Mercurial > octave
annotate src/DLD-FUNCTIONS/rand.cc @ 10154:40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
| author | John W. Eaton <jwe@octave.org> |
|---|---|
| date | Wed, 20 Jan 2010 17:33:41 -0500 |
| parents | 2cd940306a06 |
| children | d0ce5e973937 |
| rev | line source |
|---|---|
| 2928 | 1 /* |
| 2 | |
| 7017 | 3 Copyright (C) 1996, 1997, 1998, 1999, 2000, 2002, 2003, 2005, 2006, |
| 8920 | 4 2007, 2008, 2009 John W. Eaton |
|
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5 Copyright (C) 2009 VZLU Prague |
| 2928 | 6 |
| 7 This file is part of Octave. | |
| 8 | |
| 9 Octave is free software; you can redistribute it and/or modify it | |
| 10 under the terms of the GNU General Public License as published by the | |
| 7016 | 11 Free Software Foundation; either version 3 of the License, or (at your |
| 12 option) any later version. | |
| 2928 | 13 |
| 14 Octave is distributed in the hope that it will be useful, but WITHOUT | |
| 15 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
| 16 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
| 17 for more details. | |
| 18 | |
| 19 You should have received a copy of the GNU General Public License | |
| 7016 | 20 along with Octave; see the file COPYING. If not, see |
| 21 <http://www.gnu.org/licenses/>. | |
| 2928 | 22 |
| 23 */ | |
| 24 | |
| 25 #ifdef HAVE_CONFIG_H | |
| 26 #include <config.h> | |
| 27 #endif | |
| 28 | |
| 29 #include <ctime> | |
| 30 | |
| 31 #include <string> | |
| 32 | |
| 33 #include "f77-fcn.h" | |
| 34 #include "lo-mappers.h" | |
| 4307 | 35 #include "oct-rand.h" |
| 4153 | 36 #include "quit.h" |
| 2928 | 37 |
| 38 #include "defun-dld.h" | |
| 39 #include "error.h" | |
| 40 #include "gripes.h" | |
| 41 #include "oct-obj.h" | |
| 42 #include "unwind-prot.h" | |
| 43 #include "utils.h" | |
|
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44 #include "ov-re-mat.h" |
| 2928 | 45 |
| 6437 | 46 /* |
| 47 %!shared __random_statistical_tests__ | |
| 48 %! % Flag whether the statistical tests should be run in "make check" or not | |
| 49 %! __random_statistical_tests__ = 0; | |
| 50 */ | |
| 51 | |
| 4307 | 52 static octave_value |
| 5730 | 53 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
|
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54 bool additional_arg = false) |
| 2928 | 55 { |
| 4307 | 56 octave_value retval; |
| 5730 | 57 NDArray a; |
| 58 int idx = 0; | |
| 59 dim_vector dims; | |
| 2928 | 60 |
| 5730 | 61 if (additional_arg) |
| 62 { | |
| 63 if (nargin == 0) | |
|
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64 { |
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65 error ("%s: expecting at least one argument", fcn); |
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66 goto done; |
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67 } |
| 5730 | 68 else if (args(0).is_string()) |
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69 additional_arg = false; |
| 5730 | 70 else |
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71 { |
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72 a = args(0).array_value (); |
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73 if (error_state) |
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74 { |
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75 error ("%s: expecting scalar or matrix arguments", fcn); |
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76 goto done; |
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77 } |
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78 idx++; |
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79 nargin--; |
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80 } |
| 5730 | 81 } |
| 2928 | 82 |
| 4543 | 83 switch (nargin) |
| 2928 | 84 { |
| 4543 | 85 case 0: |
| 86 { | |
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87 if (additional_arg) |
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88 dims = a.dims (); |
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89 else |
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90 { |
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91 dims.resize (2); |
| 4543 | 92 |
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93 dims(0) = 1; |
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94 dims(1) = 1; |
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95 } |
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96 goto gen_matrix; |
| 4543 | 97 } |
| 98 break; | |
| 2928 | 99 |
| 4543 | 100 case 1: |
| 101 { | |
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102 octave_value tmp = args(idx); |
| 4543 | 103 |
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104 if (tmp.is_string ()) |
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105 { |
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106 std::string s_arg = tmp.string_value (); |
| 2928 | 107 |
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108 if (s_arg == "dist") |
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109 { |
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110 retval = octave_rand::distribution (); |
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111 } |
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112 else if (s_arg == "seed") |
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113 { |
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114 retval = octave_rand::seed (); |
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115 } |
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116 else if (s_arg == "state" || s_arg == "twister") |
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117 { |
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118 retval = octave_rand::state (fcn); |
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119 } |
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120 else if (s_arg == "uniform") |
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121 { |
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122 octave_rand::uniform_distribution (); |
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123 } |
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124 else if (s_arg == "normal") |
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125 { |
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126 octave_rand::normal_distribution (); |
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127 } |
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128 else if (s_arg == "exponential") |
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129 { |
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130 octave_rand::exponential_distribution (); |
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131 } |
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132 else if (s_arg == "poisson") |
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133 { |
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134 octave_rand::poisson_distribution (); |
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135 } |
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136 else if (s_arg == "gamma") |
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137 { |
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138 octave_rand::gamma_distribution (); |
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139 } |
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140 else |
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141 error ("%s: unrecognized string argument", fcn); |
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142 } |
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143 else if (tmp.is_scalar_type ()) |
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144 { |
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145 double dval = tmp.double_value (); |
| 2928 | 146 |
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147 if (xisnan (dval)) |
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148 { |
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149 error ("%s: NaN is invalid a matrix dimension", fcn); |
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150 } |
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151 else |
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152 { |
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153 dims.resize (2); |
| 4543 | 154 |
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155 dims(0) = NINTbig (tmp.double_value ()); |
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156 dims(1) = NINTbig (tmp.double_value ()); |
| 2928 | 157 |
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158 if (! error_state) |
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159 goto gen_matrix; |
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160 } |
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161 } |
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162 else if (tmp.is_range ()) |
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163 { |
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164 Range r = tmp.range_value (); |
| 4543 | 165 |
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166 if (r.all_elements_are_ints ()) |
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167 { |
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168 octave_idx_type n = r.nelem (); |
| 4543 | 169 |
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170 dims.resize (n); |
| 4543 | 171 |
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172 octave_idx_type base = NINTbig (r.base ()); |
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173 octave_idx_type incr = NINTbig (r.inc ()); |
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174 octave_idx_type lim = NINTbig (r.limit ()); |
| 2928 | 175 |
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176 if (base < 0 || lim < 0) |
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177 error ("%s: all dimensions must be nonnegative", fcn); |
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178 else |
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179 { |
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180 for (octave_idx_type i = 0; i < n; i++) |
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181 { |
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182 dims(i) = base; |
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183 base += incr; |
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184 } |
| 2928 | 185 |
|
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186 goto gen_matrix; |
|
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187 } |
|
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188 } |
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189 else |
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190 error ("%s: expecting all elements of range to be integers", |
|
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191 fcn); |
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192 } |
|
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193 else if (tmp.is_matrix_type ()) |
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194 { |
|
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195 Array<int> iv = tmp.int_vector_value (true); |
| 4543 | 196 |
|
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197 if (! error_state) |
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198 { |
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199 octave_idx_type len = iv.length (); |
| 2928 | 200 |
|
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201 dims.resize (len); |
| 4543 | 202 |
|
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203 for (octave_idx_type i = 0; i < len; i++) |
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204 { |
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205 octave_idx_type elt = iv(i); |
| 4543 | 206 |
|
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207 if (elt < 0) |
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208 { |
|
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209 error ("%s: all dimensions must be nonnegative", fcn); |
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210 goto done; |
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211 } |
| 4543 | 212 |
|
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213 dims(i) = iv(i); |
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214 } |
| 2928 | 215 |
|
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216 goto gen_matrix; |
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217 } |
|
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218 else |
|
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219 error ("%s: expecting integer vector", fcn); |
|
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220 } |
|
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221 else |
|
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222 { |
|
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223 gripe_wrong_type_arg ("rand", tmp); |
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224 return retval; |
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225 } |
| 4543 | 226 } |
| 227 break; | |
| 228 | |
| 229 default: | |
| 230 { | |
|
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231 octave_value tmp = args(idx); |
| 4543 | 232 |
|
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233 if (nargin == 2 && tmp.is_string ()) |
|
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234 { |
|
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235 std::string ts = tmp.string_value (); |
| 5164 | 236 |
|
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237 if (ts == "seed") |
|
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238 { |
|
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239 if (args(idx+1).is_real_scalar ()) |
|
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240 { |
|
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241 double d = args(idx+1).double_value (); |
| 2928 | 242 |
|
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243 if (! error_state) |
|
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244 octave_rand::seed (d); |
|
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245 } |
|
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246 else |
|
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247 error ("%s: seed must be a real scalar", fcn); |
|
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248 } |
|
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249 else if (ts == "state" || ts == "twister") |
|
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250 { |
|
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251 ColumnVector s = |
|
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252 ColumnVector (args(idx+1).vector_value(false, true)); |
| 5730 | 253 |
|
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254 if (! error_state) |
|
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255 octave_rand::state (s, fcn); |
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256 } |
|
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257 else |
|
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258 error ("%s: unrecognized string argument", fcn); |
|
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259 } |
|
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260 else |
|
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261 { |
|
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262 dims.resize (nargin); |
| 4543 | 263 |
|
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264 for (int i = 0; i < nargin; i++) |
|
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265 { |
|
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266 dims(i) = args(idx+i).int_value (); |
| 4543 | 267 |
|
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268 if (error_state) |
|
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269 { |
|
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270 error ("%s: expecting integer arguments", fcn); |
|
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271 goto done; |
|
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272 } |
|
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273 } |
| 4543 | 274 |
|
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275 goto gen_matrix; |
|
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|
276 } |
| 4543 | 277 } |
| 278 break; | |
| 2928 | 279 } |
| 280 | |
| 4543 | 281 done: |
| 2928 | 282 |
| 283 return retval; | |
| 284 | |
| 285 gen_matrix: | |
| 286 | |
| 5355 | 287 dims.chop_trailing_singletons (); |
| 288 | |
| 5730 | 289 if (additional_arg) |
| 290 { | |
| 291 if (a.length() == 1) | |
|
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292 return octave_rand::nd_array (dims, a(0)); |
| 5730 | 293 else |
|
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294 { |
|
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295 if (a.dims() != dims) |
|
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296 { |
|
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297 error ("%s: mismatch in argument size", fcn); |
|
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|
298 return retval; |
|
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|
299 } |
|
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|
300 octave_idx_type len = a.length (); |
|
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301 NDArray m (dims); |
|
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|
302 double *v = m.fortran_vec (); |
|
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changeset
|
303 for (octave_idx_type i = 0; i < len; i++) |
|
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304 v[i] = octave_rand::scalar (a(i)); |
|
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305 return m; |
|
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|
306 } |
| 5730 | 307 } |
| 308 else | |
| 309 return octave_rand::nd_array (dims); | |
| 2928 | 310 } |
| 311 | |
| 4665 | 312 DEFUN_DLD (rand, args, , |
| 3369 | 313 "-*- texinfo -*-\n\ |
| 314 @deftypefn {Loadable Function} {} rand (@var{x})\n\ | |
| 315 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m})\n\ | |
| 5730 | 316 @deftypefnx {Loadable Function} {} rand (\"state\", @var{x})\n\ |
| 317 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{x})\n\ | |
| 3369 | 318 Return a matrix with random elements uniformly distributed on the\n\ |
| 319 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
| 5730 | 320 for @code{eye}.\n\ |
| 321 \n\ | |
| 322 You can query the state of the random number generator using the\n\ | |
| 3369 | 323 form\n\ |
| 2928 | 324 \n\ |
| 3369 | 325 @example\n\ |
| 5730 | 326 v = rand (\"state\")\n\ |
| 327 @end example\n\ | |
| 328 \n\ | |
|
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|
329 This returns a column vector @var{v} of length 625. Later, you can\n\ |
| 5730 | 330 restore the random number generator to the state @var{v}\n\ |
| 331 using the form\n\ | |
| 332 \n\ | |
| 333 @example\n\ | |
| 334 rand (\"state\", v)\n\ | |
| 3369 | 335 @end example\n\ |
| 336 \n\ | |
| 337 @noindent\n\ | |
| 5730 | 338 You may also initialize the state vector from an arbitrary vector of\n\ |
| 339 length <= 625 for @var{v}. This new state will be a hash based on the\n\ | |
| 5798 | 340 value of @var{v}, not @var{v} itself.\n\ |
| 5730 | 341 \n\ |
| 342 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
| 343 available, otherwise from cpu time, wall clock time and the current\n\ | |
| 344 fraction of a second.\n\ | |
| 345 \n\ | |
|
8325
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|
346 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
|
8498
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|
347 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and T. Nishimura,\n\ |
|
8482
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|
348 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator}, ACM Trans. on\n\ |
| 7001 | 349 Modeling and Computer Simulation Vol. 8, No. 1, January pp.3-30 1998,\n\ |
| 7171 | 350 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
| 6547 | 351 Do @strong{not} use for cryptography without securely hashing\n\ |
| 352 several returned values together, otherwise the generator state\n\ | |
| 353 can be learned after reading 624 consecutive values.\n\ | |
| 5730 | 354 \n\ |
| 7096 | 355 Older versions of Octave used a different random number generator.\n\ |
| 356 The new generator is used by default\n\ | |
| 5730 | 357 as it is significantly faster than the old generator, and produces\n\ |
|
9041
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diff
changeset
|
358 random numbers with a significantly longer cycle time. However, in\n\ |
| 5798 | 359 some circumstances it might be desirable to obtain the same random\n\ |
|
9041
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Cleanup documentation file matrix.texi
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changeset
|
360 sequences as used by the old generators. To do this the keyword\n\ |
| 5730 | 361 \"seed\" is used to specify that the old generators should be use,\n\ |
| 362 as in\n\ | |
| 2928 | 363 \n\ |
| 3369 | 364 @example\n\ |
| 5730 | 365 rand (\"seed\", val)\n\ |
| 3369 | 366 @end example\n\ |
| 367 \n\ | |
|
9064
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368 which sets the seed of the generator to @var{val}. The seed of the\n\ |
| 5730 | 369 generator can be queried with\n\ |
| 370 \n\ | |
| 371 @example\n\ | |
| 372 s = rand (\"seed\")\n\ | |
| 373 @end example\n\ | |
| 374 \n\ | |
| 375 However, it should be noted that querying the seed will not cause\n\ | |
| 376 @code{rand} to use the old generators, only setting the seed will.\n\ | |
| 377 To cause @code{rand} to once again use the new generators, the\n\ | |
| 378 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
| 5798 | 379 @seealso{randn, rande, randg, randp}\n\ |
| 3369 | 380 @end deftypefn") |
| 2928 | 381 { |
| 4307 | 382 octave_value retval; |
| 2928 | 383 |
| 384 int nargin = args.length (); | |
| 385 | |
| 4543 | 386 retval = do_rand (args, nargin, "rand"); |
| 2928 | 387 |
| 388 return retval; | |
| 389 } | |
| 390 | |
| 8871 | 391 // FIXME -- The old generator (selected when "seed" is set) will not |
| 392 // work properly if compiled to use 64-bit integers. | |
| 393 | |
| 5730 | 394 /* |
| 395 %!test # 'state' can be a scalar | |
| 396 %! rand('state',12); x = rand(1,4); | |
| 397 %! rand('state',12); y = rand(1,4); | |
| 398 %! assert(x,y); | |
| 399 %!test # 'state' can be a vector | |
| 400 %! rand('state',[12,13]); x=rand(1,4); | |
| 401 %! rand('state',[12;13]); y=rand(1,4); | |
| 402 %! assert(x,y); | |
| 403 %!test # querying 'state' doesn't disturb sequence | |
| 404 %! rand('state',12); rand(1,2); x=rand(1,2); | |
| 405 %! rand('state',12); rand(1,2); | |
| 406 %! s=rand('state'); y=rand(1,2); | |
| 407 %! assert(x,y); | |
| 408 %! rand('state',s); z=rand(1,2); | |
| 409 %! assert(x,z); | |
| 410 %!test # 'seed' must be a scalar | |
| 411 %! rand('seed',12); x = rand(1,4); | |
| 412 %! rand('seed',12); y = rand(1,4); | |
| 413 %! assert(x,y); | |
| 414 %!error(rand('seed',[12,13])) | |
| 415 %!test # querying 'seed' returns a value which can be used later | |
| 416 %! s=rand('seed'); x=rand(1,2); | |
| 417 %! rand('seed',s); y=rand(1,2); | |
| 418 %! assert(x,y); | |
| 419 %!test # querying 'seed' doesn't disturb sequence | |
| 420 %! rand('seed',12); rand(1,2); x=rand(1,2); | |
| 421 %! rand('seed',12); rand(1,2); | |
| 422 %! s=rand('seed'); y=rand(1,2); | |
| 423 %! assert(x,y); | |
| 424 %! rand('seed',s); z=rand(1,2); | |
| 425 %! assert(x,z); | |
| 426 */ | |
| 427 | |
| 428 /* | |
| 429 %!test | |
| 6437 | 430 %! % Test fixed state |
| 431 %! rand("state",1); | |
| 6443 | 432 %! assert (rand(1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382],1e-6); |
| 6437 | 433 %!test |
| 6443 | 434 %! % Test fixed seed |
| 6437 | 435 %! rand("seed",1); |
| 6443 | 436 %! assert (rand(1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759],1e-6); |
| 5730 | 437 %!test |
| 6437 | 438 %! if (__random_statistical_tests__) |
| 439 %! % statistical tests may fail occasionally. | |
| 440 %! rand("state",12); | |
| 441 %! x = rand(100000,1); | |
| 442 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 443 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 444 %! assert(mean(x),0.5,0.0024); | |
| 445 %! assert(var(x),1/48,0.0632); | |
| 446 %! assert(skewness(x),0,0.012); | |
| 447 %! assert(kurtosis(x),-6/5,0.0094); | |
| 448 %! endif | |
| 449 %!test | |
| 450 %! if (__random_statistical_tests__) | |
| 451 %! % statistical tests may fail occasionally. | |
| 452 %! rand("seed",12); | |
| 453 %! x = rand(100000,1); | |
| 454 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 455 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 456 %! assert(mean(x),0.5,0.0024); | |
| 457 %! assert(var(x),1/48,0.0632); | |
| 458 %! assert(skewness(x),0,0.012); | |
| 459 %! assert(kurtosis(x),-6/5,0.0094); | |
| 460 %! endif | |
| 5730 | 461 */ |
| 462 | |
| 463 | |
| 4307 | 464 static std::string current_distribution = octave_rand::distribution (); |
| 465 | |
| 2928 | 466 static void |
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467 reset_rand_generator (void) |
| 2928 | 468 { |
| 4307 | 469 octave_rand::distribution (current_distribution); |
| 2928 | 470 } |
| 471 | |
| 4665 | 472 DEFUN_DLD (randn, args, , |
| 3369 | 473 "-*- texinfo -*-\n\ |
| 474 @deftypefn {Loadable Function} {} randn (@var{x})\n\ | |
| 475 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m})\n\ | |
| 5730 | 476 @deftypefnx {Loadable Function} {} randn (\"state\", @var{x})\n\ |
| 477 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{x})\n\ | |
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478 Return a matrix with normally distributed pseudo-random\n\ |
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479 elements having zero mean and variance one. The arguments are\n\ |
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480 handled the same as the arguments for @code{rand}.\n\ |
| 3369 | 481 \n\ |
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482 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique'' to\n\ |
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483 transform from a uniform to a normal distribution. (G. Marsaglia and\n\ |
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484 W.W. Tsang, @cite{Ziggurat method for generating random variables},\n\ |
| 5730 | 485 J. Statistical Software, vol 5, 2000,\n\ |
| 486 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 2928 | 487 \n\ |
| 6547 | 488 @seealso{rand, rande, randg, randp}\n\ |
| 3369 | 489 @end deftypefn") |
| 2928 | 490 { |
| 4307 | 491 octave_value retval; |
| 2928 | 492 |
| 493 int nargin = args.length (); | |
| 494 | |
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495 unwind_protect frame; |
| 2928 | 496 |
| 4543 | 497 // This relies on the fact that elements are popped from the unwind |
| 498 // stack in the reverse of the order they are pushed | |
| 499 // (i.e. current_distribution will be reset before calling | |
| 500 // reset_rand_generator()). | |
| 2928 | 501 |
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502 frame.add_fcn (reset_rand_generator); |
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503 frame.protect_var (current_distribution); |
| 2928 | 504 |
| 4543 | 505 current_distribution = "normal"; |
| 2928 | 506 |
| 4543 | 507 octave_rand::distribution (current_distribution); |
| 2928 | 508 |
| 4543 | 509 retval = do_rand (args, nargin, "randn"); |
| 2928 | 510 |
| 511 return retval; | |
| 512 } | |
| 513 | |
| 514 /* | |
| 5730 | 515 %!test |
| 6437 | 516 %! % Test fixed state |
| 517 %! randn("state",1); | |
| 6443 | 518 %! assert (randn(1,6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116],1e-6); |
| 6437 | 519 %!test |
| 6443 | 520 %! % Test fixed seed |
| 6437 | 521 %! randn("seed",1); |
| 6443 | 522 %! assert (randn(1,6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133],1e-6); |
| 5730 | 523 %!test |
| 6437 | 524 %! if (__random_statistical_tests__) |
| 525 %! % statistical tests may fail occasionally. | |
| 526 %! randn("state",12); | |
| 527 %! x = randn(100000,1); | |
| 528 %! assert(mean(x),0,0.01); | |
| 529 %! assert(var(x),1,0.02); | |
| 530 %! assert(skewness(x),0,0.02); | |
| 531 %! assert(kurtosis(x),0,0.04); | |
| 532 %! endif | |
| 533 %!test | |
| 534 %! if (__random_statistical_tests__) | |
| 535 %! % statistical tests may fail occasionally. | |
| 536 %! randn("seed",12); | |
| 537 %! x = randn(100000,1); | |
| 538 %! assert(mean(x),0,0.01); | |
| 539 %! assert(var(x),1,0.02); | |
| 540 %! assert(skewness(x),0,0.02); | |
| 541 %! assert(kurtosis(x),0,0.04); | |
| 542 %! endif | |
| 5730 | 543 */ |
| 544 | |
| 545 DEFUN_DLD (rande, args, , | |
| 546 "-*- texinfo -*-\n\ | |
| 547 @deftypefn {Loadable Function} {} rande (@var{x})\n\ | |
| 548 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m})\n\ | |
| 549 @deftypefnx {Loadable Function} {} rande (\"state\", @var{x})\n\ | |
| 550 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{x})\n\ | |
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551 Return a matrix with exponentially distributed random elements. The\n\ |
| 5730 | 552 arguments are handled the same as the arguments for @code{rand}.\n\ |
| 553 \n\ | |
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554 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique'' to\n\ |
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555 transform from a uniform to a exponential distribution. (G. Marsaglia and\n\ |
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556 W.W. Tsang, @cite{Ziggurat method for generating random variables},\n\ |
| 5730 | 557 J. Statistical Software, vol 5, 2000,\n\ |
| 558 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 6547 | 559 @seealso{rand, randn, randg, randp}\n\ |
| 5730 | 560 @end deftypefn") |
| 561 { | |
| 562 octave_value retval; | |
| 563 | |
| 564 int nargin = args.length (); | |
| 565 | |
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566 unwind_protect frame; |
| 5730 | 567 |
| 568 // This relies on the fact that elements are popped from the unwind | |
| 569 // stack in the reverse of the order they are pushed | |
| 570 // (i.e. current_distribution will be reset before calling | |
| 571 // reset_rand_generator()). | |
| 572 | |
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573 frame.add_fcn (reset_rand_generator); |
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574 frame.protect_var (current_distribution); |
| 5730 | 575 |
| 576 current_distribution = "exponential"; | |
| 577 | |
| 578 octave_rand::distribution (current_distribution); | |
| 579 | |
| 580 retval = do_rand (args, nargin, "rande"); | |
| 581 | |
| 582 return retval; | |
| 583 } | |
| 584 | |
| 585 /* | |
| 586 %!test | |
| 6437 | 587 %! % Test fixed state |
| 588 %! rande("state",1); | |
| 6443 | 589 %! assert (rande(1,6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291],1e-6); |
| 6437 | 590 %!test |
| 6443 | 591 %! % Test fixed seed |
| 6437 | 592 %! rande("seed",1); |
| 6443 | 593 %! assert (rande(1,6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087],1e-6); |
| 5730 | 594 %!test |
| 6437 | 595 %! if (__random_statistical_tests__) |
| 596 %! % statistical tests may fail occasionally | |
| 597 %! rande("state",1); | |
| 598 %! x = rande(100000,1); | |
| 599 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 600 %! assert(mean(x),1,0.01); | |
| 601 %! assert(var(x),1,0.03); | |
| 602 %! assert(skewness(x),2,0.06); | |
| 603 %! assert(kurtosis(x),6,0.7); | |
| 604 %! endif | |
| 605 %!test | |
| 606 %! if (__random_statistical_tests__) | |
| 607 %! % statistical tests may fail occasionally | |
| 608 %! rande("seed",1); | |
| 609 %! x = rande(100000,1); | |
| 610 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 611 %! assert(mean(x),1,0.01); | |
| 612 %! assert(var(x),1,0.03); | |
| 613 %! assert(skewness(x),2,0.06); | |
| 614 %! assert(kurtosis(x),6,0.7); | |
| 615 %! endif | |
| 5730 | 616 */ |
| 617 | |
| 618 DEFUN_DLD (randg, args, , | |
| 619 "-*- texinfo -*-\n\ | |
| 620 @deftypefn {Loadable Function} {} randg (@var{a}, @var{x})\n\ | |
| 621 @deftypefnx {Loadable Function} {} randg (@var{a}, @var{n}, @var{m})\n\ | |
| 622 @deftypefnx {Loadable Function} {} randg (\"state\", @var{x})\n\ | |
| 623 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{x})\n\ | |
| 624 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ | |
| 625 The arguments are handled the same as the arguments for @code{rand},\n\ | |
| 626 except for the argument @var{a}.\n\ | |
| 627 \n\ | |
| 628 This can be used to generate many distributions:\n\ | |
| 629 \n\ | |
| 630 @table @asis\n\ | |
| 6547 | 631 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
| 5730 | 632 @example\n\ |
| 6547 | 633 r = b * randg (a)\n\ |
| 5730 | 634 @end example\n\ |
| 6547 | 635 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
| 5730 | 636 @example\n\ |
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637 @group\n\ |
| 6547 | 638 r1 = randg (a, 1)\n\ |
| 639 r = r1 / (r1 + randg (b, 1))\n\ | |
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640 @end group\n\ |
| 5730 | 641 @end example\n\ |
| 6547 | 642 @item @code{Erlang (a, n)}\n\ |
| 5730 | 643 @example\n\ |
| 6547 | 644 r = a * randg (n)\n\ |
| 5730 | 645 @end example\n\ |
| 6547 | 646 @item @code{chisq (df)} for @code{df > 0}\n\ |
| 5730 | 647 @example\n\ |
| 6547 | 648 r = 2 * randg (df / 2)\n\ |
| 5730 | 649 @end example\n\ |
| 650 @item @code{t(df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ | |
| 651 @example\n\ | |
| 6547 | 652 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
| 5730 | 653 @end example\n\ |
| 6547 | 654 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
| 5730 | 655 @example\n\ |
| 7096 | 656 @group\n\ |
| 657 ## r1 equals 1 if n1 is infinite\n\ | |
| 658 r1 = 2 * randg (n1 / 2) / n1\n\ | |
| 659 ## r2 equals 1 if n2 is infinite\n\ | |
| 660 r2 = 2 * randg (n2 / 2) / n2\n\ | |
| 5730 | 661 r = r1 / r2\n\n\ |
| 7096 | 662 @end group\n\ |
| 5730 | 663 @end example\n\ |
| 664 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ | |
| 665 @example\n\ | |
| 6547 | 666 r = randp ((1 - p) / p * randg (n))\n\ |
| 5730 | 667 @end example\n\ |
| 6547 | 668 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
| 5730 | 669 (use chisq if @code{L = 0})\n\ |
| 670 @example\n\ | |
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671 @group\n\ |
| 6547 | 672 r = randp (L / 2)\n\ |
| 673 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
| 674 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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675 @end group\n\ |
| 5730 | 676 @end example\n\ |
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677 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
| 5730 | 678 @example\n\ |
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679 @group\n\ |
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680 r = (randg (a1), @dots{}, randg (ak))\n\ |
| 6547 | 681 r = r / sum (r)\n\ |
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682 @end group\n\ |
| 5730 | 683 @end example\n\ |
| 684 @end table\n\ | |
| 6547 | 685 @seealso{rand, randn, rande, randp}\n\ |
| 5730 | 686 @end deftypefn") |
| 687 { | |
| 688 octave_value retval; | |
| 689 | |
| 690 int nargin = args.length (); | |
| 691 | |
| 692 if (nargin < 1) | |
| 693 error ("randg: insufficient arguments"); | |
| 694 else | |
| 695 { | |
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696 unwind_protect frame; |
| 5730 | 697 |
| 698 // This relies on the fact that elements are popped from the unwind | |
| 699 // stack in the reverse of the order they are pushed | |
| 700 // (i.e. current_distribution will be reset before calling | |
| 701 // reset_rand_generator()). | |
| 702 | |
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703 frame.add_fcn (reset_rand_generator); |
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704 frame.protect_var (current_distribution); |
| 5730 | 705 |
| 706 current_distribution = "gamma"; | |
| 707 | |
| 708 octave_rand::distribution (current_distribution); | |
| 709 | |
| 710 retval = do_rand (args, nargin, "randg", true); | |
| 711 } | |
| 712 | |
| 713 return retval; | |
| 714 } | |
| 715 | |
| 716 /* | |
| 717 %!test | |
| 6437 | 718 %! randg("state",12) |
| 719 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report | |
| 720 | |
| 721 | |
| 722 %!test | |
| 723 %! % Test fixed state | |
| 724 %! randg("state",1); | |
| 6443 | 725 %! assert (randg(0.1,1,6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12],1e-6); |
| 6437 | 726 %!test |
| 727 %! % Test fixed state | |
| 728 %! randg("state",1); | |
| 6443 | 729 %! assert (randg(0.95,1,6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939],1e-6); |
| 6437 | 730 %!test |
| 731 %! % Test fixed state | |
| 732 %! randg("state",1); | |
| 6443 | 733 %! assert (randg(1,1,6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533],1e-6); |
| 6437 | 734 %!test |
| 735 %! % Test fixed state | |
| 736 %! randg("state",1); | |
| 6443 | 737 %! assert (randg(10,1,6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059],1e-5); |
| 6437 | 738 %!test |
| 739 %! % Test fixed state | |
| 740 %! randg("state",1); | |
| 6443 | 741 %! assert (randg(100,1,6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118],1e-4); |
| 6437 | 742 %!test |
| 743 %! % Test fixed seed | |
| 744 %! randg("seed",1); | |
| 6443 | 745 %! assert (randg(0.1,1,6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12],1e-6); |
| 6437 | 746 %!test |
| 747 %! % Test fixed seed | |
| 748 %! randg("seed",1); | |
| 6443 | 749 %! assert (randg(0.95,1,6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648],1e-6); |
| 6437 | 750 %!test |
| 751 %! % Test fixed seed | |
| 752 %! randg("seed",1); | |
| 6443 | 753 %! assert (randg(1,1,6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303],1e-6); |
| 6437 | 754 %!test |
| 755 %! % Test fixed seed | |
| 756 %! randg("seed",1); | |
| 6443 | 757 %! assert (randg(10,1,6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186],1e-5); |
| 6437 | 758 %!test |
| 759 %! % Test fixed seed | |
| 760 %! randg("seed",1); | |
| 6443 | 761 %! assert (randg(100,1,6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625],1e-4); |
| 6437 | 762 %!test |
| 763 %! if (__random_statistical_tests__) | |
| 764 %! % statistical tests may fail occasionally. | |
| 765 %! randg("state",12) | |
| 766 %! a=0.1; x = randg(a,100000,1); | |
| 767 %! assert(mean(x), a, 0.01); | |
| 768 %! assert(var(x), a, 0.01); | |
| 769 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 770 %! assert(kurtosis(x),6/a, 50.); | |
| 771 %! endif | |
| 772 %!test | |
| 773 %! if (__random_statistical_tests__) | |
| 774 %! % statistical tests may fail occasionally. | |
| 775 %! randg("state",12) | |
| 776 %! a=0.95; x = randg(a,100000,1); | |
| 777 %! assert(mean(x), a, 0.01); | |
| 778 %! assert(var(x), a, 0.04); | |
| 779 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 780 %! assert(kurtosis(x),6/a, 2.); | |
| 781 %! endif | |
| 782 %!test | |
| 783 %! if (__random_statistical_tests__) | |
| 784 %! % statistical tests may fail occasionally. | |
| 785 %! randg("state",12) | |
| 786 %! a=1; x = randg(a,100000,1); | |
| 787 %! assert(mean(x),a, 0.01); | |
| 788 %! assert(var(x),a, 0.04); | |
| 789 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 790 %! assert(kurtosis(x),6/a, 2.); | |
| 791 %! endif | |
| 792 %!test | |
| 793 %! if (__random_statistical_tests__) | |
| 794 %! % statistical tests may fail occasionally. | |
| 795 %! randg("state",12) | |
| 796 %! a=10; x = randg(a,100000,1); | |
| 797 %! assert(mean(x), a, 0.1); | |
| 798 %! assert(var(x), a, 0.5); | |
| 799 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 800 %! assert(kurtosis(x),6/a, 0.5); | |
| 801 %! endif | |
| 802 %!test | |
| 803 %! if (__random_statistical_tests__) | |
| 804 %! % statistical tests may fail occasionally. | |
| 805 %! randg("state",12) | |
| 806 %! a=100; x = randg(a,100000,1); | |
| 807 %! assert(mean(x), a, 0.2); | |
| 808 %! assert(var(x), a, 2.); | |
| 809 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 810 %! assert(kurtosis(x),6/a, 0.2); | |
| 811 %! endif | |
| 812 %!test | |
| 813 %! randg("seed",12) | |
| 5730 | 814 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report |
| 815 %!test | |
| 6437 | 816 %! if (__random_statistical_tests__) |
| 817 %! % statistical tests may fail occasionally. | |
| 818 %! randg("seed",12) | |
| 819 %! a=0.1; x = randg(a,100000,1); | |
| 820 %! assert(mean(x), a, 0.01); | |
| 821 %! assert(var(x), a, 0.01); | |
| 822 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 823 %! assert(kurtosis(x),6/a, 50.); | |
| 824 %! endif | |
| 5730 | 825 %!test |
| 6437 | 826 %! if (__random_statistical_tests__) |
| 827 %! % statistical tests may fail occasionally. | |
| 828 %! randg("seed",12) | |
| 829 %! a=0.95; x = randg(a,100000,1); | |
| 830 %! assert(mean(x), a, 0.01); | |
| 831 %! assert(var(x), a, 0.04); | |
| 832 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 833 %! assert(kurtosis(x),6/a, 2.); | |
| 834 %! endif | |
| 5730 | 835 %!test |
| 6437 | 836 %! if (__random_statistical_tests__) |
| 837 %! % statistical tests may fail occasionally. | |
| 838 %! randg("seed",12) | |
| 839 %! a=1; x = randg(a,100000,1); | |
| 840 %! assert(mean(x),a, 0.01); | |
| 841 %! assert(var(x),a, 0.04); | |
| 842 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 843 %! assert(kurtosis(x),6/a, 2.); | |
| 844 %! endif | |
| 5730 | 845 %!test |
| 6437 | 846 %! if (__random_statistical_tests__) |
| 847 %! % statistical tests may fail occasionally. | |
| 848 %! randg("seed",12) | |
| 849 %! a=10; x = randg(a,100000,1); | |
| 850 %! assert(mean(x), a, 0.1); | |
| 851 %! assert(var(x), a, 0.5); | |
| 852 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 853 %! assert(kurtosis(x),6/a, 0.5); | |
| 854 %! endif | |
| 5730 | 855 %!test |
| 6437 | 856 %! if (__random_statistical_tests__) |
| 857 %! % statistical tests may fail occasionally. | |
| 858 %! randg("seed",12) | |
| 859 %! a=100; x = randg(a,100000,1); | |
| 860 %! assert(mean(x), a, 0.2); | |
| 861 %! assert(var(x), a, 2.); | |
| 862 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 863 %! assert(kurtosis(x),6/a, 0.2); | |
| 864 %! endif | |
| 5730 | 865 */ |
| 866 | |
| 867 | |
| 868 DEFUN_DLD (randp, args, , | |
| 869 "-*- texinfo -*-\n\ | |
| 870 @deftypefn {Loadable Function} {} randp (@var{l}, @var{x})\n\ | |
| 871 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m})\n\ | |
| 872 @deftypefnx {Loadable Function} {} randp (\"state\", @var{x})\n\ | |
| 873 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{x})\n\ | |
|
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874 Return a matrix with Poisson distributed random elements with mean value parameter given by the first argument, @var{l}. The arguments\n\ |
| 5730 | 875 are handled the same as the arguments for @code{rand}, except for the\n\ |
| 876 argument @var{l}.\n\ | |
| 877 \n\ | |
| 878 Five different algorithms are used depending on the range of @var{l}\n\ | |
| 879 and whether or not @var{l} is a scalar or a matrix.\n\ | |
| 880 \n\ | |
| 881 @table @asis\n\ | |
| 882 @item For scalar @var{l} <= 12, use direct method.\n\ | |
| 883 Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\ | |
| 884 @item For scalar @var{l} > 12, use rejection method.[1]\n\ | |
| 885 Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\ | |
| 886 @item For matrix @var{l} <= 10, use inversion method.[2]\n\ | |
| 887 Stadlober E., et al., WinRand source code, available via FTP.\n\ | |
| 888 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ | |
| 889 Stadlober E., et al., WinRand source code, available via FTP, or\n\ | |
| 890 H. Zechner, 'Efficient sampling from continuous and discrete\n\ | |
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891 unimodal distributions', Doctoral Dissertation, 156pp., Technical\n\ |
| 5730 | 892 University Graz, Austria, 1994.\n\ |
| 893 @item For @var{l} > 1e8, use normal approximation.\n\ | |
| 894 L. Montanet, et al., 'Review of Particle Properties', Physical Review\n\ | |
| 895 D 50 p1284, 1994\n\ | |
| 896 @end table\n\ | |
| 6547 | 897 @seealso{rand, randn, rande, randg}\n\ |
| 5730 | 898 @end deftypefn") |
| 899 { | |
| 900 octave_value retval; | |
| 901 | |
| 902 int nargin = args.length (); | |
| 903 | |
| 904 if (nargin < 1) | |
| 905 error ("randp: insufficient arguments"); | |
| 906 else | |
| 907 { | |
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908 unwind_protect frame; |
| 5730 | 909 |
| 910 // This relies on the fact that elements are popped from the unwind | |
| 911 // stack in the reverse of the order they are pushed | |
| 912 // (i.e. current_distribution will be reset before calling | |
| 913 // reset_rand_generator()). | |
| 914 | |
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915 frame.add_fcn (reset_rand_generator); |
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916 frame.protect_var (current_distribution); |
| 5730 | 917 |
| 918 current_distribution = "poisson"; | |
| 919 | |
| 920 octave_rand::distribution (current_distribution); | |
| 921 | |
| 922 retval = do_rand (args, nargin, "randp", true); | |
| 923 } | |
| 924 | |
| 925 return retval; | |
| 926 } | |
| 927 | |
| 928 /* | |
| 929 %!test | |
| 6437 | 930 %! randp("state",12) |
| 931 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report | |
| 932 %!test | |
| 933 %! % Test fixed state | |
| 934 %! randp("state",1); | |
| 935 %! assert(randp(5,1,6),[5 5 3 7 7 3]) | |
| 936 %!test | |
| 937 %! % Test fixed state | |
| 938 %! randp("state",1); | |
| 939 %! assert(randp(15,1,6),[13 15 8 18 18 15]) | |
| 940 %!test | |
| 941 %! % Test fixed state | |
| 942 %! randp("state",1); | |
| 7421 | 943 %! assert(randp(1e9,1,6),[999915677 999976657 1000047684 1000019035 999985749 999977692],-1e-6) |
| 6437 | 944 %!test |
| 945 %! % Test fixed state | |
| 946 %! randp("seed",1); | |
| 6449 | 947 %! %%assert(randp(5,1,6),[8 2 3 6 6 8]) |
| 948 %! assert(randp(5,1,5),[8 2 3 6 6]) | |
| 6437 | 949 %!test |
| 950 %! % Test fixed state | |
| 951 %! randp("seed",1); | |
| 952 %! assert(randp(15,1,6),[15 16 12 10 10 12]) | |
| 953 %!test | |
| 954 %! % Test fixed state | |
| 955 %! randp("seed",1); | |
| 7421 | 956 %! assert(randp(1e9,1,6),[1000006208 1000012224 999981120 999963520 999963072 999981440],-1e-6) |
| 6437 | 957 %!test |
| 958 %! if (__random_statistical_tests__) | |
| 959 %! % statistical tests may fail occasionally. | |
| 960 %! randp("state",12) | |
| 961 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 962 %! x = randp(a(1),100000,1); | |
| 963 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 964 %! assert(mean(x),a(1),a(2)); | |
| 965 %! assert(var(x),a(1),0.02*a(1)); | |
| 966 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 967 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 968 %! endfor | |
| 969 %! endif | |
| 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)*ones(100000,1),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 %! randp("seed",12) | |
| 5730 | 985 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report |
| 986 %!test | |
| 6449 | 987 %! if (__random_statistical_tests__) |
| 988 %! % statistical tests may fail occasionally. | |
| 989 %! randp("seed",12) | |
| 990 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 991 %! x = randp(a(1),100000,1); | |
| 992 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 993 %! assert(mean(x),a(1),a(2)); | |
| 994 %! assert(var(x),a(1),0.02*a(1)); | |
| 995 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 996 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 997 %! endfor | |
| 998 %! endif | |
| 5730 | 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)*ones(100000,1),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 */ |
| 1013 | |
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1014 DEFUN_DLD (randperm, args, , |
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1015 "-*- texinfo -*-\n\ |
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1016 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
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1017 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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1018 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1019 If @var{m} is supplied, return @var{m} permutations,\n\ |
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1020 one in each row of a NxM matrix. The complexity is O(M*N) in both time and\n\ |
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1021 memory. The randomization is performed using rand().\n\ |
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1022 All permutations are equally likely.\n\ |
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1023 @seealso{perms}\n\ |
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1024 @end deftypefn") |
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1025 { |
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1026 int nargin = args.length (); |
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1027 octave_value retval; |
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1028 |
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1029 if (nargin == 1 || nargin == 2) |
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1030 { |
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1031 octave_idx_type n, m; |
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1032 |
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1033 if (nargin == 2) |
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1034 m = args(1).idx_type_value (true); |
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1035 else |
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1036 m = 1; |
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1037 |
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1038 n = args(0).idx_type_value (true); |
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1039 |
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1040 if (m < 0 || n < 0) |
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1041 error ("randperm: m and n must be non-negative"); |
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1042 |
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1043 if (! error_state) |
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1044 { |
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1045 // Generate random numbers. |
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1046 NDArray r = octave_rand::nd_array (dim_vector (m, n)); |
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1047 |
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1048 // Create transposed to allow faster access. |
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1049 Array<octave_idx_type> idx (dim_vector (n, m)); |
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1050 |
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1051 double *rvec = r.fortran_vec (); |
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1052 |
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1053 octave_idx_type *ivec = idx.fortran_vec (); |
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1054 |
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1055 // Perform the Knuth shuffle. |
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1056 for (octave_idx_type j = 0; j < m; j++) |
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1057 { |
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1058 for (octave_idx_type i = 0; i < n; i++) |
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1059 ivec[i] = i; |
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1060 |
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1061 for (octave_idx_type i = 0; i < n; i++) |
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1062 { |
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1063 octave_idx_type k = i + floor (rvec[i] * (n - i)); |
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1064 std::swap (ivec[i], ivec[k]); |
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1065 } |
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1066 |
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1067 ivec += n; |
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1068 rvec += n; |
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1069 } |
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1070 |
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1071 // Transpose. |
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1072 idx = idx.transpose (); |
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1073 |
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1074 // Re-fetch the pointers. |
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1075 ivec = idx.fortran_vec (); |
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1076 rvec = r.fortran_vec (); |
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1077 |
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1078 // Convert to doubles, reusing r. |
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1079 for (octave_idx_type i = 0, l = m*n; i < l; i++) |
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1080 rvec[i] = ivec[i] + 1; |
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1081 |
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1082 // Now create an array object with a cached idx_vector. |
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1083 retval = new octave_matrix (r, idx_vector (idx)); |
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1084 } |
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1085 } |
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1086 else |
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1087 print_usage (); |
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1088 |
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1089 return retval; |
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1090 } |
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1091 |
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1092 /* |
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1093 %!assert(sort(randperm(20)),1:20) |
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1094 %!assert(sort(randperm(20,50),2),repmat(1:20,50,1)) |
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1095 */ |
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1096 |
| 5730 | 1097 /* |
| 2928 | 1098 ;;; Local Variables: *** |
| 1099 ;;; mode: C++ *** | |
| 1100 ;;; End: *** | |
| 1101 */ |