Mercurial > octave
annotate liboctave/numeric/randmtzig.cc @ 27857:81ada4b7d85b
Backed out changeset b3a03dbde858 (uniform spacing of doubles).
author | Rik <rik@octave.org> |
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date | Fri, 20 Dec 2019 14:25:16 -0800 |
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7019 | 1 /* |
2 | |
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3 Copyright (C) 2006-2019 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 | |
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23 /* |
5742 | 24 A C-program for MT19937, with initialization improved 2002/2/10. |
25 Coded by Takuji Nishimura and Makoto Matsumoto. | |
26 This is a faster version by taking Shawn Cokus's optimization, | |
27 Matthe Bellew's simplification, Isaku Wada's real version. | |
28 David Bateman added normal and exponential distributions following | |
29 Marsaglia and Tang's Ziggurat algorithm. | |
30 | |
31 Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, | |
32 Copyright (C) 2004, David Bateman | |
33 All rights reserved. | |
34 | |
35 Redistribution and use in source and binary forms, with or without | |
36 modification, are permitted provided that the following conditions | |
37 are met: | |
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38 |
5742 | 39 1. Redistributions of source code must retain the above copyright |
40 notice, this list of conditions and the following disclaimer. | |
41 | |
42 2. Redistributions in binary form must reproduce the above copyright | |
43 notice, this list of conditions and the following disclaimer in the | |
44 documentation and/or other materials provided with the distribution. | |
45 | |
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46 3. The names of its contributors may not be used to endorse or promote |
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47 products derived from this software without specific prior written |
5742 | 48 permission. |
49 | |
50 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS | |
51 "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT | |
52 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR | |
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53 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER |
5742 | 54 OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, |
55 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, | |
56 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR | |
57 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF | |
58 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING | |
59 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS | |
60 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |
61 | |
62 | |
63 Any feedback is very welcome. | |
64 http://www.math.keio.ac.jp/matumoto/emt.html | |
65 email: matumoto@math.keio.ac.jp | |
66 | |
67 * 2004-01-19 Paul Kienzle | |
68 * * comment out main | |
69 * add init_by_entropy, get_state, set_state | |
70 * * converted to allow compiling by C++ compiler | |
71 * | |
72 * 2004-01-25 David Bateman | |
73 * * Add Marsaglia and Tsang Ziggurat code | |
74 * | |
75 * 2004-07-13 Paul Kienzle | |
76 * * make into an independent library with some docs. | |
77 * * introduce new main and test code. | |
78 * | |
79 * 2004-07-28 Paul Kienzle & David Bateman | |
80 * * add -DALLBITS flag for 32 vs. 53 bits of randomness in mantissa | |
81 * * make the naming scheme more uniform | |
82 * * add -DHAVE_X86 for faster support of 53 bit mantissa on x86 arch. | |
83 * | |
84 * 2005-02-23 Paul Kienzle | |
85 * * fix -DHAVE_X86_32 flag and add -DUSE_X86_32=0|1 for explicit control | |
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86 * |
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87 * 2006-04-01 David Bateman |
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88 * * convert for use in octave, declaring static functions only used |
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89 * here and adding oct_ to functions visible externally |
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90 * * inverse sense of ALLBITS |
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91 * |
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92 * 2012-05-18 David Bateman |
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93 * * Remove randu64 and ALLBIT option |
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94 * * Add the single precision generators |
5742 | 95 */ |
96 | |
97 /* | |
98 === Build instructions === | |
99 | |
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100 Compile with -DHAVE_GETTIMEOFDAY if the gettimeofday function is |
5742 | 101 available. This is not necessary if your architecture has |
102 /dev/urandom defined. | |
103 | |
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104 Uses implicit -Di386 or explicit -DHAVE_X86_32 to determine if CPU=x86. |
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105 You can force X86 behavior with -DUSE_X86_32=1, or suppress it with |
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106 -DUSE_X86_32=0. You should also consider -march=i686 or similar for |
5742 | 107 extra performance. Check whether -DUSE_X86_32=0 is faster on 64-bit |
108 x86 architectures. | |
109 | |
110 If you want to replace the Mersenne Twister with another | |
111 generator then redefine randi32 appropriately. | |
112 | |
113 === Usage instructions === | |
114 Before using any of the generators, initialize the state with one of | |
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115 the init_mersenne_twister functions. |
5742 | 116 |
117 All generators share the same state vector. | |
118 | |
119 === Mersenne Twister === | |
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120 random initial state: |
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121 void init_mersenne_twister (void) |
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122 |
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123 // 32-bit initial state: |
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124 void init_mersenne_twister (uint32_t s) |
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125 |
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126 // m*32-bit initial state: |
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127 void init_mersenne_twister (uint32_t k[],int m) |
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128 |
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129 // saves state in array: |
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130 void get_mersenne_twister_state (uint32_t save[MT_N+1]) |
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131 |
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132 // restores state from array |
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133 void set_mersenne_twister_state (uint32_t save[MT_N+1]) |
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134 |
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135 static uint32_t randmt (void) returns 32-bit unsigned int |
5742 | 136 |
137 === inline generators === | |
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138 static uint32_t randi32 (void) returns 32-bit unsigned int |
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139 static uint64_t randi53 (void) returns 53-bit unsigned int |
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140 static uint64_t randi54 (void) returns 54-bit unsigned int |
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141 static float randu24 (void) returns 24-bit uniform in (0,1) |
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142 static double randu53 (void) returns 53-bit uniform in (0,1) |
5742 | 143 |
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144 double rand_uniform (void) returns M-bit uniform in (0,1) |
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145 double rand_normal (void) returns M-bit standard normal |
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146 double rand_exponential (void) returns N-bit standard exponential |
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147 |
5742 | 148 === Array generators === |
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149 void rand_uniform (octave_idx_type, double []) |
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150 void rand_normal (octave_idx_type, double []) |
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151 void rand_exponential (octave_idx_type, double []) |
5742 | 152 */ |
153 | |
154 #if defined (HAVE_CONFIG_H) | |
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155 # include "config.h" |
5742 | 156 #endif |
157 | |
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158 #include <cmath> |
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159 #include <cstdio> |
5742 | 160 |
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161 #include <algorithm> |
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162 |
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163 #include "oct-time.h" |
5742 | 164 #include "randmtzig.h" |
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165 |
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166 /* FIXME: may want to suppress X86 if sizeof(long) > 4 */ |
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167 #if ! defined (USE_X86_32) |
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168 # if defined (i386) || defined (HAVE_X86_32) |
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169 # define USE_X86_32 1 |
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170 # else |
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171 # define USE_X86_32 0 |
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172 # endif |
5742 | 173 #endif |
174 | |
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175 namespace octave |
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176 { |
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177 /* ===== Mersenne Twister 32-bit generator ===== */ |
5742 | 178 |
179 #define MT_M 397 | |
180 #define MATRIX_A 0x9908b0dfUL /* constant vector a */ | |
181 #define UMASK 0x80000000UL /* most significant w-r bits */ | |
182 #define LMASK 0x7fffffffUL /* least significant r bits */ | |
183 #define MIXBITS(u,v) ( ((u) & UMASK) | ((v) & LMASK) ) | |
184 #define TWIST(u,v) ((MIXBITS(u,v) >> 1) ^ ((v)&1UL ? MATRIX_A : 0UL)) | |
185 | |
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186 static uint32_t *next; |
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187 static uint32_t state[MT_N]; /* the array for the state vector */ |
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188 static int left = 1; |
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189 static int initf = 0; |
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190 static int initt = 1; |
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191 static int inittf = 1; |
5742 | 192 |
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193 /* initializes state[MT_N] with a seed */ |
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194 void init_mersenne_twister (const uint32_t s) |
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195 { |
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196 int j; |
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197 state[0] = s & 0xffffffffUL; |
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198 for (j = 1; j < MT_N; j++) |
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199 { |
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200 state[j] = (1812433253UL * (state[j-1] ^ (state[j-1] >> 30)) + j); |
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201 /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */ |
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202 /* In the previous versions, MSBs of the seed affect */ |
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203 /* only MSBs of the array state[]. */ |
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204 /* 2002/01/09 modified by Makoto Matsumoto */ |
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205 state[j] &= 0xffffffffUL; /* for >32 bit machines */ |
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206 } |
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207 left = 1; |
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208 initf = 1; |
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209 } |
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211 /* initialize by an array with array-length */ |
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212 /* init_key is the array for initializing keys */ |
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213 /* key_length is its length */ |
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214 void init_mersenne_twister (const uint32_t *init_key, const int key_length) |
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215 { |
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216 int i, j, k; |
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217 init_mersenne_twister (19650218UL); |
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218 i = 1; |
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219 j = 0; |
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220 k = (MT_N > key_length ? MT_N : key_length); |
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221 for (; k; k--) |
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222 { |
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223 state[i] = (state[i] ^ ((state[i-1] ^ (state[i-1] >> 30)) * 1664525UL)) |
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224 + init_key[j] + j; /* non linear */ |
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225 state[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ |
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226 i++; |
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227 j++; |
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228 if (i >= MT_N) |
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229 { |
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230 state[0] = state[MT_N-1]; |
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231 i = 1; |
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232 } |
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233 if (j >= key_length) |
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234 j = 0; |
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235 } |
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236 for (k = MT_N - 1; k; k--) |
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237 { |
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238 state[i] = (state[i] ^ ((state[i-1] ^ (state[i-1] >> 30)) * 1566083941UL)) |
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239 - i; /* non linear */ |
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240 state[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */ |
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241 i++; |
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242 if (i >= MT_N) |
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243 { |
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244 state[0] = state[MT_N-1]; |
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245 i = 1; |
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246 } |
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247 } |
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249 state[0] = 0x80000000UL; /* MSB is 1; assuring nonzero initial array */ |
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250 left = 1; |
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251 initf = 1; |
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252 } |
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254 void init_mersenne_twister (void) |
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255 { |
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256 uint32_t entropy[MT_N]; |
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257 int n = 0; |
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259 /* Look for entropy in /dev/urandom */ |
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260 FILE *urandom = std::fopen ("/dev/urandom", "rb"); |
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261 if (urandom) |
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262 { |
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263 while (n < MT_N) |
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264 { |
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265 unsigned char word[4]; |
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266 if (std::fread (word, 4, 1, urandom) != 1) |
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267 break; |
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268 entropy[n++] = word[0] + (word[1]<<8) + (word[2]<<16) |
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269 + (static_cast<uint32_t> (word[3])<<24); |
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270 } |
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271 std::fclose (urandom); |
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272 } |
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274 /* If there isn't enough entropy, gather some from various sources */ |
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275 |
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276 sys::time now; |
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277 |
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278 if (n < MT_N) |
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279 entropy[n++] = now.unix_time (); /* Current time in seconds */ |
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280 |
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281 if (n < MT_N) |
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282 entropy[n++] = clock (); /* CPU time used (usec) */ |
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283 |
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284 if (n < MT_N) |
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285 entropy[n++] = now.usec (); /* Fractional part of current time */ |
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286 |
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287 /* Send all the entropy into the initial state vector */ |
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288 init_mersenne_twister (entropy,n); |
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289 } |
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291 void set_mersenne_twister_state (const uint32_t *save) |
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292 { |
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293 std::copy_n (save, MT_N, state); |
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294 left = save[MT_N]; |
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295 next = state + (MT_N - left + 1); |
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296 } |
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298 void get_mersenne_twister_state (uint32_t *save) |
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299 { |
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300 std::copy_n (state, MT_N, save); |
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301 save[MT_N] = left; |
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302 } |
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304 static void next_state (void) |
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305 { |
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306 uint32_t *p = state; |
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307 int j; |
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309 /* if init_by_int() has not been called, */ |
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310 /* a default initial seed is used */ |
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311 /* if (initf==0) init_by_int(5489UL); */ |
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312 /* Or better yet, a random seed! */ |
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313 if (initf == 0) |
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314 init_mersenne_twister (); |
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316 left = MT_N; |
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317 next = state; |
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318 |
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319 for (j = MT_N - MT_M + 1; --j; p++) |
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320 *p = p[MT_M] ^ TWIST(p[0], p[1]); |
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322 for (j = MT_M; --j; p++) |
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323 *p = p[MT_M-MT_N] ^ TWIST(p[0], p[1]); |
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325 *p = p[MT_M-MT_N] ^ TWIST(p[0], state[0]); |
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326 } |
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327 |
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328 /* generates a random number on [0,0xffffffff]-interval */ |
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329 static uint32_t randmt (void) |
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330 { |
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331 uint32_t y; |
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333 if (--left == 0) |
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334 next_state (); |
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335 y = *next++; |
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337 /* Tempering */ |
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338 y ^= (y >> 11); |
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339 y ^= (y << 7) & 0x9d2c5680UL; |
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340 y ^= (y << 15) & 0xefc60000UL; |
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341 return (y ^ (y >> 18)); |
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342 } |
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344 /* ===== Uniform generators ===== */ |
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346 /* Select which 32 bit generator to use */ |
5742 | 347 #define randi32 randmt |
348 | |
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349 static uint64_t randi53 (void) |
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350 { |
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351 const uint32_t lo = randi32 (); |
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352 const uint32_t hi = randi32 () & 0x1FFFFF; |
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353 #if defined (HAVE_X86_32) |
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354 uint64_t u; |
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355 uint32_t *p = (uint32_t *)&u; |
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356 p[0] = lo; |
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357 p[1] = hi; |
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358 return u; |
5742 | 359 #else |
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360 return ((static_cast<uint64_t> (hi) << 32) | lo); |
5742 | 361 #endif |
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362 } |
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364 static uint64_t randi54 (void) |
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365 { |
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366 const uint32_t lo = randi32 (); |
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367 const uint32_t hi = randi32 () & 0x3FFFFF; |
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368 #if defined (HAVE_X86_32) |
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369 uint64_t u; |
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370 uint32_t *p = static_cast<uint32_t *> (&u); |
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371 p[0] = lo; |
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372 p[1] = hi; |
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373 return u; |
5742 | 374 #else |
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375 return ((static_cast<uint64_t> (hi) << 32) | lo); |
5742 | 376 #endif |
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377 } |
5742 | 378 |
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379 /* generates a random number on (0,1)-real-interval */ |
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380 static float randu24 (void) |
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381 { |
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382 uint32_t i; |
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383 |
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384 do |
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385 { |
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386 i = randi32 () & static_cast<uint32_t> (0xFFFFFF); |
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387 } |
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388 while (i == 0); |
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389 |
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390 return i * (1.0f / 16777216.0f); |
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391 } |
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392 |
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393 /* generates a random number on (0,1) with 53-bit resolution */ |
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394 static double randu53 (void) |
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395 { |
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396 const uint32_t a = randi32 () >> 5; |
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397 const uint32_t b = randi32 () >> 6; |
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398 return (a*67108864.0+b+0.4) * (1.0/9007199254740992.0); |
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399 } |
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400 |
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401 /* Determine mantissa for uniform doubles */ |
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402 template <> |
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403 double |
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404 rand_uniform<double> (void) |
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405 { |
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406 return randu53 (); |
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407 } |
5742 | 408 |
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409 /* Determine mantissa for uniform floats */ |
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410 template <> |
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411 float |
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412 rand_uniform<float> (void) |
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413 { |
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414 return randu24 (); |
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415 } |
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416 |
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417 /* ===== Ziggurat normal and exponential generators ===== */ |
5742 | 418 |
419 #define ZIGGURAT_TABLE_SIZE 256 | |
420 | |
421 #define ZIGGURAT_NOR_R 3.6541528853610088 | |
422 #define ZIGGURAT_NOR_INV_R 0.27366123732975828 | |
423 #define NOR_SECTION_AREA 0.00492867323399 | |
424 | |
425 #define ZIGGURAT_EXP_R 7.69711747013104972 | |
426 #define ZIGGURAT_EXP_INV_R 0.129918765548341586 | |
427 #define EXP_SECTION_AREA 0.0039496598225815571993 | |
428 | |
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429 #define ZIGINT uint64_t |
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430 #define EMANTISSA 9007199254740992.0 /* 53 bit mantissa */ |
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431 #define ERANDI randi53() /* 53 bits for mantissa */ |
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432 #define NMANTISSA EMANTISSA |
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433 #define NRANDI randi54() /* 53 bits for mantissa + 1 bit sign */ |
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434 #define RANDU randu53() |
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435 |
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436 static ZIGINT ki[ZIGGURAT_TABLE_SIZE]; |
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437 static double wi[ZIGGURAT_TABLE_SIZE], fi[ZIGGURAT_TABLE_SIZE]; |
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438 static ZIGINT ke[ZIGGURAT_TABLE_SIZE]; |
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439 static double we[ZIGGURAT_TABLE_SIZE], fe[ZIGGURAT_TABLE_SIZE]; |
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440 |
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441 /* |
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442 This code is based on the paper Marsaglia and Tsang, "The ziggurat method |
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443 for generating random variables", Journ. Statistical Software. Code was |
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444 presented in this paper for a Ziggurat of 127 levels and using a 32 bit |
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445 integer random number generator. This version of the code, uses the |
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446 Mersenne Twister as the integer generator and uses 256 levels in the |
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447 Ziggurat. This has several advantages. |
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449 1) As Marsaglia and Tsang themselves states, the more levels the few |
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450 times the expensive tail algorithm must be called |
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451 2) The cycle time of the generator is determined by the integer |
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452 generator, thus the use of a Mersenne Twister for the core random |
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453 generator makes this cycle extremely long. |
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454 3) The license on the original code was unclear, thus rewriting the code |
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455 from the article means we are free of copyright issues. |
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456 4) Compile flag for full 53-bit random mantissa. |
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458 It should be stated that the authors made my life easier, by the fact that |
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459 the algorithm developed in the text of the article is for a 256 level |
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460 ziggurat, even if the code itself isn't... |
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462 One modification to the algorithm developed in the article, is that it is |
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463 assumed that 0 <= x < Inf, and "unsigned long"s are used, thus resulting in |
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464 terms like 2^32 in the code. As the normal distribution is defined between |
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465 -Inf < x < Inf, we effectively only have 31 bit integers plus a sign. Thus |
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466 in Marsaglia and Tsang, terms like 2^32 become 2^31. We use NMANTISSA for |
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467 this term. The exponential distribution is one sided so we use the |
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468 full 32 bits. We use EMANTISSA for this term. |
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470 It appears that I'm slightly slower than the code in the article, this |
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471 is partially due to a better generator of random integers than they |
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472 use. But might also be that the case of rapid return was optimized by |
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473 inlining the relevant code with a #define. As the basic Mersenne |
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474 Twister is only 25% faster than this code I suspect that the main |
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475 reason is just the use of the Mersenne Twister and not the inlining, |
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476 so I'm not going to try and optimize further. |
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477 */ |
5742 | 478 |
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479 void create_ziggurat_tables (void) |
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480 { |
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481 int i; |
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482 double x, x1; |
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483 |
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484 /* Ziggurat tables for the normal distribution */ |
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485 x1 = ZIGGURAT_NOR_R; |
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486 wi[255] = x1 / NMANTISSA; |
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487 fi[255] = exp (-0.5 * x1 * x1); |
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488 |
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489 /* Index zero is special for tail strip, where Marsaglia and Tsang |
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490 * defines this as |
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491 * k_0 = 2^31 * r * f(r) / v, w_0 = 0.5^31 * v / f(r), f_0 = 1, |
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492 * where v is the area of each strip of the ziggurat. |
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493 */ |
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494 ki[0] = static_cast<ZIGINT> (x1 * fi[255] / NOR_SECTION_AREA * NMANTISSA); |
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495 wi[0] = NOR_SECTION_AREA / fi[255] / NMANTISSA; |
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496 fi[0] = 1.; |
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498 for (i = 254; i > 0; i--) |
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499 { |
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500 /* New x is given by x = f^{-1}(v/x_{i+1} + f(x_{i+1})), thus |
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501 * need inverse operator of y = exp(-0.5*x*x) -> x = sqrt(-2*ln(y)) |
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502 */ |
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503 x = std::sqrt (-2. * std::log (NOR_SECTION_AREA / x1 + fi[i+1])); |
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504 ki[i+1] = static_cast<ZIGINT> (x / x1 * NMANTISSA); |
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505 wi[i] = x / NMANTISSA; |
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506 fi[i] = exp (-0.5 * x * x); |
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507 x1 = x; |
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508 } |
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510 ki[1] = 0; |
5742 | 511 |
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512 /* Zigurrat tables for the exponential distribution */ |
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513 x1 = ZIGGURAT_EXP_R; |
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514 we[255] = x1 / EMANTISSA; |
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515 fe[255] = exp (-x1); |
5742 | 516 |
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517 /* Index zero is special for tail strip, where Marsaglia and Tsang |
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518 * defines this as |
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519 * k_0 = 2^32 * r * f(r) / v, w_0 = 0.5^32 * v / f(r), f_0 = 1, |
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520 * where v is the area of each strip of the ziggurat. |
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521 */ |
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522 ke[0] = static_cast<ZIGINT> (x1 * fe[255] / EXP_SECTION_AREA * EMANTISSA); |
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523 we[0] = EXP_SECTION_AREA / fe[255] / EMANTISSA; |
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524 fe[0] = 1.; |
5742 | 525 |
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526 for (i = 254; i > 0; i--) |
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527 { |
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528 /* New x is given by x = f^{-1}(v/x_{i+1} + f(x_{i+1})), thus |
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529 * need inverse operator of y = exp(-x) -> x = -ln(y) |
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530 */ |
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531 x = - std::log (EXP_SECTION_AREA / x1 + fe[i+1]); |
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532 ke[i+1] = static_cast<ZIGINT> (x / x1 * EMANTISSA); |
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533 we[i] = x / EMANTISSA; |
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534 fe[i] = exp (-x); |
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535 x1 = x; |
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536 } |
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537 ke[1] = 0; |
5742 | 538 |
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539 initt = 0; |
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540 } |
5742 | 541 |
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542 /* |
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543 * Here is the guts of the algorithm. As Marsaglia and Tsang state the |
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544 * algorithm in their paper |
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545 * |
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546 * 1) Calculate a random signed integer j and let i be the index |
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547 * provided by the rightmost 8-bits of j |
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548 * 2) Set x = j * w_i. If j < k_i return x |
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549 * 3) If i = 0, then return x from the tail |
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550 * 4) If [f(x_{i-1}) - f(x_i)] * U < f(x) - f(x_i), return x |
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551 * 5) goto step 1 |
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552 * |
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553 * Where f is the functional form of the distribution, which for a normal |
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554 * distribution is exp(-0.5*x*x) |
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555 */ |
5742 | 556 |
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557 |
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558 template <> double rand_normal<double> (void) |
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559 { |
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560 if (initt) |
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561 create_ziggurat_tables (); |
5742 | 562 |
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563 while (1) |
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564 { |
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565 /* The following code is specialized for 32-bit mantissa. |
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566 * Compared to the arbitrary mantissa code, there is a performance |
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567 * gain for 32-bits: PPC: 2%, MIPS: 8%, x86: 40% |
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568 * There is a bigger performance gain compared to using a full |
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569 * 53-bit mantissa: PPC: 60%, MIPS: 65%, x86: 240% |
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570 * Of course, different compilers and operating systems may |
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571 * have something to do with this. |
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572 */ |
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573 # if defined (HAVE_X86_32) |
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574 /* 53-bit mantissa, 1-bit sign, x86 32-bit architecture */ |
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575 double x; |
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576 int si,idx; |
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577 uint32_t lo, hi; |
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578 int64_t rabs; |
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579 uint32_t *p = (uint32_t *)&rabs; |
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580 lo = randi32 (); |
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581 idx = lo & 0xFF; |
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582 hi = randi32 (); |
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583 si = hi & UMASK; |
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584 p[0] = lo; |
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585 p[1] = hi & 0x1FFFFF; |
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586 x = ( si ? -rabs : rabs ) * wi[idx]; |
22188 | 587 # else |
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588 /* arbitrary mantissa (selected by NRANDI, with 1 bit for sign) */ |
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589 const uint64_t r = NRANDI; |
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590 const int64_t rabs = r >> 1; |
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591 const int idx = static_cast<int> (rabs & 0xFF); |
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592 const double x = ( (r & 1) ? -rabs : rabs) * wi[idx]; |
22188 | 593 # endif |
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594 if (rabs < static_cast<int64_t> (ki[idx])) |
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595 return x; /* 99.3% of the time we return here 1st try */ |
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596 else if (idx == 0) |
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597 { |
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598 /* As stated in Marsaglia and Tsang |
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599 * |
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600 * For the normal tail, the method of Marsaglia[5] provides: |
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601 * generate x = -ln(U_1)/r, y = -ln(U_2), until y+y > x*x, |
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602 * then return r+x. Except that r+x is always in the positive |
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603 * tail!!!! Any thing random might be used to determine the |
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604 * sign, but as we already have r we might as well use it |
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605 * |
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606 * [PAK] but not the bottom 8 bits, since they are all 0 here! |
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607 */ |
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608 double xx, yy; |
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609 do |
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610 { |
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611 xx = - ZIGGURAT_NOR_INV_R * std::log (RANDU); |
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612 yy = - std::log (RANDU); |
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613 } |
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614 while ( yy+yy <= xx*xx); |
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615 return ((rabs & 0x100) ? -ZIGGURAT_NOR_R-xx : ZIGGURAT_NOR_R+xx); |
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616 } |
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617 else if ((fi[idx-1] - fi[idx]) * RANDU + fi[idx] < exp (-0.5*x*x)) |
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618 return x; |
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619 } |
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620 } |
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621 |
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622 template <> double rand_exponential<double> (void) |
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623 { |
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624 if (initt) |
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625 create_ziggurat_tables (); |
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627 while (1) |
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628 { |
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629 ZIGINT ri = ERANDI; |
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630 const int idx = static_cast<int> (ri & 0xFF); |
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631 const double x = ri * we[idx]; |
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632 if (ri < ke[idx]) |
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633 return x; /* 98.9% of the time we return here 1st try */ |
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634 else if (idx == 0) |
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635 { |
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636 /* As stated in Marsaglia and Tsang |
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637 * |
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638 * For the exponential tail, the method of Marsaglia[5] provides: |
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639 * x = r - ln(U); |
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640 */ |
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641 return ZIGGURAT_EXP_R - std::log (RANDU); |
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642 } |
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643 else if ((fe[idx-1] - fe[idx]) * RANDU + fe[idx] < exp (-x)) |
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644 return x; |
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645 } |
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646 } |
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648 template <> void rand_uniform<double> (octave_idx_type n, double *p) |
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649 { |
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650 std::generate_n (p, n, [](void) { return rand_uniform<double> (); }); |
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651 } |
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652 |
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653 template <> void rand_normal (octave_idx_type n, double *p) |
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654 { |
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655 std::generate_n (p, n, [](void) { return rand_normal<double> (); }); |
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656 } |
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657 |
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658 template <> void rand_exponential (octave_idx_type n, double *p) |
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659 { |
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660 std::generate_n (p, n, [](void) { return rand_exponential<double> (); }); |
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661 } |
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663 #undef ZIGINT |
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664 #undef EMANTISSA |
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665 #undef ERANDI |
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666 #undef NMANTISSA |
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667 #undef NRANDI |
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668 #undef RANDU |
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669 |
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670 #define ZIGINT uint32_t |
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671 #define EMANTISSA 4294967296.0 /* 32 bit mantissa */ |
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672 #define ERANDI randi32() /* 32 bits for mantissa */ |
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673 #define NMANTISSA 2147483648.0 /* 31 bit mantissa */ |
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674 #define NRANDI randi32() /* 31 bits for mantissa + 1 bit sign */ |
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675 #define RANDU randu24() |
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676 |
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677 static ZIGINT fki[ZIGGURAT_TABLE_SIZE]; |
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678 static float fwi[ZIGGURAT_TABLE_SIZE], ffi[ZIGGURAT_TABLE_SIZE]; |
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679 static ZIGINT fke[ZIGGURAT_TABLE_SIZE]; |
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680 static float fwe[ZIGGURAT_TABLE_SIZE], ffe[ZIGGURAT_TABLE_SIZE]; |
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681 |
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682 static void create_ziggurat_float_tables (void) |
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683 { |
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684 int i; |
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685 float x, x1; |
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686 |
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687 /* Ziggurat tables for the normal distribution */ |
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688 x1 = ZIGGURAT_NOR_R; |
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689 fwi[255] = x1 / NMANTISSA; |
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690 ffi[255] = exp (-0.5 * x1 * x1); |
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691 |
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692 /* Index zero is special for tail strip, where Marsaglia and Tsang |
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693 * defines this as |
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694 * k_0 = 2^31 * r * f(r) / v, w_0 = 0.5^31 * v / f(r), f_0 = 1, |
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695 * where v is the area of each strip of the ziggurat. |
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696 */ |
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697 fki[0] = static_cast<ZIGINT> (x1 * ffi[255] / NOR_SECTION_AREA * NMANTISSA); |
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698 fwi[0] = NOR_SECTION_AREA / ffi[255] / NMANTISSA; |
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699 ffi[0] = 1.; |
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700 |
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701 for (i = 254; i > 0; i--) |
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702 { |
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703 /* New x is given by x = f^{-1}(v/x_{i+1} + f(x_{i+1})), thus |
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704 * need inverse operator of y = exp(-0.5*x*x) -> x = sqrt(-2*ln(y)) |
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705 */ |
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706 x = std::sqrt (-2. * std::log (NOR_SECTION_AREA / x1 + ffi[i+1])); |
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707 fki[i+1] = static_cast<ZIGINT> (x / x1 * NMANTISSA); |
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708 fwi[i] = x / NMANTISSA; |
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709 ffi[i] = exp (-0.5 * x * x); |
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710 x1 = x; |
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711 } |
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712 |
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713 fki[1] = 0; |
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714 |
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715 /* Zigurrat tables for the exponential distribution */ |
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716 x1 = ZIGGURAT_EXP_R; |
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717 fwe[255] = x1 / EMANTISSA; |
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718 ffe[255] = exp (-x1); |
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719 |
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720 /* Index zero is special for tail strip, where Marsaglia and Tsang |
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721 * defines this as |
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722 * k_0 = 2^32 * r * f(r) / v, w_0 = 0.5^32 * v / f(r), f_0 = 1, |
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723 * where v is the area of each strip of the ziggurat. |
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724 */ |
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725 fke[0] = static_cast<ZIGINT> (x1 * ffe[255] / EXP_SECTION_AREA * EMANTISSA); |
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726 fwe[0] = EXP_SECTION_AREA / ffe[255] / EMANTISSA; |
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727 ffe[0] = 1.; |
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728 |
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729 for (i = 254; i > 0; i--) |
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730 { |
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731 /* New x is given by x = f^{-1}(v/x_{i+1} + f(x_{i+1})), thus |
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732 * need inverse operator of y = exp(-x) -> x = -ln(y) |
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733 */ |
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734 x = - std::log (EXP_SECTION_AREA / x1 + ffe[i+1]); |
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735 fke[i+1] = static_cast<ZIGINT> (x / x1 * EMANTISSA); |
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736 fwe[i] = x / EMANTISSA; |
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737 ffe[i] = exp (-x); |
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738 x1 = x; |
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739 } |
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740 fke[1] = 0; |
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741 |
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742 inittf = 0; |
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743 } |
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744 |
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745 /* |
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746 * Here is the guts of the algorithm. As Marsaglia and Tsang state the |
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747 * algorithm in their paper |
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748 * |
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749 * 1) Calculate a random signed integer j and let i be the index |
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750 * provided by the rightmost 8-bits of j |
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751 * 2) Set x = j * w_i. If j < k_i return x |
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752 * 3) If i = 0, then return x from the tail |
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753 * 4) If [f(x_{i-1}) - f(x_i)] * U < f(x) - f(x_i), return x |
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754 * 5) goto step 1 |
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755 * |
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756 * Where f is the functional form of the distribution, which for a normal |
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757 * distribution is exp(-0.5*x*x) |
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758 */ |
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759 |
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760 template <> float rand_normal<float> (void) |
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761 { |
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762 if (inittf) |
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763 create_ziggurat_float_tables (); |
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764 |
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765 while (1) |
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766 { |
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767 /* 32-bit mantissa */ |
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768 const uint32_t r = randi32 (); |
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769 const uint32_t rabs = r & LMASK; |
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770 const int idx = static_cast<int> (r & 0xFF); |
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771 const float x = static_cast<int32_t> (r) * fwi[idx]; |
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772 if (rabs < fki[idx]) |
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773 return x; /* 99.3% of the time we return here 1st try */ |
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774 else if (idx == 0) |
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775 { |
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776 /* As stated in Marsaglia and Tsang |
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777 * |
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778 * For the normal tail, the method of Marsaglia[5] provides: |
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779 * generate x = -ln(U_1)/r, y = -ln(U_2), until y+y > x*x, |
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780 * then return r+x. Except that r+x is always in the positive |
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781 * tail!!!! Any thing random might be used to determine the |
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782 * sign, but as we already have r we might as well use it |
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783 * |
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784 * [PAK] but not the bottom 8 bits, since they are all 0 here! |
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785 */ |
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786 float xx, yy; |
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787 do |
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788 { |
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789 xx = - ZIGGURAT_NOR_INV_R * std::log (RANDU); |
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790 yy = - std::log (RANDU); |
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791 } |
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792 while ( yy+yy <= xx*xx); |
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793 return ((rabs & 0x100) ? -ZIGGURAT_NOR_R-xx : ZIGGURAT_NOR_R+xx); |
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794 } |
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795 else if ((ffi[idx-1] - ffi[idx]) * RANDU + ffi[idx] < exp (-0.5*x*x)) |
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796 return x; |
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797 } |
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798 } |
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799 |
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800 template <> float rand_exponential<float> (void) |
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801 { |
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802 if (inittf) |
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803 create_ziggurat_float_tables (); |
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804 |
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805 while (1) |
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806 { |
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807 ZIGINT ri = ERANDI; |
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808 const int idx = static_cast<int> (ri & 0xFF); |
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809 const float x = ri * fwe[idx]; |
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810 if (ri < fke[idx]) |
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811 return x; /* 98.9% of the time we return here 1st try */ |
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812 else if (idx == 0) |
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813 { |
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814 /* As stated in Marsaglia and Tsang |
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815 * |
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816 * For the exponential tail, the method of Marsaglia[5] provides: |
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817 * x = r - ln(U); |
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818 */ |
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819 return ZIGGURAT_EXP_R - std::log (RANDU); |
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820 } |
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821 else if ((ffe[idx-1] - ffe[idx]) * RANDU + ffe[idx] < exp (-x)) |
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822 return x; |
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823 } |
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824 } |
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825 |
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826 template <> void rand_uniform (octave_idx_type n, float *p) |
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827 { |
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828 std::generate_n (p, n, [](void) { return rand_uniform<float> (); }); |
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829 } |
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830 |
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831 template <> void rand_normal (octave_idx_type n, float *p) |
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832 { |
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833 std::generate_n (p, n, [](void) { return rand_normal<float> (); }); |
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John W. Eaton <jwe@octave.org>
parents:
25054
diff
changeset
|
834 } |
49e0447413ad
use templates and move rand functions inside octave namespace
John W. Eaton <jwe@octave.org>
parents:
25054
diff
changeset
|
835 |
49e0447413ad
use templates and move rand functions inside octave namespace
John W. Eaton <jwe@octave.org>
parents:
25054
diff
changeset
|
836 template <> void rand_exponential (octave_idx_type n, float *p) |
49e0447413ad
use templates and move rand functions inside octave namespace
John W. Eaton <jwe@octave.org>
parents:
25054
diff
changeset
|
837 { |
49e0447413ad
use templates and move rand functions inside octave namespace
John W. Eaton <jwe@octave.org>
parents:
25054
diff
changeset
|
838 std::generate_n (p, n, [](void) { return rand_exponential<float> (); }); |
49e0447413ad
use templates and move rand functions inside octave namespace
John W. Eaton <jwe@octave.org>
parents:
25054
diff
changeset
|
839 } |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14138
diff
changeset
|
840 } |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14138
diff
changeset
|
841 |