1 | // $Id: random.h 614 2006-08-31 05:08:05Z jari $ |
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2 | |
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3 | /* |
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4 | Copyright (C) 2005, 2006 Jari Häkkinen, Peter Johansson |
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5 | |
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6 | This file is part of the thep c++ tools library, |
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7 | http://lev.thep.lu.se/trac/c++_tools |
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8 | |
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9 | The c++ tools library is free software; you can redistribute it |
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10 | and/or modify it under the terms of the GNU General Public License |
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11 | as published by the Free Software Foundation; either version 2 of |
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12 | the License, or (at your option) any later version. |
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13 | |
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14 | The c++ tools library is distributed in the hope that it will be |
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15 | useful, but WITHOUT ANY WARRANTY; without even the implied warranty |
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16 | of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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17 | General Public License for more details. |
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18 | |
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19 | You should have received a copy of the GNU General Public License |
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20 | along with this program; if not, write to the Free Software |
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21 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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22 | 02111-1307, USA. |
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23 | */ |
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24 | |
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25 | #ifndef _theplu_random_ |
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26 | #define _theplu_random_ |
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27 | |
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28 | #include <c++_tools/statistics/Histogram.h> |
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29 | |
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30 | #include <gsl/gsl_rng.h> |
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31 | #include <gsl/gsl_randist.h> |
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32 | |
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33 | #include <string> |
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34 | |
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35 | namespace theplu { |
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36 | namespace random { |
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37 | |
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38 | /// |
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39 | /// @brief Random Number Generator |
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40 | /// |
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41 | /// The RNG class is wrapper to the GSL random number generator |
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42 | /// (rng). This class provides a single global instance of the rng, |
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43 | /// and makes sure there is only one point of access to the |
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44 | /// generator. |
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45 | /// |
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46 | /// There is information about how to change seeding and generators |
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47 | /// at run time without recompilation using environment |
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48 | /// variables. RNG of course support seeding at compile time if you |
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49 | /// don't want to bother about environment variables and GSL. |
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50 | /// |
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51 | /// There are many different rng's available in GSL. Currently only |
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52 | /// the default generator is implemented and no other one is |
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53 | /// choosable through the class interface. This means that you have |
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54 | /// to fall back to the use of environment variables as described in |
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55 | /// the GSL documentation, or be bold and request support for other |
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56 | /// rng's through the class interface. |
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57 | /// |
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58 | /// Not all GSL functionality is implemented, we'll add |
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59 | /// functionality when needed and may do it when requested. Better |
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60 | /// yet, supply us with code and we will probably add it to the code |
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61 | /// (BUT remember to implement reasonable tests for your code and |
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62 | /// follow the coding style.) |
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63 | /// |
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64 | /// This implementation may be thread safe (according to GSL |
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65 | /// documentation), but should be checked to be so before trusting |
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66 | /// thread safety. |
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67 | /// |
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68 | /// @see Design Patterns (the singleton and adapter pattern). GSL |
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69 | /// documentation. |
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70 | /// |
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71 | class RNG |
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72 | { |
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73 | public: |
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74 | |
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75 | virtual ~RNG(void); |
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76 | |
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77 | /// |
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78 | /// @brief Get an instance of the random number generator. |
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79 | /// |
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80 | /// Get an instance of the random number generator. If the random |
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81 | /// number generator is not already created, the call will create |
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82 | /// a new generator and use the default seed. The seed must be |
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83 | /// changed with the seed or seed_from_devurandom member |
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84 | /// functions. |
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85 | /// |
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86 | /// @return A pointer to the random number generator. |
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87 | /// |
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88 | /// @see seed and seed_from_devurandom |
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89 | /// |
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90 | static RNG* instance(void) |
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91 | { if (!instance_) instance_=new RNG; return instance_; } |
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92 | |
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93 | /// |
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94 | /// @brief Returns the largest number that the random number |
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95 | /// generator can return. |
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96 | /// |
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97 | inline u_long max(void) const { return gsl_rng_max(rng_); } |
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98 | |
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99 | /// |
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100 | /// @brief Returns the smallest number that the random number |
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101 | /// generator can return. |
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102 | /// |
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103 | inline u_long min(void) const { return gsl_rng_min(rng_); } |
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104 | |
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105 | /// |
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106 | /// @brief Returns the name of the random number generator |
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107 | /// |
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108 | inline std::string name(void) const { return gsl_rng_name(rng_); } |
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109 | |
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110 | inline const gsl_rng* rng(void) const { return rng_; } |
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111 | |
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112 | /// |
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113 | /// @brief Set the seed \a s for the rng. |
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114 | /// |
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115 | /// Set the seed \a s for the rng. If \a s is zero, a default |
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116 | /// value from the rng's original implementation is used (cf. GSL |
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117 | /// documentation). |
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118 | /// |
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119 | /// @see seed_from_devurandom |
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120 | /// |
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121 | inline void seed(u_long s) const { gsl_rng_set(rng_,s); } |
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122 | |
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123 | /// |
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124 | /// @brief Seed the rng using the /dev/urandom device. |
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125 | /// |
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126 | /// @return The seed acquired from /dev/urandom. |
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127 | /// |
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128 | u_long seed_from_devurandom(void); |
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129 | |
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130 | private: |
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131 | RNG(void); |
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132 | |
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133 | static RNG* instance_; |
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134 | gsl_rng* rng_; |
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135 | }; |
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136 | |
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137 | // --------------------- Discrete distribtuions --------------------- |
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138 | |
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139 | /// |
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140 | /// @brief Discrete random number distributions. |
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141 | /// |
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142 | /// Abstract base class for discrete random number |
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143 | /// distributions. Given K discrete events with different |
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144 | /// probabilities \f$ P[k] \f$, produce a random value k consistent |
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145 | /// with its probability. |
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146 | /// |
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147 | class Discrete |
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148 | { |
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149 | public: |
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150 | /// |
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151 | /// @brief Constructor |
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152 | /// |
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153 | inline Discrete(void) { rng_=RNG::instance(); } |
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154 | |
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155 | inline virtual ~Discrete(void) { } |
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156 | |
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157 | /// |
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158 | /// @brief Set the seed to \a s. |
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159 | /// |
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160 | /// Set the seed to \a s in the underlying rng. If \a s is zero, a |
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161 | /// default value from the rng's original implementation is used |
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162 | /// (cf. GSL documentation). |
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163 | /// |
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164 | /// @see seed_from_devurandom, RNG::seed_from_devurandom, RNG::seed |
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165 | /// |
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166 | inline void seed(u_long s) const { rng_->seed(s); } |
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167 | |
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168 | /// |
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169 | /// @brief Set the seed using the /dev/urandom device. |
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170 | /// |
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171 | /// @return The seed acquired from /dev/urandom. |
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172 | /// |
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173 | /// @see seed, RNG::seed_from_devurandom, RNG::seed |
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174 | /// |
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175 | u_long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); } |
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176 | |
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177 | /// |
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178 | /// @return A random number. |
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179 | /// |
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180 | virtual u_long operator()(void) const = 0; |
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181 | |
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182 | protected: |
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183 | RNG* rng_; |
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184 | }; |
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185 | |
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186 | /// |
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187 | /// @brief General |
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188 | /// |
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189 | class DiscreteGeneral : public Discrete |
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190 | { |
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191 | public: |
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192 | /// |
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193 | /// @brief Constructor |
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194 | /// |
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195 | /// @param hist is a Histogram defining the probability distribution |
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196 | /// |
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197 | DiscreteGeneral(const statistics::Histogram& hist); |
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198 | |
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199 | /// |
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200 | /// @brief Destructor |
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201 | /// |
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202 | ~DiscreteGeneral(void); |
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203 | |
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204 | /// |
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205 | /// The generated number is an integer and proportinal to the |
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206 | /// frequency in the corresponding histogram bin. In other words, |
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207 | /// the probability that 0 is returned is proportinal to the size |
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208 | /// of the first bin. |
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209 | /// |
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210 | /// @return A random number. |
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211 | /// |
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212 | inline u_long |
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213 | operator()(void) const { return gsl_ran_discrete(rng_->rng(), gen_); } |
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214 | |
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215 | private: |
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216 | gsl_ran_discrete_t* gen_; |
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217 | }; |
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218 | |
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219 | /// |
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220 | /// @brief Discrete uniform distribution |
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221 | /// |
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222 | /// Discrete uniform distribution also known as the "equally likely |
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223 | /// outcomes" distribution. Each outcome, in this case an integer |
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224 | /// from [0,n-1] , have equal probability to occur. |
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225 | /// |
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226 | /// Distribution function \f$ p(k) = \frac{1}{n+1} \f$ for \f$ 0 \le |
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227 | /// k < n \f$ \n |
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228 | /// Expectation value: \f$ \frac{n-1}{2} \f$ \n |
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229 | /// Variance: \f$ \frac{1}{12}(n-1)(n+1) \f$ |
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230 | /// |
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231 | class DiscreteUniform : public Discrete |
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232 | { |
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233 | public: |
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234 | /// |
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235 | /// @brief Default constructor. |
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236 | /// |
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237 | DiscreteUniform(void) : range_(rng_->max()) {} |
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238 | |
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239 | /// |
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240 | /// @brief Constructor. |
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241 | /// |
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242 | /// The generator will generate integers from \f$ [0,n-1] \f$. If |
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243 | /// \a n is larger than the maximum number the random number |
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244 | /// generator can return, then (currently) \a n is adjusted |
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245 | /// appropriately. |
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246 | /// |
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247 | /// @todo If a too large \a n is given an exception should be |
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248 | /// thrown, i.e. the behaviour of this class will change. The case |
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249 | /// when argument is 0 is not treated gracefully (underlying GSL |
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250 | /// functionality will not return). |
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251 | /// |
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252 | DiscreteUniform(const u_long n) : range_(n) |
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253 | { if ( range_>rng_->max() ) range_=rng_->max(); } |
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254 | |
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255 | /// |
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256 | /// This function returns a random integer from 0 to n-1 |
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257 | /// inclusive. All integers in the range [0,n-1] are equally |
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258 | /// likely. n is set in constructor. |
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259 | /// |
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260 | inline u_long |
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261 | operator()(void) const { return gsl_rng_uniform_int(rng_->rng(), range_); } |
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262 | |
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263 | /// |
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264 | /// This function returns a random integer from 0 to n-1 |
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265 | /// inclusive. All integers in the range [0,n-1] are equally |
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266 | /// likely. |
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267 | /// |
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268 | inline u_long operator()(const u_long n) const |
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269 | { return gsl_rng_uniform_int(rng_->rng(), n); } |
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270 | |
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271 | private: |
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272 | u_long range_; |
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273 | }; |
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274 | |
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275 | /// |
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276 | /// @brief Poisson Distribution |
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277 | /// |
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278 | /// Having a Poisson process (i.e. no memory), number of occurences |
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279 | /// within a given time window is Poisson distributed. This |
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280 | /// distribution is the limit of a Binomial distribution when number |
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281 | /// of attempts is large, and the probability for one attempt to be |
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282 | /// succesful is small (in such a way that the expected number of |
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283 | /// succesful attempts is \f$ m \f$. |
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284 | /// |
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285 | /// Probability function \f$ p(k) = e^{-m}\frac{m^k}{k!} \f$ for \f$ 0 \le |
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286 | /// k \f$ \n |
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287 | /// Expectation value: \f$ m \f$ \n |
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288 | /// Variance: \f$ m \f$ |
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289 | /// |
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290 | class Poisson : public Discrete |
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291 | { |
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292 | public: |
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293 | /// |
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294 | /// @brief Constructor |
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295 | /// |
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296 | /// @param m is expectation value |
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297 | inline Poisson(const double m=1) : m_(m) {} |
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298 | |
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299 | /// |
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300 | /// @return A Poisson distributed number. |
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301 | /// |
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302 | inline u_long |
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303 | operator()(void) const { return gsl_ran_poisson(rng_->rng(), m_); } |
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304 | |
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305 | /// |
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306 | /// @return A Poisson distributed number with expectation value \a |
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307 | /// m |
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308 | /// |
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309 | /// @note this operator ignores parameters set in Constructor |
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310 | /// |
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311 | inline u_long |
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312 | operator()(const double m) const { return gsl_ran_poisson(rng_->rng(), m); } |
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313 | |
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314 | private: |
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315 | double m_; |
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316 | }; |
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317 | |
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318 | // --------------------- Continuous distribtuions --------------------- |
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319 | |
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320 | /// |
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321 | /// @brief Continuous random number distributions. |
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322 | /// |
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323 | /// Abstract base class for continuous random number distributions. |
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324 | /// |
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325 | class Continuous |
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326 | { |
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327 | public: |
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328 | |
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329 | /// |
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330 | /// @brief Constructor |
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331 | /// |
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332 | inline Continuous(void) { rng_=RNG::instance(); } |
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333 | |
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334 | inline virtual ~Continuous(void) { } |
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335 | |
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336 | /// |
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337 | /// @brief Set the seed to \a s. |
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338 | /// |
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339 | /// Set the seed to \a s in the underlying rng. If \a s is zero, a |
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340 | /// default value from the rng's original implementation is used |
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341 | /// (cf. GSL documentation). |
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342 | /// |
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343 | /// @see seed_from_devurandom, RNG::seed_from_devurandom, RNG::seed |
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344 | /// |
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345 | inline void seed(u_long s) const { rng_->seed(s); } |
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346 | |
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347 | /// |
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348 | /// @brief Set the seed using the /dev/urandom device. |
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349 | /// |
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350 | /// @return The seed acquired from /dev/urandom. |
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351 | /// |
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352 | /// @see seed, RNG::seed_from_devurandom, RNG::seed |
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353 | /// |
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354 | u_long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); } |
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355 | |
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356 | /// |
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357 | /// @return A random number |
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358 | /// |
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359 | virtual double operator()(void) const = 0; |
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360 | |
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361 | protected: |
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362 | RNG* rng_; |
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363 | }; |
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364 | |
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365 | /// |
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366 | /// @brief Uniform distribution |
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367 | /// |
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368 | /// Class for generating a random number from a uniform distribution |
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369 | /// in the range [0,1), i.e. zero is included but not 1. |
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370 | /// |
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371 | /// Distribution function \f$ f(x) = 1 \f$ for \f$ 0 \le x < 1 \f$ \n |
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372 | /// Expectation value: 0.5 \n |
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373 | /// Variance: \f$ \frac{1}{12} \f$ |
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374 | /// |
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375 | class ContinuousUniform : public Continuous |
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376 | { |
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377 | public: |
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378 | inline double operator()(void) const { return gsl_rng_uniform(rng_->rng());} |
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379 | }; |
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380 | |
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381 | /// |
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382 | /// Class to generate numbers from a histogram in a continuous manner. |
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383 | /// |
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384 | class ContinuousGeneral : public Continuous |
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385 | { |
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386 | public: |
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387 | /// |
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388 | /// @brief Constructor |
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389 | /// |
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390 | /// @param hist is a Histogram defining the probability distribution |
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391 | /// |
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392 | inline ContinuousGeneral(const statistics::Histogram& hist) |
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393 | : discrete_(DiscreteGeneral(hist)), hist_(hist) {} |
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394 | |
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395 | /// |
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396 | /// @brief Destructor |
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397 | /// |
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398 | ~ContinuousGeneral(void); |
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399 | |
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400 | /// |
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401 | /// The number is generated in a two step process. First the bin |
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402 | /// in the histogram is randomly selected (see |
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403 | /// DiscreteGeneral). Then a number is generated uniformly from |
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404 | /// the interval defined by the bin. |
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405 | /// |
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406 | /// @return A random number. |
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407 | /// |
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408 | inline double operator()(void) const |
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409 | { return hist_.observation_value(discrete_())+(u_()-0.5)*hist_.spacing(); } |
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410 | |
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411 | private: |
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412 | const DiscreteGeneral discrete_; |
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413 | const statistics::Histogram& hist_; |
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414 | ContinuousUniform u_; |
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415 | }; |
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416 | |
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417 | /// |
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418 | /// @brief Generator of random numbers from an exponential |
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419 | /// distribution. |
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420 | /// |
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421 | /// The distribution function is \f$ f(x) = \frac{1}{m}\exp(-x/a) |
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422 | /// \f$ for \f$ x \f$ with the expectation value \f$ m \f$ and |
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423 | /// variance \f$ m^2 \f$ |
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424 | /// |
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425 | class Exponential : public Continuous |
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426 | { |
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427 | public: |
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428 | /// |
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429 | /// @brief Constructor |
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430 | /// |
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431 | /// @param \a m is the expectation value of the distribution. |
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432 | /// |
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433 | inline Exponential(const double m=1) : m_(m) {} |
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434 | |
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435 | /// |
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436 | /// @return A random number from exponential distribution. |
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437 | /// |
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438 | inline double |
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439 | operator()(void) const { return gsl_ran_exponential(rng_->rng(), m_); } |
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440 | |
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441 | /// |
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442 | /// @return A random number from exponential distribution, with |
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443 | /// expectation value \a m |
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444 | /// |
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445 | /// @note This operator ignores parameters given in constructor. |
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446 | /// |
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447 | inline double operator()(const double m) const |
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448 | { return gsl_ran_exponential(rng_->rng(), m); } |
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449 | |
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450 | private: |
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451 | double m_; |
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452 | }; |
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453 | |
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454 | /// |
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455 | /// @brief Gaussian distribution |
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456 | /// |
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457 | /// Class for generating a random number from a Gaussian |
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458 | /// distribution between zero and unity. Utilizes the Box-Muller |
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459 | /// algorithm, which needs two calls to random generator. |
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460 | /// |
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461 | /// Distribution function \f$ f(x) = |
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462 | /// \frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{(x-\mu)^2}{2\sigma^2}) |
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463 | /// \f$ \n |
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464 | /// Expectation value: \f$ \mu \f$ \n |
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465 | /// Variance: \f$ \sigma^2 \f$ |
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466 | /// |
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467 | class Gaussian : public Continuous |
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468 | { |
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469 | public: |
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470 | /// |
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471 | /// @brief Constructor |
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472 | /// @param s is the standard deviation \f$ \sigma \f$ of distribution |
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473 | /// m is the expectation value \f$ \mu \f$ of the distribution |
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474 | /// |
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475 | inline Gaussian(const double s=1, const double m=0) : m_(m), s_(s) {} |
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476 | |
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477 | /// |
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478 | /// @return A random Gaussian number |
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479 | /// |
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480 | inline double |
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481 | operator()(void) const { return gsl_ran_gaussian(rng_->rng(), s_)+m_; } |
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482 | |
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483 | /// |
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484 | /// @return A random Gaussian number with standard deviation \a s |
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485 | /// and expectation value 0. |
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486 | /// @note this operator ignores parameters given in Constructor |
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487 | /// |
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488 | inline double |
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489 | operator()(const double s) const { return gsl_ran_gaussian(rng_->rng(), s); } |
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490 | |
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491 | /// |
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492 | /// @return A random Gaussian number with standard deviation \a s |
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493 | /// and expectation value \a m. |
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494 | /// @note this operator ignores parameters given in Constructor |
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495 | /// |
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496 | inline double operator()(const double s, const double m) const |
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497 | { return gsl_ran_gaussian(rng_->rng(), s)+m; } |
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498 | |
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499 | private: |
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500 | double m_; |
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501 | double s_; |
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502 | }; |
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503 | |
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504 | |
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505 | }} // of namespace random and namespace theplu |
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506 | |
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507 | #endif |
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