514 lines
19 KiB
C++
514 lines
19 KiB
C++
// wald_example.cpp or inverse_gaussian_example.cpp
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// Copyright Paul A. Bristow 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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// Example of using the Inverse Gaussian (or Inverse Normal) distribution.
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// The Wald Distribution is
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// Note that this file contains Quickbook mark-up as well as code
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// and comments, don't change any of the special comment mark-ups!
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//[inverse_gaussian_basic1
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/*`
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First we need some includes to access the normal distribution
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(and some std output of course).
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*/
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#ifdef _MSC_VER
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# pragma warning (disable : 4224)
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# pragma warning (disable : 4189)
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# pragma warning (disable : 4100)
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# pragma warning (disable : 4224)
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# pragma warning (disable : 4512)
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# pragma warning (disable : 4702)
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# pragma warning (disable : 4127)
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#endif
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//#define BOOST_MATH_INSTRUMENT
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#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
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#define BOOST_MATH_DOMAIN_ERROR_POLICY ignore_error
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#include <boost/math/distributions/inverse_gaussian.hpp> // for inverse_gaussian_distribution
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using boost::math::inverse_gaussian; // typedef provides default type is double.
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using boost::math::inverse_gaussian_distribution; // for inverse gaussian distribution.
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#include <boost/math/distributions/normal.hpp> // for normal_distribution
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using boost::math::normal; // typedef provides default type is double.
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#include <boost/array.hpp>
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using boost::array;
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#include <iostream>
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using std::cout; using std::endl; using std::left; using std::showpoint; using std::noshowpoint;
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#include <iomanip>
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using std::setw; using std::setprecision;
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#include <limits>
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using std::numeric_limits;
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#include <sstream>
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using std::string;
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#include <string>
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using std::stringstream;
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// const double tol = 3 * numeric_limits<double>::epsilon();
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int main()
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{
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cout << "Example: Inverse Gaussian Distribution."<< endl;
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try
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{
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double tolfeweps = numeric_limits<double>::epsilon();
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//cout << "Tolerance = " << tol << endl;
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int precision = 17; // traditional tables are only computed to much lower precision.
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cout.precision(17); // std::numeric_limits<double>::max_digits10; for 64-bit doubles.
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// Traditional tables and values.
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double step = 0.2; // in z
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double range = 4; // min and max z = -range to +range.
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// Construct a (standard) inverse gaussian distribution s
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inverse_gaussian w11(1, 1);
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// (default mean = units, and standard deviation = unity)
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cout << "(Standard) Inverse Gaussian distribution, mean = "<< w11.mean()
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<< ", scale = " << w11.scale() << endl;
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/*` First the probability distribution function (pdf).
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*/
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cout << "Probability distribution function (pdf) values" << endl;
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cout << " z " " pdf " << endl;
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cout.precision(5);
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for (double z = (numeric_limits<double>::min)(); z < range + step; z += step)
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{
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cout << left << setprecision(3) << setw(6) << z << " "
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<< setprecision(precision) << setw(12) << pdf(w11, z) << endl;
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}
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cout.precision(6); // default
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/*`And the area under the normal curve from -[infin] up to z,
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the cumulative distribution function (cdf).
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*/
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// For a (default) inverse gaussian distribution.
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cout << "Integral (area under the curve) from 0 up to z (cdf) " << endl;
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cout << " z " " cdf " << endl;
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for (double z = (numeric_limits<double>::min)(); z < range + step; z += step)
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{
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cout << left << setprecision(3) << setw(6) << z << " "
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<< setprecision(precision) << setw(12) << cdf(w11, z) << endl;
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}
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/*`giving the following table:
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[pre
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z pdf
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2.23e-308 -1.#IND
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0.2 0.90052111680384117
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0.4 1.0055127039453111
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0.6 0.75123750098955733
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0.8 0.54377310461643302
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1 0.3989422804014327
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1.2 0.29846949816803292
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1.4 0.2274579835638664
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1.6 0.17614566625628389
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1.8 0.13829083543591469
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2 0.10984782236693062
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2.2 0.088133964251182237
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2.4 0.071327382959107177
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2.6 0.058162562161661699
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2.8 0.047742223328567722
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3 0.039418357969819712
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3.2 0.032715223861241892
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3.4 0.027278388940958308
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3.6 0.022840312999395804
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3.8 0.019196657941016954
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4 0.016189699458236451
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Integral (area under the curve) from 0 up to z (cdf)
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z cdf
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2.23e-308 0
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0.2 0.063753567519976254
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0.4 0.2706136704424541
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0.6 0.44638391340412931
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0.8 0.57472390962590925
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1 0.66810200122317065
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1.2 0.73724578422952536
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1.4 0.78944214237790356
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1.6 0.82953458108474554
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1.8 0.86079282968276671
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2 0.88547542598600626
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2.2 0.90517870624273966
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2.4 0.92105495653509362
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2.6 0.93395164268166786
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2.8 0.94450240360053817
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3 0.95318792074278835
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3.2 0.96037753019309191
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3.4 0.96635823989417369
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3.6 0.97135533107998406
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3.8 0.97554722413538364
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4 0.97907636417888622
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]
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/*`We can get the inverse, the quantile, percentile, percentage point, or critical value
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for a probability for a few probability from the above table, for z = 0.4, 1.0, 2.0:
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*/
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cout << quantile(w11, 0.27061367044245421 ) << endl; // 0.4
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cout << quantile(w11, 0.66810200122317065) << endl; // 1.0
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cout << quantile(w11, 0.88547542598600615) << endl; // 2.0
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/*`turning the expect values apart from some 'computational noise' in the least significant bit or two.
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[pre
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0.40000000000000008
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0.99999999999999967
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1.9999999999999973
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]
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*/
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// cout << "pnorm01(-0.406053) " << pnorm01(-0.406053) << ", cdfn01(-0.406053) = " << cdf(n01, -0.406053) << endl;
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//cout << "pnorm01(0.5) = " << pnorm01(0.5) << endl; // R pnorm(0.5,0,1) = 0.6914625 == 0.69146246127401312
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// R qnorm(0.6914625,0,1) = 0.5
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// formatC(SuppDists::qinvGauss(0.3649755481729598, 1, 1), digits=17) [1] "0.50000000969034875"
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// formatC(SuppDists::dinvGauss(0.01, 1, 1), digits=17) [1] "2.0811768202028392e-19"
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// formatC(SuppDists::pinvGauss(0.01, 1, 1), digits=17) [1] "4.122313403318778e-23"
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//cout << " qinvgauss(0.3649755481729598, 1, 1) = " << qinvgauss(0.3649755481729598, 1, 1) << endl; // 0.5
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// cout << quantile(s, 0.66810200122317065) << endl; // expect 1, get 0.50517388467190727
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//cout << " qinvgauss(0.62502320258649202, 1, 1) = " << qinvgauss(0.62502320258649202, 1, 1) << endl; // 0.9
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//cout << " qinvgauss(0.063753567519976254, 1, 1) = " << qinvgauss(0.063753567519976254, 1, 1) << endl; // 0.2
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//cout << " qinvgauss(0.0040761113207110162, 1, 1) = " << qinvgauss(0.0040761113207110162, 1, 1) << endl; // 0.1
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//double x = 1.; // SuppDists::pinvGauss(0.4, 1,1) [1] 0.2706137
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//double c = pinvgauss(x, 1, 1); // 0.3649755481729598 == cdf(x, 1,1) 0.36497554817295974
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//cout << " pinvgauss(x, 1, 1) = " << c << endl; // pinvgauss(x, 1, 1) = 0.27061367044245421
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//double p = pdf(w11, x);
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//double c = cdf(w11, x); // cdf(1, 1, 1) = 0.66810200122317065
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//cout << "cdf(" << x << ", " << w11.mean() << ", "<< w11.scale() << ") = " << c << endl; // cdf(x, 1, 1) 0.27061367044245421
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//cout << "pdf(" << x << ", " << w11.mean() << ", "<< w11.scale() << ") = " << p << endl;
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//double q = quantile(w11, c);
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//cout << "quantile(w11, " << c << ") = " << q << endl;
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//cout << "quantile(w11, 4.122313403318778e-23) = "<< quantile(w11, 4.122313403318778e-23) << endl; // quantile
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//cout << "quantile(w11, 4.8791443010851493e-219) = " << quantile(w11, 4.8791443010851493e-219) << endl; // quantile
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//double c1 = 1 - cdf(w11, x); // 1 - cdf(1, 1, 1) = 0.33189799877682935
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//cout << "1 - cdf(" << x << ", " << w11.mean() << ", " << w11.scale() << ") = " << c1 << endl; // cdf(x, 1, 1) 0.27061367044245421
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//double cc = cdf(complement(w11, x));
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//cout << "cdf(complement(" << x << ", " << w11.mean() << ", "<< w11.scale() << ")) = " << cc << endl; // cdf(x, 1, 1) 0.27061367044245421
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//// 1 - cdf(1000, 1, 1) = 0
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//// cdf(complement(1000, 1, 1)) = 4.8694344366900402e-222
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//cout << "quantile(w11, " << c << ") = "<< quantile(w11, c) << endl; // quantile = 0.99999999999999978 == x = 1
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//cout << "quantile(w11, " << c << ") = "<< quantile(w11, 1 - c) << endl; // quantile complement. quantile(w11, 0.66810200122317065) = 0.46336593652340152
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// cout << "quantile(complement(w11, " << c << ")) = " << quantile(complement(w11, c)) << endl; // quantile complement = 0.46336593652340163
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// cdf(1, 1, 1) = 0.66810200122317065
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// 1 - cdf(1, 1, 1) = 0.33189799877682935
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// cdf(complement(1, 1, 1)) = 0.33189799877682929
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// quantile(w11, 0.66810200122317065) = 0.99999999999999978
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// 1 - quantile(w11, 0.66810200122317065) = 2.2204460492503131e-016
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// quantile(complement(w11, 0.33189799877682929)) = 0.99999999999999989
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// qinvgauss(c, 1, 1) = 0.3999999999999998
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// SuppDists::qinvGauss(0.270613670442454, 1, 1) [1] 0.4
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/*
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double qs = pinvgaussU(c, 1, 1); //
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cout << "qinvgaussU(c, 1, 1) = " << qs << endl; // qinvgaussU(c, 1, 1) = 0.86567442459240929
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// > z=q - exp(c) * p [1] 0.8656744 qs 0.86567442459240929 double
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// Is this the complement?
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cout << "qgamma(0.2, 0.5, 1) expect 0.0320923 = " << qgamma(0.2, 0.5, 1) << endl;
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// qgamma(0.2, 0.5, 1) expect 0.0320923 = 0.032092377333650807
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cout << "qinvgauss(pinvgauss(x, 1, 1) = " << q
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<< ", diff = " << x - q << ", fraction = " << (x - q) /x << endl; // 0.5
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*/ // > SuppDists::pinvGauss(0.02, 1,1) [1] 4.139176e-12
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// > SuppDists::qinvGauss(4.139176e-12, 1,1) [1] 0.02000000
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// pinvGauss(1,1,1) = 0.668102 C++ == 0.66810200122317065
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// qinvGauss(0.668102,1,1) = 1
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// SuppDists::pinvGauss(0.3,1,1) = 0.1657266
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// cout << "qinvGauss(0.0040761113207110162, 1, 1) = " << qinvgauss(0.0040761113207110162, 1, 1) << endl;
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//cout << "quantile(s, 0.1657266) = " << quantile(s, 0.1657266) << endl; // expect 1.
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//wald s12(2, 1);
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//cout << "qinvGauss(0.3, 2, 1) = " << qinvgauss(0.3, 2, 1) << endl; // SuppDists::qinvGauss(0.3,2,1) == 0.58288065635052944
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//// but actually get qinvGauss(0.3, 2, 1) = 0.58288064777632187
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//cout << "cdf(s12, 0.3) = " << cdf(s12, 0.3) << endl; // cdf(s12, 0.3) = 0.10895339868447573
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// using boost::math::wald;
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//cout.precision(6);
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/*
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double m = 1;
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double l = 1;
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double x = 0.1;
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//c = cdf(w, x);
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double p = pinvgauss(x, m, l);
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cout << "x = " << x << ", pinvgauss(x, m, l) = " << p << endl; // R 0.4 0.2706137
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double qg = qgamma(1.- p, 0.5, 1.0, true, false);
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cout << " qgamma(1.- p, 0.5, 1.0, true, false) = " << qg << endl; // 0.606817
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double g = guess_whitmore(p, m, l);
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cout << "m = " << m << ", l = " << l << ", x = " << x << ", guess = " << g
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<< ", diff = " << (x - g) << endl;
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g = guess_wheeler(p, m, l);
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cout << "m = " << m << ", l = " << l << ", x = " << x << ", guess = " << g
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<< ", diff = " << (x - g) << endl;
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g = guess_bagshaw(p, m, l);
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cout << "m = " << m << ", l = " << l << ", x = " << x << ", guess = " << g
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<< ", diff = " << (x - g) << endl;
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// m = 1, l = 10, x = 0.9, guess = 0.89792, diff = 0.00231075 so a better fit.
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// x = 0.9, guess = 0.887907
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// x = 0.5, guess = 0.474977
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// x = 0.4, guess = 0.369597
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// x = 0.2, guess = 0.155196
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// m = 1, l = 2, x = 0.9, guess = 1.0312, diff = -0.145778
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// m = 1, l = 2, x = 0.1, guess = 0.122201, diff = -0.222013
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// m = 1, l = 2, x = 0.2, guess = 0.299326, diff = -0.49663
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// m = 1, l = 2, x = 0.5, guess = 1.00437, diff = -1.00875
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// m = 1, l = 2, x = 0.7, guess = 1.01517, diff = -0.450247
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double ls[7] = {0.1, 0.2, 0.5, 1., 2., 10, 100}; // scale values.
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double ms[10] = {0.001, 0.02, 0.1, 0.2, 0.5, 0.9, 1., 2., 10, 100}; // mean values.
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*/
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cout.precision(6); // Restore to default.
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} // try
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catch(const std::exception& e)
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{ // Always useful to include try & catch blocks because default policies
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// are to throw exceptions on arguments that cause errors like underflow, overflow.
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// Lacking try & catch blocks, the program will abort without a message below,
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// which may give some helpful clues as to the cause of the exception.
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std::cout <<
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"\n""Message from thrown exception was:\n " << e.what() << std::endl;
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}
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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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inverse_gaussian_example.cpp
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inverse_gaussian_example.vcxproj -> J:\Cpp\MathToolkit\test\Math_test\Debug\inverse_gaussian_example.exe
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Example: Inverse Gaussian Distribution.
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(Standard) Inverse Gaussian distribution, mean = 1, scale = 1
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Probability distribution function (pdf) values
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z pdf
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2.23e-308 -1.#IND
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0.2 0.90052111680384117
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0.4 1.0055127039453111
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0.6 0.75123750098955733
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0.8 0.54377310461643302
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1 0.3989422804014327
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1.2 0.29846949816803292
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1.4 0.2274579835638664
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1.6 0.17614566625628389
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1.8 0.13829083543591469
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2 0.10984782236693062
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2.2 0.088133964251182237
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2.4 0.071327382959107177
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2.6 0.058162562161661699
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2.8 0.047742223328567722
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3 0.039418357969819712
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3.2 0.032715223861241892
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3.4 0.027278388940958308
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3.6 0.022840312999395804
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3.8 0.019196657941016954
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4 0.016189699458236451
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Integral (area under the curve) from 0 up to z (cdf)
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z cdf
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2.23e-308 0
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0.2 0.063753567519976254
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0.4 0.2706136704424541
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0.6 0.44638391340412931
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0.8 0.57472390962590925
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1 0.66810200122317065
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1.2 0.73724578422952536
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1.4 0.78944214237790356
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1.6 0.82953458108474554
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1.8 0.86079282968276671
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2 0.88547542598600626
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2.2 0.90517870624273966
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2.4 0.92105495653509362
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2.6 0.93395164268166786
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2.8 0.94450240360053817
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3 0.95318792074278835
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3.2 0.96037753019309191
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3.4 0.96635823989417369
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3.6 0.97135533107998406
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3.8 0.97554722413538364
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4 0.97907636417888622
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0.40000000000000008
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0.99999999999999967
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1.9999999999999973
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> SuppDists::dinvGauss(2, 1, 1) [1] 0.1098478
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> SuppDists::dinvGauss(0.4, 1, 1) [1] 1.005513
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> SuppDists::dinvGauss(0.5, 1, 1) [1] 0.8787826
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> SuppDists::dinvGauss(0.39, 1, 1) [1] 1.016559
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> SuppDists::dinvGauss(0.38, 1, 1) [1] 1.027006
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> SuppDists::dinvGauss(0.37, 1, 1) [1] 1.036748
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> SuppDists::dinvGauss(0.36, 1, 1) [1] 1.045661
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> SuppDists::dinvGauss(0.35, 1, 1) [1] 1.053611
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> SuppDists::dinvGauss(0.3, 1, 1) [1] 1.072888
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> SuppDists::dinvGauss(0.1, 1, 1) [1] 0.2197948
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> SuppDists::dinvGauss(0.2, 1, 1) [1] 0.9005211
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>
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x = 0.3 [1, 1] 1.0728879234594337 // R SuppDists::dinvGauss(0.3, 1, 1) [1] 1.072888
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x = 1 [1, 1] 0.3989422804014327
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0 " NA"
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1 "0.3989422804014327"
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2 "0.10984782236693059"
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3 "0.039418357969819733"
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4 "0.016189699458236468"
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5 "0.007204168934430732"
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6 "0.003379893528659049"
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7 "0.0016462878258114036"
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8 "0.00082460931140859956"
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9 "0.00042207355643694234"
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10 "0.00021979480031862676"
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[1] " NA" " 0.690988298942671" "0.11539974210409144"
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[4] "0.01799698883772935" "0.0029555399206496469" "0.00050863023587406587"
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[7] "9.0761842931362914e-05" "1.6655279133132795e-05" "3.1243174913715429e-06"
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[10] "5.96530227727434e-07" "1.1555606328299836e-07"
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matC(dinvGauss(0:10, 1, 3), digits=17) df = 3
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[1] " NA" " 0.690988298942671" "0.11539974210409144"
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[4] "0.01799698883772935" "0.0029555399206496469" "0.00050863023587406587"
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[7] "9.0761842931362914e-05" "1.6655279133132795e-05" "3.1243174913715429e-06"
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[10] "5.96530227727434e-07" "1.1555606328299836e-07"
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$title
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[1] "Inverse Gaussian"
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$nu
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[1] 1
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$lambda
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[1] 3
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$Mean
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[1] 1
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$Median
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[1] 0.8596309
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$Mode
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[1] 0.618034
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$Variance
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[1] 0.3333333
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$SD
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[1] 0.5773503
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$ThirdCentralMoment
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[1] 0.3333333
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$FourthCentralMoment
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[1] 0.8888889
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$PearsonsSkewness...mean.minus.mode.div.SD
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[1] 0.6615845
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$Skewness...sqrtB1
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[1] 1.732051
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$Kurtosis...B2.minus.3
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[1] 5
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Example: Wald distribution.
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(Standard) Wald distribution, mean = 1, scale = 1
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1 dx = 0.24890250442652451, x = 0.70924622051646713
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2 dx = -0.038547954953794553, x = 0.46034371608994262
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3 dx = -0.0011074090830291131, x = 0.49889167104373716
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4 dx = -9.1987259926368029e-007, x = 0.49999908012676625
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5 dx = -6.346513344581067e-013, x = 0.49999999999936551
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dx = 6.3168242705156857e-017 at i = 6
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qinvgauss(0.3649755481729598, 1, 1) = 0.50000000000000011
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1 dx = 0.6719944578376621, x = 1.3735318786222666
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2 dx = -0.16997432635769361, x = 0.70153742078460446
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3 dx = -0.027865119206495724, x = 0.87151174714229807
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4 dx = -0.00062283290009492603, x = 0.89937686634879377
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5 dx = -3.0075104108208687e-007, x = 0.89999969924888867
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6 dx = -7.0485322513588089e-014, x = 0.89999999999992975
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7 dx = 9.557331866250277e-016, x = 0.90000000000000024
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dx = 0 at i = 8
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qinvgauss(0.62502320258649202, 1, 1) = 0.89999999999999925
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1 dx = -0.0052296256747447678, x = 0.19483508278446249
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2 dx = 6.4699046853900505e-005, x = 0.20006470845920726
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3 dx = 9.4123530465288137e-009, x = 0.20000000941235335
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4 dx = 2.7739513919147025e-016, x = 0.20000000000000032
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dx = 1.5410841066192808e-016 at i = 5
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qinvgauss(0.063753567519976254, 1, 1) = 0.20000000000000004
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1 dx = -1, x = -0.46073286697416105
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2 dx = 0.47665501251497061, x = 0.53926713302583895
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3 dx = -0.171105768635964, x = 0.062612120510868341
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4 dx = 0.091490360797512563, x = 0.23371788914683234
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5 dx = 0.029410311722649803, x = 0.14222752834931979
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6 dx = 0.010761845493592421, x = 0.11281721662666999
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7 dx = 0.0019864890597643035, x = 0.10205537113307757
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8 dx = 6.8800383732599561e-005, x = 0.10006888207331327
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9 dx = 8.1689466405590418e-008, x = 0.10000008168958067
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10 dx = 1.133634672475146e-013, x = 0.10000000000011428
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11 dx = 5.9588135045224526e-016, x = 0.10000000000000091
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12 dx = 3.433223674791152e-016, x = 0.10000000000000031
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dx = 9.0763384505974048e-017 at i = 13
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qinvgauss(0.0040761113207110162, 1, 1) = 0.099999999999999964
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wald_example.vcxproj -> J:\Cpp\MathToolkit\test\Math_test\Debug\wald_example.exe
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Example: Wald distribution.
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Tolerance = 6.66134e-016
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(Standard) Wald distribution, mean = 1, scale = 1
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cdf(x, 1,1) 4.1390252102096375e-012
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qinvgauss(pinvgauss(x, 1, 1) = 0.020116801973767886, diff = -0.00011680197376788548, fraction = -0.005840098688394274
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____________________________________________________________
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wald 1, 1
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x = 0.02, diff x - qinvgauss(cdf) = -0.00011680197376788548
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x = 0.10000000000000001, diff x - qinvgauss(cdf) = 8.7430063189231078e-016
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x = 0.20000000000000001, diff x - qinvgauss(cdf) = -1.1102230246251565e-016
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x = 0.29999999999999999, diff x - qinvgauss(cdf) = 0
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x = 0.40000000000000002, diff x - qinvgauss(cdf) = 2.2204460492503131e-016
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x = 0.5, diff x - qinvgauss(cdf) = -1.1102230246251565e-016
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x = 0.59999999999999998, diff x - qinvgauss(cdf) = 1.1102230246251565e-016
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x = 0.80000000000000004, diff x - qinvgauss(cdf) = 1.1102230246251565e-016
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x = 0.90000000000000002, diff x - qinvgauss(cdf) = 0
|
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x = 0.98999999999999999, diff x - qinvgauss(cdf) = -1.1102230246251565e-016
|
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x = 0.999, diff x - qinvgauss(cdf) = -1.1102230246251565e-016
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*/
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