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1 //  (C) Copyright Eric Niebler, Olivier Gygi 2006.
2 //  Use, modification and distribution are subject to the
3 //  Boost Software License, Version 1.0. (See accompanying file
4 //  LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
5 
6 // Test case for weighted_p_square_cumul_dist.hpp
7 
8 #include <cmath>
9 #include <boost/random.hpp>
10 #include <boost/test/unit_test.hpp>
11 #include <boost/test/floating_point_comparison.hpp>
12 #include <boost/accumulators/numeric/functional/vector.hpp>
13 #include <boost/accumulators/numeric/functional/complex.hpp>
14 #include <boost/accumulators/numeric/functional/valarray.hpp>
15 #include <boost/accumulators/accumulators.hpp>
16 #include <boost/accumulators/statistics/stats.hpp>
17 #include <boost/accumulators/statistics/weighted_p_square_cumul_dist.hpp>
18 
19 using namespace boost;
20 using namespace unit_test;
21 using namespace boost::accumulators;
22 
23 ///////////////////////////////////////////////////////////////////////////////
24 // erf() not known by VC++ compiler!
25 // my_erf() computes error function by numerically integrating with trapezoidal rule
26 //
my_erf(double const & x,int const & n=1000)27 double my_erf(double const& x, int const& n = 1000)
28 {
29     double sum = 0.;
30     double delta = x/n;
31     for (int i = 1; i < n; ++i)
32         sum += std::exp(-i*i*delta*delta) * delta;
33     sum += 0.5 * delta * (1. + std::exp(-x*x));
34     return sum * 2. / std::sqrt(3.141592653);
35 }
36 
37 ///////////////////////////////////////////////////////////////////////////////
38 // test_stat
39 //
test_stat()40 void test_stat()
41 {
42     // tolerance in %
43     double epsilon = 4;
44 
45     typedef accumulator_set<double, stats<tag::weighted_p_square_cumulative_distribution>, double > accumulator_t;
46 
47     accumulator_t acc_upper(p_square_cumulative_distribution_num_cells = 100);
48     accumulator_t acc_lower(p_square_cumulative_distribution_num_cells = 100);
49 
50     // two random number generators
51     double mu_upper = 1.0;
52     double mu_lower = -1.0;
53     boost::lagged_fibonacci607 rng;
54     boost::normal_distribution<> mean_sigma_upper(mu_upper,1);
55     boost::normal_distribution<> mean_sigma_lower(mu_lower,1);
56     boost::variate_generator<boost::lagged_fibonacci607&, boost::normal_distribution<> > normal_upper(rng, mean_sigma_upper);
57     boost::variate_generator<boost::lagged_fibonacci607&, boost::normal_distribution<> > normal_lower(rng, mean_sigma_lower);
58 
59     for (std::size_t i=0; i<100000; ++i)
60     {
61         double sample = normal_upper();
62         acc_upper(sample, weight = std::exp(-mu_upper * (sample - 0.5 * mu_upper)));
63     }
64 
65     for (std::size_t i=0; i<100000; ++i)
66     {
67         double sample = normal_lower();
68         acc_lower(sample, weight = std::exp(-mu_lower * (sample - 0.5 * mu_lower)));
69     }
70 
71     typedef iterator_range<std::vector<std::pair<double, double> >::iterator > histogram_type;
72     histogram_type histogram_upper = weighted_p_square_cumulative_distribution(acc_upper);
73     histogram_type histogram_lower = weighted_p_square_cumulative_distribution(acc_lower);
74 
75     // Note that applying importance sampling results in a region of the distribution
76     // to be estimated more accurately and another region to be estimated less accurately
77     // than without importance sampling, i.e., with unweighted samples
78 
79     for (std::size_t i = 0; i < histogram_upper.size(); ++i)
80     {
81         // problem with small results: epsilon is relative (in percent), not absolute!
82 
83         // check upper region of distribution
84         if ( histogram_upper[i].second > 0.1 )
85             BOOST_CHECK_CLOSE( 0.5 * (1.0 + my_erf( histogram_upper[i].first / std::sqrt(2.0) )), histogram_upper[i].second, epsilon );
86         // check lower region of distribution
87         if ( histogram_lower[i].second < -0.1 )
88             BOOST_CHECK_CLOSE( 0.5 * (1.0 + my_erf( histogram_lower[i].first / std::sqrt(2.0) )), histogram_lower[i].second, epsilon );
89     }
90 }
91 
92 ///////////////////////////////////////////////////////////////////////////////
93 // init_unit_test_suite
94 //
init_unit_test_suite(int argc,char * argv[])95 test_suite* init_unit_test_suite( int argc, char* argv[] )
96 {
97     test_suite *test = BOOST_TEST_SUITE("weighted_p_square_cumulative_distribution test");
98 
99     test->add(BOOST_TEST_CASE(&test_stat));
100 
101     return test;
102 }
103 
104