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183 lines
8.1 KiB
C++
183 lines
8.1 KiB
C++
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// negative_binomial_example2.cpp
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// Copyright Paul A. Bristow 2007, 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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// Simple example demonstrating use of the Negative Binomial Distribution.
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#include <boost/math/distributions/negative_binomial.hpp>
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using boost::math::negative_binomial_distribution;
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using boost::math::negative_binomial; // typedef
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// In a sequence of trials or events
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// (Bernoulli, independent, yes or no, succeed or fail)
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// with success_fraction probability p,
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// negative_binomial is the probability that k or fewer failures
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// preceed the r th trial's success.
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#include <iostream>
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using std::cout;
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using std::endl;
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using std::setprecision;
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using std::showpoint;
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using std::setw;
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using std::left;
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using std::right;
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#include <limits>
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using std::numeric_limits;
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int main()
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{
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cout << "Negative_binomial distribution - simple example 2" << endl;
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// Construct a negative binomial distribution with:
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// 8 successes (r), success fraction (p) 0.25 = 25% or 1 in 4 successes.
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negative_binomial mynbdist(8, 0.25); // Shorter method using typedef.
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// Display (to check) properties of the distribution just constructed.
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cout << "mean(mynbdist) = " << mean(mynbdist) << endl; // 24
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cout << "mynbdist.successes() = " << mynbdist.successes() << endl; // 8
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// r th successful trial, after k failures, is r + k th trial.
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cout << "mynbdist.success_fraction() = " << mynbdist.success_fraction() << endl;
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// success_fraction = failures/successes or k/r = 0.25 or 25%.
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cout << "mynbdist.percent success = " << mynbdist.success_fraction() * 100 << "%" << endl;
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// Show as % too.
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// Show some cumulative distribution function values for failures k = 2 and 8
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cout << "cdf(mynbdist, 2.) = " << cdf(mynbdist, 2.) << endl; // 0.000415802001953125
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cout << "cdf(mynbdist, 8.) = " << cdf(mynbdist, 8.) << endl; // 0.027129956288263202
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cout << "cdf(complement(mynbdist, 8.)) = " << cdf(complement(mynbdist, 8.)) << endl; // 0.9728700437117368
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// Check that cdf plus its complement is unity.
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cout << "cdf + complement = " << cdf(mynbdist, 8.) + cdf(complement(mynbdist, 8.)) << endl; // 1
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// Note: No complement for pdf!
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// Compare cdf with sum of pdfs.
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double sum = 0.; // Calculate the sum of all the pdfs,
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int k = 20; // for 20 failures
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for(signed i = 0; i <= k; ++i)
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{
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sum += pdf(mynbdist, double(i));
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}
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// Compare with the cdf
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double cdf8 = cdf(mynbdist, static_cast<double>(k));
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double diff = sum - cdf8; // Expect the diference to be very small.
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cout << setprecision(17) << "Sum pdfs = " << sum << ' ' // sum = 0.40025683281803698
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<< ", cdf = " << cdf(mynbdist, static_cast<double>(k)) // cdf = 0.40025683281803687
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<< ", difference = " // difference = 0.50000000000000000
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<< setprecision(1) << diff/ (std::numeric_limits<double>::epsilon() * sum)
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<< " in epsilon units." << endl;
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// Note: Use boost::math::tools::epsilon rather than std::numeric_limits
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// to cover RealTypes that do not specialize numeric_limits.
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//[neg_binomial_example2
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// Print a table of values that can be used to plot
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// using Excel, or some other superior graphical display tool.
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cout.precision(17); // Use max_digits10 precision, the maximum available for a reference table.
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cout << showpoint << endl; // include trailing zeros.
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// This is a maximum possible precision for the type (here double) to suit a reference table.
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int maxk = static_cast<int>(2. * mynbdist.successes() / mynbdist.success_fraction());
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// This maxk shows most of the range of interest, probability about 0.0001 to 0.999.
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cout << "\n"" k pdf cdf""\n" << endl;
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for (int k = 0; k < maxk; k++)
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{
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cout << right << setprecision(17) << showpoint
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<< right << setw(3) << k << ", "
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<< left << setw(25) << pdf(mynbdist, static_cast<double>(k))
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<< left << setw(25) << cdf(mynbdist, static_cast<double>(k))
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<< endl;
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}
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cout << endl;
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//] [/ neg_binomial_example2]
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return 0;
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} // int main()
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/*
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Output is:
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negative_binomial distribution - simple example 2
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mean(mynbdist) = 24
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mynbdist.successes() = 8
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mynbdist.success_fraction() = 0.25
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mynbdist.percent success = 25%
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cdf(mynbdist, 2.) = 0.000415802001953125
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cdf(mynbdist, 8.) = 0.027129956288263202
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cdf(complement(mynbdist, 8.)) = 0.9728700437117368
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cdf + complement = 1
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Sum pdfs = 0.40025683281803692 , cdf = 0.40025683281803687, difference = 0.25 in epsilon units.
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//[neg_binomial_example2_1
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k pdf cdf
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0, 1.5258789062500000e-005 1.5258789062500003e-005
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1, 9.1552734375000000e-005 0.00010681152343750000
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2, 0.00030899047851562522 0.00041580200195312500
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3, 0.00077247619628906272 0.0011882781982421875
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4, 0.0015932321548461918 0.0027815103530883789
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5, 0.0028678178787231476 0.0056493282318115234
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6, 0.0046602040529251142 0.010309532284736633
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7, 0.0069903060793876605 0.017299838364124298
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8, 0.0098301179241389001 0.027129956288263202
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9, 0.013106823898851871 0.040236780187115073
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10, 0.016711200471036140 0.056947980658151209
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11, 0.020509200578089786 0.077457181236241013
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12, 0.024354675686481652 0.10181185692272265
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13, 0.028101548869017230 0.12991340579173993
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14, 0.031614242477644432 0.16152764826938440
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15, 0.034775666725408917 0.19630331499479325
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16, 0.037492515688331451 0.23379583068312471
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17, 0.039697957787645101 0.27349378847076977
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18, 0.041352039362130305 0.31484582783290005
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19, 0.042440250924291580 0.35728607875719176
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20, 0.042970754060845245 0.40025683281803687
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21, 0.042970754060845225 0.44322758687888220
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22, 0.042482450037426581 0.48571003691630876
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23, 0.041558918514873783 0.52726895543118257
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24, 0.040260202311284021 0.56752915774246648
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25, 0.038649794218832620 0.60617895196129912
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26, 0.036791631035234917 0.64297058299653398
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27, 0.034747651533277427 0.67771823452981139
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28, 0.032575923312447595 0.71029415784225891
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29, 0.030329307911589130 0.74062346575384819
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30, 0.028054609818219924 0.76867807557206813
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31, 0.025792141284492545 0.79447021685656061
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32, 0.023575629142856460 0.81804584599941710
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33, 0.021432390129869489 0.83947823612928651
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34, 0.019383705779220189 0.85886194190850684
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35, 0.017445335201298231 0.87630727710980494
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36, 0.015628112784496322 0.89193538989430121
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37, 0.013938587078064250 0.90587397697236549
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38, 0.012379666154859701 0.91825364312722524
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39, 0.010951243136991251 0.92920488626421649
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40, 0.0096507830144735539 0.93885566927869002
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41, 0.0084738582566109364 0.94732952753530097
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42, 0.0074146259745345548 0.95474415350983555
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43, 0.0064662435824429246 0.96121039709227851
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44, 0.0056212231142827853 0.96683162020656122
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45, 0.0048717266990450708 0.97170334690560634
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46, 0.0042098073105878630 0.97591315421619418
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47, 0.0036275999165703964 0.97954075413276465
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48, 0.0031174686783026818 0.98265822281106729
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49, 0.0026721160099737302 0.98533033882104104
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50, 0.0022846591885275322 0.98761499800956853
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51, 0.0019486798960970148 0.98956367790566557
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52, 0.0016582516423517923 0.99122192954801736
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53, 0.0014079495076571762 0.99262987905567457
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54, 0.0011928461106539983 0.99382272516632852
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55, 0.0010084971662802015 0.99483122233260868
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56, 0.00085091948404891532 0.99568214181665760
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57, 0.00071656377604119542 0.99639870559269883
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58, 0.00060228420831048650 0.99700098980100937
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59, 0.00050530624256557675 0.99750629604357488
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60, 0.00042319397814867202 0.99792949002172360
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61, 0.00035381791615708398 0.99828330793788067
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62, 0.00029532382517950324 0.99857863176306016
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63, 0.00024610318764958566 0.99882473495070978
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//] [neg_binomial_example2_1 end of Quickbook]
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*/
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