Mercurial > octave
annotate liboctave/numeric/randpoisson.cc @ 25433:49e0447413ad
use templates and move rand functions inside octave namespace
* oct-rand.h, oct-rand.cc (rand): Move class inside octave namespace
and rename from octave_rand. Use templates to eliminate duplicate
code. Change all uses.
* randgamma.h, randgamma.cc, randmtzig.h, randmtzig.cc, randpoisson.h,
randpoisson.cc: Move functions inside Octave namespace.
Use templates to eliminate duplicate code. Change all uses.
author | John W. Eaton <jwe@octave.org> |
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date | Sat, 17 Mar 2018 07:02:30 -0500 |
parents | 6652d3823428 |
children | 859ad1f0b85e |
rev | line source |
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7019 | 1 /* |
2 | |
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3 Copyright (C) 2006-2018 John W. Eaton |
7019 | 4 |
5 This file is part of Octave. | |
6 | |
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7 Octave is free software: you can redistribute it and/or modify it |
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8 under the terms of the GNU General Public License as published by |
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9 the Free Software Foundation, either version 3 of the License, or |
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10 (at your option) any later version. |
7019 | 11 |
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12 Octave is distributed in the hope that it will be useful, but |
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13 WITHOUT ANY WARRANTY; without even the implied warranty of |
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14 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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15 GNU General Public License for more details. |
7019 | 16 |
17 You should have received a copy of the GNU General Public License | |
18 along with Octave; see the file COPYING. If not, see | |
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19 <https://www.gnu.org/licenses/>. |
7019 | 20 |
21 */ | |
22 | |
23 /* Original version written by Paul Kienzle distributed as free | |
24 software in the in the public domain. */ | |
5742 | 25 |
26 #if defined (HAVE_CONFIG_H) | |
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27 # include "config.h" |
5742 | 28 #endif |
29 | |
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30 #include <cmath> |
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31 #include <cstddef> |
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32 |
5742 | 33 #include "f77-fcn.h" |
7231 | 34 #include "lo-error.h" |
5742 | 35 #include "lo-ieee.h" |
36 #include "randmtzig.h" | |
37 #include "randpoisson.h" | |
38 | |
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39 namespace octave |
5742 | 40 { |
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41 static double xlgamma (double x) |
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42 { |
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43 return std::lgamma (x); |
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44 } |
5742 | 45 |
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46 /* ---- pprsc.c from Stadloeber's winrand --- */ |
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47 |
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48 /* flogfak(k) = ln(k!) */ |
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49 static double flogfak (double k) |
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50 { |
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51 #define C0 9.18938533204672742e-01 |
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52 #define C1 8.33333333333333333e-02 |
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53 #define C3 -2.77777777777777778e-03 |
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54 #define C5 7.93650793650793651e-04 |
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55 #define C7 -5.95238095238095238e-04 |
5742 | 56 |
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57 static double logfak[30L] = |
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58 { |
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59 0.00000000000000000, 0.00000000000000000, 0.69314718055994531, |
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60 1.79175946922805500, 3.17805383034794562, 4.78749174278204599, |
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61 6.57925121201010100, 8.52516136106541430, 10.60460290274525023, |
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62 12.80182748008146961, 15.10441257307551530, 17.50230784587388584, |
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63 19.98721449566188615, 22.55216385312342289, 25.19122118273868150, |
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64 27.89927138384089157, 30.67186010608067280, 33.50507345013688888, |
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65 36.39544520803305358, 39.33988418719949404, 42.33561646075348503, |
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66 45.38013889847690803, 48.47118135183522388, 51.60667556776437357, |
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67 54.78472939811231919, 58.00360522298051994, 61.26170176100200198, |
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68 64.55753862700633106, 67.88974313718153498, 71.25703896716800901 |
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69 }; |
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70 |
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71 double r, rr; |
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72 |
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73 if (k >= 30.0) |
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74 { |
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75 r = 1.0 / k; |
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76 rr = r * r; |
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77 return ((k + 0.5)*std::log (k) - k + C0 |
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78 + r*(C1 + rr*(C3 + rr*(C5 + rr*C7)))); |
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79 } |
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80 else |
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81 return (logfak[static_cast<int> (k)]); |
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82 } |
5742 | 83 |
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84 /****************************************************************** |
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85 * * |
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86 * Poisson Distribution - Patchwork Rejection/Inversion * |
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87 * * |
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88 ****************************************************************** |
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89 * * |
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90 * For parameter my < 10, Tabulated Inversion is applied. * |
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91 * For my >= 10, Patchwork Rejection is employed: * |
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92 * The area below the histogram function f(x) is rearranged in * |
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93 * its body by certain point reflections. Within a large center * |
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94 * interval variates are sampled efficiently by rejection from * |
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95 * uniform hats. Rectangular immediate acceptance regions speed * |
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96 * up the generation. The remaining tails are covered by * |
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97 * exponential functions. * |
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98 * * |
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99 ****************************************************************** |
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100 * * |
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101 * FUNCTION : - pprsc samples a random number from the Poisson * |
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102 * distribution with parameter my > 0. * |
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103 * REFERENCE : - H. Zechner (1994): Efficient sampling from * |
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104 * continuous and discrete unimodal distributions, * |
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105 * Doctoral Dissertation, 156 pp., Technical * |
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106 * University Graz, Austria. * |
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107 * SUBPROGRAM : - drand(seed) ... (0,1)-Uniform generator with * |
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108 * unsigned long integer *seed. * |
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109 * * |
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110 * Implemented by H. Zechner, January 1994 * |
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111 * Revised by F. Niederl, July 1994 * |
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112 * * |
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113 ******************************************************************/ |
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115 static double f (double k, double l_nu, double c_pm) |
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116 { |
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117 return exp (k * l_nu - flogfak (k) - c_pm); |
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118 } |
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120 static double pprsc (double my) |
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121 { |
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122 static double my_last = -1.0; |
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123 static double m, k2, k4, k1, k5; |
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124 static double dl, dr, r1, r2, r4, r5, ll, lr, l_my, c_pm, |
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125 f1, f2, f4, f5, p1, p2, p3, p4, p5, p6; |
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126 double Dk, X, Y; |
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127 double Ds, U, V, W; |
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128 |
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129 if (my != my_last) |
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130 { /* set-up */ |
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131 my_last = my; |
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132 /* approximate deviation of reflection points k2, k4 from my - 1/2 */ |
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133 Ds = std::sqrt (my + 0.25); |
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134 |
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135 /* mode m, reflection points k2 and k4, and points k1 and k5, */ |
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136 /* which delimit the centre region of h(x) */ |
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137 m = std::floor (my); |
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138 k2 = ceil (my - 0.5 - Ds); |
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139 k4 = std::floor (my - 0.5 + Ds); |
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140 k1 = k2 + k2 - m + 1L; |
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141 k5 = k4 + k4 - m; |
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142 |
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143 /* range width of the critical left and right centre region */ |
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144 dl = (k2 - k1); |
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145 dr = (k5 - k4); |
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146 |
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147 /* recurrence constants r(k)=p(k)/p(k-1) at k = k1, k2, k4+1, k5+1 */ |
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148 r1 = my / k1; |
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149 r2 = my / k2; |
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150 r4 = my / (k4 + 1.0); |
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151 r5 = my / (k5 + 1.0); |
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153 /* reciprocal values of the scale parameters of exp. tail envelope */ |
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154 ll = std::log (r1); /* expon. tail left */ |
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155 lr = -std::log (r5); /* expon. tail right*/ |
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156 |
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157 /* Poisson constants, necessary for computing function values f(k) */ |
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158 l_my = std::log (my); |
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159 c_pm = m * l_my - flogfak (m); |
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160 |
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161 /* function values f(k) = p(k)/p(m) at k = k2, k4, k1, k5 */ |
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162 f2 = f (k2, l_my, c_pm); |
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163 f4 = f (k4, l_my, c_pm); |
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164 f1 = f (k1, l_my, c_pm); |
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165 f5 = f (k5, l_my, c_pm); |
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166 |
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167 /* area of the two centre and the two exponential tail regions */ |
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168 /* area of the two immediate acceptance regions between k2, k4 */ |
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169 p1 = f2 * (dl + 1.0); /* immed. left */ |
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170 p2 = f2 * dl + p1; /* centre left */ |
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171 p3 = f4 * (dr + 1.0) + p2; /* immed. right */ |
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172 p4 = f4 * dr + p3; /* centre right */ |
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173 p5 = f1 / ll + p4; /* exp. tail left */ |
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174 p6 = f5 / lr + p5; /* exp. tail right*/ |
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175 } |
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176 |
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177 for (;;) |
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178 { |
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179 /* generate uniform number U -- U(0, p6) */ |
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180 /* case distinction corresponding to U */ |
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181 if ((U = octave::rand_uniform<double> () * p6) < p2) |
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182 { /* centre left */ |
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183 |
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184 /* immediate acceptance region |
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185 R2 = [k2, m) *[0, f2), X = k2, ... m -1 */ |
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186 if ((V = U - p1) < 0.0) return (k2 + std::floor (U/f2)); |
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187 /* immediate acceptance region |
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188 R1 = [k1, k2)*[0, f1), X = k1, ... k2-1 */ |
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189 if ((W = V / dl) < f1 ) return (k1 + std::floor (V/f1)); |
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190 |
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191 /* computation of candidate X < k2, and its counterpart Y > k2 */ |
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192 /* either squeeze-acceptance of X or acceptance-rejection of Y */ |
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193 Dk = std::floor (dl * octave::rand_uniform<double> ()) + 1.0; |
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194 if (W <= f2 - Dk * (f2 - f2/r2)) |
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195 { /* quick accept of */ |
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196 return (k2 - Dk); /* X = k2 - Dk */ |
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197 } |
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198 if ((V = f2 + f2 - W) < 1.0) |
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199 { /* quick reject of Y*/ |
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200 Y = k2 + Dk; |
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201 if (V <= f2 + Dk * (1.0 - f2)/(dl + 1.0)) |
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202 { /* quick accept of */ |
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203 return (Y); /* Y = k2 + Dk */ |
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204 } |
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205 if (V <= f (Y, l_my, c_pm)) return (Y); /* final accept of Y*/ |
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206 } |
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207 X = k2 - Dk; |
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208 } |
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209 else if (U < p4) |
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210 { /* centre right */ |
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211 /* immediate acceptance region |
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212 R3 = [m, k4+1)*[0, f4), X = m, ... k4 */ |
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213 if ((V = U - p3) < 0.0) return (k4 - std::floor ((U - p2)/f4)); |
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214 /* immediate acceptance region |
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215 R4 = [k4+1, k5+1)*[0, f5) */ |
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216 if ((W = V / dr) < f5 ) return (k5 - std::floor (V/f5)); |
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217 |
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218 /* computation of candidate X > k4, and its counterpart Y < k4 */ |
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219 /* either squeeze-acceptance of X or acceptance-rejection of Y */ |
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220 Dk = std::floor (dr * octave::rand_uniform<double> ()) + 1.0; |
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221 if (W <= f4 - Dk * (f4 - f4*r4)) |
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222 { /* quick accept of */ |
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223 return (k4 + Dk); /* X = k4 + Dk */ |
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224 } |
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225 if ((V = f4 + f4 - W) < 1.0) |
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226 { /* quick reject of Y*/ |
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227 Y = k4 - Dk; |
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228 if (V <= f4 + Dk * (1.0 - f4)/ dr) |
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229 { /* quick accept of */ |
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230 return (Y); /* Y = k4 - Dk */ |
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231 } |
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232 if (V <= f (Y, l_my, c_pm)) return (Y); /* final accept of Y*/ |
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233 } |
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234 X = k4 + Dk; |
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235 } |
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236 else |
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237 { |
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238 W = octave::rand_uniform<double> (); |
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239 if (U < p5) |
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240 { /* expon. tail left */ |
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241 Dk = std::floor (1.0 - std::log (W)/ll); |
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242 if ((X = k1 - Dk) < 0L) continue; /* 0 <= X <= k1 - 1 */ |
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243 W *= (U - p4) * ll; /* W -- U(0, h(x)) */ |
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244 if (W <= f1 - Dk * (f1 - f1/r1)) |
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245 return (X); /* quick accept of X*/ |
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246 } |
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247 else |
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248 { /* expon. tail right*/ |
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249 Dk = std::floor (1.0 - std::log (W)/lr); |
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250 X = k5 + Dk; /* X >= k5 + 1 */ |
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251 W *= (U - p5) * lr; /* W -- U(0, h(x)) */ |
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252 if (W <= f5 - Dk * (f5 - f5*r5)) |
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253 return (X); /* quick accept of X*/ |
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254 } |
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255 } |
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257 /* acceptance-rejection test of candidate X from the original area */ |
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258 /* test, whether W <= f(k), with W = U*h(x) and U -- U(0, 1)*/ |
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259 /* log f(X) = (X - m)*log(my) - log X! + log m! */ |
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260 if (std::log (W) <= X * l_my - flogfak (X) - c_pm) return (X); |
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261 } |
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262 } |
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263 /* ---- pprsc.c end ------ */ |
5742 | 264 |
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265 /* The remainder of the file is by Paul Kienzle */ |
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267 /* Given uniform u, find x such that CDF(L,x)==u. Return x. */ |
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268 static void poisson_cdf_lookup (double lambda, double *p, size_t n) |
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269 { |
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270 /* Table size is predicated on the maximum value of lambda |
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271 * we want to store in the table, and the maximum value of |
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272 * returned by the uniform random number generator on [0,1). |
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273 * With lambda==10 and u_max = 1 - 1/(2^32+1), we |
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274 * have poisson_pdf(lambda,36) < 1-u_max. If instead our |
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275 * generator uses more bits of mantissa or returns a value |
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276 * in the range [0,1], then for lambda==10 we need a table |
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277 * size of 46 instead. For long doubles, the table size |
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278 * will need to be longer still. */ |
5742 | 279 #define TABLESIZE 46 |
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280 double t[TABLESIZE]; |
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281 |
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282 /* Precompute the table for the u up to and including 0.458. |
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283 * We will almost certainly need it. */ |
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284 int intlambda = static_cast<int> (std::floor (lambda)); |
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285 double P; |
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286 int tableidx; |
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287 size_t i = n; |
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288 |
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289 t[0] = P = exp (-lambda); |
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290 for (tableidx = 1; tableidx <= intlambda; tableidx++) |
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291 { |
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292 P = P*lambda/static_cast<double> (tableidx); |
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293 t[tableidx] = t[tableidx-1] + P; |
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294 } |
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295 |
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296 while (i-- > 0) |
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297 { |
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298 double u = octave::rand_uniform<double> (); |
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299 |
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300 /* If u > 0.458 we know we can jump to floor(lambda) before |
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301 * comparing (this observation is based on Stadlober's winrand |
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302 * code). For lambda >= 1, this will be a win. Lambda < 1 |
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303 * is already fast, so adding an extra comparison is not a |
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304 * problem. */ |
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305 int k = (u > 0.458 ? intlambda : 0); |
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306 |
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307 /* We aren't using a for loop here because when we find the |
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308 * right k we want to jump to the next iteration of the |
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309 * outer loop, and the continue statement will only work for |
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310 * the inner loop. */ |
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311 nextk: |
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312 if (u <= t[k]) |
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313 { |
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314 p[i] = static_cast<double> (k); |
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315 continue; |
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316 } |
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317 if (++k < tableidx) |
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318 goto nextk; |
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319 |
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320 /* We only need high values of the table very rarely so we |
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321 * don't automatically compute the entire table. */ |
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322 while (tableidx < TABLESIZE) |
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323 { |
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324 P = P*lambda/static_cast<double> (tableidx); |
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325 t[tableidx] = t[tableidx-1] + P; |
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326 /* Make sure we converge to 1.0 just in case u is uniform |
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327 * on [0,1] rather than [0,1). */ |
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328 if (t[tableidx] == t[tableidx-1]) t[tableidx] = 1.0; |
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329 tableidx++; |
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330 if (u <= t[tableidx-1]) break; |
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331 } |
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332 |
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333 /* We are assuming that the table size is big enough here. |
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334 * This should be true even if rand_uniform is returning values in |
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335 * the range [0,1] rather than [0,1). */ |
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336 p[i] = static_cast<double> (tableidx-1); |
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337 } |
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338 } |
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340 static void poisson_cdf_lookup_float (double lambda, float *p, size_t n) |
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341 { |
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342 double t[TABLESIZE]; |
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343 |
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344 /* Precompute the table for the u up to and including 0.458. |
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345 * We will almost certainly need it. */ |
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346 int intlambda = static_cast<int> (std::floor (lambda)); |
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347 double P; |
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348 int tableidx; |
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349 size_t i = n; |
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350 |
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351 t[0] = P = exp (-lambda); |
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352 for (tableidx = 1; tableidx <= intlambda; tableidx++) |
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353 { |
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354 P = P*lambda/static_cast<double> (tableidx); |
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355 t[tableidx] = t[tableidx-1] + P; |
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356 } |
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357 |
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358 while (i-- > 0) |
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359 { |
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360 double u = octave::rand_uniform<double> (); |
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361 int k = (u > 0.458 ? intlambda : 0); |
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362 nextk: |
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363 if (u <= t[k]) |
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364 { |
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365 p[i] = static_cast<float> (k); |
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366 continue; |
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367 } |
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368 if (++k < tableidx) |
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369 goto nextk; |
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370 |
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371 while (tableidx < TABLESIZE) |
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372 { |
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373 P = P*lambda/static_cast<double> (tableidx); |
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374 t[tableidx] = t[tableidx-1] + P; |
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375 if (t[tableidx] == t[tableidx-1]) t[tableidx] = 1.0; |
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376 tableidx++; |
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377 if (u <= t[tableidx-1]) break; |
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378 } |
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379 |
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380 p[i] = static_cast<float> (tableidx-1); |
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381 } |
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382 } |
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383 |
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384 /* From Press, et al., Numerical Recipes */ |
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385 static void poisson_rejection (double lambda, double *p, size_t n) |
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386 { |
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387 double sq = std::sqrt (2.0*lambda); |
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388 double alxm = std::log (lambda); |
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389 double g = lambda*alxm - xlgamma (lambda+1.0); |
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390 size_t i; |
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391 |
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392 for (i = 0; i < n; i++) |
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393 { |
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394 double y, em, t; |
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395 do |
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396 { |
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397 do |
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398 { |
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399 y = tan (M_PI*octave::rand_uniform<double> ()); |
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400 em = sq * y + lambda; |
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401 } while (em < 0.0); |
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402 em = std::floor (em); |
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403 t = 0.9*(1.0+y*y)*exp (em*alxm-flogfak (em)-g); |
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404 } while (octave::rand_uniform<double> () > t); |
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405 p[i] = em; |
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406 } |
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407 } |
5742 | 408 |
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409 /* From Press, et al., Numerical Recipes */ |
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410 static void poisson_rejection_float (double lambda, float *p, size_t n) |
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411 { |
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412 double sq = std::sqrt (2.0*lambda); |
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413 double alxm = std::log (lambda); |
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414 double g = lambda*alxm - xlgamma (lambda+1.0); |
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415 size_t i; |
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416 |
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417 for (i = 0; i < n; i++) |
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418 { |
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419 double y, em, t; |
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420 do |
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421 { |
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422 do |
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423 { |
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424 y = tan (M_PI*octave::rand_uniform<double> ()); |
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425 em = sq * y + lambda; |
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426 } while (em < 0.0); |
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427 em = std::floor (em); |
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428 t = 0.9*(1.0+y*y)*exp (em*alxm-flogfak (em)-g); |
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429 } while (octave::rand_uniform<double> () > t); |
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430 p[i] = em; |
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431 } |
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432 } |
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433 |
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434 /* The cutoff of L <= 1e8 in the following two functions before using |
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435 * the normal approximation is based on: |
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436 * > L=1e8; x=floor(linspace(0,2*L,1000)); |
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437 * > max(abs(normal_pdf(x,L,L)-poisson_pdf(x,L))) |
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438 * ans = 1.1376e-28 |
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439 * For L=1e7, the max is around 1e-9, which is within the step size of |
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440 * rand_uniform. For L>1e10 the pprsc function breaks down, as I saw |
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441 * from the histogram of a large sample, so 1e8 is both small enough |
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442 * and large enough. */ |
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444 /* Generate a set of poisson numbers with the same distribution */ |
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445 template <> void rand_poisson<double> (double L, octave_idx_type n, double *p) |
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446 { |
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447 octave_idx_type i; |
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448 if (L < 0.0 || lo_ieee_isinf (L)) |
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449 { |
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450 for (i=0; i<n; i++) |
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451 p[i] = numeric_limits<double>::NaN (); |
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452 } |
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453 else if (L <= 10.0) |
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454 { |
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455 poisson_cdf_lookup (L, p, n); |
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456 } |
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457 else if (L <= 1e8) |
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458 { |
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459 for (i=0; i<n; i++) |
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460 p[i] = pprsc (L); |
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461 } |
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462 else |
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463 { |
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464 /* normal approximation: from Phys. Rev. D (1994) v50 p1284 */ |
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465 const double sqrtL = std::sqrt (L); |
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466 for (i = 0; i < n; i++) |
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467 { |
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468 p[i] = std::floor (rand_normal<double> () * sqrtL + L + 0.5); |
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469 if (p[i] < 0.0) |
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470 p[i] = 0.0; /* will probably never happen */ |
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471 } |
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472 } |
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473 } |
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475 /* Generate one poisson variate */ |
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476 template <> double rand_poisson<double> (double L) |
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477 { |
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478 double ret; |
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479 if (L < 0.0) ret = numeric_limits<double>::NaN (); |
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480 else if (L <= 12.0) |
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481 { |
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482 /* From Press, et al. Numerical recipes */ |
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483 double g = exp (-L); |
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484 int em = -1; |
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485 double t = 1.0; |
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486 do |
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487 { |
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488 ++em; |
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489 t *= rand_uniform<double> (); |
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490 } while (t > g); |
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491 ret = em; |
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492 } |
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493 else if (L <= 1e8) |
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494 { |
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495 /* numerical recipes */ |
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496 poisson_rejection (L, &ret, 1); |
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497 } |
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498 else if (lo_ieee_isinf (L)) |
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499 { |
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500 /* FIXME: R uses NaN, but the normal approximation suggests that |
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501 * limit should be Inf. Which is correct? */ |
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502 ret = numeric_limits<double>::NaN (); |
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503 } |
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504 else |
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505 { |
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506 /* normal approximation: from Phys. Rev. D (1994) v50 p1284 */ |
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507 ret = std::floor (rand_normal<double> () * std::sqrt (L) + L + 0.5); |
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508 if (ret < 0.0) ret = 0.0; /* will probably never happen */ |
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509 } |
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510 return ret; |
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511 } |
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512 |
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513 /* Generate a set of poisson numbers with the same distribution */ |
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514 template <> void rand_poisson<float> (float FL, octave_idx_type n, float *p) |
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515 { |
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516 double L = FL; |
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517 octave_idx_type i; |
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518 if (L < 0.0 || lo_ieee_isinf (L)) |
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519 { |
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520 for (i=0; i<n; i++) |
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521 p[i] = numeric_limits<double>::NaN (); |
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522 } |
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523 else if (L <= 10.0) |
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524 { |
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525 poisson_cdf_lookup_float (L, p, n); |
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526 } |
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527 else if (L <= 1e8) |
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528 { |
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529 for (i=0; i<n; i++) |
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530 p[i] = pprsc (L); |
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531 } |
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532 else |
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533 { |
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534 /* normal approximation: from Phys. Rev. D (1994) v50 p1284 */ |
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535 const double sqrtL = std::sqrt (L); |
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536 for (i = 0; i < n; i++) |
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537 { |
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538 p[i] = std::floor (rand_normal<float> () * sqrtL + L + 0.5); |
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539 if (p[i] < 0.0) |
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540 p[i] = 0.0; /* will probably never happen */ |
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541 } |
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542 } |
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543 } |
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544 |
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545 /* Generate one poisson variate */ |
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546 template <> float rand_poisson<float> (float FL) |
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547 { |
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548 double L = FL; |
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549 float ret; |
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550 if (L < 0.0) ret = numeric_limits<float>::NaN (); |
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551 else if (L <= 12.0) |
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552 { |
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553 /* From Press, et al. Numerical recipes */ |
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554 double g = exp (-L); |
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555 int em = -1; |
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556 double t = 1.0; |
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557 do |
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558 { |
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559 ++em; |
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560 t *= rand_uniform<float> (); |
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561 } while (t > g); |
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562 ret = em; |
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563 } |
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564 else if (L <= 1e8) |
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565 { |
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566 /* numerical recipes */ |
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567 poisson_rejection_float (L, &ret, 1); |
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568 } |
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569 else if (lo_ieee_isinf (L)) |
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570 { |
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571 /* FIXME: R uses NaN, but the normal approximation suggests that |
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572 * limit should be Inf. Which is correct? */ |
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573 ret = numeric_limits<float>::NaN (); |
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574 } |
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575 else |
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576 { |
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577 /* normal approximation: from Phys. Rev. D (1994) v50 p1284 */ |
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578 ret = std::floor (rand_normal<float> () * std::sqrt (L) + L + 0.5); |
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579 if (ret < 0.0) ret = 0.0; /* will probably never happen */ |
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580 } |
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581 return ret; |
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582 } |
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583 } |