boost/random
2
0
Fork 0
mirror of https://github.com/boostorg/random.git synced 2026-01-19 04:22:17 +00:00
Code Issues Projects Releases Wiki Activity
Files
9febe9be3fbb731279038d413dca775ecfdfe850
random/statistic_tests.cpp

670 lines
19 KiB
C++
Raw Blame History

/* statistic_tests.cpp file
*
* Copyright Jens Maurer 2000, 2002
* Permission to use, copy, modify, sell, and distribute this software
* is hereby granted without fee provided that the above copyright notice
* appears in all copies and that both that copyright notice and this
* permission notice appear in supporting documentation,
*
* Jens Maurer makes no representations about the suitability of this
* software for any purpose. It is provided "as is" without express or
* implied warranty.
*
* $Id$
*
* Revision history
*/
/*
* NOTE: This is not part of the official boost submission. It exists
* only as a collection of ideas.
*/
#include <iostream>
#include <iomanip>
#include <string>
#include <functional>
#include <math.h> // lgamma is not in namespace std
#include <vector>
#include <algorithm>
#include <boost/cstdint.hpp>
#include <boost/random.hpp>
#include "statistic_tests.hpp"
#include "integrate.hpp"
namespace boost {
namespace random {
// Wikramaratna 1989 ACORN
template<class IntType, int k, IntType m, IntType val>
class additive_congruential
{
public:
typedef IntType result_type;
#ifndef BOOST_NO_INCLASS_MEMBER_INITIALIZATION
static const bool has_fixed_range = true;
static const result_type min_value = 0;
static const result_type max_value = m-1;
#else
enum {
has_fixed_range = true,
min_value = 0,
max_value = m-1
};
#endif
template<class InputIterator>
explicit additive_congruential(InputIterator start) { seed(start); }
template<class InputIterator>
void seed(InputIterator start)
{
for(int i = 0; i <= k; ++i, ++start)
values[i] = *start;
}
result_type operator()()
{
for(int i = 1; i <= k; ++i) {
IntType tmp = values[i-1] + values[i];
if(tmp >= m)
tmp -= m;
values[i] = tmp;
}
return values[k];
}
result_type validation() const { return val; }
private:
IntType values[k+1];
};
template<class IntType, int r, int s, IntType m, IntType val>
class lagged_fibonacci_int
{
public:
typedef IntType result_type;
#ifndef BOOST_NO_INCLASS_MEMBER_INITIALIZATION
static const bool has_fixed_range = true;
static const result_type min_value = 0;
static const result_type max_value = m-1;
#else
enum {
has_fixed_range = true,
min_value = 0,
max_value = m-1
};
#endif
explicit lagged_fibonacci_int(IntType start) { seed(start); }
template<class Generator>
explicit lagged_fibonacci_int(Generator & gen) { seed(gen); }
void seed(IntType start)
{
linear_congruential<uint32_t, 299375077, 0, 0, 0> init;
seed(init);
}
template<class Generator>
void seed(Generator & gen)
{
assert(r > s);
for(int i = 0; i < 607; ++i)
values[i] = gen();
current = 0;
lag = r-s;
}
result_type operator()()
{
result_type tmp = values[current] + values[lag];
if(tmp >= m)
tmp -= m;
values[current] = tmp;
++current;
if(current >= r)
current = 0;
++lag;
if(lag >= r)
lag = 0;
return tmp;
}
result_type validation() const { return val; }
private:
result_type values[r];
int current, lag;
};
} // namespace random
} // namespace boost
// distributions from Haertel's dissertation
// (additional parameterizations of the basic templates)
namespace Haertel {
typedef boost::random::linear_congruential<boost::uint64_t, 45965, 453816691,
(boost::uint64_t(1)<<31), 0> LCG_Af2;
typedef boost::random::linear_congruential<boost::uint64_t, 211936855, 0,
(boost::uint64_t(1)<<29)-3, 0> LCG_Die1;
typedef boost::random::linear_congruential<boost::uint32_t, 2824527309u, 0,
0, 0> LCG_Fis;
typedef boost::random::linear_congruential<boost::uint64_t, 950706376u, 0,
(boost::uint64_t(1)<<31)-1, 0> LCG_FM;
typedef boost::random::linear_congruential<boost::int32_t, 51081, 0,
2147483647, 0> LCG_Hae;
typedef boost::random::linear_congruential<boost::uint32_t, 69069, 1,
0, 0> LCG_VAX;
typedef boost::random::inversive_congruential<boost::int64_t, 240318, 197,
1000081, 0> NLG_Inv1;
typedef boost::random::inversive_congruential<boost::int64_t, 15707262,
13262967, (1<<24)-17, 0> NLG_Inv2;
typedef boost::random::inversive_congruential<boost::int32_t, 1, 1,
2147483647, 0> NLG_Inv4;
typedef boost::random::inversive_congruential<boost::int32_t, 1, 2,
1<<30, 0> NLG_Inv5;
typedef boost::random::additive_congruential<boost::int32_t, 6,
(1<<30)-35, 0> MRG_Acorn7;
typedef boost::random::lagged_fibonacci_int<boost::uint32_t, 607, 273,
0, 0> MRG_Fib2;
template<class Gen, class T>
inline void check_validation(Gen & gen, T value, const std::string & name)
{
for(int i = 0; i < 100000-1; ++i)
gen();
if(value != gen())
std::cout << name << ": validation failed" << std::endl;
}
// we have validation after 100000 steps with Haertel's generators
template<class Gen, class T>
void validate(T value, const std::string & name)
{
Gen gen(1234567);
check_validation(gen, value, name);
}
void validate_all()
{
validate<LCG_Af2>(183269031u, "LCG_Af2");
validate<LCG_Die1>(522319944u, "LCG_Die1");
validate<LCG_Fis>(-2065162233u, "LCG_Fis");
validate<LCG_FM>(581815473u, "LCG_FM");
validate<LCG_Hae>(28931709, "LCG_Hae");
validate<LCG_VAX>(1508154087u, "LCG_VAX");
validate<NLG_Inv2>(6666884, "NLG_Inv2");
validate<NLG_Inv4>(1521640076, "NLG_Inv4");
validate<NLG_Inv5>(641840839, "NLG_Inv5");
static const int acorn7_init[]
= { 1234567, 7654321, 246810, 108642, 13579, 97531, 555555 };
MRG_Acorn7 acorn7(acorn7_init);
check_validation(acorn7, 874294697, "MRG_Acorn7");
validate<MRG_Fib2>(1234567u, "MRG_Fib2");
}
} // namespace Haertel
double normal_density(double x)
{
const double pi = 3.14159265358979323846;
return 1/std::sqrt(2*pi) * std::exp(-x*x/2);
}
namespace std {
#ifdef _CXXRTCF_H__
using _CS_swamp::lgamma;
#elif defined __SGI_STL_PORT
using ::lgamma;
#endif
}
class chi_square_density : public std::unary_function<double, double>
{
public:
chi_square_density(int freedom)
: _exponent( static_cast<double>(freedom)/2-1 ),
_factor(1/(std::pow(2, _exponent+1) * std::exp(lgamma(_exponent+1))))
{ }
double operator()(double x)
{
return _factor*std::pow(x, _exponent)*std::exp(-x/2);
}
private:
double _exponent, _factor;
};
// computes F(x) or F(y) - F(x)
class chi_square_probability : public distribution_function<double>
{
public:
chi_square_probability(int freedom) : dens(freedom) {}
double operator()(double x) { return operator()(0, x); }
double operator()(double x, double y)
{ return trapezoid(dens, x, y, 1000); }
private:
chi_square_density dens;
};
class uniform_distribution : public distribution_function<double>
{
public:
uniform_distribution(double from, double to) : from(from), to(to)
{ assert(from < to); }
double operator()(double x)
{
if(x < from)
return 0;
else if(x > to)
return 1;
else
return (x-from)/(to-from);
}
double operator()(double x, double delta)
{ return operator()(x+delta) - operator()(x); }
private:
double from, to;
};
class test_environment;
class test_base
{
protected:
explicit test_base(test_environment & env) : environment(env) { }
void check(double val) const;
private:
test_environment & environment;
};
class equidistribution_test : test_base
{
public:
equidistribution_test(test_environment & env, unsigned int classes,
unsigned int high_classes)
: test_base(env), classes(classes),
test_distrib_chi_square(chi_square_probability(classes-1), high_classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "equidistribution: " << std::flush;
equidistribution_experiment equi(classes);
uniform_smallint<RNG> uint_linear(rng, 0, classes-1);
check(run_experiment(test_distrib_chi_square,
experiment_generator(equi, uint_linear, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(equi, uint_linear, n1), 2*n2));
std::cout << " 2D: " << std::flush;
equidistribution_2d_experiment equi_2d(classes);
unsigned int root = static_cast<unsigned int>(std::sqrt(double(classes)));
assert(root * root == classes);
uniform_smallint<RNG> uint_square(rng, 0, root-1);
check(run_experiment(test_distrib_chi_square,
experiment_generator(equi_2d, uint_square, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(equi_2d, uint_square, n1), 2*n2));
std::cout << std::endl;
}
private:
unsigned int classes;
distribution_experiment test_distrib_chi_square;
};
class ks_equidistribution_test : test_base
{
public:
ks_equidistribution_test(test_environment & env, unsigned int classes)
: test_base(env),
test_distrib_chi_square(kolmogorov_smirnov_probability(5000),
classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "KS: " << std::flush;
// generator_reference_t<RNG> gen_ref(rng);
RNG& gen_ref(rng);
kolmogorov_experiment ks(n1);
uniform_distribution ud((rng.min)(), (rng.max)());
check(run_experiment(test_distrib_chi_square,
ks_experiment_generator(ks, gen_ref, ud), n2));
check(run_experiment(test_distrib_chi_square,
ks_experiment_generator(ks, gen_ref, ud), 2*n2));
}
private:
distribution_experiment test_distrib_chi_square;
};
class runs_test : test_base
{
public:
runs_test(test_environment & env, unsigned int classes,
unsigned int high_classes)
: test_base(env), classes(classes),
test_distrib_chi_square(chi_square_probability(classes-1), high_classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "runs: up: " << std::flush;
runs_experiment<true> r_up(classes);
// generator_reference_t<RNG> gen_ref(rng);
RNG& gen_ref(rng);
check(run_experiment(test_distrib_chi_square,
experiment_generator(r_up, gen_ref, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(r_up, gen_ref, n1), 2*n2));
std::cout << " down: " << std::flush;
runs_experiment<false> r_down(classes);
check(run_experiment(test_distrib_chi_square,
experiment_generator(r_down, gen_ref, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(r_down, gen_ref, n1), 2*n2));
std::cout << std::endl;
}
private:
unsigned int classes;
distribution_experiment test_distrib_chi_square;
};
class gap_test : test_base
{
public:
gap_test(test_environment & env, unsigned int classes,
unsigned int high_classes)
: test_base(env), classes(classes),
test_distrib_chi_square(chi_square_probability(classes-1), high_classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "gaps: " << std::flush;
gap_experiment gap(classes, 0.2, 0.8);
// generator_reference_t<RNG> gen_ref(rng);
RNG& gen_ref(rng);
check(run_experiment(test_distrib_chi_square,
experiment_generator(gap, gen_ref, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(gap, gen_ref, n1), 2*n2));
std::cout << std::endl;
}
private:
unsigned int classes;
distribution_experiment test_distrib_chi_square;
};
class poker_test : test_base
{
public:
poker_test(test_environment & env, unsigned int classes,
unsigned int high_classes)
: test_base(env), classes(classes),
test_distrib_chi_square(chi_square_probability(classes-1), high_classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "poker: " << std::flush;
poker_experiment poker(8, classes);
uniform_smallint<RNG> usmall(rng, 0, 7);
check(run_experiment(test_distrib_chi_square,
experiment_generator(poker, usmall, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(poker, usmall, n1), 2*n2));
std::cout << std::endl;
}
private:
unsigned int classes;
distribution_experiment test_distrib_chi_square;
};
class coupon_collector_test : test_base
{
public:
coupon_collector_test(test_environment & env, unsigned int classes,
unsigned int high_classes)
: test_base(env), classes(classes),
test_distrib_chi_square(chi_square_probability(classes-1), high_classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "coupon collector: " << std::flush;
coupon_collector_experiment coupon(5, classes);
uniform_smallint<RNG> usmall(rng, 0, 4);
check(run_experiment(test_distrib_chi_square,
experiment_generator(coupon, usmall, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(coupon, usmall, n1), 2*n2));
std::cout << std::endl;
}
private:
unsigned int classes;
distribution_experiment test_distrib_chi_square;
};
class permutation_test : test_base
{
public:
permutation_test(test_environment & env, unsigned int classes,
unsigned int high_classes)
: test_base(env), classes(classes),
test_distrib_chi_square(chi_square_probability(fac<int>(classes)-1),
high_classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "permutation: " << std::flush;
permutation_experiment perm(classes);
// generator_reference_t<RNG> gen_ref(rng);
RNG& gen_ref(rng);
check(run_experiment(test_distrib_chi_square,
experiment_generator(perm, gen_ref, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(perm, gen_ref, n1), 2*n2));
std::cout << std::endl;
}
private:
unsigned int classes;
distribution_experiment test_distrib_chi_square;
};
class maximum_test : test_base
{
public:
maximum_test(test_environment & env, unsigned int high_classes)
: test_base(env),
test_distrib_chi_square(kolmogorov_smirnov_probability(1000),
high_classes)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "maximum-of-t: " << std::flush;
maximum_experiment<RNG> mx(rng, n1, 5);
check(run_experiment(test_distrib_chi_square, mx, n2));
check(run_experiment(test_distrib_chi_square, mx, 2*n2));
std::cout << std::endl;
}
private:
distribution_experiment test_distrib_chi_square;
};
class birthday_test : test_base
{
public:
birthday_test(test_environment & env)
: test_base(env)
{ }
template<class RNG>
void run(RNG & rng, int n1, int n2)
{
using namespace boost;
std::cout << "birthday spacing: " << std::flush;
uniform_int<RNG> uni(rng, 0, (1<<25)-1);
birthday_spacing_experiment bsp(4, 512, (1<<25));
std::cout << run_experiment(bsp, uni, n1);
#if 0
check(run_experiment(test_distrib_chi_square,
experiment_generator(perm, gen_ref, n1), n2));
check(run_experiment(test_distrib_chi_square,
experiment_generator(perm, gen_ref, n1), 2*n2));
#endif
std::cout << std::endl;
}
};
class test_environment
{
public:
static const int classes = 20;
explicit test_environment(double confid)
: confidence(confid),
confidence_chi_square_quantil(quantil(chi_square_density(classes-1), 0, confidence, 1e-4)),
test_distrib_chi_square6(chi_square_probability(7-1), classes),
ksequi_test(*this, classes),
equi_test(*this, 100, classes),
rns_test(*this, 7, classes),
gp_test(*this, 7, classes),
pk_test(*this, 5, classes),
cpn_test(*this, 15, classes),
perm_test(*this, 5, classes),
max_test(*this, classes),
bday_test(*this)
{
std::cout << "Confidence level: " << confid
<< "; 1-alpha = " << (1-confid)
<< "; chi_square(" << (classes-1)
<< ", " << confidence_chi_square_quantil
<< ") = "
<< chi_square_probability(classes-1)(0, confidence_chi_square_quantil)
<< std::endl;
}
bool check_confidence(double val, double chi_square_conf) const
{
std::cout << val;
bool result = (val <= chi_square_conf);
if(!result) {
std::cout << "* [";
double prob = (val > 10*chi_square_conf ? 1 :
chi_square_probability(classes-1)(0, val));
std::cout << (1-prob) << "]";
}
std::cout << " " << std::flush;
return result;
}
bool check(double chi_square_value) const
{
return check_confidence(chi_square_value, confidence_chi_square_quantil);
}
template<class RNG>
void run_test(const std::string & name)
{
using namespace boost;
std::cout << "Running tests on " << name << std::endl;
RNG rng(1234567);
typedef boost::uniform_01<RNG> UGen;
#if 1
ksequi_test.run(rng, 5000, 250);
equi_test.run(rng, 5000, 250);
rns_test.run(rng, 100000, 250);
gp_test.run(rng, 10000, 250);
pk_test.run(rng, 5000, 250);
cpn_test.run(rng, 500, 250);
perm_test.run(rng, 1200, 250);
max_test.run(rng, 1000, 250);
#endif
bday_test.run(rng, 1000, 150);
std::cout << std::endl;
}
private:
double confidence;
double confidence_chi_square_quantil;
distribution_experiment test_distrib_chi_square6;
ks_equidistribution_test ksequi_test;
equidistribution_test equi_test;
runs_test rns_test;
gap_test gp_test;
poker_test pk_test;
coupon_collector_test cpn_test;
permutation_test perm_test;
maximum_test max_test;
birthday_test bday_test;
};
void test_base::check(double val) const
{
environment.check(val);
}
void print_ks_table()
{
std::cout.setf(std::ios::fixed);
std::cout.precision(5);
static const double all_p[] = { 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99 };
for(int n = 0; n <= 10000; (n < 55 ? ++n : n *= 10)) {
std::cout << std::setw(4) << n << " ";
for(unsigned int i = 0; i < sizeof(all_p)/sizeof(all_p[0]); ++i) {
std::cout << std::setw(8)
<< (n == 0 ? all_p[i] :
invert_monotone_inc(kolmogorov_smirnov_probability(n), all_p[i], 0, 10))
<< " ";
}
std::cout << std::endl;
}
}
int main()
{
// Haertel::validate_all();
test_environment env(0.99);
env.run_test<boost::minstd_rand>("minstd_rand");
env.run_test<boost::mt19937>("mt19937");
env.run_test<Haertel::LCG_Af2>("LCG_Af2");
env.run_test<Haertel::LCG_Die1>("LCG_Die1");
env.run_test<Haertel::LCG_Fis>("LCG_Fis");
env.run_test<Haertel::LCG_FM>("LCG_FM");
env.run_test<Haertel::LCG_Hae>("LCG_Hae");
env.run_test<Haertel::LCG_VAX>("LCG_VAX");
env.run_test<Haertel::NLG_Inv1>("NLG_Inv1");
env.run_test<Haertel::NLG_Inv2>("NLG_Inv2");
env.run_test<Haertel::NLG_Inv4>("NLG_Inv4");
env.run_test<Haertel::NLG_Inv5>("NLG_Inv5");
}
Reference in New Issue View Git Blame Copy Permalink