Mercurial > hg > octave-lyh
annotate src/DLD-FUNCTIONS/rand.cc @ 13026:9a2a6054460f
maint: Fix 2 compiler warnings
rand.cc: Remove unused variable from function.
load-save.cc: Remove unused initialization in for loop.
| author | Rik <octave@nomad.inbox5.com> |
|---|---|
| date | Mon, 29 Aug 2011 09:14:11 -0700 |
| parents | f7a8d1dafda3 |
| children | 2896c083576a |
| rev | line source |
|---|---|
| 2928 | 1 /* |
| 2 | |
| 11523 | 3 Copyright (C) 1996-2011 John W. Eaton |
|
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4 Copyright (C) 2009 VZLU Prague |
| 2928 | 5 |
| 6 This file is part of Octave. | |
| 7 | |
| 8 Octave is free software; you can redistribute it and/or modify it | |
| 9 under the terms of the GNU General Public License as published by the | |
| 7016 | 10 Free Software Foundation; either version 3 of the License, or (at your |
| 11 option) any later version. | |
| 2928 | 12 |
| 13 Octave is distributed in the hope that it will be useful, but WITHOUT | |
| 14 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
| 15 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
| 16 for more details. | |
| 17 | |
| 18 You should have received a copy of the GNU General Public License | |
| 7016 | 19 along with Octave; see the file COPYING. If not, see |
| 20 <http://www.gnu.org/licenses/>. | |
| 2928 | 21 |
| 22 */ | |
| 23 | |
| 24 #ifdef HAVE_CONFIG_H | |
| 25 #include <config.h> | |
| 26 #endif | |
| 27 | |
| 28 #include <ctime> | |
| 29 | |
| 30 #include <string> | |
| 31 | |
| 32 #include "f77-fcn.h" | |
| 33 #include "lo-mappers.h" | |
| 4307 | 34 #include "oct-rand.h" |
| 4153 | 35 #include "quit.h" |
| 2928 | 36 |
| 37 #include "defun-dld.h" | |
| 38 #include "error.h" | |
| 39 #include "gripes.h" | |
| 40 #include "oct-obj.h" | |
| 41 #include "unwind-prot.h" | |
| 42 #include "utils.h" | |
|
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43 #include "ov-re-mat.h" |
| 2928 | 44 |
| 6437 | 45 /* |
| 46 %!shared __random_statistical_tests__ | |
| 47 %! % Flag whether the statistical tests should be run in "make check" or not | |
| 48 %! __random_statistical_tests__ = 0; | |
| 49 */ | |
| 50 | |
| 4307 | 51 static octave_value |
| 5730 | 52 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
|
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53 const std::string& distribution, bool additional_arg = false) |
| 2928 | 54 { |
| 4307 | 55 octave_value retval; |
| 5730 | 56 NDArray a; |
| 57 int idx = 0; | |
| 58 dim_vector dims; | |
| 2928 | 59 |
|
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60 unwind_protect frame; |
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61 // Restore current distribution on any exit. |
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62 frame.add_fcn (octave_rand::distribution, |
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63 octave_rand::distribution ()); |
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64 |
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65 octave_rand::distribution (distribution); |
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66 |
| 5730 | 67 if (additional_arg) |
| 68 { | |
| 69 if (nargin == 0) | |
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70 { |
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71 error ("%s: expecting at least one argument", fcn); |
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72 goto done; |
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73 } |
| 5730 | 74 else if (args(0).is_string()) |
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75 additional_arg = false; |
| 5730 | 76 else |
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77 { |
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78 a = args(0).array_value (); |
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79 if (error_state) |
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80 { |
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81 error ("%s: expecting scalar or matrix arguments", fcn); |
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82 goto done; |
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83 } |
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84 idx++; |
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85 nargin--; |
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86 } |
| 5730 | 87 } |
| 2928 | 88 |
| 4543 | 89 switch (nargin) |
| 2928 | 90 { |
| 4543 | 91 case 0: |
| 92 { | |
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93 if (additional_arg) |
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94 dims = a.dims (); |
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95 else |
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96 { |
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97 dims.resize (2); |
| 4543 | 98 |
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99 dims(0) = 1; |
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100 dims(1) = 1; |
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101 } |
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102 goto gen_matrix; |
| 4543 | 103 } |
| 104 break; | |
| 2928 | 105 |
| 4543 | 106 case 1: |
| 107 { | |
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108 octave_value tmp = args(idx); |
| 4543 | 109 |
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110 if (tmp.is_string ()) |
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111 { |
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112 std::string s_arg = tmp.string_value (); |
| 2928 | 113 |
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114 if (s_arg == "dist") |
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115 { |
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116 retval = octave_rand::distribution (); |
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117 } |
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118 else if (s_arg == "seed") |
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119 { |
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120 retval = octave_rand::seed (); |
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121 } |
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122 else if (s_arg == "state" || s_arg == "twister") |
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123 { |
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124 retval = octave_rand::state (fcn); |
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125 } |
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126 else if (s_arg == "uniform") |
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127 { |
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128 octave_rand::uniform_distribution (); |
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129 } |
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130 else if (s_arg == "normal") |
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131 { |
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132 octave_rand::normal_distribution (); |
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133 } |
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134 else if (s_arg == "exponential") |
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135 { |
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136 octave_rand::exponential_distribution (); |
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137 } |
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138 else if (s_arg == "poisson") |
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139 { |
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140 octave_rand::poisson_distribution (); |
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141 } |
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142 else if (s_arg == "gamma") |
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143 { |
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144 octave_rand::gamma_distribution (); |
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145 } |
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146 else |
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147 error ("%s: unrecognized string argument", fcn); |
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148 } |
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149 else if (tmp.is_scalar_type ()) |
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150 { |
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151 double dval = tmp.double_value (); |
| 2928 | 152 |
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153 if (xisnan (dval)) |
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154 { |
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155 error ("%s: NaN is invalid matrix dimension", fcn); |
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156 } |
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157 else |
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158 { |
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159 dims.resize (2); |
| 4543 | 160 |
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161 dims(0) = NINTbig (tmp.double_value ()); |
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162 dims(1) = NINTbig (tmp.double_value ()); |
| 2928 | 163 |
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164 if (! error_state) |
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165 goto gen_matrix; |
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166 } |
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167 } |
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168 else if (tmp.is_range ()) |
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169 { |
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170 Range r = tmp.range_value (); |
| 4543 | 171 |
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172 if (r.all_elements_are_ints ()) |
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173 { |
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174 octave_idx_type n = r.nelem (); |
| 4543 | 175 |
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176 dims.resize (n); |
| 4543 | 177 |
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178 octave_idx_type base = NINTbig (r.base ()); |
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179 octave_idx_type incr = NINTbig (r.inc ()); |
| 2928 | 180 |
|
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181 for (octave_idx_type i = 0; i < n; i++) |
|
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182 { |
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183 //Negative dimensions are treated as zero for Matlab |
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184 //compatibility |
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185 dims(i) = base >= 0 ? base : 0; |
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186 base += incr; |
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187 } |
| 2928 | 188 |
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189 goto gen_matrix; |
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190 |
|
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191 } |
|
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192 else |
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193 error ("%s: all elements of range must be integers", |
|
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194 fcn); |
|
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195 } |
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196 else if (tmp.is_matrix_type ()) |
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197 { |
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198 Array<int> iv = tmp.int_vector_value (true); |
| 4543 | 199 |
|
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200 if (! error_state) |
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201 { |
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202 octave_idx_type len = iv.length (); |
| 2928 | 203 |
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204 dims.resize (len); |
| 4543 | 205 |
|
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206 for (octave_idx_type i = 0; i < len; i++) |
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207 { |
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208 //Negative dimensions are treated as zero for Matlab |
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209 //compatibility |
|
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210 octave_idx_type elt = iv(i); |
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211 dims(i) = elt >=0 ? elt : 0; |
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212 } |
| 2928 | 213 |
|
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214 goto gen_matrix; |
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215 } |
|
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216 else |
|
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217 error ("%s: expecting integer vector", fcn); |
|
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218 } |
|
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219 else |
|
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220 { |
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221 gripe_wrong_type_arg ("rand", tmp); |
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222 return retval; |
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223 } |
| 4543 | 224 } |
| 225 break; | |
| 226 | |
| 227 default: | |
| 228 { | |
|
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229 octave_value tmp = args(idx); |
| 4543 | 230 |
|
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231 if (nargin == 2 && tmp.is_string ()) |
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232 { |
|
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233 std::string ts = tmp.string_value (); |
| 5164 | 234 |
|
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235 if (ts == "seed") |
|
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236 { |
|
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237 if (args(idx+1).is_real_scalar ()) |
|
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238 { |
|
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239 double d = args(idx+1).double_value (); |
| 2928 | 240 |
|
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241 if (! error_state) |
|
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242 octave_rand::seed (d); |
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243 } |
|
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244 else if (args(idx+1).is_string () |
|
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245 && args(idx+1).string_value() == "reset") |
|
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246 octave_rand::reset (); |
|
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247 else |
|
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248 error ("%s: seed must be a real scalar", fcn); |
|
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249 } |
|
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250 else if (ts == "state" || ts == "twister") |
|
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251 { |
|
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252 if (args(idx+1).is_string () |
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253 && args(idx+1).string_value() == "reset") |
|
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254 octave_rand::reset (fcn); |
|
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255 else |
|
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|
256 { |
|
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|
257 ColumnVector s = |
|
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258 ColumnVector (args(idx+1).vector_value(false, true)); |
| 5730 | 259 |
|
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260 if (! error_state) |
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|
261 octave_rand::state (s, fcn); |
|
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262 } |
|
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263 } |
|
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264 else |
|
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265 error ("%s: unrecognized string argument", fcn); |
|
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266 } |
|
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267 else |
|
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268 { |
|
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269 dims.resize (nargin); |
| 4543 | 270 |
|
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271 for (int i = 0; i < nargin; i++) |
|
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272 { |
|
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273 octave_idx_type elt = args(idx+i).int_value (); |
|
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274 if (error_state) |
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275 { |
|
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276 error ("%s: expecting integer arguments", fcn); |
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277 goto done; |
|
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|
278 } |
|
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279 //Negative is zero for Matlab compatibility |
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280 dims(i) = elt >= 0 ? elt : 0; |
|
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|
281 } |
| 4543 | 282 |
|
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283 goto gen_matrix; |
|
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|
284 } |
| 4543 | 285 } |
| 286 break; | |
| 2928 | 287 } |
| 288 | |
| 4543 | 289 done: |
| 2928 | 290 |
| 291 return retval; | |
| 292 | |
| 293 gen_matrix: | |
| 294 | |
| 5355 | 295 dims.chop_trailing_singletons (); |
| 296 | |
| 5730 | 297 if (additional_arg) |
| 298 { | |
| 299 if (a.length() == 1) | |
|
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300 return octave_rand::nd_array (dims, a(0)); |
| 5730 | 301 else |
|
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|
302 { |
|
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|
303 if (a.dims() != dims) |
|
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304 { |
|
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|
305 error ("%s: mismatch in argument size", fcn); |
|
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306 return retval; |
|
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|
307 } |
|
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|
308 octave_idx_type len = a.length (); |
|
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|
309 NDArray m (dims); |
|
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|
310 double *v = m.fortran_vec (); |
|
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|
311 for (octave_idx_type i = 0; i < len; i++) |
|
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312 v[i] = octave_rand::scalar (a(i)); |
|
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313 return m; |
|
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|
314 } |
| 5730 | 315 } |
| 316 else | |
| 317 return octave_rand::nd_array (dims); | |
| 2928 | 318 } |
| 319 | |
| 4665 | 320 DEFUN_DLD (rand, args, , |
| 3369 | 321 "-*- texinfo -*-\n\ |
|
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|
322 @deftypefn {Loadable Function} {} rand (@var{n})\n\ |
|
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|
323 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m}, @dots{})\n\ |
|
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|
324 @deftypefnx {Loadable Function} {} rand ([@var{n} @var{m} @dots{}])\n\ |
|
11553
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changeset
|
325 @deftypefnx {Loadable Function} {@var{v} =} rand (\"state\")\n\ |
|
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326 @deftypefnx {Loadable Function} {} rand (\"state\", @var{v})\n\ |
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327 @deftypefnx {Loadable Function} {} rand (\"state\", \"reset\")\n\ |
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328 @deftypefnx {Loadable Function} {@var{v} =} rand (\"seed\")\n\ |
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329 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{v})\n\ |
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330 @deftypefnx {Loadable Function} {} rand (\"seed\", \"reset\")\n\ |
| 3369 | 331 Return a matrix with random elements uniformly distributed on the\n\ |
| 332 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
| 5730 | 333 for @code{eye}.\n\ |
| 334 \n\ | |
| 335 You can query the state of the random number generator using the\n\ | |
| 3369 | 336 form\n\ |
| 2928 | 337 \n\ |
| 3369 | 338 @example\n\ |
| 5730 | 339 v = rand (\"state\")\n\ |
| 340 @end example\n\ | |
| 341 \n\ | |
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342 This returns a column vector @var{v} of length 625. Later, you can\n\ |
| 5730 | 343 restore the random number generator to the state @var{v}\n\ |
| 344 using the form\n\ | |
| 345 \n\ | |
| 346 @example\n\ | |
| 347 rand (\"state\", v)\n\ | |
| 3369 | 348 @end example\n\ |
| 349 \n\ | |
| 350 @noindent\n\ | |
| 5730 | 351 You may also initialize the state vector from an arbitrary vector of\n\ |
| 10840 | 352 length @leq{} 625 for @var{v}. This new state will be a hash based on the\n\ |
| 5798 | 353 value of @var{v}, not @var{v} itself.\n\ |
| 5730 | 354 \n\ |
| 355 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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356 available, otherwise from CPU time, wall clock time, and the current\n\ |
| 5730 | 357 fraction of a second.\n\ |
| 358 \n\ | |
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359 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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360 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and\n\ |
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361 T. Nishimura,\n\ |
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362 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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363 pseudorandom number generator}, ACM Trans. on\n\ |
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364 Modeling and Computer Simulation Vol. 8, No. 1, pp. 3-30, January 1998,\n\ |
| 7171 | 365 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
| 6547 | 366 Do @strong{not} use for cryptography without securely hashing\n\ |
| 367 several returned values together, otherwise the generator state\n\ | |
| 368 can be learned after reading 624 consecutive values.\n\ | |
| 5730 | 369 \n\ |
| 7096 | 370 Older versions of Octave used a different random number generator.\n\ |
| 371 The new generator is used by default\n\ | |
| 5730 | 372 as it is significantly faster than the old generator, and produces\n\ |
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373 random numbers with a significantly longer cycle time. However, in\n\ |
| 5798 | 374 some circumstances it might be desirable to obtain the same random\n\ |
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375 sequences as used by the old generators. To do this the keyword\n\ |
| 5730 | 376 \"seed\" is used to specify that the old generators should be use,\n\ |
| 377 as in\n\ | |
| 2928 | 378 \n\ |
| 3369 | 379 @example\n\ |
| 5730 | 380 rand (\"seed\", val)\n\ |
| 3369 | 381 @end example\n\ |
| 382 \n\ | |
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383 @noindent\n\ |
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384 which sets the seed of the generator to @var{val}. The seed of the\n\ |
| 5730 | 385 generator can be queried with\n\ |
| 386 \n\ | |
| 387 @example\n\ | |
| 388 s = rand (\"seed\")\n\ | |
| 389 @end example\n\ | |
| 390 \n\ | |
| 391 However, it should be noted that querying the seed will not cause\n\ | |
| 392 @code{rand} to use the old generators, only setting the seed will.\n\ | |
| 393 To cause @code{rand} to once again use the new generators, the\n\ | |
| 394 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
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395 \n\ |
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396 The state or seed of the generator can be reset to a new random value\n\ |
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397 using the \"reset\" keyword.\n\ |
| 5798 | 398 @seealso{randn, rande, randg, randp}\n\ |
| 3369 | 399 @end deftypefn") |
| 2928 | 400 { |
| 4307 | 401 octave_value retval; |
| 2928 | 402 |
| 403 int nargin = args.length (); | |
| 404 | |
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405 retval = do_rand (args, nargin, "rand", "uniform"); |
| 2928 | 406 |
| 407 return retval; | |
| 408 } | |
| 409 | |
| 8871 | 410 // FIXME -- The old generator (selected when "seed" is set) will not |
| 411 // work properly if compiled to use 64-bit integers. | |
| 412 | |
| 5730 | 413 /* |
| 414 %!test # 'state' can be a scalar | |
| 415 %! rand('state',12); x = rand(1,4); | |
| 416 %! rand('state',12); y = rand(1,4); | |
| 417 %! assert(x,y); | |
| 418 %!test # 'state' can be a vector | |
| 419 %! rand('state',[12,13]); x=rand(1,4); | |
| 420 %! rand('state',[12;13]); y=rand(1,4); | |
| 421 %! assert(x,y); | |
| 422 %!test # querying 'state' doesn't disturb sequence | |
| 423 %! rand('state',12); rand(1,2); x=rand(1,2); | |
| 424 %! rand('state',12); rand(1,2); | |
| 425 %! s=rand('state'); y=rand(1,2); | |
| 426 %! assert(x,y); | |
| 427 %! rand('state',s); z=rand(1,2); | |
| 428 %! assert(x,z); | |
| 429 %!test # 'seed' must be a scalar | |
| 430 %! rand('seed',12); x = rand(1,4); | |
| 431 %! rand('seed',12); y = rand(1,4); | |
| 432 %! assert(x,y); | |
| 433 %!error(rand('seed',[12,13])) | |
| 434 %!test # querying 'seed' returns a value which can be used later | |
| 435 %! s=rand('seed'); x=rand(1,2); | |
| 436 %! rand('seed',s); y=rand(1,2); | |
| 437 %! assert(x,y); | |
| 438 %!test # querying 'seed' doesn't disturb sequence | |
| 439 %! rand('seed',12); rand(1,2); x=rand(1,2); | |
| 440 %! rand('seed',12); rand(1,2); | |
| 441 %! s=rand('seed'); y=rand(1,2); | |
| 442 %! assert(x,y); | |
| 443 %! rand('seed',s); z=rand(1,2); | |
| 444 %! assert(x,z); | |
| 445 */ | |
| 446 | |
| 447 /* | |
| 448 %!test | |
| 6437 | 449 %! % Test fixed state |
| 450 %! rand("state",1); | |
| 6443 | 451 %! assert (rand(1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382],1e-6); |
| 6437 | 452 %!test |
| 6443 | 453 %! % Test fixed seed |
| 6437 | 454 %! rand("seed",1); |
| 6443 | 455 %! assert (rand(1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759],1e-6); |
| 5730 | 456 %!test |
| 6437 | 457 %! if (__random_statistical_tests__) |
| 458 %! % statistical tests may fail occasionally. | |
| 459 %! rand("state",12); | |
| 460 %! x = rand(100000,1); | |
| 461 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 462 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 463 %! assert(mean(x),0.5,0.0024); | |
| 464 %! assert(var(x),1/48,0.0632); | |
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465 %! assert(skewness(x),0,0.012); |
| 6437 | 466 %! assert(kurtosis(x),-6/5,0.0094); |
| 467 %! endif | |
| 468 %!test | |
| 469 %! if (__random_statistical_tests__) | |
| 470 %! % statistical tests may fail occasionally. | |
| 471 %! rand("seed",12); | |
| 472 %! x = rand(100000,1); | |
| 473 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 474 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 475 %! assert(mean(x),0.5,0.0024); | |
| 476 %! assert(var(x),1/48,0.0632); | |
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477 %! assert(skewness(x),0,0.012); |
| 6437 | 478 %! assert(kurtosis(x),-6/5,0.0094); |
| 479 %! endif | |
| 5730 | 480 */ |
| 481 | |
| 482 | |
| 4307 | 483 static std::string current_distribution = octave_rand::distribution (); |
| 484 | |
| 4665 | 485 DEFUN_DLD (randn, args, , |
| 3369 | 486 "-*- texinfo -*-\n\ |
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487 @deftypefn {Loadable Function} {} randn (@var{n})\n\ |
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488 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m}, @dots{})\n\ |
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489 @deftypefnx {Loadable Function} {} randn ([@var{n} @var{m} @dots{}])\n\ |
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490 @deftypefnx {Loadable Function} {@var{v} =} randn (\"state\")\n\ |
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491 @deftypefnx {Loadable Function} {} randn (\"state\", @var{v})\n\ |
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492 @deftypefnx {Loadable Function} {} randn (\"state\", \"reset\")\n\ |
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493 @deftypefnx {Loadable Function} {@var{v} =} randn (\"seed\")\n\ |
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494 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{v})\n\ |
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495 @deftypefnx {Loadable Function} {} randn (\"seed\", \"reset\")\n\ |
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496 Return a matrix with normally distributed random\n\ |
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497 elements having zero mean and variance one. The arguments are\n\ |
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498 handled the same as the arguments for @code{rand}.\n\ |
| 3369 | 499 \n\ |
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500 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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501 to transform from a uniform to a normal distribution.\n\ |
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502 \n\ |
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503 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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504 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 505 J. Statistical Software, vol 5, 2000,\n\ |
| 506 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 2928 | 507 \n\ |
| 6547 | 508 @seealso{rand, rande, randg, randp}\n\ |
| 3369 | 509 @end deftypefn") |
| 2928 | 510 { |
| 4307 | 511 octave_value retval; |
| 2928 | 512 |
| 513 int nargin = args.length (); | |
| 514 | |
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515 retval = do_rand (args, nargin, "randn", "normal"); |
| 2928 | 516 |
| 517 return retval; | |
| 518 } | |
| 519 | |
| 520 /* | |
| 5730 | 521 %!test |
| 6437 | 522 %! % Test fixed state |
| 523 %! randn("state",1); | |
| 6443 | 524 %! assert (randn(1,6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116],1e-6); |
| 6437 | 525 %!test |
| 6443 | 526 %! % Test fixed seed |
| 6437 | 527 %! randn("seed",1); |
| 6443 | 528 %! assert (randn(1,6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133],1e-6); |
| 5730 | 529 %!test |
| 6437 | 530 %! if (__random_statistical_tests__) |
| 531 %! % statistical tests may fail occasionally. | |
| 532 %! randn("state",12); | |
| 533 %! x = randn(100000,1); | |
| 534 %! assert(mean(x),0,0.01); | |
| 535 %! assert(var(x),1,0.02); | |
| 536 %! assert(skewness(x),0,0.02); | |
| 537 %! assert(kurtosis(x),0,0.04); | |
| 538 %! endif | |
| 539 %!test | |
| 540 %! if (__random_statistical_tests__) | |
| 541 %! % statistical tests may fail occasionally. | |
| 542 %! randn("seed",12); | |
| 543 %! x = randn(100000,1); | |
| 544 %! assert(mean(x),0,0.01); | |
| 545 %! assert(var(x),1,0.02); | |
| 546 %! assert(skewness(x),0,0.02); | |
| 547 %! assert(kurtosis(x),0,0.04); | |
| 548 %! endif | |
| 5730 | 549 */ |
| 550 | |
| 551 DEFUN_DLD (rande, args, , | |
| 552 "-*- texinfo -*-\n\ | |
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553 @deftypefn {Loadable Function} {} rande (@var{n})\n\ |
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554 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m}, @dots{})\n\ |
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555 @deftypefnx {Loadable Function} {} rande ([@var{n} @var{m} @dots{}])\n\ |
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556 @deftypefnx {Loadable Function} {@var{v} =} rande (\"state\")\n\ |
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557 @deftypefnx {Loadable Function} {} rande (\"state\", @var{v})\n\ |
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558 @deftypefnx {Loadable Function} {} rande (\"state\", \"reset\")\n\ |
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559 @deftypefnx {Loadable Function} {@var{v} =} rande (\"seed\")\n\ |
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560 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{v})\n\ |
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561 @deftypefnx {Loadable Function} {} rande (\"seed\", \"reset\")\n\ |
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562 Return a matrix with exponentially distributed random elements. The\n\ |
| 5730 | 563 arguments are handled the same as the arguments for @code{rand}.\n\ |
| 564 \n\ | |
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565 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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566 to transform from a uniform to an exponential distribution.\n\ |
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567 \n\ |
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568 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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569 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 570 J. Statistical Software, vol 5, 2000,\n\ |
| 571 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
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572 \n\ |
| 6547 | 573 @seealso{rand, randn, randg, randp}\n\ |
| 5730 | 574 @end deftypefn") |
| 575 { | |
| 576 octave_value retval; | |
| 577 | |
| 578 int nargin = args.length (); | |
| 579 | |
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580 retval = do_rand (args, nargin, "rande", "exponential"); |
| 5730 | 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\ | |
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620 @deftypefn {Loadable Function} {} randg (@var{n})\n\ |
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621 @deftypefnx {Loadable Function} {} randg (@var{n}, @var{m}, @dots{})\n\ |
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622 @deftypefnx {Loadable Function} {} randg ([@var{n} @var{m} @dots{}])\n\ |
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623 @deftypefnx {Loadable Function} {@var{v} =} randg (\"state\")\n\ |
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624 @deftypefnx {Loadable Function} {} randg (\"state\", @var{v})\n\ |
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625 @deftypefnx {Loadable Function} {} randg (\"state\", \"reset\")\n\ |
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626 @deftypefnx {Loadable Function} {@var{v} =} randg (\"seed\")\n\ |
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627 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{v})\n\ |
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628 @deftypefnx {Loadable Function} {} randg (\"seed\", \"reset\")\n\ |
| 5730 | 629 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ |
| 630 The arguments are handled the same as the arguments for @code{rand},\n\ | |
| 631 except for the argument @var{a}.\n\ | |
| 632 \n\ | |
| 633 This can be used to generate many distributions:\n\ | |
| 634 \n\ | |
| 635 @table @asis\n\ | |
| 6547 | 636 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
| 10840 | 637 \n\ |
| 5730 | 638 @example\n\ |
| 6547 | 639 r = b * randg (a)\n\ |
| 5730 | 640 @end example\n\ |
| 10840 | 641 \n\ |
| 6547 | 642 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
| 10840 | 643 \n\ |
| 5730 | 644 @example\n\ |
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645 @group\n\ |
| 6547 | 646 r1 = randg (a, 1)\n\ |
| 647 r = r1 / (r1 + randg (b, 1))\n\ | |
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648 @end group\n\ |
| 5730 | 649 @end example\n\ |
| 10840 | 650 \n\ |
| 6547 | 651 @item @code{Erlang (a, n)}\n\ |
| 10840 | 652 \n\ |
| 5730 | 653 @example\n\ |
| 6547 | 654 r = a * randg (n)\n\ |
| 5730 | 655 @end example\n\ |
| 10840 | 656 \n\ |
| 6547 | 657 @item @code{chisq (df)} for @code{df > 0}\n\ |
| 10840 | 658 \n\ |
| 5730 | 659 @example\n\ |
| 6547 | 660 r = 2 * randg (df / 2)\n\ |
| 5730 | 661 @end example\n\ |
| 10840 | 662 \n\ |
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663 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
| 10840 | 664 \n\ |
| 5730 | 665 @example\n\ |
| 6547 | 666 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
| 5730 | 667 @end example\n\ |
| 10840 | 668 \n\ |
| 6547 | 669 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
| 10840 | 670 \n\ |
| 5730 | 671 @example\n\ |
| 7096 | 672 @group\n\ |
| 673 ## r1 equals 1 if n1 is infinite\n\ | |
| 674 r1 = 2 * randg (n1 / 2) / n1\n\ | |
| 675 ## r2 equals 1 if n2 is infinite\n\ | |
| 676 r2 = 2 * randg (n2 / 2) / n2\n\ | |
| 5730 | 677 r = r1 / r2\n\n\ |
| 7096 | 678 @end group\n\ |
| 5730 | 679 @end example\n\ |
| 10840 | 680 \n\ |
| 5730 | 681 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
| 10840 | 682 \n\ |
| 5730 | 683 @example\n\ |
| 6547 | 684 r = randp ((1 - p) / p * randg (n))\n\ |
| 5730 | 685 @end example\n\ |
| 10840 | 686 \n\ |
| 6547 | 687 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
| 5730 | 688 (use chisq if @code{L = 0})\n\ |
| 10840 | 689 \n\ |
| 5730 | 690 @example\n\ |
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691 @group\n\ |
| 6547 | 692 r = randp (L / 2)\n\ |
| 693 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
| 694 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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695 @end group\n\ |
| 5730 | 696 @end example\n\ |
| 10840 | 697 \n\ |
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698 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
| 10840 | 699 \n\ |
| 5730 | 700 @example\n\ |
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701 @group\n\ |
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702 r = (randg (a1), @dots{}, randg (ak))\n\ |
| 6547 | 703 r = r / sum (r)\n\ |
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704 @end group\n\ |
| 5730 | 705 @end example\n\ |
| 10840 | 706 \n\ |
| 5730 | 707 @end table\n\ |
| 6547 | 708 @seealso{rand, randn, rande, randp}\n\ |
| 5730 | 709 @end deftypefn") |
| 710 { | |
| 711 octave_value retval; | |
| 712 | |
| 713 int nargin = args.length (); | |
| 714 | |
| 715 if (nargin < 1) | |
| 716 error ("randg: insufficient arguments"); | |
| 717 else | |
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718 retval = do_rand (args, nargin, "randg", "gamma", true); |
| 5730 | 719 |
| 720 return retval; | |
| 721 } | |
| 722 | |
| 723 /* | |
| 724 %!test | |
| 6437 | 725 %! randg("state",12) |
| 726 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report | |
| 727 | |
| 728 | |
| 729 %!test | |
| 730 %! % Test fixed state | |
| 731 %! randg("state",1); | |
| 6443 | 732 %! assert (randg(0.1,1,6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12],1e-6); |
| 6437 | 733 %!test |
| 734 %! % Test fixed state | |
| 735 %! randg("state",1); | |
| 6443 | 736 %! assert (randg(0.95,1,6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939],1e-6); |
| 6437 | 737 %!test |
| 738 %! % Test fixed state | |
| 739 %! randg("state",1); | |
| 6443 | 740 %! assert (randg(1,1,6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533],1e-6); |
| 6437 | 741 %!test |
| 742 %! % Test fixed state | |
| 743 %! randg("state",1); | |
| 6443 | 744 %! assert (randg(10,1,6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059],1e-5); |
| 6437 | 745 %!test |
| 746 %! % Test fixed state | |
| 747 %! randg("state",1); | |
| 6443 | 748 %! assert (randg(100,1,6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118],1e-4); |
| 6437 | 749 %!test |
| 750 %! % Test fixed seed | |
| 751 %! randg("seed",1); | |
| 6443 | 752 %! assert (randg(0.1,1,6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12],1e-6); |
| 6437 | 753 %!test |
| 754 %! % Test fixed seed | |
| 755 %! randg("seed",1); | |
| 6443 | 756 %! assert (randg(0.95,1,6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648],1e-6); |
| 6437 | 757 %!test |
| 758 %! % Test fixed seed | |
| 759 %! randg("seed",1); | |
| 6443 | 760 %! assert (randg(1,1,6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303],1e-6); |
| 6437 | 761 %!test |
| 762 %! % Test fixed seed | |
| 763 %! randg("seed",1); | |
| 6443 | 764 %! assert (randg(10,1,6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186],1e-5); |
| 6437 | 765 %!test |
| 766 %! % Test fixed seed | |
| 767 %! randg("seed",1); | |
| 6443 | 768 %! assert (randg(100,1,6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625],1e-4); |
| 6437 | 769 %!test |
| 770 %! if (__random_statistical_tests__) | |
| 771 %! % statistical tests may fail occasionally. | |
| 772 %! randg("state",12) | |
| 773 %! a=0.1; x = randg(a,100000,1); | |
| 774 %! assert(mean(x), a, 0.01); | |
| 775 %! assert(var(x), a, 0.01); | |
| 776 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 777 %! assert(kurtosis(x),6/a, 50.); | |
| 778 %! endif | |
| 779 %!test | |
| 780 %! if (__random_statistical_tests__) | |
| 781 %! % statistical tests may fail occasionally. | |
| 782 %! randg("state",12) | |
| 783 %! a=0.95; x = randg(a,100000,1); | |
| 784 %! assert(mean(x), a, 0.01); | |
| 785 %! assert(var(x), a, 0.04); | |
| 786 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 787 %! assert(kurtosis(x),6/a, 2.); | |
| 788 %! endif | |
| 789 %!test | |
| 790 %! if (__random_statistical_tests__) | |
| 791 %! % statistical tests may fail occasionally. | |
| 792 %! randg("state",12) | |
| 793 %! a=1; x = randg(a,100000,1); | |
| 794 %! assert(mean(x),a, 0.01); | |
| 795 %! assert(var(x),a, 0.04); | |
| 796 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 797 %! assert(kurtosis(x),6/a, 2.); | |
| 798 %! endif | |
| 799 %!test | |
| 800 %! if (__random_statistical_tests__) | |
| 801 %! % statistical tests may fail occasionally. | |
| 802 %! randg("state",12) | |
| 803 %! a=10; x = randg(a,100000,1); | |
| 804 %! assert(mean(x), a, 0.1); | |
| 805 %! assert(var(x), a, 0.5); | |
| 806 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 807 %! assert(kurtosis(x),6/a, 0.5); | |
| 808 %! endif | |
| 809 %!test | |
| 810 %! if (__random_statistical_tests__) | |
| 811 %! % statistical tests may fail occasionally. | |
| 812 %! randg("state",12) | |
| 813 %! a=100; x = randg(a,100000,1); | |
| 814 %! assert(mean(x), a, 0.2); | |
| 815 %! assert(var(x), a, 2.); | |
| 816 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 817 %! assert(kurtosis(x),6/a, 0.2); | |
| 818 %! endif | |
| 819 %!test | |
| 820 %! randg("seed",12) | |
| 5730 | 821 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report |
| 822 %!test | |
| 6437 | 823 %! if (__random_statistical_tests__) |
| 824 %! % statistical tests may fail occasionally. | |
| 825 %! randg("seed",12) | |
| 826 %! a=0.1; x = randg(a,100000,1); | |
| 827 %! assert(mean(x), a, 0.01); | |
| 828 %! assert(var(x), a, 0.01); | |
| 829 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 830 %! assert(kurtosis(x),6/a, 50.); | |
| 831 %! endif | |
| 5730 | 832 %!test |
| 6437 | 833 %! if (__random_statistical_tests__) |
| 834 %! % statistical tests may fail occasionally. | |
| 835 %! randg("seed",12) | |
| 836 %! a=0.95; x = randg(a,100000,1); | |
| 837 %! assert(mean(x), a, 0.01); | |
| 838 %! assert(var(x), a, 0.04); | |
| 839 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 840 %! assert(kurtosis(x),6/a, 2.); | |
| 841 %! endif | |
| 5730 | 842 %!test |
| 6437 | 843 %! if (__random_statistical_tests__) |
| 844 %! % statistical tests may fail occasionally. | |
| 845 %! randg("seed",12) | |
| 846 %! a=1; x = randg(a,100000,1); | |
| 847 %! assert(mean(x),a, 0.01); | |
| 848 %! assert(var(x),a, 0.04); | |
| 849 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 850 %! assert(kurtosis(x),6/a, 2.); | |
| 851 %! endif | |
| 5730 | 852 %!test |
| 6437 | 853 %! if (__random_statistical_tests__) |
| 854 %! % statistical tests may fail occasionally. | |
| 855 %! randg("seed",12) | |
| 856 %! a=10; x = randg(a,100000,1); | |
| 857 %! assert(mean(x), a, 0.1); | |
| 858 %! assert(var(x), a, 0.5); | |
| 859 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 860 %! assert(kurtosis(x),6/a, 0.5); | |
| 861 %! endif | |
| 5730 | 862 %!test |
| 6437 | 863 %! if (__random_statistical_tests__) |
| 864 %! % statistical tests may fail occasionally. | |
| 865 %! randg("seed",12) | |
| 866 %! a=100; x = randg(a,100000,1); | |
| 867 %! assert(mean(x), a, 0.2); | |
| 868 %! assert(var(x), a, 2.); | |
| 869 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 870 %! assert(kurtosis(x),6/a, 0.2); | |
| 871 %! endif | |
| 5730 | 872 */ |
| 873 | |
| 874 | |
| 875 DEFUN_DLD (randp, args, , | |
| 876 "-*- texinfo -*-\n\ | |
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877 @deftypefn {Loadable Function} {} randp (@var{l}, @var{n})\n\ |
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878 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m}, @dots{})\n\ |
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879 @deftypefnx {Loadable Function} {} randp (@var{l}, [@var{n} @var{m} @dots{}])\n\ |
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880 @deftypefnx {Loadable Function} {@var{v} =} randp (\"state\")\n\ |
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881 @deftypefnx {Loadable Function} {} randp (\"state\", @var{v})\n\ |
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882 @deftypefnx {Loadable Function} {} randp (\"state\", \"reset\")\n\ |
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883 @deftypefnx {Loadable Function} {@var{v} =} randp (\"seed\")\n\ |
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884 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{v})\n\ |
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885 @deftypefnx {Loadable Function} {} randp (\"seed\", \"reset\")\n\ |
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886 Return a matrix with Poisson distributed random elements with mean value\n\ |
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887 parameter given by the first argument, @var{l}. The arguments\n\ |
| 5730 | 888 are handled the same as the arguments for @code{rand}, except for the\n\ |
| 889 argument @var{l}.\n\ | |
| 890 \n\ | |
| 891 Five different algorithms are used depending on the range of @var{l}\n\ | |
| 892 and whether or not @var{l} is a scalar or a matrix.\n\ | |
| 893 \n\ | |
| 894 @table @asis\n\ | |
| 10840 | 895 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
| 896 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
| 897 Cambridge University Press, 1992.\n\ | |
| 898 \n\ | |
| 5730 | 899 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
| 10840 | 900 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
| 901 Cambridge University Press, 1992.\n\ | |
| 902 \n\ | |
| 903 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
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904 E. Stadlober, et al., WinRand source code, available via FTP.\n\ |
| 10840 | 905 \n\ |
| 5730 | 906 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
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907 E. Stadlober, et al., WinRand source code, available via FTP, or\n\ |
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908 H. Zechner, @cite{Efficient sampling from continuous and discrete\n\ |
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909 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
| 5730 | 910 University Graz, Austria, 1994.\n\ |
| 10840 | 911 \n\ |
| 5730 | 912 @item For @var{l} > 1e8, use normal approximation.\n\ |
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913 L. Montanet, et al., @cite{Review of Particle Properties}, Physical Review\n\ |
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914 D 50 p1284, 1994.\n\ |
| 5730 | 915 @end table\n\ |
| 6547 | 916 @seealso{rand, randn, rande, randg}\n\ |
| 5730 | 917 @end deftypefn") |
| 918 { | |
| 919 octave_value retval; | |
| 920 | |
| 921 int nargin = args.length (); | |
| 922 | |
| 923 if (nargin < 1) | |
| 924 error ("randp: insufficient arguments"); | |
| 925 else | |
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926 retval = do_rand (args, nargin, "randp", "poisson", true); |
| 5730 | 927 |
| 928 return retval; | |
| 929 } | |
| 930 | |
| 931 /* | |
| 932 %!test | |
| 6437 | 933 %! randp("state",12) |
| 934 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report | |
| 935 %!test | |
| 936 %! % Test fixed state | |
| 937 %! randp("state",1); | |
| 938 %! assert(randp(5,1,6),[5 5 3 7 7 3]) | |
| 939 %!test | |
| 940 %! % Test fixed state | |
| 941 %! randp("state",1); | |
| 942 %! assert(randp(15,1,6),[13 15 8 18 18 15]) | |
| 943 %!test | |
| 944 %! % Test fixed state | |
| 945 %! randp("state",1); | |
| 7421 | 946 %! assert(randp(1e9,1,6),[999915677 999976657 1000047684 1000019035 999985749 999977692],-1e-6) |
| 6437 | 947 %!test |
| 948 %! % Test fixed state | |
| 949 %! randp("seed",1); | |
| 6449 | 950 %! %%assert(randp(5,1,6),[8 2 3 6 6 8]) |
| 951 %! assert(randp(5,1,5),[8 2 3 6 6]) | |
| 6437 | 952 %!test |
| 953 %! % Test fixed state | |
| 954 %! randp("seed",1); | |
| 955 %! assert(randp(15,1,6),[15 16 12 10 10 12]) | |
| 956 %!test | |
| 957 %! % Test fixed state | |
| 958 %! randp("seed",1); | |
| 7421 | 959 %! assert(randp(1e9,1,6),[1000006208 1000012224 999981120 999963520 999963072 999981440],-1e-6) |
| 6437 | 960 %!test |
| 961 %! if (__random_statistical_tests__) | |
| 962 %! % statistical tests may fail occasionally. | |
| 963 %! randp("state",12) | |
| 964 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 965 %! x = randp(a(1),100000,1); | |
| 966 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 967 %! assert(mean(x),a(1),a(2)); | |
| 968 %! assert(var(x),a(1),0.02*a(1)); | |
| 969 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 970 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 971 %! endfor | |
| 972 %! endif | |
| 973 %!test | |
| 974 %! if (__random_statistical_tests__) | |
| 975 %! % statistical tests may fail occasionally. | |
| 976 %! randp("state",12) | |
| 977 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 978 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 979 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 980 %! assert(mean(x),a(1),a(2)); | |
| 981 %! assert(var(x),a(1),0.02*a(1)); | |
| 982 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 983 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 984 %! endfor | |
| 985 %! endif | |
| 986 %!test | |
| 987 %! randp("seed",12) | |
| 5730 | 988 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report |
| 989 %!test | |
| 6449 | 990 %! if (__random_statistical_tests__) |
| 991 %! % statistical tests may fail occasionally. | |
| 992 %! randp("seed",12) | |
| 993 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 994 %! x = randp(a(1),100000,1); | |
| 995 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 996 %! assert(mean(x),a(1),a(2)); | |
| 997 %! assert(var(x),a(1),0.02*a(1)); | |
| 998 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 999 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1000 %! endfor | |
| 1001 %! endif | |
| 5730 | 1002 %!test |
| 6449 | 1003 %! if (__random_statistical_tests__) |
| 1004 %! % statistical tests may fail occasionally. | |
| 1005 %! randp("seed",12) | |
| 1006 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 1007 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 1008 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 1009 %! assert(mean(x),a(1),a(2)); | |
| 1010 %! assert(var(x),a(1),0.02*a(1)); | |
| 1011 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 1012 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1013 %! endfor | |
| 1014 %! endif | |
| 5730 | 1015 */ |
| 1016 | |
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1017 DEFUN_DLD (randperm, args, , |
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1018 "-*- texinfo -*-\n\ |
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1019 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
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1020 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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1021 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1022 If @var{m} is supplied, return @var{m} permutations,\n\ |
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1023 one in each row of an @nospell{MxN} matrix. The complexity is O(M*N) in both\n\ |
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1024 time and memory. The randomization is performed using rand().\n\ |
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1025 All permutations are equally likely.\n\ |
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1026 @seealso{perms}\n\ |
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1027 @end deftypefn") |
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1028 { |
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1029 int nargin = args.length (); |
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1030 octave_value retval; |
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1031 |
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1032 if (nargin == 1 || nargin == 2) |
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1033 { |
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1034 octave_idx_type n, m; |
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1035 |
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1036 if (nargin == 2) |
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1037 m = args(1).idx_type_value (true); |
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1038 else |
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1039 m = 1; |
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1040 |
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1041 n = args(0).idx_type_value (true); |
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1042 |
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1043 if (m < 0 || n < 0) |
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1044 error ("randperm: M and N must be non-negative"); |
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1045 |
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1046 if (! error_state) |
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1047 { |
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1048 // Generate random numbers. |
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1049 NDArray r = octave_rand::nd_array (dim_vector (m, n)); |
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1050 |
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1051 // Create transposed to allow faster access. |
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1052 Array<octave_idx_type> idx (dim_vector (n, m)); |
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1053 |
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1054 double *rvec = r.fortran_vec (); |
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1055 |
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1056 octave_idx_type *ivec = idx.fortran_vec (); |
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1057 |
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1058 // Perform the Knuth shuffle. |
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1059 for (octave_idx_type j = 0; j < m; j++) |
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1060 { |
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1061 for (octave_idx_type i = 0; i < n; i++) |
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1062 ivec[i] = i; |
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1063 |
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1064 for (octave_idx_type i = 0; i < n; i++) |
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1065 { |
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1066 octave_idx_type k = i + gnulib::floor (rvec[i] * (n - i)); |
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1067 std::swap (ivec[i], ivec[k]); |
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1068 } |
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1069 |
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1070 ivec += n; |
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1071 rvec += n; |
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1072 } |
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1073 |
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1074 // Transpose. |
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1075 idx = idx.transpose (); |
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1076 |
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1077 // Re-fetch the pointers. |
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1078 ivec = idx.fortran_vec (); |
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1079 rvec = r.fortran_vec (); |
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1080 |
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1081 // Convert to doubles, reusing r. |
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1082 for (octave_idx_type i = 0, l = m*n; i < l; i++) |
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1083 rvec[i] = ivec[i] + 1; |
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1084 |
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1085 // Now create an array object with a cached idx_vector. |
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1086 retval = new octave_matrix (r, idx_vector (idx)); |
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1087 } |
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1088 } |
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1089 else |
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1090 print_usage (); |
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1091 |
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1092 return retval; |
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1093 } |
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1094 |
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1095 /* |
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1096 %!assert(sort(randperm(20)),1:20) |
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1097 %!assert(sort(randperm(20,50),2),repmat(1:20,50,1)) |
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1098 */ |