Mercurial > hg > octave-lyh
annotate src/DLD-FUNCTIONS/rand.cc @ 13727:478efc95cb7a
Test unordered_map header location and namespace.
* configure.ac: Add tests for <unordered_map> and <tr1/unordered_map>.
Add test to determine whether std::tr1 namespace is required for unordered_map.
* rand.cc: Use tests result.
| author | Michael Goffioul <michael.goffioul@gmail.com> |
|---|---|
| date | Thu, 20 Oct 2011 12:51:31 +0100 |
| parents | dd3c5325039c |
| children | 0206484682c6 |
| rev | line source |
|---|---|
| 2928 | 1 /* |
| 2 | |
| 11523 | 3 Copyright (C) 1996-2011 John W. Eaton |
|
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4 Copyright (C) 2009 VZLU Prague |
| 2928 | 5 |
| 6 This file is part of Octave. | |
| 7 | |
| 8 Octave is free software; you can redistribute it and/or modify it | |
| 9 under the terms of the GNU General Public License as published by the | |
| 7016 | 10 Free Software Foundation; either version 3 of the License, or (at your |
| 11 option) any later version. | |
| 2928 | 12 |
| 13 Octave is distributed in the hope that it will be useful, but WITHOUT | |
| 14 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
| 15 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
| 16 for more details. | |
| 17 | |
| 18 You should have received a copy of the GNU General Public License | |
| 7016 | 19 along with Octave; see the file COPYING. If not, see |
| 20 <http://www.gnu.org/licenses/>. | |
| 2928 | 21 |
| 22 */ | |
| 23 | |
| 24 #ifdef HAVE_CONFIG_H | |
| 25 #include <config.h> | |
| 26 #endif | |
| 27 | |
| 28 #include <ctime> | |
|
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29 #if defined (HAVE_UNORDERED_MAP) |
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30 #include <unordered_map> |
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31 #elif defined (HAVE_TR1_UNORDERED_MAP) |
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32 #include <tr1/unordered_map> |
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33 #endif |
| 2928 | 34 #include <string> |
| 35 | |
| 36 #include "f77-fcn.h" | |
| 37 #include "lo-mappers.h" | |
| 4307 | 38 #include "oct-rand.h" |
| 4153 | 39 #include "quit.h" |
| 2928 | 40 |
| 41 #include "defun-dld.h" | |
| 42 #include "error.h" | |
| 43 #include "gripes.h" | |
| 44 #include "oct-obj.h" | |
| 45 #include "unwind-prot.h" | |
| 46 #include "utils.h" | |
|
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47 #include "ov-re-mat.h" |
| 2928 | 48 |
|
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49 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
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50 using namespace std::tr1; |
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51 #else |
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52 using namespace std; |
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53 #endif |
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54 |
| 6437 | 55 /* |
| 56 %!shared __random_statistical_tests__ | |
| 57 %! % Flag whether the statistical tests should be run in "make check" or not | |
| 58 %! __random_statistical_tests__ = 0; | |
| 59 */ | |
| 60 | |
| 4307 | 61 static octave_value |
| 5730 | 62 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
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63 const std::string& distribution, bool additional_arg = false) |
| 2928 | 64 { |
| 4307 | 65 octave_value retval; |
| 5730 | 66 NDArray a; |
| 67 int idx = 0; | |
| 68 dim_vector dims; | |
| 2928 | 69 |
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70 unwind_protect frame; |
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71 // Restore current distribution on any exit. |
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72 frame.add_fcn (octave_rand::distribution, |
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73 octave_rand::distribution ()); |
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74 |
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75 octave_rand::distribution (distribution); |
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76 |
| 5730 | 77 if (additional_arg) |
| 78 { | |
| 79 if (nargin == 0) | |
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80 { |
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81 error ("%s: expecting at least one argument", fcn); |
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82 goto done; |
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83 } |
| 5730 | 84 else if (args(0).is_string()) |
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85 additional_arg = false; |
| 5730 | 86 else |
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87 { |
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88 a = args(0).array_value (); |
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89 if (error_state) |
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90 { |
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91 error ("%s: expecting scalar or matrix arguments", fcn); |
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92 goto done; |
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93 } |
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94 idx++; |
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95 nargin--; |
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96 } |
| 5730 | 97 } |
| 2928 | 98 |
| 4543 | 99 switch (nargin) |
| 2928 | 100 { |
| 4543 | 101 case 0: |
| 102 { | |
|
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103 if (additional_arg) |
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104 dims = a.dims (); |
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105 else |
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106 { |
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107 dims.resize (2); |
| 4543 | 108 |
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109 dims(0) = 1; |
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110 dims(1) = 1; |
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111 } |
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112 goto gen_matrix; |
| 4543 | 113 } |
| 114 break; | |
| 2928 | 115 |
| 4543 | 116 case 1: |
| 117 { | |
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118 octave_value tmp = args(idx); |
| 4543 | 119 |
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120 if (tmp.is_string ()) |
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121 { |
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122 std::string s_arg = tmp.string_value (); |
| 2928 | 123 |
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124 if (s_arg == "dist") |
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125 { |
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126 retval = octave_rand::distribution (); |
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127 } |
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128 else if (s_arg == "seed") |
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129 { |
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130 retval = octave_rand::seed (); |
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131 } |
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132 else if (s_arg == "state" || s_arg == "twister") |
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133 { |
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134 retval = octave_rand::state (fcn); |
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135 } |
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136 else if (s_arg == "uniform") |
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137 { |
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138 octave_rand::uniform_distribution (); |
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139 } |
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140 else if (s_arg == "normal") |
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141 { |
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142 octave_rand::normal_distribution (); |
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143 } |
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144 else if (s_arg == "exponential") |
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145 { |
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146 octave_rand::exponential_distribution (); |
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147 } |
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148 else if (s_arg == "poisson") |
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149 { |
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150 octave_rand::poisson_distribution (); |
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151 } |
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152 else if (s_arg == "gamma") |
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153 { |
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154 octave_rand::gamma_distribution (); |
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155 } |
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156 else |
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157 error ("%s: unrecognized string argument", fcn); |
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158 } |
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159 else if (tmp.is_scalar_type ()) |
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160 { |
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161 double dval = tmp.double_value (); |
| 2928 | 162 |
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163 if (xisnan (dval)) |
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164 { |
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165 error ("%s: NaN is invalid matrix dimension", fcn); |
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166 } |
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167 else |
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168 { |
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169 dims.resize (2); |
| 4543 | 170 |
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171 dims(0) = NINTbig (tmp.double_value ()); |
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172 dims(1) = NINTbig (tmp.double_value ()); |
| 2928 | 173 |
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174 if (! error_state) |
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175 goto gen_matrix; |
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176 } |
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177 } |
|
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178 else if (tmp.is_range ()) |
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179 { |
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180 Range r = tmp.range_value (); |
| 4543 | 181 |
|
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182 if (r.all_elements_are_ints ()) |
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183 { |
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184 octave_idx_type n = r.nelem (); |
| 4543 | 185 |
|
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186 dims.resize (n); |
| 4543 | 187 |
|
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188 octave_idx_type base = NINTbig (r.base ()); |
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189 octave_idx_type incr = NINTbig (r.inc ()); |
| 2928 | 190 |
|
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191 for (octave_idx_type i = 0; i < n; i++) |
|
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192 { |
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193 //Negative dimensions are treated as zero for Matlab |
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194 //compatibility |
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195 dims(i) = base >= 0 ? base : 0; |
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196 base += incr; |
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197 } |
| 2928 | 198 |
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199 goto gen_matrix; |
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200 |
|
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201 } |
|
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202 else |
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203 error ("%s: all elements of range must be integers", |
|
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204 fcn); |
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205 } |
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206 else if (tmp.is_matrix_type ()) |
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207 { |
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208 Array<int> iv = tmp.int_vector_value (true); |
| 4543 | 209 |
|
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210 if (! error_state) |
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211 { |
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212 octave_idx_type len = iv.length (); |
| 2928 | 213 |
|
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214 dims.resize (len); |
| 4543 | 215 |
|
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216 for (octave_idx_type i = 0; i < len; i++) |
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217 { |
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218 //Negative dimensions are treated as zero for Matlab |
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219 //compatibility |
|
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220 octave_idx_type elt = iv(i); |
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221 dims(i) = elt >=0 ? elt : 0; |
|
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222 } |
| 2928 | 223 |
|
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224 goto gen_matrix; |
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225 } |
|
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226 else |
|
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227 error ("%s: expecting integer vector", fcn); |
|
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228 } |
|
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229 else |
|
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230 { |
|
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231 gripe_wrong_type_arg ("rand", tmp); |
|
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232 return retval; |
|
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233 } |
| 4543 | 234 } |
| 235 break; | |
| 236 | |
| 237 default: | |
| 238 { | |
|
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239 octave_value tmp = args(idx); |
| 4543 | 240 |
|
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241 if (nargin == 2 && tmp.is_string ()) |
|
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242 { |
|
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243 std::string ts = tmp.string_value (); |
| 5164 | 244 |
|
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245 if (ts == "seed") |
|
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246 { |
|
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247 if (args(idx+1).is_real_scalar ()) |
|
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248 { |
|
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249 double d = args(idx+1).double_value (); |
| 2928 | 250 |
|
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251 if (! error_state) |
|
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252 octave_rand::seed (d); |
|
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253 } |
|
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|
254 else if (args(idx+1).is_string () |
|
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92a85ed5b86e
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255 && args(idx+1).string_value() == "reset") |
|
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|
256 octave_rand::reset (); |
|
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257 else |
|
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258 error ("%s: seed must be a real scalar", fcn); |
|
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259 } |
|
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260 else if (ts == "state" || ts == "twister") |
|
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|
261 { |
|
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|
262 if (args(idx+1).is_string () |
|
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|
263 && args(idx+1).string_value() == "reset") |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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changeset
|
264 octave_rand::reset (fcn); |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
265 else |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
266 { |
|
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changeset
|
267 ColumnVector s = |
|
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|
268 ColumnVector (args(idx+1).vector_value(false, true)); |
| 5730 | 269 |
|
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|
270 if (! error_state) |
|
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|
271 octave_rand::state (s, fcn); |
|
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|
272 } |
|
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273 } |
|
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|
274 else |
|
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|
275 error ("%s: unrecognized string argument", fcn); |
|
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276 } |
|
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277 else |
|
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278 { |
|
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|
279 dims.resize (nargin); |
| 4543 | 280 |
|
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281 for (int i = 0; i < nargin; i++) |
|
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282 { |
|
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changeset
|
283 octave_idx_type elt = args(idx+i).int_value (); |
|
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284 if (error_state) |
|
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285 { |
|
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286 error ("%s: expecting integer arguments", fcn); |
|
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|
287 goto done; |
|
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|
288 } |
|
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|
289 //Negative is zero for Matlab compatibility |
|
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changeset
|
290 dims(i) = elt >= 0 ? elt : 0; |
|
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|
291 } |
| 4543 | 292 |
|
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293 goto gen_matrix; |
|
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|
294 } |
| 4543 | 295 } |
| 296 break; | |
| 2928 | 297 } |
| 298 | |
| 4543 | 299 done: |
| 2928 | 300 |
| 301 return retval; | |
| 302 | |
| 303 gen_matrix: | |
| 304 | |
| 5355 | 305 dims.chop_trailing_singletons (); |
| 306 | |
| 5730 | 307 if (additional_arg) |
| 308 { | |
| 309 if (a.length() == 1) | |
|
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310 return octave_rand::nd_array (dims, a(0)); |
| 5730 | 311 else |
|
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|
312 { |
|
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|
313 if (a.dims() != dims) |
|
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|
314 { |
|
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|
315 error ("%s: mismatch in argument size", fcn); |
|
40dfc0c99116
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|
316 return retval; |
|
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|
317 } |
|
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|
318 octave_idx_type len = a.length (); |
|
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changeset
|
319 NDArray m (dims); |
|
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320 double *v = m.fortran_vec (); |
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321 for (octave_idx_type i = 0; i < len; i++) |
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322 v[i] = octave_rand::scalar (a(i)); |
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323 return m; |
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324 } |
| 5730 | 325 } |
| 326 else | |
| 327 return octave_rand::nd_array (dims); | |
| 2928 | 328 } |
| 329 | |
| 4665 | 330 DEFUN_DLD (rand, args, , |
| 3369 | 331 "-*- texinfo -*-\n\ |
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332 @deftypefn {Loadable Function} {} rand (@var{n})\n\ |
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333 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m}, @dots{})\n\ |
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334 @deftypefnx {Loadable Function} {} rand ([@var{n} @var{m} @dots{}])\n\ |
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335 @deftypefnx {Loadable Function} {@var{v} =} rand (\"state\")\n\ |
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336 @deftypefnx {Loadable Function} {} rand (\"state\", @var{v})\n\ |
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337 @deftypefnx {Loadable Function} {} rand (\"state\", \"reset\")\n\ |
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338 @deftypefnx {Loadable Function} {@var{v} =} rand (\"seed\")\n\ |
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339 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{v})\n\ |
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340 @deftypefnx {Loadable Function} {} rand (\"seed\", \"reset\")\n\ |
| 3369 | 341 Return a matrix with random elements uniformly distributed on the\n\ |
| 342 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
| 5730 | 343 for @code{eye}.\n\ |
| 344 \n\ | |
| 345 You can query the state of the random number generator using the\n\ | |
| 3369 | 346 form\n\ |
| 2928 | 347 \n\ |
| 3369 | 348 @example\n\ |
| 5730 | 349 v = rand (\"state\")\n\ |
| 350 @end example\n\ | |
| 351 \n\ | |
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352 This returns a column vector @var{v} of length 625. Later, you can\n\ |
| 5730 | 353 restore the random number generator to the state @var{v}\n\ |
| 354 using the form\n\ | |
| 355 \n\ | |
| 356 @example\n\ | |
| 357 rand (\"state\", v)\n\ | |
| 3369 | 358 @end example\n\ |
| 359 \n\ | |
| 360 @noindent\n\ | |
| 5730 | 361 You may also initialize the state vector from an arbitrary vector of\n\ |
| 10840 | 362 length @leq{} 625 for @var{v}. This new state will be a hash based on the\n\ |
| 5798 | 363 value of @var{v}, not @var{v} itself.\n\ |
| 5730 | 364 \n\ |
| 365 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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366 available, otherwise from CPU time, wall clock time, and the current\n\ |
| 5730 | 367 fraction of a second.\n\ |
| 368 \n\ | |
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369 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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370 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and\n\ |
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371 T. Nishimura,\n\ |
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372 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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373 pseudorandom number generator}, ACM Trans. on\n\ |
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374 Modeling and Computer Simulation Vol. 8, No. 1, pp. 3-30, January 1998,\n\ |
| 7171 | 375 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
| 6547 | 376 Do @strong{not} use for cryptography without securely hashing\n\ |
| 377 several returned values together, otherwise the generator state\n\ | |
| 378 can be learned after reading 624 consecutive values.\n\ | |
| 5730 | 379 \n\ |
| 7096 | 380 Older versions of Octave used a different random number generator.\n\ |
| 381 The new generator is used by default\n\ | |
| 5730 | 382 as it is significantly faster than the old generator, and produces\n\ |
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383 random numbers with a significantly longer cycle time. However, in\n\ |
| 5798 | 384 some circumstances it might be desirable to obtain the same random\n\ |
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385 sequences as used by the old generators. To do this the keyword\n\ |
| 5730 | 386 \"seed\" is used to specify that the old generators should be use,\n\ |
| 387 as in\n\ | |
| 2928 | 388 \n\ |
| 3369 | 389 @example\n\ |
| 5730 | 390 rand (\"seed\", val)\n\ |
| 3369 | 391 @end example\n\ |
| 392 \n\ | |
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393 @noindent\n\ |
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394 which sets the seed of the generator to @var{val}. The seed of the\n\ |
| 5730 | 395 generator can be queried with\n\ |
| 396 \n\ | |
| 397 @example\n\ | |
| 398 s = rand (\"seed\")\n\ | |
| 399 @end example\n\ | |
| 400 \n\ | |
| 401 However, it should be noted that querying the seed will not cause\n\ | |
| 402 @code{rand} to use the old generators, only setting the seed will.\n\ | |
| 403 To cause @code{rand} to once again use the new generators, the\n\ | |
| 404 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
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405 \n\ |
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406 The state or seed of the generator can be reset to a new random value\n\ |
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407 using the \"reset\" keyword.\n\ |
| 5798 | 408 @seealso{randn, rande, randg, randp}\n\ |
| 3369 | 409 @end deftypefn") |
| 2928 | 410 { |
| 4307 | 411 octave_value retval; |
| 2928 | 412 |
| 413 int nargin = args.length (); | |
| 414 | |
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415 retval = do_rand (args, nargin, "rand", "uniform"); |
| 2928 | 416 |
| 417 return retval; | |
| 418 } | |
| 419 | |
| 8871 | 420 // FIXME -- The old generator (selected when "seed" is set) will not |
| 421 // work properly if compiled to use 64-bit integers. | |
| 422 | |
| 5730 | 423 /* |
| 424 %!test # 'state' can be a scalar | |
| 425 %! rand('state',12); x = rand(1,4); | |
| 426 %! rand('state',12); y = rand(1,4); | |
| 427 %! assert(x,y); | |
| 428 %!test # 'state' can be a vector | |
| 429 %! rand('state',[12,13]); x=rand(1,4); | |
| 430 %! rand('state',[12;13]); y=rand(1,4); | |
| 431 %! assert(x,y); | |
| 432 %!test # querying 'state' doesn't disturb sequence | |
| 433 %! rand('state',12); rand(1,2); x=rand(1,2); | |
| 434 %! rand('state',12); rand(1,2); | |
| 435 %! s=rand('state'); y=rand(1,2); | |
| 436 %! assert(x,y); | |
| 437 %! rand('state',s); z=rand(1,2); | |
| 438 %! assert(x,z); | |
| 439 %!test # 'seed' must be a scalar | |
| 440 %! rand('seed',12); x = rand(1,4); | |
| 441 %! rand('seed',12); y = rand(1,4); | |
| 442 %! assert(x,y); | |
| 443 %!error(rand('seed',[12,13])) | |
| 444 %!test # querying 'seed' returns a value which can be used later | |
| 445 %! s=rand('seed'); x=rand(1,2); | |
| 446 %! rand('seed',s); y=rand(1,2); | |
| 447 %! assert(x,y); | |
| 448 %!test # querying 'seed' doesn't disturb sequence | |
| 449 %! rand('seed',12); rand(1,2); x=rand(1,2); | |
| 450 %! rand('seed',12); rand(1,2); | |
| 451 %! s=rand('seed'); y=rand(1,2); | |
| 452 %! assert(x,y); | |
| 453 %! rand('seed',s); z=rand(1,2); | |
| 454 %! assert(x,z); | |
| 455 */ | |
| 456 | |
| 457 /* | |
| 458 %!test | |
| 6437 | 459 %! % Test fixed state |
| 460 %! rand("state",1); | |
| 6443 | 461 %! assert (rand(1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382],1e-6); |
| 6437 | 462 %!test |
| 6443 | 463 %! % Test fixed seed |
| 6437 | 464 %! rand("seed",1); |
| 6443 | 465 %! assert (rand(1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759],1e-6); |
| 5730 | 466 %!test |
| 6437 | 467 %! if (__random_statistical_tests__) |
| 468 %! % statistical tests may fail occasionally. | |
| 469 %! rand("state",12); | |
| 470 %! x = rand(100000,1); | |
| 471 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 472 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 473 %! assert(mean(x),0.5,0.0024); | |
| 474 %! assert(var(x),1/48,0.0632); | |
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475 %! assert(skewness(x),0,0.012); |
| 6437 | 476 %! assert(kurtosis(x),-6/5,0.0094); |
| 477 %! endif | |
| 478 %!test | |
| 479 %! if (__random_statistical_tests__) | |
| 480 %! % statistical tests may fail occasionally. | |
| 481 %! rand("seed",12); | |
| 482 %! x = rand(100000,1); | |
| 483 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 484 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 485 %! assert(mean(x),0.5,0.0024); | |
| 486 %! assert(var(x),1/48,0.0632); | |
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487 %! assert(skewness(x),0,0.012); |
| 6437 | 488 %! assert(kurtosis(x),-6/5,0.0094); |
| 489 %! endif | |
| 5730 | 490 */ |
| 491 | |
| 492 | |
| 4307 | 493 static std::string current_distribution = octave_rand::distribution (); |
| 494 | |
| 4665 | 495 DEFUN_DLD (randn, args, , |
| 3369 | 496 "-*- texinfo -*-\n\ |
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497 @deftypefn {Loadable Function} {} randn (@var{n})\n\ |
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498 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m}, @dots{})\n\ |
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499 @deftypefnx {Loadable Function} {} randn ([@var{n} @var{m} @dots{}])\n\ |
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500 @deftypefnx {Loadable Function} {@var{v} =} randn (\"state\")\n\ |
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501 @deftypefnx {Loadable Function} {} randn (\"state\", @var{v})\n\ |
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502 @deftypefnx {Loadable Function} {} randn (\"state\", \"reset\")\n\ |
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503 @deftypefnx {Loadable Function} {@var{v} =} randn (\"seed\")\n\ |
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504 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{v})\n\ |
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505 @deftypefnx {Loadable Function} {} randn (\"seed\", \"reset\")\n\ |
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506 Return a matrix with normally distributed random\n\ |
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507 elements having zero mean and variance one. The arguments are\n\ |
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508 handled the same as the arguments for @code{rand}.\n\ |
| 3369 | 509 \n\ |
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510 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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511 to transform from a uniform to a normal distribution.\n\ |
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512 \n\ |
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513 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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514 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 515 J. Statistical Software, vol 5, 2000,\n\ |
| 516 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 2928 | 517 \n\ |
| 6547 | 518 @seealso{rand, rande, randg, randp}\n\ |
| 3369 | 519 @end deftypefn") |
| 2928 | 520 { |
| 4307 | 521 octave_value retval; |
| 2928 | 522 |
| 523 int nargin = args.length (); | |
| 524 | |
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525 retval = do_rand (args, nargin, "randn", "normal"); |
| 2928 | 526 |
| 527 return retval; | |
| 528 } | |
| 529 | |
| 530 /* | |
| 5730 | 531 %!test |
| 6437 | 532 %! % Test fixed state |
| 533 %! randn("state",1); | |
| 6443 | 534 %! assert (randn(1,6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116],1e-6); |
| 6437 | 535 %!test |
| 6443 | 536 %! % Test fixed seed |
| 6437 | 537 %! randn("seed",1); |
| 6443 | 538 %! assert (randn(1,6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133],1e-6); |
| 5730 | 539 %!test |
| 6437 | 540 %! if (__random_statistical_tests__) |
| 541 %! % statistical tests may fail occasionally. | |
| 542 %! randn("state",12); | |
| 543 %! x = randn(100000,1); | |
| 544 %! assert(mean(x),0,0.01); | |
| 545 %! assert(var(x),1,0.02); | |
| 546 %! assert(skewness(x),0,0.02); | |
| 547 %! assert(kurtosis(x),0,0.04); | |
| 548 %! endif | |
| 549 %!test | |
| 550 %! if (__random_statistical_tests__) | |
| 551 %! % statistical tests may fail occasionally. | |
| 552 %! randn("seed",12); | |
| 553 %! x = randn(100000,1); | |
| 554 %! assert(mean(x),0,0.01); | |
| 555 %! assert(var(x),1,0.02); | |
| 556 %! assert(skewness(x),0,0.02); | |
| 557 %! assert(kurtosis(x),0,0.04); | |
| 558 %! endif | |
| 5730 | 559 */ |
| 560 | |
| 561 DEFUN_DLD (rande, args, , | |
| 562 "-*- texinfo -*-\n\ | |
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563 @deftypefn {Loadable Function} {} rande (@var{n})\n\ |
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564 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m}, @dots{})\n\ |
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565 @deftypefnx {Loadable Function} {} rande ([@var{n} @var{m} @dots{}])\n\ |
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566 @deftypefnx {Loadable Function} {@var{v} =} rande (\"state\")\n\ |
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567 @deftypefnx {Loadable Function} {} rande (\"state\", @var{v})\n\ |
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568 @deftypefnx {Loadable Function} {} rande (\"state\", \"reset\")\n\ |
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569 @deftypefnx {Loadable Function} {@var{v} =} rande (\"seed\")\n\ |
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570 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{v})\n\ |
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571 @deftypefnx {Loadable Function} {} rande (\"seed\", \"reset\")\n\ |
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572 Return a matrix with exponentially distributed random elements. The\n\ |
| 5730 | 573 arguments are handled the same as the arguments for @code{rand}.\n\ |
| 574 \n\ | |
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575 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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576 to transform from a uniform to an exponential distribution.\n\ |
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577 \n\ |
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578 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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579 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 580 J. Statistical Software, vol 5, 2000,\n\ |
| 581 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
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582 \n\ |
| 6547 | 583 @seealso{rand, randn, randg, randp}\n\ |
| 5730 | 584 @end deftypefn") |
| 585 { | |
| 586 octave_value retval; | |
| 587 | |
| 588 int nargin = args.length (); | |
| 589 | |
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590 retval = do_rand (args, nargin, "rande", "exponential"); |
| 5730 | 591 |
| 592 return retval; | |
| 593 } | |
| 594 | |
| 595 /* | |
| 596 %!test | |
| 6437 | 597 %! % Test fixed state |
| 598 %! rande("state",1); | |
| 6443 | 599 %! assert (rande(1,6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291],1e-6); |
| 6437 | 600 %!test |
| 6443 | 601 %! % Test fixed seed |
| 6437 | 602 %! rande("seed",1); |
| 6443 | 603 %! assert (rande(1,6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087],1e-6); |
| 5730 | 604 %!test |
| 6437 | 605 %! if (__random_statistical_tests__) |
| 606 %! % statistical tests may fail occasionally | |
| 607 %! rande("state",1); | |
| 608 %! x = rande(100000,1); | |
| 609 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 610 %! assert(mean(x),1,0.01); | |
| 611 %! assert(var(x),1,0.03); | |
| 612 %! assert(skewness(x),2,0.06); | |
| 613 %! assert(kurtosis(x),6,0.7); | |
| 614 %! endif | |
| 615 %!test | |
| 616 %! if (__random_statistical_tests__) | |
| 617 %! % statistical tests may fail occasionally | |
| 618 %! rande("seed",1); | |
| 619 %! x = rande(100000,1); | |
| 620 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 621 %! assert(mean(x),1,0.01); | |
| 622 %! assert(var(x),1,0.03); | |
| 623 %! assert(skewness(x),2,0.06); | |
| 624 %! assert(kurtosis(x),6,0.7); | |
| 625 %! endif | |
| 5730 | 626 */ |
| 627 | |
| 628 DEFUN_DLD (randg, args, , | |
| 629 "-*- texinfo -*-\n\ | |
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630 @deftypefn {Loadable Function} {} randg (@var{n})\n\ |
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631 @deftypefnx {Loadable Function} {} randg (@var{n}, @var{m}, @dots{})\n\ |
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632 @deftypefnx {Loadable Function} {} randg ([@var{n} @var{m} @dots{}])\n\ |
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633 @deftypefnx {Loadable Function} {@var{v} =} randg (\"state\")\n\ |
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634 @deftypefnx {Loadable Function} {} randg (\"state\", @var{v})\n\ |
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635 @deftypefnx {Loadable Function} {} randg (\"state\", \"reset\")\n\ |
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636 @deftypefnx {Loadable Function} {@var{v} =} randg (\"seed\")\n\ |
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637 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{v})\n\ |
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638 @deftypefnx {Loadable Function} {} randg (\"seed\", \"reset\")\n\ |
| 5730 | 639 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ |
| 640 The arguments are handled the same as the arguments for @code{rand},\n\ | |
| 641 except for the argument @var{a}.\n\ | |
| 642 \n\ | |
| 643 This can be used to generate many distributions:\n\ | |
| 644 \n\ | |
| 645 @table @asis\n\ | |
| 6547 | 646 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
| 10840 | 647 \n\ |
| 5730 | 648 @example\n\ |
| 6547 | 649 r = b * randg (a)\n\ |
| 5730 | 650 @end example\n\ |
| 10840 | 651 \n\ |
| 6547 | 652 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
| 10840 | 653 \n\ |
| 5730 | 654 @example\n\ |
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655 @group\n\ |
| 6547 | 656 r1 = randg (a, 1)\n\ |
| 657 r = r1 / (r1 + randg (b, 1))\n\ | |
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658 @end group\n\ |
| 5730 | 659 @end example\n\ |
| 10840 | 660 \n\ |
| 6547 | 661 @item @code{Erlang (a, n)}\n\ |
| 10840 | 662 \n\ |
| 5730 | 663 @example\n\ |
| 6547 | 664 r = a * randg (n)\n\ |
| 5730 | 665 @end example\n\ |
| 10840 | 666 \n\ |
| 6547 | 667 @item @code{chisq (df)} for @code{df > 0}\n\ |
| 10840 | 668 \n\ |
| 5730 | 669 @example\n\ |
| 6547 | 670 r = 2 * randg (df / 2)\n\ |
| 5730 | 671 @end example\n\ |
| 10840 | 672 \n\ |
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673 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
| 10840 | 674 \n\ |
| 5730 | 675 @example\n\ |
| 6547 | 676 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
| 5730 | 677 @end example\n\ |
| 10840 | 678 \n\ |
| 6547 | 679 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
| 10840 | 680 \n\ |
| 5730 | 681 @example\n\ |
| 7096 | 682 @group\n\ |
| 683 ## r1 equals 1 if n1 is infinite\n\ | |
| 684 r1 = 2 * randg (n1 / 2) / n1\n\ | |
| 685 ## r2 equals 1 if n2 is infinite\n\ | |
| 686 r2 = 2 * randg (n2 / 2) / n2\n\ | |
| 5730 | 687 r = r1 / r2\n\n\ |
| 7096 | 688 @end group\n\ |
| 5730 | 689 @end example\n\ |
| 10840 | 690 \n\ |
| 5730 | 691 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
| 10840 | 692 \n\ |
| 5730 | 693 @example\n\ |
| 6547 | 694 r = randp ((1 - p) / p * randg (n))\n\ |
| 5730 | 695 @end example\n\ |
| 10840 | 696 \n\ |
| 6547 | 697 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
| 5730 | 698 (use chisq if @code{L = 0})\n\ |
| 10840 | 699 \n\ |
| 5730 | 700 @example\n\ |
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701 @group\n\ |
| 6547 | 702 r = randp (L / 2)\n\ |
| 703 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
| 704 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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705 @end group\n\ |
| 5730 | 706 @end example\n\ |
| 10840 | 707 \n\ |
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708 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
| 10840 | 709 \n\ |
| 5730 | 710 @example\n\ |
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711 @group\n\ |
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712 r = (randg (a1), @dots{}, randg (ak))\n\ |
| 6547 | 713 r = r / sum (r)\n\ |
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714 @end group\n\ |
| 5730 | 715 @end example\n\ |
| 10840 | 716 \n\ |
| 5730 | 717 @end table\n\ |
| 6547 | 718 @seealso{rand, randn, rande, randp}\n\ |
| 5730 | 719 @end deftypefn") |
| 720 { | |
| 721 octave_value retval; | |
| 722 | |
| 723 int nargin = args.length (); | |
| 724 | |
| 725 if (nargin < 1) | |
| 726 error ("randg: insufficient arguments"); | |
| 727 else | |
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728 retval = do_rand (args, nargin, "randg", "gamma", true); |
| 5730 | 729 |
| 730 return retval; | |
| 731 } | |
| 732 | |
| 733 /* | |
| 734 %!test | |
| 6437 | 735 %! randg("state",12) |
| 736 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report | |
| 737 | |
| 738 | |
| 739 %!test | |
| 740 %! % Test fixed state | |
| 741 %! randg("state",1); | |
| 6443 | 742 %! assert (randg(0.1,1,6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12],1e-6); |
| 6437 | 743 %!test |
| 744 %! % Test fixed state | |
| 745 %! randg("state",1); | |
| 6443 | 746 %! assert (randg(0.95,1,6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939],1e-6); |
| 6437 | 747 %!test |
| 748 %! % Test fixed state | |
| 749 %! randg("state",1); | |
| 6443 | 750 %! assert (randg(1,1,6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533],1e-6); |
| 6437 | 751 %!test |
| 752 %! % Test fixed state | |
| 753 %! randg("state",1); | |
| 6443 | 754 %! assert (randg(10,1,6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059],1e-5); |
| 6437 | 755 %!test |
| 756 %! % Test fixed state | |
| 757 %! randg("state",1); | |
| 6443 | 758 %! assert (randg(100,1,6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118],1e-4); |
| 6437 | 759 %!test |
| 760 %! % Test fixed seed | |
| 761 %! randg("seed",1); | |
| 6443 | 762 %! assert (randg(0.1,1,6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12],1e-6); |
| 6437 | 763 %!test |
| 764 %! % Test fixed seed | |
| 765 %! randg("seed",1); | |
| 6443 | 766 %! assert (randg(0.95,1,6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648],1e-6); |
| 6437 | 767 %!test |
| 768 %! % Test fixed seed | |
| 769 %! randg("seed",1); | |
| 6443 | 770 %! assert (randg(1,1,6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303],1e-6); |
| 6437 | 771 %!test |
| 772 %! % Test fixed seed | |
| 773 %! randg("seed",1); | |
| 6443 | 774 %! assert (randg(10,1,6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186],1e-5); |
| 6437 | 775 %!test |
| 776 %! % Test fixed seed | |
| 777 %! randg("seed",1); | |
| 6443 | 778 %! assert (randg(100,1,6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625],1e-4); |
| 6437 | 779 %!test |
| 780 %! if (__random_statistical_tests__) | |
| 781 %! % statistical tests may fail occasionally. | |
| 782 %! randg("state",12) | |
| 783 %! a=0.1; x = randg(a,100000,1); | |
| 784 %! assert(mean(x), a, 0.01); | |
| 785 %! assert(var(x), a, 0.01); | |
| 786 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 787 %! assert(kurtosis(x),6/a, 50.); | |
| 788 %! endif | |
| 789 %!test | |
| 790 %! if (__random_statistical_tests__) | |
| 791 %! % statistical tests may fail occasionally. | |
| 792 %! randg("state",12) | |
| 793 %! a=0.95; x = randg(a,100000,1); | |
| 794 %! assert(mean(x), a, 0.01); | |
| 795 %! assert(var(x), a, 0.04); | |
| 796 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 797 %! assert(kurtosis(x),6/a, 2.); | |
| 798 %! endif | |
| 799 %!test | |
| 800 %! if (__random_statistical_tests__) | |
| 801 %! % statistical tests may fail occasionally. | |
| 802 %! randg("state",12) | |
| 803 %! a=1; x = randg(a,100000,1); | |
| 804 %! assert(mean(x),a, 0.01); | |
| 805 %! assert(var(x),a, 0.04); | |
| 806 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 807 %! assert(kurtosis(x),6/a, 2.); | |
| 808 %! endif | |
| 809 %!test | |
| 810 %! if (__random_statistical_tests__) | |
| 811 %! % statistical tests may fail occasionally. | |
| 812 %! randg("state",12) | |
| 813 %! a=10; x = randg(a,100000,1); | |
| 814 %! assert(mean(x), a, 0.1); | |
| 815 %! assert(var(x), a, 0.5); | |
| 816 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 817 %! assert(kurtosis(x),6/a, 0.5); | |
| 818 %! endif | |
| 819 %!test | |
| 820 %! if (__random_statistical_tests__) | |
| 821 %! % statistical tests may fail occasionally. | |
| 822 %! randg("state",12) | |
| 823 %! a=100; x = randg(a,100000,1); | |
| 824 %! assert(mean(x), a, 0.2); | |
| 825 %! assert(var(x), a, 2.); | |
| 826 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 827 %! assert(kurtosis(x),6/a, 0.2); | |
| 828 %! endif | |
| 829 %!test | |
| 830 %! randg("seed",12) | |
| 5730 | 831 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report |
| 832 %!test | |
| 6437 | 833 %! if (__random_statistical_tests__) |
| 834 %! % statistical tests may fail occasionally. | |
| 835 %! randg("seed",12) | |
| 836 %! a=0.1; x = randg(a,100000,1); | |
| 837 %! assert(mean(x), a, 0.01); | |
| 838 %! assert(var(x), a, 0.01); | |
| 839 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 840 %! assert(kurtosis(x),6/a, 50.); | |
| 841 %! endif | |
| 5730 | 842 %!test |
| 6437 | 843 %! if (__random_statistical_tests__) |
| 844 %! % statistical tests may fail occasionally. | |
| 845 %! randg("seed",12) | |
| 846 %! a=0.95; x = randg(a,100000,1); | |
| 847 %! assert(mean(x), a, 0.01); | |
| 848 %! assert(var(x), a, 0.04); | |
| 849 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 850 %! assert(kurtosis(x),6/a, 2.); | |
| 851 %! endif | |
| 5730 | 852 %!test |
| 6437 | 853 %! if (__random_statistical_tests__) |
| 854 %! % statistical tests may fail occasionally. | |
| 855 %! randg("seed",12) | |
| 856 %! a=1; x = randg(a,100000,1); | |
| 857 %! assert(mean(x),a, 0.01); | |
| 858 %! assert(var(x),a, 0.04); | |
| 859 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 860 %! assert(kurtosis(x),6/a, 2.); | |
| 861 %! endif | |
| 5730 | 862 %!test |
| 6437 | 863 %! if (__random_statistical_tests__) |
| 864 %! % statistical tests may fail occasionally. | |
| 865 %! randg("seed",12) | |
| 866 %! a=10; x = randg(a,100000,1); | |
| 867 %! assert(mean(x), a, 0.1); | |
| 868 %! assert(var(x), a, 0.5); | |
| 869 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 870 %! assert(kurtosis(x),6/a, 0.5); | |
| 871 %! endif | |
| 5730 | 872 %!test |
| 6437 | 873 %! if (__random_statistical_tests__) |
| 874 %! % statistical tests may fail occasionally. | |
| 875 %! randg("seed",12) | |
| 876 %! a=100; x = randg(a,100000,1); | |
| 877 %! assert(mean(x), a, 0.2); | |
| 878 %! assert(var(x), a, 2.); | |
| 879 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 880 %! assert(kurtosis(x),6/a, 0.2); | |
| 881 %! endif | |
| 5730 | 882 */ |
| 883 | |
| 884 | |
| 885 DEFUN_DLD (randp, args, , | |
| 886 "-*- texinfo -*-\n\ | |
|
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887 @deftypefn {Loadable Function} {} randp (@var{l}, @var{n})\n\ |
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888 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m}, @dots{})\n\ |
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889 @deftypefnx {Loadable Function} {} randp (@var{l}, [@var{n} @var{m} @dots{}])\n\ |
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890 @deftypefnx {Loadable Function} {@var{v} =} randp (\"state\")\n\ |
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891 @deftypefnx {Loadable Function} {} randp (\"state\", @var{v})\n\ |
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892 @deftypefnx {Loadable Function} {} randp (\"state\", \"reset\")\n\ |
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893 @deftypefnx {Loadable Function} {@var{v} =} randp (\"seed\")\n\ |
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894 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{v})\n\ |
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895 @deftypefnx {Loadable Function} {} randp (\"seed\", \"reset\")\n\ |
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896 Return a matrix with Poisson distributed random elements with mean value\n\ |
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897 parameter given by the first argument, @var{l}. The arguments\n\ |
| 5730 | 898 are handled the same as the arguments for @code{rand}, except for the\n\ |
| 899 argument @var{l}.\n\ | |
| 900 \n\ | |
| 901 Five different algorithms are used depending on the range of @var{l}\n\ | |
| 902 and whether or not @var{l} is a scalar or a matrix.\n\ | |
| 903 \n\ | |
| 904 @table @asis\n\ | |
| 10840 | 905 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
| 906 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
| 907 Cambridge University Press, 1992.\n\ | |
| 908 \n\ | |
| 5730 | 909 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
| 10840 | 910 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
| 911 Cambridge University Press, 1992.\n\ | |
| 912 \n\ | |
| 913 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
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914 E. Stadlober, et al., WinRand source code, available via FTP.\n\ |
| 10840 | 915 \n\ |
| 5730 | 916 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
|
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917 E. Stadlober, et al., WinRand source code, available via FTP, or\n\ |
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918 H. Zechner, @cite{Efficient sampling from continuous and discrete\n\ |
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919 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
| 5730 | 920 University Graz, Austria, 1994.\n\ |
| 10840 | 921 \n\ |
| 5730 | 922 @item For @var{l} > 1e8, use normal approximation.\n\ |
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923 L. Montanet, et al., @cite{Review of Particle Properties}, Physical Review\n\ |
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924 D 50 p1284, 1994.\n\ |
| 5730 | 925 @end table\n\ |
| 6547 | 926 @seealso{rand, randn, rande, randg}\n\ |
| 5730 | 927 @end deftypefn") |
| 928 { | |
| 929 octave_value retval; | |
| 930 | |
| 931 int nargin = args.length (); | |
| 932 | |
| 933 if (nargin < 1) | |
| 934 error ("randp: insufficient arguments"); | |
| 935 else | |
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936 retval = do_rand (args, nargin, "randp", "poisson", true); |
| 5730 | 937 |
| 938 return retval; | |
| 939 } | |
| 940 | |
| 941 /* | |
| 942 %!test | |
| 6437 | 943 %! randp("state",12) |
| 944 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report | |
| 945 %!test | |
| 946 %! % Test fixed state | |
| 947 %! randp("state",1); | |
| 948 %! assert(randp(5,1,6),[5 5 3 7 7 3]) | |
| 949 %!test | |
| 950 %! % Test fixed state | |
| 951 %! randp("state",1); | |
| 952 %! assert(randp(15,1,6),[13 15 8 18 18 15]) | |
| 953 %!test | |
| 954 %! % Test fixed state | |
| 955 %! randp("state",1); | |
| 7421 | 956 %! assert(randp(1e9,1,6),[999915677 999976657 1000047684 1000019035 999985749 999977692],-1e-6) |
| 6437 | 957 %!test |
| 958 %! % Test fixed state | |
| 959 %! randp("seed",1); | |
| 6449 | 960 %! %%assert(randp(5,1,6),[8 2 3 6 6 8]) |
| 961 %! assert(randp(5,1,5),[8 2 3 6 6]) | |
| 6437 | 962 %!test |
| 963 %! % Test fixed state | |
| 964 %! randp("seed",1); | |
| 965 %! assert(randp(15,1,6),[15 16 12 10 10 12]) | |
| 966 %!test | |
| 967 %! % Test fixed state | |
| 968 %! randp("seed",1); | |
| 7421 | 969 %! assert(randp(1e9,1,6),[1000006208 1000012224 999981120 999963520 999963072 999981440],-1e-6) |
| 6437 | 970 %!test |
| 971 %! if (__random_statistical_tests__) | |
| 972 %! % statistical tests may fail occasionally. | |
| 973 %! randp("state",12) | |
| 974 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 975 %! x = randp(a(1),100000,1); | |
| 976 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 977 %! assert(mean(x),a(1),a(2)); | |
| 978 %! assert(var(x),a(1),0.02*a(1)); | |
| 979 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 980 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 981 %! endfor | |
| 982 %! endif | |
| 983 %!test | |
| 984 %! if (__random_statistical_tests__) | |
| 985 %! % statistical tests may fail occasionally. | |
| 986 %! randp("state",12) | |
| 987 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 988 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 989 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 990 %! assert(mean(x),a(1),a(2)); | |
| 991 %! assert(var(x),a(1),0.02*a(1)); | |
| 992 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 993 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 994 %! endfor | |
| 995 %! endif | |
| 996 %!test | |
| 997 %! randp("seed",12) | |
| 5730 | 998 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report |
| 999 %!test | |
| 6449 | 1000 %! if (__random_statistical_tests__) |
| 1001 %! % statistical tests may fail occasionally. | |
| 1002 %! randp("seed",12) | |
| 1003 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 1004 %! x = randp(a(1),100000,1); | |
| 1005 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 1006 %! assert(mean(x),a(1),a(2)); | |
| 1007 %! assert(var(x),a(1),0.02*a(1)); | |
| 1008 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 1009 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1010 %! endfor | |
| 1011 %! endif | |
| 5730 | 1012 %!test |
| 6449 | 1013 %! if (__random_statistical_tests__) |
| 1014 %! % statistical tests may fail occasionally. | |
| 1015 %! randp("seed",12) | |
| 1016 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 1017 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 1018 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 1019 %! assert(mean(x),a(1),a(2)); | |
| 1020 %! assert(var(x),a(1),0.02*a(1)); | |
| 1021 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 1022 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1023 %! endfor | |
| 1024 %! endif | |
| 5730 | 1025 */ |
| 1026 | |
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1027 DEFUN_DLD (randperm, args, , |
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1028 "-*- texinfo -*-\n\ |
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1029 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
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1030 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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1031 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1032 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
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1033 replacement from @code{1:@var{n}}. The complexity is O(@var{n}) in\n\ |
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1034 memory and O(@var{m}) in time, unless @var{m} < @var{n}/5, in which case\n\ |
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1035 O(@var{m}) memory is used as well. The randomization is performed using\n\ |
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1036 rand(). All permutations are equally likely.\n\ |
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1037 @seealso{perms}\n\ |
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1038 @end deftypefn") |
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1039 { |
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1040 int nargin = args.length (); |
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1041 octave_value retval; |
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1042 |
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1043 if (nargin == 1 || nargin == 2) |
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1044 { |
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1045 octave_idx_type n, m; |
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1046 |
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1047 n = args(0).idx_type_value (true); |
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1048 |
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1049 if (nargin == 2) |
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1050 m = args(1).idx_type_value (true); |
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1051 else |
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1052 m = n; |
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1053 |
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1054 if (m < 0 || n < 0) |
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1055 error ("randperm: M and N must be non-negative"); |
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1056 |
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1057 if (m > n) |
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1058 error ("randperm: M must be less than or equal to N"); |
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1059 |
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1060 // Quick and dirty heuristic to decide if we allocate or not the |
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1061 // whole vector for tracking the truncated shuffle. |
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1062 bool short_shuffle = m < n/5 && m < 1e5; |
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1063 |
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1064 if (! error_state) |
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1065 { |
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1066 // Generate random numbers. |
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1067 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
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1068 double *rvec = r.fortran_vec (); |
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1069 |
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1070 octave_idx_type idx_len = short_shuffle ? m : n; |
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1071 Array<octave_idx_type> idx (dim_vector (1, idx_len)); |
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1072 octave_idx_type *ivec = idx.fortran_vec (); |
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1073 |
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1074 for (octave_idx_type i = 0; i < idx_len; i++) |
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1075 ivec[i] = i; |
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1076 |
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1077 if (short_shuffle) |
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1078 { |
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13727
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Test unordered_map header location and namespace.
Michael Goffioul <michael.goffioul@gmail.com>
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1079 unordered_map<octave_idx_type, octave_idx_type> map (m); |
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1080 |
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1081 // Perform the Knuth shuffle only keeping track of moved |
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1082 // entries in the map |
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1083 for (octave_idx_type i = 0; i < m; i++) |
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1084 { |
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1085 octave_idx_type k = i + |
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1086 gnulib::floor (rvec[i] * (n - i)); |
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1087 |
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1088 if (map.find(k) == map.end()) |
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1089 { |
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1090 map[k] = ivec[i]; |
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1091 ivec[i] = k; |
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1092 } |
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1093 else |
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1094 std::swap (ivec[i], map[k]); |
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1095 |
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1096 } |
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1097 } |
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1098 else |
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1099 { |
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1100 |
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1101 // Perform the Knuth shuffle of the first m entries |
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1102 for (octave_idx_type i = 0; i < m; i++) |
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1103 { |
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1104 octave_idx_type k = i + |
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1105 gnulib::floor (rvec[i] * (n - i)); |
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1106 std::swap (ivec[i], ivec[k]); |
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1107 } |
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1108 } |
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1109 |
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1110 // Convert to doubles, reusing r. |
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1111 for (octave_idx_type i = 0; i < m; i++) |
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1112 rvec[i] = ivec[i] + 1; |
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1113 |
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1114 if (m < n) |
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1115 idx.resize (dim_vector (1, m)); |
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1116 |
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1117 // Now create an array object with a cached idx_vector. |
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11586
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strip trailing whitespace from source files
John W. Eaton <jwe@octave.org>
parents:
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diff
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1118 retval = new octave_matrix (r, idx_vector (idx)); |
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1119 } |
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1120 } |
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1121 else |
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1122 print_usage (); |
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1123 |
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1124 return retval; |
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1125 } |
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1126 |
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1127 /* |
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1128 %!assert(sort (randperm (20)),1:20) |
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1129 %!assert(length (randperm (20,10)), 10) |
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1130 */ |