Mercurial > octave
annotate src/DLD-FUNCTIONS/rand.cc @ 14610:41d7e23f5734
Document rand's seed difference from Matlab
* rand.cc (Frand): Mention in docstring that seed is randomly chosen,
not always fixed at startup. Give hint on how to obtain Matlab-like
behaviour.
| author | Jordi Gutiérrez Hermoso <jordigh@octave.org> |
|---|---|
| date | Tue, 08 May 2012 23:23:02 -0400 |
| parents | 60e5cf354d80 |
| children | cd375519eab0 |
| rev | line source |
|---|---|
| 2928 | 1 /* |
| 2 | |
|
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3 Copyright (C) 1996-2012 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 |
| 6437 | 49 /* |
| 50 %!shared __random_statistical_tests__ | |
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51 %! # Flag whether the statistical tests should be run in "make check" or not |
| 6437 | 52 %! __random_statistical_tests__ = 0; |
| 53 */ | |
| 54 | |
| 4307 | 55 static octave_value |
| 5730 | 56 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
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57 const std::string& distribution, bool additional_arg = false) |
| 2928 | 58 { |
| 4307 | 59 octave_value retval; |
| 5730 | 60 NDArray a; |
| 61 int idx = 0; | |
| 62 dim_vector dims; | |
| 2928 | 63 |
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64 unwind_protect frame; |
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65 // Restore current distribution on any exit. |
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66 frame.add_fcn (octave_rand::distribution, |
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67 octave_rand::distribution ()); |
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68 |
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69 octave_rand::distribution (distribution); |
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70 |
| 5730 | 71 if (additional_arg) |
| 72 { | |
| 73 if (nargin == 0) | |
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74 { |
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75 error ("%s: expecting at least one argument", fcn); |
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76 goto done; |
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77 } |
| 5730 | 78 else if (args(0).is_string()) |
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79 additional_arg = false; |
| 5730 | 80 else |
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81 { |
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82 a = args(0).array_value (); |
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83 if (error_state) |
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84 { |
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85 error ("%s: expecting scalar or matrix arguments", fcn); |
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86 goto done; |
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87 } |
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88 idx++; |
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89 nargin--; |
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90 } |
| 5730 | 91 } |
| 2928 | 92 |
| 4543 | 93 switch (nargin) |
| 2928 | 94 { |
| 4543 | 95 case 0: |
| 96 { | |
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97 if (additional_arg) |
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98 dims = a.dims (); |
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99 else |
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100 { |
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101 dims.resize (2); |
| 4543 | 102 |
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103 dims(0) = 1; |
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104 dims(1) = 1; |
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105 } |
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106 goto gen_matrix; |
| 4543 | 107 } |
| 108 break; | |
| 2928 | 109 |
| 4543 | 110 case 1: |
| 111 { | |
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112 octave_value tmp = args(idx); |
| 4543 | 113 |
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114 if (tmp.is_string ()) |
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115 { |
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116 std::string s_arg = tmp.string_value (); |
| 2928 | 117 |
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118 if (s_arg == "dist") |
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119 { |
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120 retval = octave_rand::distribution (); |
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121 } |
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122 else if (s_arg == "seed") |
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123 { |
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124 retval = octave_rand::seed (); |
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125 } |
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126 else if (s_arg == "state" || s_arg == "twister") |
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127 { |
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128 retval = octave_rand::state (fcn); |
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129 } |
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130 else if (s_arg == "uniform") |
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131 { |
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132 octave_rand::uniform_distribution (); |
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133 } |
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134 else if (s_arg == "normal") |
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135 { |
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136 octave_rand::normal_distribution (); |
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137 } |
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138 else if (s_arg == "exponential") |
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139 { |
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140 octave_rand::exponential_distribution (); |
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141 } |
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142 else if (s_arg == "poisson") |
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143 { |
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144 octave_rand::poisson_distribution (); |
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145 } |
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146 else if (s_arg == "gamma") |
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147 { |
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148 octave_rand::gamma_distribution (); |
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149 } |
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150 else |
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151 error ("%s: unrecognized string argument", fcn); |
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152 } |
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153 else if (tmp.is_scalar_type ()) |
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154 { |
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155 double dval = tmp.double_value (); |
| 2928 | 156 |
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157 if (xisnan (dval)) |
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158 { |
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159 error ("%s: NaN is invalid matrix dimension", fcn); |
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160 } |
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161 else |
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162 { |
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163 dims.resize (2); |
| 4543 | 164 |
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165 dims(0) = NINTbig (tmp.double_value ()); |
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166 dims(1) = NINTbig (tmp.double_value ()); |
| 2928 | 167 |
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168 if (! error_state) |
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169 goto gen_matrix; |
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170 } |
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171 } |
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172 else if (tmp.is_range ()) |
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173 { |
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174 Range r = tmp.range_value (); |
| 4543 | 175 |
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176 if (r.all_elements_are_ints ()) |
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177 { |
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178 octave_idx_type n = r.nelem (); |
| 4543 | 179 |
|
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180 dims.resize (n); |
| 4543 | 181 |
|
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182 octave_idx_type base = NINTbig (r.base ()); |
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183 octave_idx_type incr = NINTbig (r.inc ()); |
| 2928 | 184 |
|
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185 for (octave_idx_type i = 0; i < n; i++) |
|
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186 { |
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187 //Negative dimensions are treated as zero for Matlab |
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188 //compatibility |
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189 dims(i) = base >= 0 ? base : 0; |
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190 base += incr; |
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191 } |
| 2928 | 192 |
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193 goto gen_matrix; |
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194 |
|
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195 } |
|
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196 else |
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197 error ("%s: all elements of range must be integers", |
|
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198 fcn); |
|
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199 } |
|
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200 else if (tmp.is_matrix_type ()) |
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201 { |
|
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202 Array<int> iv = tmp.int_vector_value (true); |
| 4543 | 203 |
|
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204 if (! error_state) |
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205 { |
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206 octave_idx_type len = iv.length (); |
| 2928 | 207 |
|
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208 dims.resize (len); |
| 4543 | 209 |
|
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210 for (octave_idx_type i = 0; i < len; i++) |
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211 { |
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212 //Negative dimensions are treated as zero for Matlab |
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213 //compatibility |
|
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214 octave_idx_type elt = iv(i); |
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215 dims(i) = elt >=0 ? elt : 0; |
|
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216 } |
| 2928 | 217 |
|
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218 goto gen_matrix; |
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219 } |
|
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220 else |
|
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221 error ("%s: expecting integer vector", fcn); |
|
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222 } |
|
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223 else |
|
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224 { |
|
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225 gripe_wrong_type_arg ("rand", tmp); |
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226 return retval; |
|
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227 } |
| 4543 | 228 } |
| 229 break; | |
| 230 | |
| 231 default: | |
| 232 { | |
|
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233 octave_value tmp = args(idx); |
| 4543 | 234 |
|
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235 if (nargin == 2 && tmp.is_string ()) |
|
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236 { |
|
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237 std::string ts = tmp.string_value (); |
| 5164 | 238 |
|
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239 if (ts == "seed") |
|
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240 { |
|
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241 if (args(idx+1).is_real_scalar ()) |
|
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242 { |
|
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243 double d = args(idx+1).double_value (); |
| 2928 | 244 |
|
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245 if (! error_state) |
|
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246 octave_rand::seed (d); |
|
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247 } |
|
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|
248 else if (args(idx+1).is_string () |
|
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92a85ed5b86e
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249 && args(idx+1).string_value() == "reset") |
|
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|
250 octave_rand::reset (); |
|
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251 else |
|
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252 error ("%s: seed must be a real scalar", fcn); |
|
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253 } |
|
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254 else if (ts == "state" || ts == "twister") |
|
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|
255 { |
|
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|
256 if (args(idx+1).is_string () |
|
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|
257 && 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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|
258 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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|
259 else |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
260 { |
|
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|
261 ColumnVector s = |
|
10709
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Don't special case color_property type when emitting factory default (bug #30118)
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|
262 ColumnVector (args(idx+1).vector_value(false, true)); |
| 5730 | 263 |
|
10709
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|
264 if (! error_state) |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
265 octave_rand::state (s, fcn); |
|
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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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|
267 } |
|
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|
268 else |
|
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|
269 error ("%s: unrecognized string argument", fcn); |
|
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270 } |
|
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271 else |
|
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272 { |
|
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273 dims.resize (nargin); |
| 4543 | 274 |
|
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275 for (int i = 0; i < nargin; i++) |
|
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276 { |
|
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|
277 octave_idx_type elt = args(idx+i).int_value (); |
|
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278 if (error_state) |
|
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279 { |
|
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280 error ("%s: expecting integer arguments", fcn); |
|
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|
281 goto done; |
|
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|
282 } |
|
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|
283 //Negative is zero for Matlab compatibility |
|
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|
284 dims(i) = elt >= 0 ? elt : 0; |
|
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|
285 } |
| 4543 | 286 |
|
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|
287 goto gen_matrix; |
|
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|
288 } |
| 4543 | 289 } |
| 290 break; | |
| 2928 | 291 } |
| 292 | |
| 4543 | 293 done: |
| 2928 | 294 |
| 295 return retval; | |
| 296 | |
| 297 gen_matrix: | |
| 298 | |
| 5355 | 299 dims.chop_trailing_singletons (); |
| 300 | |
| 5730 | 301 if (additional_arg) |
| 302 { | |
| 303 if (a.length() == 1) | |
|
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|
304 return octave_rand::nd_array (dims, a(0)); |
| 5730 | 305 else |
|
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|
306 { |
|
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|
307 if (a.dims() != dims) |
|
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|
308 { |
|
40dfc0c99116
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|
309 error ("%s: mismatch in argument size", fcn); |
|
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|
310 return retval; |
|
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|
311 } |
|
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|
312 octave_idx_type len = a.length (); |
|
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changeset
|
313 NDArray m (dims); |
|
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changeset
|
314 double *v = m.fortran_vec (); |
|
40dfc0c99116
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changeset
|
315 for (octave_idx_type i = 0; i < len; i++) |
|
40dfc0c99116
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changeset
|
316 v[i] = octave_rand::scalar (a(i)); |
|
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|
317 return m; |
|
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318 } |
| 5730 | 319 } |
| 320 else | |
| 321 return octave_rand::nd_array (dims); | |
| 2928 | 322 } |
| 323 | |
| 4665 | 324 DEFUN_DLD (rand, args, , |
| 3369 | 325 "-*- texinfo -*-\n\ |
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326 @deftypefn {Loadable Function} {} rand (@var{n})\n\ |
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327 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m}, @dots{})\n\ |
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328 @deftypefnx {Loadable Function} {} rand ([@var{n} @var{m} @dots{}])\n\ |
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329 @deftypefnx {Loadable Function} {@var{v} =} rand (\"state\")\n\ |
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330 @deftypefnx {Loadable Function} {} rand (\"state\", @var{v})\n\ |
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331 @deftypefnx {Loadable Function} {} rand (\"state\", \"reset\")\n\ |
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332 @deftypefnx {Loadable Function} {@var{v} =} rand (\"seed\")\n\ |
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333 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{v})\n\ |
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334 @deftypefnx {Loadable Function} {} rand (\"seed\", \"reset\")\n\ |
| 3369 | 335 Return a matrix with random elements uniformly distributed on the\n\ |
| 336 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
| 5730 | 337 for @code{eye}.\n\ |
| 338 \n\ | |
| 339 You can query the state of the random number generator using the\n\ | |
| 3369 | 340 form\n\ |
| 2928 | 341 \n\ |
| 3369 | 342 @example\n\ |
| 5730 | 343 v = rand (\"state\")\n\ |
| 344 @end example\n\ | |
| 345 \n\ | |
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346 This returns a column vector @var{v} of length 625. Later, you can\n\ |
| 5730 | 347 restore the random number generator to the state @var{v}\n\ |
| 348 using the form\n\ | |
| 349 \n\ | |
| 350 @example\n\ | |
| 351 rand (\"state\", v)\n\ | |
| 3369 | 352 @end example\n\ |
| 353 \n\ | |
| 354 @noindent\n\ | |
| 5730 | 355 You may also initialize the state vector from an arbitrary vector of\n\ |
| 10840 | 356 length @leq{} 625 for @var{v}. This new state will be a hash based on the\n\ |
| 5798 | 357 value of @var{v}, not @var{v} itself.\n\ |
| 5730 | 358 \n\ |
| 359 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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360 available, otherwise from CPU time, wall clock time, and the current\n\ |
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361 fraction of a second. Note that this differs from @sc{Matlab}, which\n\ |
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362 always initializes the state to the same state at startup. To obtain\n\ |
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363 behavior comparable to @sc{Matlab}, initialize with a deterministic state\n\ |
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364 vector in Octave's startup files (@pxref{Startup Files}).\n\ |
| 5730 | 365 \n\ |
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366 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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367 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and\n\ |
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368 T. Nishimura,\n\ |
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369 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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370 pseudorandom number generator}, ACM Trans. on\n\ |
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371 Modeling and Computer Simulation Vol. 8, No. 1, pp. 3-30, January 1998,\n\ |
| 7171 | 372 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
| 6547 | 373 Do @strong{not} use for cryptography without securely hashing\n\ |
| 374 several returned values together, otherwise the generator state\n\ | |
| 375 can be learned after reading 624 consecutive values.\n\ | |
| 5730 | 376 \n\ |
| 7096 | 377 Older versions of Octave used a different random number generator.\n\ |
| 378 The new generator is used by default\n\ | |
| 5730 | 379 as it is significantly faster than the old generator, and produces\n\ |
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380 random numbers with a significantly longer cycle time. However, in\n\ |
| 5798 | 381 some circumstances it might be desirable to obtain the same random\n\ |
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382 sequences as used by the old generators. To do this the keyword\n\ |
| 5730 | 383 \"seed\" is used to specify that the old generators should be use,\n\ |
| 384 as in\n\ | |
| 2928 | 385 \n\ |
| 3369 | 386 @example\n\ |
| 5730 | 387 rand (\"seed\", val)\n\ |
| 3369 | 388 @end example\n\ |
| 389 \n\ | |
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390 @noindent\n\ |
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391 which sets the seed of the generator to @var{val}. The seed of the\n\ |
| 5730 | 392 generator can be queried with\n\ |
| 393 \n\ | |
| 394 @example\n\ | |
| 395 s = rand (\"seed\")\n\ | |
| 396 @end example\n\ | |
| 397 \n\ | |
| 398 However, it should be noted that querying the seed will not cause\n\ | |
| 399 @code{rand} to use the old generators, only setting the seed will.\n\ | |
| 400 To cause @code{rand} to once again use the new generators, the\n\ | |
| 401 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
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402 \n\ |
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403 The state or seed of the generator can be reset to a new random value\n\ |
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404 using the \"reset\" keyword.\n\ |
| 5798 | 405 @seealso{randn, rande, randg, randp}\n\ |
| 3369 | 406 @end deftypefn") |
| 2928 | 407 { |
| 4307 | 408 octave_value retval; |
| 2928 | 409 |
| 410 int nargin = args.length (); | |
| 411 | |
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412 retval = do_rand (args, nargin, "rand", "uniform"); |
| 2928 | 413 |
| 414 return retval; | |
| 415 } | |
| 416 | |
| 8871 | 417 // FIXME -- The old generator (selected when "seed" is set) will not |
| 418 // work properly if compiled to use 64-bit integers. | |
| 419 | |
| 5730 | 420 /* |
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421 %!test # "state" can be a scalar |
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422 %! rand ("state", 12); x = rand (1,4); |
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423 %! rand ("state", 12); y = rand (1,4); |
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424 %! assert (x, y); |
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425 %!test # "state" can be a vector |
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426 %! rand ("state", [12,13]); x = rand (1,4); |
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427 %! rand ("state", [12;13]); y = rand (1,4); |
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428 %! assert (x, y); |
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429 %!test # querying "state" doesn't disturb sequence |
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430 %! rand ("state", 12); rand (1,2); x = rand (1,2); |
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431 %! rand ("state", 12); rand (1,2); |
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432 %! s = rand ("state"); y = rand (1,2); |
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433 %! assert (x, y); |
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434 %! rand ("state", s); z = rand (1,2); |
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435 %! assert (x, z); |
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436 %!test # "seed" must be a scalar |
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437 %! rand ("seed", 12); x = rand (1,4); |
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438 %! rand ("seed", 12); y = rand (1,4); |
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439 %! assert (x, y); |
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440 %!error <seed must be a real scalar> rand ("seed", [12,13]) |
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441 %!test # querying "seed" returns a value which can be used later |
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442 %! s = rand ("seed"); x = rand (1,2); |
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443 %! rand ("seed", s); y = rand (1,2); |
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444 %! assert (x, y); |
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445 %!test # querying "seed" doesn't disturb sequence |
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446 %! rand ("seed", 12); rand (1,2); x = rand (1,2); |
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447 %! rand ("seed", 12); rand (1,2); |
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448 %! s = rand ("seed"); y = rand (1,2); |
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449 %! assert (x, y); |
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450 %! rand ("seed", s); z = rand (1,2); |
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451 %! assert (x, z); |
| 5730 | 452 */ |
| 453 | |
| 454 /* | |
| 455 %!test | |
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456 %! # Test fixed state |
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457 %! rand ("state", 1); |
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458 %! assert (rand (1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382], 1e-6); |
| 6437 | 459 %!test |
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460 %! # Test fixed seed |
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461 %! rand ("seed", 1); |
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462 %! assert (rand (1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759], 1e-6); |
| 5730 | 463 %!test |
| 6437 | 464 %! if (__random_statistical_tests__) |
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465 %! # statistical tests may fail occasionally. |
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466 %! rand ("state", 12); |
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467 %! x = rand (100000, 1); |
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468 %! assert (max (x) < 1); #*** Please report this!!! *** |
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469 %! assert (min (x) > 0); #*** Please report this!!! *** |
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470 %! assert (mean (x), 0.5, 0.0024); |
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471 %! assert (var (x), 1/48, 0.0632); |
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472 %! assert (skewness (x), 0, 0.012); |
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473 %! assert (kurtosis (x), -6/5, 0.0094); |
| 6437 | 474 %! endif |
| 475 %!test | |
| 476 %! if (__random_statistical_tests__) | |
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477 %! # statistical tests may fail occasionally. |
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478 %! rand ("seed", 12); |
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479 %! x = rand (100000, 1); |
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480 %! assert (max (x) < 1); #*** Please report this!!! *** |
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481 %! assert (min (x) > 0); #*** Please report this!!! *** |
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482 %! assert (mean (x), 0.5, 0.0024); |
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483 %! assert (var (x), 1/48, 0.0632); |
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484 %! assert (skewness (x), 0, 0.012); |
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485 %! assert (kurtosis (x), -6/5, 0.0094); |
| 6437 | 486 %! endif |
| 5730 | 487 */ |
| 488 | |
| 4307 | 489 static std::string current_distribution = octave_rand::distribution (); |
| 490 | |
| 4665 | 491 DEFUN_DLD (randn, args, , |
| 3369 | 492 "-*- texinfo -*-\n\ |
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493 @deftypefn {Loadable Function} {} randn (@var{n})\n\ |
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494 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m}, @dots{})\n\ |
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495 @deftypefnx {Loadable Function} {} randn ([@var{n} @var{m} @dots{}])\n\ |
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496 @deftypefnx {Loadable Function} {@var{v} =} randn (\"state\")\n\ |
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497 @deftypefnx {Loadable Function} {} randn (\"state\", @var{v})\n\ |
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498 @deftypefnx {Loadable Function} {} randn (\"state\", \"reset\")\n\ |
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499 @deftypefnx {Loadable Function} {@var{v} =} randn (\"seed\")\n\ |
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500 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{v})\n\ |
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501 @deftypefnx {Loadable Function} {} randn (\"seed\", \"reset\")\n\ |
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502 Return a matrix with normally distributed random\n\ |
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503 elements having zero mean and variance one. The arguments are\n\ |
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504 handled the same as the arguments for @code{rand}.\n\ |
| 3369 | 505 \n\ |
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506 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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507 to transform from a uniform to a normal distribution.\n\ |
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508 \n\ |
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509 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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510 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 511 J. Statistical Software, vol 5, 2000,\n\ |
| 512 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 2928 | 513 \n\ |
| 6547 | 514 @seealso{rand, rande, randg, randp}\n\ |
| 3369 | 515 @end deftypefn") |
| 2928 | 516 { |
| 4307 | 517 octave_value retval; |
| 2928 | 518 |
| 519 int nargin = args.length (); | |
| 520 | |
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521 retval = do_rand (args, nargin, "randn", "normal"); |
| 2928 | 522 |
| 523 return retval; | |
| 524 } | |
| 525 | |
| 526 /* | |
| 5730 | 527 %!test |
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528 %! # Test fixed state |
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529 %! randn ("state", 1); |
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530 %! assert (randn (1, 6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116], 1e-6); |
| 6437 | 531 %!test |
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532 %! # Test fixed seed |
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533 %! randn ("seed", 1); |
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534 %! assert (randn (1, 6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133], 1e-6); |
| 5730 | 535 %!test |
| 6437 | 536 %! if (__random_statistical_tests__) |
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537 %! # statistical tests may fail occasionally. |
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538 %! randn ("state", 12); |
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539 %! x = randn (100000, 1); |
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540 %! assert (mean (x), 0, 0.01); |
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541 %! assert (var (x), 1, 0.02); |
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542 %! assert (skewness (x), 0, 0.02); |
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543 %! assert (kurtosis (x), 0, 0.04); |
| 6437 | 544 %! endif |
| 545 %!test | |
| 546 %! if (__random_statistical_tests__) | |
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547 %! # statistical tests may fail occasionally. |
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548 %! randn ("seed", 12); |
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549 %! x = randn (100000, 1); |
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550 %! assert (mean (x), 0, 0.01); |
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551 %! assert (var (x), 1, 0.02); |
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552 %! assert (skewness (x), 0, 0.02); |
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553 %! assert (kurtosis (x), 0, 0.04); |
| 6437 | 554 %! endif |
| 5730 | 555 */ |
| 556 | |
| 557 DEFUN_DLD (rande, args, , | |
| 558 "-*- texinfo -*-\n\ | |
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559 @deftypefn {Loadable Function} {} rande (@var{n})\n\ |
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560 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m}, @dots{})\n\ |
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561 @deftypefnx {Loadable Function} {} rande ([@var{n} @var{m} @dots{}])\n\ |
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562 @deftypefnx {Loadable Function} {@var{v} =} rande (\"state\")\n\ |
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563 @deftypefnx {Loadable Function} {} rande (\"state\", @var{v})\n\ |
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564 @deftypefnx {Loadable Function} {} rande (\"state\", \"reset\")\n\ |
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565 @deftypefnx {Loadable Function} {@var{v} =} rande (\"seed\")\n\ |
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566 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{v})\n\ |
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567 @deftypefnx {Loadable Function} {} rande (\"seed\", \"reset\")\n\ |
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568 Return a matrix with exponentially distributed random elements. The\n\ |
| 5730 | 569 arguments are handled the same as the arguments for @code{rand}.\n\ |
| 570 \n\ | |
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571 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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572 to transform from a uniform to an exponential distribution.\n\ |
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573 \n\ |
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574 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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575 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 576 J. Statistical Software, vol 5, 2000,\n\ |
| 577 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
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578 \n\ |
| 6547 | 579 @seealso{rand, randn, randg, randp}\n\ |
| 5730 | 580 @end deftypefn") |
| 581 { | |
| 582 octave_value retval; | |
| 583 | |
| 584 int nargin = args.length (); | |
| 585 | |
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586 retval = do_rand (args, nargin, "rande", "exponential"); |
| 5730 | 587 |
| 588 return retval; | |
| 589 } | |
| 590 | |
| 591 /* | |
| 592 %!test | |
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593 %! # Test fixed state |
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594 %! rande ("state", 1); |
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595 %! assert (rande (1, 6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291], 1e-6); |
| 6437 | 596 %!test |
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597 %! # Test fixed seed |
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598 %! rande ("seed", 1); |
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599 %! assert (rande (1, 6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087], 1e-6); |
| 5730 | 600 %!test |
| 6437 | 601 %! if (__random_statistical_tests__) |
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602 %! # statistical tests may fail occasionally |
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603 %! rande ("state", 1); |
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604 %! x = rande (100000, 1); |
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605 %! assert (min (x) > 0); # *** Please report this!!! *** |
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606 %! assert (mean (x), 1, 0.01); |
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607 %! assert (var (x), 1, 0.03); |
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608 %! assert (skewness (x), 2, 0.06); |
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609 %! assert (kurtosis (x), 6, 0.7); |
| 6437 | 610 %! endif |
| 611 %!test | |
| 612 %! if (__random_statistical_tests__) | |
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613 %! # statistical tests may fail occasionally |
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614 %! rande ("seed", 1); |
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615 %! x = rande (100000, 1); |
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616 %! assert (min (x)>0); # *** Please report this!!! *** |
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617 %! assert (mean (x), 1, 0.01); |
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618 %! assert (var (x), 1, 0.03); |
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619 %! assert (skewness (x), 2, 0.06); |
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620 %! assert (kurtosis (x), 6, 0.7); |
| 6437 | 621 %! endif |
| 5730 | 622 */ |
| 623 | |
| 624 DEFUN_DLD (randg, args, , | |
| 625 "-*- texinfo -*-\n\ | |
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626 @deftypefn {Loadable Function} {} randg (@var{n})\n\ |
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627 @deftypefnx {Loadable Function} {} randg (@var{n}, @var{m}, @dots{})\n\ |
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628 @deftypefnx {Loadable Function} {} randg ([@var{n} @var{m} @dots{}])\n\ |
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629 @deftypefnx {Loadable Function} {@var{v} =} randg (\"state\")\n\ |
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630 @deftypefnx {Loadable Function} {} randg (\"state\", @var{v})\n\ |
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631 @deftypefnx {Loadable Function} {} randg (\"state\", \"reset\")\n\ |
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632 @deftypefnx {Loadable Function} {@var{v} =} randg (\"seed\")\n\ |
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633 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{v})\n\ |
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634 @deftypefnx {Loadable Function} {} randg (\"seed\", \"reset\")\n\ |
| 5730 | 635 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ |
| 636 The arguments are handled the same as the arguments for @code{rand},\n\ | |
| 637 except for the argument @var{a}.\n\ | |
| 638 \n\ | |
| 639 This can be used to generate many distributions:\n\ | |
| 640 \n\ | |
| 641 @table @asis\n\ | |
| 6547 | 642 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
| 10840 | 643 \n\ |
| 5730 | 644 @example\n\ |
| 6547 | 645 r = b * randg (a)\n\ |
| 5730 | 646 @end example\n\ |
| 10840 | 647 \n\ |
| 6547 | 648 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
| 10840 | 649 \n\ |
| 5730 | 650 @example\n\ |
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651 @group\n\ |
| 6547 | 652 r1 = randg (a, 1)\n\ |
| 653 r = r1 / (r1 + randg (b, 1))\n\ | |
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654 @end group\n\ |
| 5730 | 655 @end example\n\ |
| 10840 | 656 \n\ |
| 6547 | 657 @item @code{Erlang (a, n)}\n\ |
| 10840 | 658 \n\ |
| 5730 | 659 @example\n\ |
| 6547 | 660 r = a * randg (n)\n\ |
| 5730 | 661 @end example\n\ |
| 10840 | 662 \n\ |
| 6547 | 663 @item @code{chisq (df)} for @code{df > 0}\n\ |
| 10840 | 664 \n\ |
| 5730 | 665 @example\n\ |
| 6547 | 666 r = 2 * randg (df / 2)\n\ |
| 5730 | 667 @end example\n\ |
| 10840 | 668 \n\ |
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669 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
| 10840 | 670 \n\ |
| 5730 | 671 @example\n\ |
| 6547 | 672 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
| 5730 | 673 @end example\n\ |
| 10840 | 674 \n\ |
| 6547 | 675 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
| 10840 | 676 \n\ |
| 5730 | 677 @example\n\ |
| 7096 | 678 @group\n\ |
| 679 ## r1 equals 1 if n1 is infinite\n\ | |
| 680 r1 = 2 * randg (n1 / 2) / n1\n\ | |
| 681 ## r2 equals 1 if n2 is infinite\n\ | |
| 682 r2 = 2 * randg (n2 / 2) / n2\n\ | |
| 5730 | 683 r = r1 / r2\n\n\ |
| 7096 | 684 @end group\n\ |
| 5730 | 685 @end example\n\ |
| 10840 | 686 \n\ |
| 5730 | 687 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
| 10840 | 688 \n\ |
| 5730 | 689 @example\n\ |
| 6547 | 690 r = randp ((1 - p) / p * randg (n))\n\ |
| 5730 | 691 @end example\n\ |
| 10840 | 692 \n\ |
| 6547 | 693 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
| 5730 | 694 (use chisq if @code{L = 0})\n\ |
| 10840 | 695 \n\ |
| 5730 | 696 @example\n\ |
|
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697 @group\n\ |
| 6547 | 698 r = randp (L / 2)\n\ |
| 699 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
| 700 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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701 @end group\n\ |
| 5730 | 702 @end example\n\ |
| 10840 | 703 \n\ |
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704 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
| 10840 | 705 \n\ |
| 5730 | 706 @example\n\ |
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707 @group\n\ |
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708 r = (randg (a1), @dots{}, randg (ak))\n\ |
| 6547 | 709 r = r / sum (r)\n\ |
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710 @end group\n\ |
| 5730 | 711 @end example\n\ |
| 10840 | 712 \n\ |
| 5730 | 713 @end table\n\ |
| 6547 | 714 @seealso{rand, randn, rande, randp}\n\ |
| 5730 | 715 @end deftypefn") |
| 716 { | |
| 717 octave_value retval; | |
| 718 | |
| 719 int nargin = args.length (); | |
| 720 | |
| 721 if (nargin < 1) | |
| 722 error ("randg: insufficient arguments"); | |
| 723 else | |
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724 retval = do_rand (args, nargin, "randg", "gamma", true); |
| 5730 | 725 |
| 726 return retval; | |
| 727 } | |
| 728 | |
| 729 /* | |
| 730 %!test | |
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731 %! randg ("state", 12) |
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732 %! assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]); # *** Please report |
| 6437 | 733 |
| 734 %!test | |
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735 %! # Test fixed state |
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736 %! randg ("state", 1); |
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737 %! assert (randg (0.1, 1, 6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12], 1e-6); |
| 6437 | 738 %!test |
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739 %! # Test fixed state |
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740 %! randg ("state", 1); |
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741 %! assert (randg (0.95, 1, 6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939], 1e-6); |
| 6437 | 742 %!test |
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743 %! # Test fixed state |
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744 %! randg ("state", 1); |
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745 %! assert (randg (1, 1, 6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533], 1e-6); |
| 6437 | 746 %!test |
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747 %! # Test fixed state |
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748 %! randg ("state", 1); |
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749 %! assert (randg (10, 1, 6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059], 1e-5); |
| 6437 | 750 %!test |
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751 %! # Test fixed state |
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752 %! randg ("state", 1); |
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753 %! assert (randg (100, 1, 6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118], 1e-4); |
| 6437 | 754 %!test |
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755 %! # Test fixed seed |
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756 %! randg ("seed", 1); |
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757 %! assert (randg (0.1, 1, 6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12], 1e-6); |
| 6437 | 758 %!test |
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759 %! # Test fixed seed |
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760 %! randg ("seed", 1); |
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761 %! assert (randg (0.95, 1, 6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648], 1e-6); |
| 6437 | 762 %!test |
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763 %! # Test fixed seed |
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764 %! randg ("seed", 1); |
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765 %! assert (randg (1, 1, 6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303], 1e-6); |
| 6437 | 766 %!test |
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767 %! # Test fixed seed |
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768 %! randg ("seed", 1); |
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769 %! assert (randg (10, 1, 6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186], 1e-5); |
| 6437 | 770 %!test |
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771 %! # Test fixed seed |
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772 %! randg ("seed", 1); |
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773 %! assert (randg (100, 1, 6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625], 1e-4); |
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774 |
| 6437 | 775 %!test |
| 776 %! if (__random_statistical_tests__) | |
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777 %! # statistical tests may fail occasionally. |
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778 %! randg ("state", 12); |
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779 %! a = 0.1; |
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780 %! x = randg (a, 100000, 1); |
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781 %! assert (mean (x), a, 0.01); |
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782 %! assert (var (x), a, 0.01); |
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783 %! assert (skewness (x), 2/sqrt (a), 1); |
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784 %! assert (kurtosis (x), 6/a, 50); |
| 6437 | 785 %! endif |
| 786 %!test | |
| 787 %! if (__random_statistical_tests__) | |
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788 %! # statistical tests may fail occasionally. |
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789 %! randg ("state", 12); |
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790 %! a = 0.95; |
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791 %! x = randg (a, 100000, 1); |
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792 %! assert (mean (x), a, 0.01); |
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793 %! assert (var (x), a, 0.04); |
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794 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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795 %! assert (kurtosis (x), 6/a, 2); |
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796 %! endif |
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797 %!test |
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798 %! if (__random_statistical_tests__) |
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799 %! # statistical tests may fail occasionally. |
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800 %! randg ("state", 12); |
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801 %! a = 1; |
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802 %! x = randg (a, 100000, 1); |
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803 %! assert (mean (x), a, 0.01); |
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804 %! assert (var (x), a, 0.04); |
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805 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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806 %! assert (kurtosis (x), 6/a, 2); |
| 6437 | 807 %! endif |
| 808 %!test | |
| 809 %! if (__random_statistical_tests__) | |
|
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810 %! # statistical tests may fail occasionally. |
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811 %! randg ("state", 12); |
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812 %! a = 10; |
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813 %! x = randg (a, 100000, 1); |
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814 %! assert (mean (x), a, 0.1); |
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815 %! assert (var (x), a, 0.5); |
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816 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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817 %! assert (kurtosis (x), 6/a, 0.5); |
| 6437 | 818 %! endif |
| 819 %!test | |
| 820 %! if (__random_statistical_tests__) | |
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821 %! # statistical tests may fail occasionally. |
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822 %! randg ("state", 12); |
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823 %! a = 100; |
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824 %! x = randg (a, 100000, 1); |
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825 %! assert (mean (x), a, 0.2); |
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826 %! assert (var (x), a, 2); |
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827 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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828 %! assert (kurtosis (x), 6/a, 0.2); |
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829 %! endif |
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830 %!test |
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831 %! randg ("seed", 12); |
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832 %!assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]) # *** Please report |
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833 %!test |
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834 %! if (__random_statistical_tests__) |
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835 %! # statistical tests may fail occasionally. |
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836 %! randg ("seed", 12); |
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837 %! a = 0.1; |
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838 %! x = randg (a, 100000, 1); |
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839 %! assert (mean (x), a, 0.01); |
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840 %! assert (var (x), a, 0.01); |
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841 %! assert (skewness (x), 2/sqrt (a), 1); |
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842 %! assert (kurtosis (x), 6/a, 50); |
| 6437 | 843 %! endif |
| 844 %!test | |
| 845 %! if (__random_statistical_tests__) | |
|
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846 %! # statistical tests may fail occasionally. |
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847 %! randg ("seed", 12); |
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848 %! a = 0.95; |
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849 %! x = randg (a, 100000, 1); |
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850 %! assert (mean (x), a, 0.01); |
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851 %! assert (var (x), a, 0.04); |
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852 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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853 %! assert (kurtosis (x), 6/a, 2); |
| 6437 | 854 %! endif |
| 855 %!test | |
| 856 %! if (__random_statistical_tests__) | |
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857 %! # statistical tests may fail occasionally. |
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858 %! randg ("seed", 12); |
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859 %! a = 1; |
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860 %! x = randg (a, 100000, 1); |
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861 %! assert (mean (x), a, 0.01); |
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862 %! assert (var (x), a, 0.04); |
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863 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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864 %! assert (kurtosis (x), 6/a, 2); |
| 6437 | 865 %! endif |
| 5730 | 866 %!test |
| 6437 | 867 %! if (__random_statistical_tests__) |
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868 %! # statistical tests may fail occasionally. |
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869 %! randg ("seed", 12); |
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870 %! a = 10; |
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871 %! x = randg (a, 100000, 1); |
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872 %! assert (mean (x), a, 0.1); |
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873 %! assert (var (x), a, 0.5); |
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874 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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875 %! assert (kurtosis (x), 6/a, 0.5); |
| 6437 | 876 %! endif |
| 5730 | 877 %!test |
| 6437 | 878 %! if (__random_statistical_tests__) |
|
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879 %! # statistical tests may fail occasionally. |
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880 %! randg ("seed", 12); |
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881 %! a = 100; |
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882 %! x = randg (a, 100000, 1); |
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883 %! assert (mean (x), a, 0.2); |
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884 %! assert (var (x), a, 2); |
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885 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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886 %! assert (kurtosis (x), 6/a, 0.2); |
| 6437 | 887 %! endif |
| 5730 | 888 */ |
| 889 | |
| 890 DEFUN_DLD (randp, args, , | |
| 891 "-*- texinfo -*-\n\ | |
|
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892 @deftypefn {Loadable Function} {} randp (@var{l}, @var{n})\n\ |
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893 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m}, @dots{})\n\ |
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894 @deftypefnx {Loadable Function} {} randp (@var{l}, [@var{n} @var{m} @dots{}])\n\ |
|
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895 @deftypefnx {Loadable Function} {@var{v} =} randp (\"state\")\n\ |
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896 @deftypefnx {Loadable Function} {} randp (\"state\", @var{v})\n\ |
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897 @deftypefnx {Loadable Function} {} randp (\"state\", \"reset\")\n\ |
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898 @deftypefnx {Loadable Function} {@var{v} =} randp (\"seed\")\n\ |
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899 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{v})\n\ |
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900 @deftypefnx {Loadable Function} {} randp (\"seed\", \"reset\")\n\ |
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901 Return a matrix with Poisson distributed random elements with mean value\n\ |
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902 parameter given by the first argument, @var{l}. The arguments\n\ |
| 5730 | 903 are handled the same as the arguments for @code{rand}, except for the\n\ |
| 904 argument @var{l}.\n\ | |
| 905 \n\ | |
| 906 Five different algorithms are used depending on the range of @var{l}\n\ | |
| 907 and whether or not @var{l} is a scalar or a matrix.\n\ | |
| 908 \n\ | |
| 909 @table @asis\n\ | |
| 10840 | 910 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
| 911 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
| 912 Cambridge University Press, 1992.\n\ | |
| 913 \n\ | |
| 5730 | 914 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
| 10840 | 915 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
| 916 Cambridge University Press, 1992.\n\ | |
| 917 \n\ | |
| 918 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
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919 E. Stadlober, et al., WinRand source code, available via FTP.\n\ |
| 10840 | 920 \n\ |
| 5730 | 921 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
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922 E. Stadlober, et al., WinRand source code, available via FTP, or\n\ |
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923 H. Zechner, @cite{Efficient sampling from continuous and discrete\n\ |
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924 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
| 5730 | 925 University Graz, Austria, 1994.\n\ |
| 10840 | 926 \n\ |
| 5730 | 927 @item For @var{l} > 1e8, use normal approximation.\n\ |
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928 L. Montanet, et al., @cite{Review of Particle Properties}, Physical Review\n\ |
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929 D 50 p1284, 1994.\n\ |
| 5730 | 930 @end table\n\ |
| 6547 | 931 @seealso{rand, randn, rande, randg}\n\ |
| 5730 | 932 @end deftypefn") |
| 933 { | |
| 934 octave_value retval; | |
| 935 | |
| 936 int nargin = args.length (); | |
| 937 | |
| 938 if (nargin < 1) | |
| 939 error ("randp: insufficient arguments"); | |
| 940 else | |
|
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Jaroslav Hajek <highegg@gmail.com>
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|
941 retval = do_rand (args, nargin, "randp", "poisson", true); |
| 5730 | 942 |
| 943 return retval; | |
| 944 } | |
| 945 | |
| 946 /* | |
| 947 %!test | |
|
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|
948 %! randp ("state", 12); |
|
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|
949 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
| 6437 | 950 %!test |
|
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|
951 %! # Test fixed state |
|
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|
952 %! randp ("state", 1); |
|
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|
953 %! assert (randp (5, 1, 6), [5 5 3 7 7 3]) |
| 6437 | 954 %!test |
|
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|
955 %! # Test fixed state |
|
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956 %! randp ("state", 1); |
|
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957 %! assert (randp (15, 1, 6), [13 15 8 18 18 15]) |
| 6437 | 958 %!test |
|
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959 %! # Test fixed state |
|
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960 %! randp ("state", 1); |
|
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|
961 %! assert (randp (1e9, 1, 6), [999915677 999976657 1000047684 1000019035 999985749 999977692], -1e-6) |
| 6437 | 962 %!test |
|
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963 %! # Test fixed state |
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964 %! randp ("seed", 1); |
|
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965 %! %%assert (randp (5, 1, 6), [8 2 3 6 6 8]) |
|
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|
966 %! assert (randp (5, 1, 5), [8 2 3 6 6]) |
| 6437 | 967 %!test |
|
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968 %! # Test fixed state |
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969 %! randp ("seed", 1); |
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970 %! assert (randp (15, 1, 6), [15 16 12 10 10 12]) |
| 6437 | 971 %!test |
|
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|
972 %! # Test fixed state |
|
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973 %! randp ("seed", 1); |
|
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|
974 %! assert (randp (1e9, 1, 6), [1000006208 1000012224 999981120 999963520 999963072 999981440], -1e-6) |
| 6437 | 975 %!test |
| 976 %! if (__random_statistical_tests__) | |
|
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|
977 %! # statistical tests may fail occasionally. |
|
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978 %! randp ("state", 12); |
|
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|
979 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
|
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980 %! x = randp (a (1), 100000, 1); |
|
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981 %! assert (min (x) >= 0); # *** Please report this!!! *** |
|
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|
982 %! assert (mean (x), a(1), a(2)); |
|
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|
983 %! assert (var (x), a(1), 0.02*a(1)); |
|
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|
984 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
|
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|
985 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
| 6437 | 986 %! endfor |
| 987 %! endif | |
| 988 %!test | |
| 989 %! if (__random_statistical_tests__) | |
|
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990 %! # statistical tests may fail occasionally. |
|
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991 %! randp ("state", 12); |
|
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992 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
|
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993 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
|
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994 %! assert (min (x) >= 0); # *** Please report this!!! *** |
|
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995 %! assert (mean (x), a(1), a(2)); |
|
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996 %! assert (var (x), a(1), 0.02*a(1)); |
|
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997 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
|
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998 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
| 6437 | 999 %! endfor |
| 1000 %! endif | |
| 1001 %!test | |
|
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1002 %! randp ("seed", 12); |
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1003 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
| 5730 | 1004 %!test |
| 6449 | 1005 %! if (__random_statistical_tests__) |
|
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|
1006 %! # statistical tests may fail occasionally. |
|
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1007 %! randp ("seed", 12); |
|
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1008 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
|
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1009 %! x = randp (a(1), 100000, 1); |
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1010 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1011 %! assert (mean (x), a(1), a(2)); |
|
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|
1012 %! assert (var (x), a(1), 0.02*a(1)); |
|
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1013 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
|
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1014 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
| 6449 | 1015 %! endfor |
| 1016 %! endif | |
| 5730 | 1017 %!test |
| 6449 | 1018 %! if (__random_statistical_tests__) |
|
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1019 %! # statistical tests may fail occasionally. |
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|
1020 %! randp ("seed", 12); |
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1021 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1022 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
|
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1023 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1024 %! assert (mean (x), a(1), a(2)); |
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|
1025 %! assert (var (x), a(1), 0.02*a(1)); |
|
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1026 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
|
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|
1027 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
| 6449 | 1028 %! endfor |
| 1029 %! endif | |
| 5730 | 1030 */ |
| 1031 | |
|
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|
1032 DEFUN_DLD (randperm, args, , |
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1033 "-*- texinfo -*-\n\ |
|
10687
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1034 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
|
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1035 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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1036 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
|
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1037 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
|
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1038 replacement from @code{1:@var{n}}. The complexity is O(@var{n}) in\n\ |
|
13255
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1039 memory and O(@var{m}) in time, unless @var{m} < @var{n}/5, in which case\n\ |
|
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1040 O(@var{m}) memory is used as well. The randomization is performed using\n\ |
|
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1041 rand(). All permutations are equally likely.\n\ |
|
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1042 @seealso{perms}\n\ |
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1043 @end deftypefn") |
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1044 { |
|
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1045 |
|
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1046 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
|
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1047 using std::tr1::unordered_map; |
|
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1048 #else |
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1049 using std::unordered_map; |
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1050 #endif |
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1051 |
|
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1052 int nargin = args.length (); |
|
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1053 octave_value retval; |
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1054 |
|
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1055 if (nargin == 1 || nargin == 2) |
|
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1056 { |
|
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|
1057 octave_idx_type n, m; |
|
11586
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1058 |
|
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1059 n = args(0).idx_type_value (true); |
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|
1060 |
|
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|
1061 if (nargin == 2) |
|
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optimize & extend randperm
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|
1062 m = args(1).idx_type_value (true); |
|
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|
1063 else |
|
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|
1064 m = n; |
|
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|
1065 |
|
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|
1066 if (m < 0 || n < 0) |
|
11553
01f703952eff
Improve docstrings for functions in DLD-FUNCTIONS directory.
Rik <octave@nomad.inbox5.com>
parents:
11523
diff
changeset
|
1067 error ("randperm: M and N must be non-negative"); |
|
9647
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|
1068 |
|
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|
1069 if (m > n) |
|
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|
1070 error ("randperm: M must be less than or equal to N"); |
|
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|
1071 |
|
13255
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|
1072 // Quick and dirty heuristic to decide if we allocate or not the |
|
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|
1073 // whole vector for tracking the truncated shuffle. |
|
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|
1074 bool short_shuffle = m < n/5 && m < 1e5; |
|
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|
1075 |
|
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|
1076 if (! error_state) |
|
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|
1077 { |
|
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1078 // Generate random numbers. |
|
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1079 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
|
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1080 double *rvec = r.fortran_vec (); |
|
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1081 |
|
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1082 octave_idx_type idx_len = short_shuffle ? m : n; |
|
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1083 Array<octave_idx_type> idx (dim_vector (1, idx_len)); |
|
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1084 octave_idx_type *ivec = idx.fortran_vec (); |
|
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1085 |
|
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1086 for (octave_idx_type i = 0; i < idx_len; i++) |
|
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1087 ivec[i] = i; |
|
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1088 |
|
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|
1089 if (short_shuffle) |
|
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|
1090 { |
|
13727
478efc95cb7a
Test unordered_map header location and namespace.
Michael Goffioul <michael.goffioul@gmail.com>
parents:
13255
diff
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|
1091 unordered_map<octave_idx_type, octave_idx_type> map (m); |
|
13255
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|
1092 |
|
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1093 // Perform the Knuth shuffle only keeping track of moved |
|
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|
1094 // entries in the map |
|
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|
1095 for (octave_idx_type i = 0; i < m; i++) |
|
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|
1096 { |
|
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|
1097 octave_idx_type k = i + |
|
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|
1098 gnulib::floor (rvec[i] * (n - i)); |
|
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|
1099 |
|
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1100 if (map.find(k) == map.end()) |
|
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|
1101 { |
|
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|
1102 map[k] = ivec[i]; |
|
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1103 ivec[i] = k; |
|
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1104 } |
|
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|
1105 else |
|
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|
1106 std::swap (ivec[i], map[k]); |
|
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|
1107 |
|
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|
1108 } |
|
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|
1109 } |
|
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|
1110 else |
|
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|
1111 { |
|
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|
1112 |
|
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1113 // Perform the Knuth shuffle of the first m entries |
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|
1114 for (octave_idx_type i = 0; i < m; i++) |
|
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|
1115 { |
|
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1116 octave_idx_type k = i + |
|
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1117 gnulib::floor (rvec[i] * (n - i)); |
|
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|
1118 std::swap (ivec[i], ivec[k]); |
|
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|
1119 } |
|
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1120 } |
|
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|
1121 |
|
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|
1122 // Convert to doubles, reusing r. |
|
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1123 for (octave_idx_type i = 0; i < m; i++) |
|
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|
1124 rvec[i] = ivec[i] + 1; |
|
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1125 |
|
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|
1126 if (m < n) |
|
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|
1127 idx.resize (dim_vector (1, m)); |
|
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1128 |
|
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|
1129 // Now create an array object with a cached idx_vector. |
|
11586
12df7854fa7c
strip trailing whitespace from source files
John W. Eaton <jwe@octave.org>
parents:
11553
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changeset
|
1130 retval = new octave_matrix (r, idx_vector (idx)); |
|
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|
1131 } |
|
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|
1132 } |
|
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|
1133 else |
|
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|
1134 print_usage (); |
|
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|
1135 |
|
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|
1136 return retval; |
|
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|
1137 } |
|
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|
1138 |
|
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|
1139 /* |
|
14501
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|
1140 %!assert (sort (randperm (20)), 1:20) |
|
60e5cf354d80
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changeset
|
1141 %!assert (length (randperm (20,10)), 10) |
|
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|
1142 */ |