mirror of
https://github.com/saitohirga/WSJT-X.git
synced 2025-11-04 05:50:31 -05:00
723 lines
34 KiB
C++
723 lines
34 KiB
C++
/*! \file dist_graphs.cpp
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\brief Produces Scalable Vector Graphic (.svg) files for all distributions.
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\details These files can be viewed using most browsers,
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though MS Internet Explorer requires a plugin from Adobe.
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These file can be converted to .png using Inkscape
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(see www.inkscape.org) Export Bit option which by default produces
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a Portable Network Graphic file with that same filename but .png suffix instead of .svg.
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Using Python, generate.sh does this conversion automatically for all .svg files in a folder.
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\author John Maddock and Paul A. Bristow
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*/
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// Copyright John Maddock 2008.
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// Copyright Paul A. Bristow 2008, 2009, 2012
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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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#ifdef _MSC_VER
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# pragma warning (disable : 4180) // qualifier applied to function type has no meaning; ignored
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# pragma warning (disable : 4503) // decorated name length exceeded, name was truncated
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# pragma warning (disable : 4512) // assignment operator could not be generated
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# pragma warning (disable : 4224) // nonstandard extension used : formal parameter 'function_ptr' was previously defined as a type
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# pragma warning (disable : 4127) // conditional expression is constant
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#endif
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#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
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#include <boost/math/distributions.hpp>
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#include <boost/math/tools/roots.hpp>
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#include <boost/svg_plot/svg_2d_plot.hpp>
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#include <list>
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#include <map>
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#include <string>
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template <class Dist>
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struct is_discrete_distribution
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: public boost::mpl::false_{};
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template<class T, class P>
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struct is_discrete_distribution<boost::math::bernoulli_distribution<T,P> >
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: public boost::mpl::true_{};
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template<class T, class P>
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struct is_discrete_distribution<boost::math::binomial_distribution<T,P> >
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: public boost::mpl::true_{};
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template<class T, class P>
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struct is_discrete_distribution<boost::math::negative_binomial_distribution<T,P> >
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: public boost::mpl::true_{};
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template<class T, class P>
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struct is_discrete_distribution<boost::math::poisson_distribution<T,P> >
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: public boost::mpl::true_{};
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template<class T, class P>
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struct is_discrete_distribution<boost::math::hypergeometric_distribution<T,P> >
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: public boost::mpl::true_{};
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template <class Dist>
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struct value_finder
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{
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value_finder(Dist const& d, typename Dist::value_type v)
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: m_dist(d), m_value(v) {}
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inline typename Dist::value_type operator()(const typename Dist::value_type& x)
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{
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return pdf(m_dist, x) - m_value;
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}
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private:
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Dist m_dist;
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typename Dist::value_type m_value;
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};
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template <class Dist>
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class distribution_plotter
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{
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public:
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distribution_plotter() : m_pdf(true), m_min_x(0), m_max_x(0), m_min_y(0), m_max_y(0) {}
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distribution_plotter(bool pdf) : m_pdf(pdf), m_min_x(0), m_max_x(0), m_min_y(0), m_max_y(0) {}
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void add(const Dist& d, const std::string& name)
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{
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// Add name of distribution to our list for later:
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m_distributions.push_back(std::make_pair(name, d));
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//
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// Get the extent of the distribution from the support:
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double a, b;
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std::tr1::tie(a, b) = support(d);
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//
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// PDF maximimum is at the mode (probably):
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double mod;
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try
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{
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mod = mode(d);
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}
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catch(const std::domain_error& )
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{ // but if not use the lower limit of support.
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mod = a;
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}
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if((mod <= a) && !is_discrete_distribution<Dist>::value)
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{ // Continuous distribution at or below lower limit of support.
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double margin = 1e-2; // Margin of 1% (say) to get lowest off the 'end stop'.
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if((a != 0) && (fabs(a) > margin))
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{
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mod = a * (1 + ((a > 0) ? margin : -margin));
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}
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else
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{ // Case of mod near zero?
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mod = margin;
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}
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}
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double peek_y = pdf(d, mod);
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double min_y = peek_y / 20;
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//
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// If the extent is "infinite" then find out how large it
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// has to be for the PDF to decay to min_y:
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//
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if(a <= -(std::numeric_limits<double>::max)())
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{
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boost::uintmax_t max_iter = 500;
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double guess = mod;
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if((pdf(d, 0) > min_y) || (guess == 0))
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guess = -1e-3;
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a = boost::math::tools::bracket_and_solve_root(
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value_finder<Dist>(d, min_y),
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guess,
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8.0,
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true,
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boost::math::tools::eps_tolerance<double>(10),
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max_iter).first;
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}
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if(b >= (std::numeric_limits<double>::max)())
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{
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boost::uintmax_t max_iter = 500;
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double guess = mod;
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if(a <= 0)
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if((pdf(d, 0) > min_y) || (guess == 0))
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guess = 1e-3;
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b = boost::math::tools::bracket_and_solve_root(
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value_finder<Dist>(d, min_y),
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guess,
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8.0,
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false,
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boost::math::tools::eps_tolerance<double>(10),
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max_iter).first;
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}
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//
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// Recalculate peek_y and location of mod so that
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// it's not too close to one end of the graph:
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// otherwise we may be shooting off to infinity.
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//
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if(!is_discrete_distribution<Dist>::value)
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{
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if(mod <= a + (b-a)/50)
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{
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mod = a + (b-a)/50;
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}
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if(mod >= b - (b-a)/50)
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{
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mod = b - (b-a)/50;
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}
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peek_y = pdf(d, mod);
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}
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//
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// Now set our limits:
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//
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if(peek_y > m_max_y)
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m_max_y = peek_y;
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if(m_max_x == m_min_x)
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{
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m_max_x = b;
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m_min_x = a;
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}
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else
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{
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if(a < m_min_x)
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m_min_x = a;
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if(b > m_max_x)
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m_max_x = b;
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}
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}
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void plot(const std::string& title, const std::string& file)
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{
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using namespace boost::svg;
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static const svg_color colors[5] =
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{
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darkblue,
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darkred,
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darkgreen,
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darkorange,
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chartreuse
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};
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if(m_pdf == false)
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{
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m_min_y = 0;
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m_max_y = 1;
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}
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svg_2d_plot plot;
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plot.image_x_size(750);
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plot.image_y_size(400);
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plot.coord_precision(4); // Avoids any visible steps.
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plot.title_font_size(20);
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plot.legend_title_font_size(15);
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plot.title(title);
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if((m_distributions.size() == 1) && (m_distributions.begin()->first == ""))
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plot.legend_on(false);
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else
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plot.legend_on(true);
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plot.title_on(true);
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//plot.x_major_labels_on(true).y_major_labels_on(true);
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//double x_delta = (m_max_x - m_min_x) / 10;
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double y_delta = (m_max_y - m_min_y) / 10;
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if(is_discrete_distribution<Dist>::value)
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plot.x_range(m_min_x - 0.5, m_max_x + 0.5)
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.y_range(m_min_y, m_max_y + y_delta);
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else
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plot.x_range(m_min_x, m_max_x)
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.y_range(m_min_y, m_max_y + y_delta);
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plot.x_label_on(true).x_label("Random Variable");
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plot.y_label_on(true).y_label("Probability");
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plot.plot_border_color(lightslategray)
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.background_border_color(lightslategray)
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.legend_border_color(lightslategray)
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.legend_background_color(white);
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//
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// Work out axis tick intervals:
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//
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double l = std::floor(std::log10((m_max_x - m_min_x) / 10) + 0.5);
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double interval = std::pow(10.0, (int)l);
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if(((m_max_x - m_min_x) / interval) > 10)
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interval *= 5;
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if(is_discrete_distribution<Dist>::value)
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{
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interval = interval > 1 ? std::floor(interval) : 1;
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plot.x_num_minor_ticks(0);
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}
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plot.x_major_interval(interval);
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l = std::floor(std::log10((m_max_y - m_min_y) / 10) + 0.5);
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interval = std::pow(10.0, (int)l);
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if(((m_max_y - m_min_y) / interval) > 10)
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interval *= 5;
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plot.y_major_interval(interval);
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int color_index = 0;
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if(!is_discrete_distribution<Dist>::value)
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{
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//
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// Continuous distribution:
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//
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for(std::list<std::pair<std::string, Dist> >::const_iterator i = m_distributions.begin();
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i != m_distributions.end(); ++i)
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{
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double x = m_min_x;
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double interval = (m_max_x - m_min_x) / 200;
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std::map<double, double> data;
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while(x <= m_max_x)
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{
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data[x] = m_pdf ? pdf(i->second, x) : cdf(i->second, x);
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x += interval;
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}
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plot.plot(data, i->first)
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.line_on(true)
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.line_color(colors[color_index])
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.line_width(1.)
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.shape(none);
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//.bezier_on(true) // Bezier can't cope with badly behaved like uniform & triangular.
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++color_index;
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color_index = color_index % (sizeof(colors)/sizeof(colors[0]));
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}
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}
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else
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{
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//
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// Discrete distribution:
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//
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double x_width = 0.75 / m_distributions.size();
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double x_off = -0.5 * 0.75;
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for(std::list<std::pair<std::string, Dist> >::const_iterator i = m_distributions.begin();
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i != m_distributions.end(); ++i)
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{
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double x = ceil(m_min_x);
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double interval = 1;
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std::map<double, double> data;
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while(x <= m_max_x)
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{
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double p;
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try{
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p = m_pdf ? pdf(i->second, x) : cdf(i->second, x);
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}
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catch(const std::domain_error&)
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{
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p = 0;
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}
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data[x + x_off] = 0;
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data[x + x_off + 0.00001] = p;
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data[x + x_off + x_width] = p;
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data[x + x_off + x_width + 0.00001] = 0;
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x += interval;
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}
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x_off += x_width;
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svg_2d_plot_series& s = plot.plot(data, i->first);
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s.line_on(true)
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.line_color(colors[color_index])
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.line_width(1.)
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.shape(none)
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.area_fill(colors[color_index]);
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++color_index;
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color_index = color_index % (sizeof(colors)/sizeof(colors[0]));
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}
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}
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plot.write(file);
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}
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private:
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bool m_pdf;
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std::list<std::pair<std::string, Dist> > m_distributions;
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double m_min_x, m_max_x, m_min_y, m_max_y;
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};
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int main()
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{
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try
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{
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distribution_plotter<boost::math::gamma_distribution<> >
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gamma_plotter;
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gamma_plotter.add(boost::math::gamma_distribution<>(0.75), "shape = 0.75");
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gamma_plotter.add(boost::math::gamma_distribution<>(1), "shape = 1");
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gamma_plotter.add(boost::math::gamma_distribution<>(3), "shape = 3");
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gamma_plotter.plot("Gamma Distribution PDF With Scale = 1", "gamma1_pdf.svg");
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distribution_plotter<boost::math::gamma_distribution<> >
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gamma_plotter2;
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gamma_plotter2.add(boost::math::gamma_distribution<>(2, 0.5), "scale = 0.5");
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gamma_plotter2.add(boost::math::gamma_distribution<>(2, 1), "scale = 1");
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gamma_plotter2.add(boost::math::gamma_distribution<>(2, 2), "scale = 2");
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gamma_plotter2.plot("Gamma Distribution PDF With Shape = 2", "gamma2_pdf.svg");
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distribution_plotter<boost::math::normal>
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normal_plotter;
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normal_plotter.add(boost::math::normal(0, 1), "μ = 0, σ = 1");
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normal_plotter.add(boost::math::normal(0, 0.5), "μ = 0, σ = 0.5");
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normal_plotter.add(boost::math::normal(0, 2), "μ = 0, σ = 2");
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normal_plotter.add(boost::math::normal(-1, 1), "μ = -1, σ = 1");
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normal_plotter.add(boost::math::normal(1, 1), "μ = 1, σ = 1");
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normal_plotter.plot("Normal Distribution PDF", "normal_pdf.svg");
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distribution_plotter<boost::math::laplace>
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laplace_plotter;
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laplace_plotter.add(boost::math::laplace(0, 1), "μ = 0, σ = 1");
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laplace_plotter.add(boost::math::laplace(0, 0.5), "μ = 0, σ = 0.5");
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laplace_plotter.add(boost::math::laplace(0, 2), "μ = 0, σ = 2");
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laplace_plotter.add(boost::math::laplace(-1, 1), "μ = -1, σ = 1");
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laplace_plotter.add(boost::math::laplace(1, 1), "μ = 1, σ = 1");
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laplace_plotter.plot("Laplace Distribution PDF", "laplace_pdf.svg");
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distribution_plotter<boost::math::non_central_chi_squared>
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nc_cs_plotter;
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nc_cs_plotter.add(boost::math::non_central_chi_squared(20, 0), "v=20, λ=0");
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nc_cs_plotter.add(boost::math::non_central_chi_squared(20, 1), "v=20, λ=1");
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nc_cs_plotter.add(boost::math::non_central_chi_squared(20, 5), "v=20, λ=5");
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nc_cs_plotter.add(boost::math::non_central_chi_squared(20, 10), "v=20, λ=10");
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nc_cs_plotter.add(boost::math::non_central_chi_squared(20, 20), "v=20, λ=20");
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nc_cs_plotter.add(boost::math::non_central_chi_squared(20, 100), "v=20, λ=100");
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nc_cs_plotter.plot("Non Central Chi Squared PDF", "nccs_pdf.svg");
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distribution_plotter<boost::math::non_central_beta>
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nc_beta_plotter;
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nc_beta_plotter.add(boost::math::non_central_beta(10, 15, 0), "α=10, β=15, δ=0");
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nc_beta_plotter.add(boost::math::non_central_beta(10, 15, 1), "α=10, β=15, δ=1");
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nc_beta_plotter.add(boost::math::non_central_beta(10, 15, 5), "α=10, β=15, δ=5");
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nc_beta_plotter.add(boost::math::non_central_beta(10, 15, 10), "α=10, β=15, δ=10");
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nc_beta_plotter.add(boost::math::non_central_beta(10, 15, 40), "α=10, β=15, δ=40");
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nc_beta_plotter.add(boost::math::non_central_beta(10, 15, 100), "α=10, β=15, δ=100");
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nc_beta_plotter.plot("Non Central Beta PDF", "nc_beta_pdf.svg");
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distribution_plotter<boost::math::non_central_f>
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nc_f_plotter;
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nc_f_plotter.add(boost::math::non_central_f(10, 20, 0), "v1=10, v2=20, λ=0");
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nc_f_plotter.add(boost::math::non_central_f(10, 20, 1), "v1=10, v2=20, λ=1");
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nc_f_plotter.add(boost::math::non_central_f(10, 20, 5), "v1=10, v2=20, λ=5");
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nc_f_plotter.add(boost::math::non_central_f(10, 20, 10), "v1=10, v2=20, λ=10");
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nc_f_plotter.add(boost::math::non_central_f(10, 20, 40), "v1=10, v2=20, λ=40");
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nc_f_plotter.add(boost::math::non_central_f(10, 20, 100), "v1=10, v2=20, λ=100");
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nc_f_plotter.plot("Non Central F PDF", "nc_f_pdf.svg");
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distribution_plotter<boost::math::non_central_t>
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nc_t_plotter;
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nc_t_plotter.add(boost::math::non_central_t(10, -10), "v=10, δ=-10");
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nc_t_plotter.add(boost::math::non_central_t(10, -5), "v=10, δ=-5");
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nc_t_plotter.add(boost::math::non_central_t(10, 0), "v=10, δ=0");
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nc_t_plotter.add(boost::math::non_central_t(10, 5), "v=10, δ=5");
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nc_t_plotter.add(boost::math::non_central_t(10, 10), "v=10, δ=10");
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nc_t_plotter.add(boost::math::non_central_t(std::numeric_limits<double>::infinity(), 15), "v=inf, δ=15");
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nc_t_plotter.plot("Non Central T PDF", "nc_t_pdf.svg");
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distribution_plotter<boost::math::non_central_t>
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nc_t_CDF_plotter(false);
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nc_t_CDF_plotter.add(boost::math::non_central_t(10, -10), "v=10, δ=-10");
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nc_t_CDF_plotter.add(boost::math::non_central_t(10, -5), "v=10, δ=-5");
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nc_t_CDF_plotter.add(boost::math::non_central_t(10, 0), "v=10, δ=0");
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nc_t_CDF_plotter.add(boost::math::non_central_t(10, 5), "v=10, δ=5");
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nc_t_CDF_plotter.add(boost::math::non_central_t(10, 10), "v=10, δ=10");
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nc_t_CDF_plotter.add(boost::math::non_central_t(std::numeric_limits<double>::infinity(), 15), "v=inf, δ=15");
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nc_t_CDF_plotter.plot("Non Central T CDF", "nc_t_cdf.svg");
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distribution_plotter<boost::math::beta_distribution<> >
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beta_plotter;
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beta_plotter.add(boost::math::beta_distribution<>(0.5, 0.5), "alpha=0.5, beta=0.5");
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beta_plotter.add(boost::math::beta_distribution<>(5, 1), "alpha=5, beta=1");
|
|
beta_plotter.add(boost::math::beta_distribution<>(1, 3), "alpha=1, beta=3");
|
|
beta_plotter.add(boost::math::beta_distribution<>(2, 2), "alpha=2, beta=2");
|
|
beta_plotter.add(boost::math::beta_distribution<>(2, 5), "alpha=2, beta=5");
|
|
beta_plotter.plot("Beta Distribution PDF", "beta_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::cauchy_distribution<> >
|
|
cauchy_plotter;
|
|
cauchy_plotter.add(boost::math::cauchy_distribution<>(-5, 1), "location = -5");
|
|
cauchy_plotter.add(boost::math::cauchy_distribution<>(0, 1), "location = 0");
|
|
cauchy_plotter.add(boost::math::cauchy_distribution<>(5, 1), "location = 5");
|
|
cauchy_plotter.plot("Cauchy Distribution PDF (scale = 1)", "cauchy_pdf1.svg");
|
|
|
|
distribution_plotter<boost::math::cauchy_distribution<> >
|
|
cauchy_plotter2;
|
|
cauchy_plotter2.add(boost::math::cauchy_distribution<>(0, 0.5), "scale = 0.5");
|
|
cauchy_plotter2.add(boost::math::cauchy_distribution<>(0, 1), "scale = 1");
|
|
cauchy_plotter2.add(boost::math::cauchy_distribution<>(0, 2), "scale = 2");
|
|
cauchy_plotter2.plot("Cauchy Distribution PDF (location = 0)", "cauchy_pdf2.svg");
|
|
|
|
distribution_plotter<boost::math::chi_squared_distribution<> >
|
|
chi_squared_plotter;
|
|
//chi_squared_plotter.add(boost::math::chi_squared_distribution<>(1), "v=1");
|
|
chi_squared_plotter.add(boost::math::chi_squared_distribution<>(2), "v=2");
|
|
chi_squared_plotter.add(boost::math::chi_squared_distribution<>(5), "v=5");
|
|
chi_squared_plotter.add(boost::math::chi_squared_distribution<>(10), "v=10");
|
|
chi_squared_plotter.plot("Chi Squared Distribution PDF", "chi_squared_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::exponential_distribution<> >
|
|
exponential_plotter;
|
|
exponential_plotter.add(boost::math::exponential_distribution<>(0.5), "λ=0.5");
|
|
exponential_plotter.add(boost::math::exponential_distribution<>(1), "λ=1");
|
|
exponential_plotter.add(boost::math::exponential_distribution<>(2), "λ=2");
|
|
exponential_plotter.plot("Exponential Distribution PDF", "exponential_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::extreme_value_distribution<> >
|
|
extreme_value_plotter;
|
|
extreme_value_plotter.add(boost::math::extreme_value_distribution<>(-5), "location=-5");
|
|
extreme_value_plotter.add(boost::math::extreme_value_distribution<>(0), "location=0");
|
|
extreme_value_plotter.add(boost::math::extreme_value_distribution<>(5), "location=5");
|
|
extreme_value_plotter.plot("Extreme Value Distribution PDF (shape=1)", "extreme_value_pdf1.svg");
|
|
|
|
distribution_plotter<boost::math::extreme_value_distribution<> >
|
|
extreme_value_plotter2;
|
|
extreme_value_plotter2.add(boost::math::extreme_value_distribution<>(0, 0.5), "shape=0.5");
|
|
extreme_value_plotter2.add(boost::math::extreme_value_distribution<>(0, 1), "shape=1");
|
|
extreme_value_plotter2.add(boost::math::extreme_value_distribution<>(0, 2), "shape=2");
|
|
extreme_value_plotter2.plot("Extreme Value Distribution PDF (location=0)", "extreme_value_pdf2.svg");
|
|
|
|
distribution_plotter<boost::math::fisher_f_distribution<> >
|
|
fisher_f_plotter;
|
|
fisher_f_plotter.add(boost::math::fisher_f_distribution<>(4, 4), "n=4, m=4");
|
|
fisher_f_plotter.add(boost::math::fisher_f_distribution<>(10, 4), "n=10, m=4");
|
|
fisher_f_plotter.add(boost::math::fisher_f_distribution<>(10, 10), "n=10, m=10");
|
|
fisher_f_plotter.add(boost::math::fisher_f_distribution<>(4, 10), "n=4, m=10");
|
|
fisher_f_plotter.plot("F Distribution PDF", "fisher_f_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::lognormal_distribution<> >
|
|
lognormal_plotter;
|
|
lognormal_plotter.add(boost::math::lognormal_distribution<>(-1), "location=-1");
|
|
lognormal_plotter.add(boost::math::lognormal_distribution<>(0), "location=0");
|
|
lognormal_plotter.add(boost::math::lognormal_distribution<>(1), "location=1");
|
|
lognormal_plotter.plot("Lognormal Distribution PDF (scale=1)", "lognormal_pdf1.svg");
|
|
|
|
distribution_plotter<boost::math::lognormal_distribution<> >
|
|
lognormal_plotter2;
|
|
lognormal_plotter2.add(boost::math::lognormal_distribution<>(0, 0.5), "scale=0.5");
|
|
lognormal_plotter2.add(boost::math::lognormal_distribution<>(0, 1), "scale=1");
|
|
lognormal_plotter2.add(boost::math::lognormal_distribution<>(0, 2), "scale=2");
|
|
lognormal_plotter2.plot("Lognormal Distribution PDF (location=0)", "lognormal_pdf2.svg");
|
|
|
|
distribution_plotter<boost::math::pareto_distribution<> >
|
|
pareto_plotter; // Rely on 2nd parameter shape = 1 default.
|
|
pareto_plotter.add(boost::math::pareto_distribution<>(1), "scale=1");
|
|
pareto_plotter.add(boost::math::pareto_distribution<>(2), "scale=2");
|
|
pareto_plotter.add(boost::math::pareto_distribution<>(3), "scale=3");
|
|
pareto_plotter.plot("Pareto Distribution PDF (shape=1)", "pareto_pdf1.svg");
|
|
|
|
distribution_plotter<boost::math::pareto_distribution<> >
|
|
pareto_plotter2;
|
|
pareto_plotter2.add(boost::math::pareto_distribution<>(1, 0.5), "shape=0.5");
|
|
pareto_plotter2.add(boost::math::pareto_distribution<>(1, 1), "shape=1");
|
|
pareto_plotter2.add(boost::math::pareto_distribution<>(1, 2), "shape=2");
|
|
pareto_plotter2.plot("Pareto Distribution PDF (scale=1)", "pareto_pdf2.svg");
|
|
|
|
distribution_plotter<boost::math::rayleigh_distribution<> >
|
|
rayleigh_plotter;
|
|
rayleigh_plotter.add(boost::math::rayleigh_distribution<>(0.5), "σ=0.5");
|
|
rayleigh_plotter.add(boost::math::rayleigh_distribution<>(1), "σ=1");
|
|
rayleigh_plotter.add(boost::math::rayleigh_distribution<>(2), "σ=2");
|
|
rayleigh_plotter.add(boost::math::rayleigh_distribution<>(4), "σ=4");
|
|
rayleigh_plotter.add(boost::math::rayleigh_distribution<>(10), "σ=10");
|
|
rayleigh_plotter.plot("Rayleigh Distribution PDF", "rayleigh_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::rayleigh_distribution<> >
|
|
rayleigh_cdf_plotter(false);
|
|
rayleigh_cdf_plotter.add(boost::math::rayleigh_distribution<>(0.5), "σ=0.5");
|
|
rayleigh_cdf_plotter.add(boost::math::rayleigh_distribution<>(1), "σ=1");
|
|
rayleigh_cdf_plotter.add(boost::math::rayleigh_distribution<>(2), "σ=2");
|
|
rayleigh_cdf_plotter.add(boost::math::rayleigh_distribution<>(4), "σ=4");
|
|
rayleigh_cdf_plotter.add(boost::math::rayleigh_distribution<>(10), "σ=10");
|
|
rayleigh_cdf_plotter.plot("Rayleigh Distribution CDF", "rayleigh_cdf.svg");
|
|
|
|
distribution_plotter<boost::math::skew_normal_distribution<> >
|
|
skew_normal_plotter;
|
|
skew_normal_plotter.add(boost::math::skew_normal_distribution<>(0,1,0), "{0,1,0}");
|
|
skew_normal_plotter.add(boost::math::skew_normal_distribution<>(0,1,1), "{0,1,1}");
|
|
skew_normal_plotter.add(boost::math::skew_normal_distribution<>(0,1,4), "{0,1,4}");
|
|
skew_normal_plotter.add(boost::math::skew_normal_distribution<>(0,1,20), "{0,1,20}");
|
|
skew_normal_plotter.add(boost::math::skew_normal_distribution<>(0,1,-2), "{0,1,-2}");
|
|
skew_normal_plotter.add(boost::math::skew_normal_distribution<>(-2,0.5,-1), "{-2,0.5,-1}");
|
|
skew_normal_plotter.plot("Skew Normal Distribution PDF", "skew_normal_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::skew_normal_distribution<> >
|
|
skew_normal_cdf_plotter(false);
|
|
skew_normal_cdf_plotter.add(boost::math::skew_normal_distribution<>(0,1,0), "{0,1,0}");
|
|
skew_normal_cdf_plotter.add(boost::math::skew_normal_distribution<>(0,1,1), "{0,1,1}");
|
|
skew_normal_cdf_plotter.add(boost::math::skew_normal_distribution<>(0,1,4), "{0,1,4}");
|
|
skew_normal_cdf_plotter.add(boost::math::skew_normal_distribution<>(0,1,20), "{0,1,20}");
|
|
skew_normal_cdf_plotter.add(boost::math::skew_normal_distribution<>(0,1,-2), "{0,1,-2}");
|
|
skew_normal_cdf_plotter.add(boost::math::skew_normal_distribution<>(-2,0.5,-1), "{-2,0.5,-1}");
|
|
skew_normal_cdf_plotter.plot("Skew Normal Distribution CDF", "skew_normal_cdf.svg");
|
|
|
|
distribution_plotter<boost::math::triangular_distribution<> >
|
|
triangular_plotter;
|
|
triangular_plotter.add(boost::math::triangular_distribution<>(-1,0,1), "{-1,0,1}");
|
|
triangular_plotter.add(boost::math::triangular_distribution<>(0,1,1), "{0,1,1}");
|
|
triangular_plotter.add(boost::math::triangular_distribution<>(0,1,3), "{0,1,3}");
|
|
triangular_plotter.add(boost::math::triangular_distribution<>(0,0.5,1), "{0,0.5,1}");
|
|
triangular_plotter.add(boost::math::triangular_distribution<>(-2,0,3), "{-2,0,3}");
|
|
triangular_plotter.plot("Triangular Distribution PDF", "triangular_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::triangular_distribution<> >
|
|
triangular_cdf_plotter(false);
|
|
triangular_cdf_plotter.add(boost::math::triangular_distribution<>(-1,0,1), "{-1,0,1}");
|
|
triangular_cdf_plotter.add(boost::math::triangular_distribution<>(0,1,1), "{0,1,1}");
|
|
triangular_cdf_plotter.add(boost::math::triangular_distribution<>(0,1,3), "{0,1,3}");
|
|
triangular_cdf_plotter.add(boost::math::triangular_distribution<>(0,0.5,1), "{0,0.5,1}");
|
|
triangular_cdf_plotter.add(boost::math::triangular_distribution<>(-2,0,3), "{-2,0,3}");
|
|
triangular_cdf_plotter.plot("Triangular Distribution CDF", "triangular_cdf.svg");
|
|
|
|
distribution_plotter<boost::math::students_t_distribution<> >
|
|
students_t_plotter;
|
|
students_t_plotter.add(boost::math::students_t_distribution<>(1), "v=1");
|
|
students_t_plotter.add(boost::math::students_t_distribution<>(5), "v=5");
|
|
students_t_plotter.add(boost::math::students_t_distribution<>(30), "v=30");
|
|
students_t_plotter.plot("Students T Distribution PDF", "students_t_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::weibull_distribution<> >
|
|
weibull_plotter;
|
|
weibull_plotter.add(boost::math::weibull_distribution<>(0.75), "shape=0.75");
|
|
weibull_plotter.add(boost::math::weibull_distribution<>(1), "shape=1");
|
|
weibull_plotter.add(boost::math::weibull_distribution<>(5), "shape=5");
|
|
weibull_plotter.add(boost::math::weibull_distribution<>(10), "shape=10");
|
|
weibull_plotter.plot("Weibull Distribution PDF (scale=1)", "weibull_pdf1.svg");
|
|
|
|
distribution_plotter<boost::math::weibull_distribution<> >
|
|
weibull_plotter2;
|
|
weibull_plotter2.add(boost::math::weibull_distribution<>(3, 0.5), "scale=0.5");
|
|
weibull_plotter2.add(boost::math::weibull_distribution<>(3, 1), "scale=1");
|
|
weibull_plotter2.add(boost::math::weibull_distribution<>(3, 2), "scale=2");
|
|
weibull_plotter2.plot("Weibull Distribution PDF (shape=3)", "weibull_pdf2.svg");
|
|
|
|
distribution_plotter<boost::math::uniform_distribution<> >
|
|
uniform_plotter;
|
|
uniform_plotter.add(boost::math::uniform_distribution<>(0, 1), "{0,1}");
|
|
uniform_plotter.add(boost::math::uniform_distribution<>(0, 3), "{0,3}");
|
|
uniform_plotter.add(boost::math::uniform_distribution<>(-2, 3), "{-2,3}");
|
|
uniform_plotter.add(boost::math::uniform_distribution<>(-1, 1), "{-1,1}");
|
|
uniform_plotter.plot("Uniform Distribution PDF", "uniform_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::uniform_distribution<> >
|
|
uniform_cdf_plotter(false);
|
|
uniform_cdf_plotter.add(boost::math::uniform_distribution<>(0, 1), "{0,1}");
|
|
uniform_cdf_plotter.add(boost::math::uniform_distribution<>(0, 3), "{0,3}");
|
|
uniform_cdf_plotter.add(boost::math::uniform_distribution<>(-2, 3), "{-2,3}");
|
|
uniform_cdf_plotter.add(boost::math::uniform_distribution<>(-1, 1), "{-1,1}");
|
|
uniform_cdf_plotter.plot("Uniform Distribution CDF", "uniform_cdf.svg");
|
|
|
|
distribution_plotter<boost::math::bernoulli_distribution<> >
|
|
bernoulli_plotter;
|
|
bernoulli_plotter.add(boost::math::bernoulli_distribution<>(0.25), "p=0.25");
|
|
bernoulli_plotter.add(boost::math::bernoulli_distribution<>(0.5), "p=0.5");
|
|
bernoulli_plotter.add(boost::math::bernoulli_distribution<>(0.75), "p=0.75");
|
|
bernoulli_plotter.plot("Bernoulli Distribution PDF", "bernoulli_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::bernoulli_distribution<> >
|
|
bernoulli_cdf_plotter(false);
|
|
bernoulli_cdf_plotter.add(boost::math::bernoulli_distribution<>(0.25), "p=0.25");
|
|
bernoulli_cdf_plotter.add(boost::math::bernoulli_distribution<>(0.5), "p=0.5");
|
|
bernoulli_cdf_plotter.add(boost::math::bernoulli_distribution<>(0.75), "p=0.75");
|
|
bernoulli_cdf_plotter.plot("Bernoulli Distribution CDF", "bernoulli_cdf.svg");
|
|
|
|
distribution_plotter<boost::math::binomial_distribution<> >
|
|
binomial_plotter;
|
|
binomial_plotter.add(boost::math::binomial_distribution<>(5, 0.5), "n=5 p=0.5");
|
|
binomial_plotter.add(boost::math::binomial_distribution<>(20, 0.5), "n=20 p=0.5");
|
|
binomial_plotter.add(boost::math::binomial_distribution<>(50, 0.5), "n=50 p=0.5");
|
|
binomial_plotter.plot("Binomial Distribution PDF", "binomial_pdf_1.svg");
|
|
|
|
distribution_plotter<boost::math::binomial_distribution<> >
|
|
binomial_plotter2;
|
|
binomial_plotter2.add(boost::math::binomial_distribution<>(20, 0.1), "n=20 p=0.1");
|
|
binomial_plotter2.add(boost::math::binomial_distribution<>(20, 0.5), "n=20 p=0.5");
|
|
binomial_plotter2.add(boost::math::binomial_distribution<>(20, 0.9), "n=20 p=0.9");
|
|
binomial_plotter2.plot("Binomial Distribution PDF", "binomial_pdf_2.svg");
|
|
|
|
distribution_plotter<boost::math::negative_binomial_distribution<> >
|
|
negative_binomial_plotter;
|
|
negative_binomial_plotter.add(boost::math::negative_binomial_distribution<>(20, 0.25), "n=20 p=0.25");
|
|
negative_binomial_plotter.add(boost::math::negative_binomial_distribution<>(20, 0.5), "n=20 p=0.5");
|
|
negative_binomial_plotter.add(boost::math::negative_binomial_distribution<>(20, 0.75), "n=20 p=0.75");
|
|
negative_binomial_plotter.plot("Negative Binomial Distribution PDF", "negative_binomial_pdf_1.svg");
|
|
|
|
distribution_plotter<boost::math::negative_binomial_distribution<> >
|
|
negative_binomial_plotter2;
|
|
negative_binomial_plotter2.add(boost::math::negative_binomial_distribution<>(10, 0.5), "n=10 p=0.5");
|
|
negative_binomial_plotter2.add(boost::math::negative_binomial_distribution<>(20, 0.5), "n=20 p=0.5");
|
|
negative_binomial_plotter2.add(boost::math::negative_binomial_distribution<>(70, 0.5), "n=70 p=0.5");
|
|
negative_binomial_plotter2.plot("Negative Binomial Distribution PDF", "negative_binomial_pdf_2.svg");
|
|
|
|
distribution_plotter<boost::math::poisson_distribution<> >
|
|
poisson_plotter;
|
|
poisson_plotter.add(boost::math::poisson_distribution<>(5), "λ=5");
|
|
poisson_plotter.add(boost::math::poisson_distribution<>(10), "λ=10");
|
|
poisson_plotter.add(boost::math::poisson_distribution<>(20), "λ=20");
|
|
poisson_plotter.plot("Poisson Distribution PDF", "poisson_pdf_1.svg");
|
|
|
|
distribution_plotter<boost::math::hypergeometric_distribution<> >
|
|
hypergeometric_plotter;
|
|
hypergeometric_plotter.add(boost::math::hypergeometric_distribution<>(30, 50, 500), "N=500, r=50, n=30");
|
|
hypergeometric_plotter.add(boost::math::hypergeometric_distribution<>(30, 100, 500), "N=500, r=100, n=30");
|
|
hypergeometric_plotter.add(boost::math::hypergeometric_distribution<>(30, 250, 500), "N=500, r=250, n=30");
|
|
hypergeometric_plotter.add(boost::math::hypergeometric_distribution<>(30, 400, 500), "N=500, r=400, n=30");
|
|
hypergeometric_plotter.add(boost::math::hypergeometric_distribution<>(30, 450, 500), "N=500, r=450, n=30");
|
|
hypergeometric_plotter.plot("Hypergeometric Distribution PDF", "hypergeometric_pdf_1.svg");
|
|
|
|
distribution_plotter<boost::math::hypergeometric_distribution<> >
|
|
hypergeometric_plotter2;
|
|
hypergeometric_plotter2.add(boost::math::hypergeometric_distribution<>(50, 50, 500), "N=500, r=50, n=50");
|
|
hypergeometric_plotter2.add(boost::math::hypergeometric_distribution<>(100, 50, 500), "N=500, r=50, n=100");
|
|
hypergeometric_plotter2.add(boost::math::hypergeometric_distribution<>(250, 50, 500), "N=500, r=50, n=250");
|
|
hypergeometric_plotter2.add(boost::math::hypergeometric_distribution<>(400, 50, 500), "N=500, r=50, n=400");
|
|
hypergeometric_plotter2.add(boost::math::hypergeometric_distribution<>(450, 50, 500), "N=500, r=50, n=450");
|
|
hypergeometric_plotter2.plot("Hypergeometric Distribution PDF", "hypergeometric_pdf_2.svg");
|
|
|
|
}
|
|
catch (std::exception ex)
|
|
{
|
|
std::cout << ex.what() << std::endl;
|
|
}
|
|
|
|
|
|
|
|
/* these graphs for hyperexponential distribution not used.
|
|
|
|
distribution_plotter<boost::math::hyperexponential_distribution<> >
|
|
hyperexponential_plotter;
|
|
{
|
|
const double probs1_1[] = {1.0};
|
|
const double rates1_1[] = {1.0};
|
|
hyperexponential_plotter.add(boost::math::hyperexponential_distribution<>(probs1_1,rates1_1), "α=(1.0), λ=(1.0)");
|
|
const double probs2_1[] = {0.1,0.9};
|
|
const double rates2_1[] = {0.5,1.5};
|
|
hyperexponential_plotter.add(boost::math::hyperexponential_distribution<>(probs2_1,rates2_1), "α=(0.1,0.9), λ=(0.5,1.5)");
|
|
const double probs2_2[] = {0.9,0.1};
|
|
const double rates2_2[] = {0.5,1.5};
|
|
hyperexponential_plotter.add(boost::math::hyperexponential_distribution<>(probs2_2,rates2_2), "α=(0.9,0.1), λ=(0.5,1.5)");
|
|
const double probs3_1[] = {0.2,0.3,0.5};
|
|
const double rates3_1[] = {0.5,1.0,1.5};
|
|
hyperexponential_plotter.add(boost::math::hyperexponential_distribution<>(probs3_1,rates3_1), "α=(0.2,0.3,0.5), λ=(0.5,1.0,1.5)");
|
|
const double probs3_2[] = {0.5,0.3,0.2};
|
|
const double rates3_2[] = {0.5,1.0,1.5};
|
|
hyperexponential_plotter.add(boost::math::hyperexponential_distribution<>(probs3_1,rates3_1), "α=(0.5,0.3,0.2), λ=(0.5,1.0,1.5)");
|
|
}
|
|
hyperexponential_plotter.plot("Hyperexponential Distribution PDF", "hyperexponential_pdf.svg");
|
|
|
|
distribution_plotter<boost::math::hyperexponential_distribution<> >
|
|
hyperexponential_plotter2;
|
|
{
|
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const double rates[] = {0.5,1.5};
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const double probs1[] = {0.1,0.9};
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hyperexponential_plotter2.add(boost::math::hyperexponential_distribution<>(probs1,rates), "α=(0.1,0.9), λ=(0.5,1.5)");
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const double probs2[] = {0.6,0.4};
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hyperexponential_plotter2.add(boost::math::hyperexponential_distribution<>(probs2,rates), "α=(0.6,0.4), λ=(0.5,1.5)");
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const double probs3[] = {0.9,0.1};
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hyperexponential_plotter2.add(boost::math::hyperexponential_distribution<>(probs3,rates), "α=(0.9,0.1), λ=(0.5,1.5)");
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}
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hyperexponential_plotter2.plot("Hyperexponential Distribution PDF (Different Probabilities, Same Rates)", "hyperexponential_pdf_samerate.svg");
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distribution_plotter<boost::math::hyperexponential_distribution<> >
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hyperexponential_plotter3;
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{
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const double probs1[] = {1.0};
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const double rates1[] = {2.0};
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hyperexponential_plotter3.add(boost::math::hyperexponential_distribution<>(probs1,rates1), "α=(1.0), λ=(2.0)");
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const double probs2[] = {0.5,0.5};
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const double rates2[] = {0.3,1.5};
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hyperexponential_plotter3.add(boost::math::hyperexponential_distribution<>(probs2,rates2), "α=(0.5,0.5), λ=(0.3,1.5)");
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const double probs3[] = {1.0/3.0,1.0/3.0,1.0/3.0};
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const double rates3[] = {0.2,1.5,3.0};
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hyperexponential_plotter3.add(boost::math::hyperexponential_distribution<>(probs2,rates2), "α=(1.0/3.0,1.0/3.0,1.0/3.0), λ=(0.2,1.5,3.0)");
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}
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hyperexponential_plotter3.plot("Hyperexponential Distribution PDF (Different Number of Phases, Same Mean)", "hyperexponential_pdf_samemean.svg");
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*/
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} // int main()
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