mirror of https://github.com/saitohirga/WSJT-X.git
194 lines
7.9 KiB
C++
194 lines
7.9 KiB
C++
// (C) Copyright John Maddock 2007.
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// Use, modification and distribution are subject to the
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// Boost Software License, Version 1.0. (See accompanying file
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// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
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#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
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#include <boost/math/concepts/real_concept.hpp>
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#define BOOST_TEST_MAIN
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#include <boost/test/unit_test.hpp>
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#include <boost/test/floating_point_comparison.hpp>
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#include <boost/math/distributions/non_central_beta.hpp>
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#include <boost/math/distributions/poisson.hpp>
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#include <boost/type_traits/is_floating_point.hpp>
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#include <boost/array.hpp>
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#include "functor.hpp"
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#include "handle_test_result.hpp"
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#include "table_type.hpp"
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#define BOOST_CHECK_CLOSE_EX(a, b, prec, i) \
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{\
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unsigned int failures = boost::unit_test::results_collector.results( boost::unit_test::framework::current_test_case().p_id ).p_assertions_failed;\
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BOOST_CHECK_CLOSE(a, b, prec); \
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if(failures != boost::unit_test::results_collector.results( boost::unit_test::framework::current_test_case().p_id ).p_assertions_failed)\
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{\
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std::cerr << "Failure was at row " << i << std::endl;\
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std::cerr << std::setprecision(35); \
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std::cerr << "{ " << data[i][0] << " , " << data[i][1] << " , " << data[i][2];\
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std::cerr << " , " << data[i][3] << " , " << data[i][4] << " } " << std::endl;\
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}\
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}
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#define BOOST_CHECK_EX(a, i) \
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{\
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unsigned int failures = boost::unit_test::results_collector.results( boost::unit_test::framework::current_test_case().p_id ).p_assertions_failed;\
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BOOST_CHECK(a); \
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if(failures != boost::unit_test::results_collector.results( boost::unit_test::framework::current_test_case().p_id ).p_assertions_failed)\
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{\
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std::cerr << "Failure was at row " << i << std::endl;\
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std::cerr << std::setprecision(35); \
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std::cerr << "{ " << data[i][0] << " , " << data[i][1] << " , " << data[i][2];\
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std::cerr << " , " << data[i][3] << " , " << data[i][4] << " } " << std::endl;\
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}\
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}
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template <class T>
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T nc_beta_cdf(T a, T b, T nc, T x)
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{
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#ifdef NC_BETA_CDF_FUNCTION_TO_TEST
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return NC_BETA_CDF_FUNCTION_TO_TEST(a, b, nc, x);
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#else
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return cdf(boost::math::non_central_beta_distribution<T>(a, b, nc), x);
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#endif
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}
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template <class T>
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T nc_beta_ccdf(T a, T b, T nc, T x)
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{
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#ifdef NC_BETA_CCDF_FUNCTION_TO_TEST
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return NC_BETA_CCDF_FUNCTION_TO_TEST(a, b, nc, x);
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#else
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return cdf(complement(boost::math::non_central_beta_distribution<T>(a, b, nc), x));
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#endif
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}
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template <typename Real, typename T>
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void do_test_nc_chi_squared(T& data, const char* type_name, const char* test)
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{
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typedef Real value_type;
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std::cout << "Testing: " << test << std::endl;
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value_type(*fp1)(value_type, value_type, value_type, value_type) = nc_beta_cdf;
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boost::math::tools::test_result<value_type> result;
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#if !(defined(ERROR_REPORTING_MODE) && !defined(NC_BETA_CDF_FUNCTION_TO_TEST))
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result = boost::math::tools::test_hetero<Real>(
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data,
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bind_func<Real>(fp1, 0, 1, 2, 3),
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extract_result<Real>(4));
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handle_test_result(result, data[result.worst()], result.worst(),
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type_name, "non central beta CDF", test);
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#endif
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#if !(defined(ERROR_REPORTING_MODE) && !defined(NC_BETA_CCDF_FUNCTION_TO_TEST))
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fp1 = nc_beta_ccdf;
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result = boost::math::tools::test_hetero<Real>(
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data,
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bind_func<Real>(fp1, 0, 1, 2, 3),
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extract_result<Real>(5));
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handle_test_result(result, data[result.worst()], result.worst(),
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type_name, "non central beta CDF complement", test);
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#endif
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std::cout << std::endl;
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}
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template <typename Real, typename T>
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void quantile_sanity_check(T& data, const char* type_name, const char* test)
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{
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#ifndef ERROR_REPORTING_MODE
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typedef Real value_type;
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//
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// Tests with type real_concept take rather too long to run, so
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// for now we'll disable them:
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//
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if(!boost::is_floating_point<value_type>::value)
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return;
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std::cout << "Testing: " << type_name << " quantile sanity check, with tests " << test << std::endl;
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//
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// These sanity checks test for a round trip accuracy of one half
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// of the bits in T, unless T is type float, in which case we check
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// for just one decimal digit. The problem here is the sensitivity
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// of the functions, not their accuracy. This test data was generated
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// for the forward functions, which means that when it is used as
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// the input to the inverses then it is necessarily inexact. This rounding
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// of the input is what makes the data unsuitable for use as an accuracy check,
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// and also demonstrates that you can't in general round-trip these functions.
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// It is however a useful sanity check.
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//
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value_type precision = static_cast<value_type>(ldexp(1.0, 1 - boost::math::policies::digits<value_type, boost::math::policies::policy<> >() / 2)) * 100;
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if(boost::math::policies::digits<value_type, boost::math::policies::policy<> >() < 50)
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precision = 1; // 1% or two decimal digits, all we can hope for when the input is truncated to float
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for(unsigned i = 0; i < data.size(); ++i)
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{
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//
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// Test case 493 fails at float precision: not enough bits to get
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// us back where we started:
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//
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if((i == 493) && boost::is_same<float, value_type>::value)
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continue;
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if(data[i][4] == 0)
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{
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BOOST_CHECK(0 == quantile(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]), data[i][4]));
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}
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else if(data[i][4] < 0.9999f)
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{
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value_type p = quantile(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]), data[i][4]);
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value_type pt = data[i][3];
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BOOST_CHECK_CLOSE_EX(pt, p, precision, i);
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}
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if(data[i][5] == 0)
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{
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BOOST_CHECK(1 == quantile(complement(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]), data[i][5])));
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}
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else if(data[i][5] < 0.9999f)
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{
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value_type p = quantile(complement(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]), data[i][5]));
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value_type pt = data[i][3];
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BOOST_CHECK_CLOSE_EX(pt, p, precision, i);
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}
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if(boost::math::tools::digits<value_type>() > 50)
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{
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//
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// Sanity check mode, accuracy of
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// the mode is at *best* the square root of the accuracy of the PDF:
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//
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value_type m = mode(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]));
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if((m == 1) || (m == 0))
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break;
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value_type p = pdf(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]), m);
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if(m * (1 + sqrt(precision) * 10) < 1)
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{
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BOOST_CHECK_EX(pdf(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]), m * (1 + sqrt(precision) * 10)) <= p, i);
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}
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if(m * (1 - sqrt(precision)) * 10 > boost::math::tools::min_value<value_type>())
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{
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BOOST_CHECK_EX(pdf(boost::math::non_central_beta_distribution<value_type>(data[i][0], data[i][1], data[i][2]), m * (1 - sqrt(precision)) * 10) <= p, i);
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}
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}
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}
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#endif
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}
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template <typename T>
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void test_accuracy(T, const char* type_name)
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{
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#if !defined(TEST_DATA) || (TEST_DATA == 1)
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#include "ncbeta.ipp"
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do_test_nc_chi_squared<T>(ncbeta, type_name, "Non Central Beta, medium parameters");
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quantile_sanity_check<T>(ncbeta, type_name, "Non Central Beta, medium parameters");
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#endif
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#if !defined(TEST_DATA) || (TEST_DATA == 2)
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#include "ncbeta_big.ipp"
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do_test_nc_chi_squared<T>(ncbeta_big, type_name, "Non Central Beta, large parameters");
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// Takes too long to run:
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// quantile_sanity_check(ncbeta_big, type_name, "Non Central Beta, large parameters");
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#endif
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}
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