naive_pdf(df, scale, x), pdf(ichsq11, x), tol_few_eps);
|
|
|
|
//inverse_gamma_distribution<> igd(df/2, (df * scale)/2);
|
|
inverse_gamma_distribution<> igd11(df/2, df * scale/2);
|
|
BOOST_CHECK_CLOSE_FRACTION(naive_pdf(df, scale, x), pdf(igd11, x), tol_few_eps);
|
|
BOOST_CHECK_CLOSE_FRACTION(naive_pdf(df, scale, x), pdf(ichsq11, x), tol_few_eps);
|
|
|
|
df = 2; scale = 1;
|
|
inverse_gamma_distribution<> igd21(df/2, df * scale/2);
|
|
inverse_chi_squared_distribution<> ichsq21(df, scale);
|
|
BOOST_CHECK_CLOSE_FRACTION(naive_pdf(df, scale, x), pdf(igd21, x), tol_few_eps); // 0.54134113294645081 OK
|
|
BOOST_CHECK_CLOSE_FRACTION(naive_pdf(df, scale, x), pdf(ichsq21, x), tol_few_eps);
|
|
|
|
df = 2; scale = 2;
|
|
inverse_gamma_distribution<> igd22(df/2, df * scale/2);
|
|
inverse_chi_squared_distribution<> ichsq22(df, scale);
|
|
BOOST_CHECK_CLOSE_FRACTION(naive_pdf(df, scale, x), pdf(igd22, x), tol_few_eps);
|
|
BOOST_CHECK_CLOSE_FRACTION(naive_pdf(df, scale, x), pdf(ichsq22, x), tol_few_eps);
|
|
}
|
|
|
|
// Check using float.
|
|
inverse_chi_squared_distribution<float> igf23(1.f, 2.f); // Using explicit RealType float.
|
|
BOOST_CHECK_EQUAL(igf23.degrees_of_freedom(), 1.f); //
|
|
BOOST_CHECK_EQUAL(igf23.scale(), 2.f); //
|
|
|
|
// Check throws from bad parameters.
|
|
inverse_chi_squared ig051(0.5, 1.); // degrees_of_freedom < 1, so wrong for mean.
|
|
BOOST_MATH_CHECK_THROW(mean(ig051), std::domain_error);
|
|
inverse_chi_squared ig191(1.9999, 1.); // degrees_of_freedom < 2, so wrong for variance.
|
|
BOOST_MATH_CHECK_THROW(variance(ig191), std::domain_error);
|
|
inverse_chi_squared ig291(2.9999, 1.); // degrees_of_freedom < 3, so wrong for skewness.
|
|
BOOST_MATH_CHECK_THROW(skewness(ig291), std::domain_error);
|
|
inverse_chi_squared ig391(3.9999, 1.); // degrees_of_freedom < 1, so wrong for kurtosis and kurtosis_excess.
|
|
BOOST_MATH_CHECK_THROW(kurtosis(ig391), std::domain_error);
|
|
BOOST_MATH_CHECK_THROW(kurtosis_excess(ig391), std::domain_error);
|
|
|
|
inverse_chi_squared ig102(10, 2); // Wolfram.com/ page 2, quantile = 2.96859.
|
|
//http://reference.wolfram.com/mathematica/ref/InverseChiSquareDistribution.html
|
|
BOOST_CHECK_CLOSE_FRACTION(quantile(ig102, 0.75), 2.96859, 0.000001);
|
|
BOOST_CHECK_CLOSE_FRACTION(cdf(ig102, 2.96859), 0.75 , 0.000001);
|
|
BOOST_CHECK_CLOSE_FRACTION(cdf(complement(ig102, 2.96859)), 1 - 0.75 , 0.00001);
|
|
BOOST_CHECK_CLOSE_FRACTION(quantile(complement(ig102, 1 - 0.75)), 2.96859, 0.000001);
|
|
|
|
// Basic sanity-check spot values.
|
|
// (Parameter value, arbitrarily zero, only communicates the floating point type).
|
|
test_spots(0.0F); // Test float. OK at decdigits = 0 tolerance = 0.0001 %
|
|
test_spots(0.0); // Test double. OK at decdigits 7, tolerance = 1e07 %
|
|
#ifndef BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS
|
|
test_spots(0.0L); // Test long double.
|
|
#if !BOOST_WORKAROUND(__BORLANDC__, BOOST_TESTED_AT(0x0582))
|
|
test_spots(boost::math::concepts::real_concept(0.)); // Test real concept.
|
|
#endif
|
|
#else
|
|
std::cout << "<note>The long double tests have been disabled on this platform "
|
|
"either because the long double overloads of the usual math functions are "
|
|
"not available at all, or because they are too inaccurate for these tests "
|
|
"to pass.</note>" << std::endl;
|
|
#endif
|
|
|
|
/* */
|
|
|
|
} // BOOST_AUTO_TEST_CASE( test_main )
|
|
|
|
/*
|
|
|
|
Output:
|
|
|
|
|
|
|
|
|
|
*/
|
|
|
|
|
|
|