Mercurial > hg > octave-lyh
annotate src/DLD-FUNCTIONS/rand.cc @ 14688:42edbe6f6a8d stable
Fix bad two-arg call of randperm (bug #36535)
* rand.cc (Frandperm): Rewrite algorithm for short_shuffle. Add another test.
| author | Jordi Gutiérrez Hermoso <jordigh@octave.org> |
|---|---|
| date | Fri, 25 May 2012 11:24:02 -0400 |
| parents | 72c96de7a403 |
| children | 52c5fb67fa5f |
| rev | line source |
|---|---|
| 2928 | 1 /* |
| 2 | |
|
14138
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3 Copyright (C) 1996-2012 John W. Eaton |
|
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4 Copyright (C) 2009 VZLU Prague |
| 2928 | 5 |
| 6 This file is part of Octave. | |
| 7 | |
| 8 Octave is free software; you can redistribute it and/or modify it | |
| 9 under the terms of the GNU General Public License as published by the | |
| 7016 | 10 Free Software Foundation; either version 3 of the License, or (at your |
| 11 option) any later version. | |
| 2928 | 12 |
| 13 Octave is distributed in the hope that it will be useful, but WITHOUT | |
| 14 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
| 15 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
| 16 for more details. | |
| 17 | |
| 18 You should have received a copy of the GNU General Public License | |
| 7016 | 19 along with Octave; see the file COPYING. If not, see |
| 20 <http://www.gnu.org/licenses/>. | |
| 2928 | 21 |
| 22 */ | |
| 23 | |
| 24 #ifdef HAVE_CONFIG_H | |
| 25 #include <config.h> | |
| 26 #endif | |
| 27 | |
| 28 #include <ctime> | |
|
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29 #if defined (HAVE_UNORDERED_MAP) |
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30 #include <unordered_map> |
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31 #elif defined (HAVE_TR1_UNORDERED_MAP) |
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32 #include <tr1/unordered_map> |
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33 #endif |
| 2928 | 34 #include <string> |
| 35 | |
| 36 #include "f77-fcn.h" | |
| 37 #include "lo-mappers.h" | |
| 4307 | 38 #include "oct-rand.h" |
| 4153 | 39 #include "quit.h" |
| 2928 | 40 |
| 41 #include "defun-dld.h" | |
| 42 #include "error.h" | |
| 43 #include "gripes.h" | |
| 44 #include "oct-obj.h" | |
| 45 #include "unwind-prot.h" | |
| 46 #include "utils.h" | |
|
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47 #include "ov-re-mat.h" |
| 2928 | 48 |
| 6437 | 49 /* |
| 50 %!shared __random_statistical_tests__ | |
| 51 %! % Flag whether the statistical tests should be run in "make check" or not | |
| 52 %! __random_statistical_tests__ = 0; | |
| 53 */ | |
| 54 | |
| 4307 | 55 static octave_value |
| 5730 | 56 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
|
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57 const std::string& distribution, bool additional_arg = false) |
| 2928 | 58 { |
| 4307 | 59 octave_value retval; |
| 5730 | 60 NDArray a; |
| 61 int idx = 0; | |
| 62 dim_vector dims; | |
| 2928 | 63 |
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64 unwind_protect frame; |
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65 // Restore current distribution on any exit. |
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66 frame.add_fcn (octave_rand::distribution, |
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67 octave_rand::distribution ()); |
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68 |
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69 octave_rand::distribution (distribution); |
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70 |
| 5730 | 71 if (additional_arg) |
| 72 { | |
| 73 if (nargin == 0) | |
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74 { |
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75 error ("%s: expecting at least one argument", fcn); |
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76 goto done; |
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77 } |
| 5730 | 78 else if (args(0).is_string()) |
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79 additional_arg = false; |
| 5730 | 80 else |
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81 { |
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82 a = args(0).array_value (); |
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83 if (error_state) |
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84 { |
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85 error ("%s: expecting scalar or matrix arguments", fcn); |
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86 goto done; |
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87 } |
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88 idx++; |
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89 nargin--; |
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90 } |
| 5730 | 91 } |
| 2928 | 92 |
| 4543 | 93 switch (nargin) |
| 2928 | 94 { |
| 4543 | 95 case 0: |
| 96 { | |
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97 if (additional_arg) |
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98 dims = a.dims (); |
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99 else |
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100 { |
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101 dims.resize (2); |
| 4543 | 102 |
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103 dims(0) = 1; |
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104 dims(1) = 1; |
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105 } |
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106 goto gen_matrix; |
| 4543 | 107 } |
| 108 break; | |
| 2928 | 109 |
| 4543 | 110 case 1: |
| 111 { | |
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112 octave_value tmp = args(idx); |
| 4543 | 113 |
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114 if (tmp.is_string ()) |
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115 { |
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116 std::string s_arg = tmp.string_value (); |
| 2928 | 117 |
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118 if (s_arg == "dist") |
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119 { |
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120 retval = octave_rand::distribution (); |
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121 } |
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122 else if (s_arg == "seed") |
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123 { |
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124 retval = octave_rand::seed (); |
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125 } |
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126 else if (s_arg == "state" || s_arg == "twister") |
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127 { |
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128 retval = octave_rand::state (fcn); |
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129 } |
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130 else if (s_arg == "uniform") |
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131 { |
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132 octave_rand::uniform_distribution (); |
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133 } |
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134 else if (s_arg == "normal") |
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135 { |
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136 octave_rand::normal_distribution (); |
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137 } |
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138 else if (s_arg == "exponential") |
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139 { |
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140 octave_rand::exponential_distribution (); |
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141 } |
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142 else if (s_arg == "poisson") |
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143 { |
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144 octave_rand::poisson_distribution (); |
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145 } |
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146 else if (s_arg == "gamma") |
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147 { |
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148 octave_rand::gamma_distribution (); |
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149 } |
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150 else |
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151 error ("%s: unrecognized string argument", fcn); |
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152 } |
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153 else if (tmp.is_scalar_type ()) |
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154 { |
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155 double dval = tmp.double_value (); |
| 2928 | 156 |
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157 if (xisnan (dval)) |
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158 { |
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159 error ("%s: NaN is invalid matrix dimension", fcn); |
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160 } |
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161 else |
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162 { |
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163 dims.resize (2); |
| 4543 | 164 |
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165 dims(0) = NINTbig (tmp.double_value ()); |
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166 dims(1) = NINTbig (tmp.double_value ()); |
| 2928 | 167 |
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168 if (! error_state) |
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169 goto gen_matrix; |
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170 } |
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171 } |
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172 else if (tmp.is_range ()) |
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173 { |
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174 Range r = tmp.range_value (); |
| 4543 | 175 |
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176 if (r.all_elements_are_ints ()) |
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177 { |
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178 octave_idx_type n = r.nelem (); |
| 4543 | 179 |
|
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180 dims.resize (n); |
| 4543 | 181 |
|
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182 octave_idx_type base = NINTbig (r.base ()); |
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183 octave_idx_type incr = NINTbig (r.inc ()); |
| 2928 | 184 |
|
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185 for (octave_idx_type i = 0; i < n; i++) |
|
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186 { |
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187 //Negative dimensions are treated as zero for Matlab |
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188 //compatibility |
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189 dims(i) = base >= 0 ? base : 0; |
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190 base += incr; |
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191 } |
| 2928 | 192 |
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193 goto gen_matrix; |
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194 |
|
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195 } |
|
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196 else |
|
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197 error ("%s: all elements of range must be integers", |
|
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198 fcn); |
|
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199 } |
|
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200 else if (tmp.is_matrix_type ()) |
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201 { |
|
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202 Array<int> iv = tmp.int_vector_value (true); |
| 4543 | 203 |
|
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204 if (! error_state) |
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205 { |
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206 octave_idx_type len = iv.length (); |
| 2928 | 207 |
|
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208 dims.resize (len); |
| 4543 | 209 |
|
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210 for (octave_idx_type i = 0; i < len; i++) |
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211 { |
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212 //Negative dimensions are treated as zero for Matlab |
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213 //compatibility |
|
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214 octave_idx_type elt = iv(i); |
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215 dims(i) = elt >=0 ? elt : 0; |
|
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216 } |
| 2928 | 217 |
|
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218 goto gen_matrix; |
|
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219 } |
|
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220 else |
|
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221 error ("%s: expecting integer vector", fcn); |
|
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222 } |
|
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223 else |
|
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224 { |
|
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225 gripe_wrong_type_arg ("rand", tmp); |
|
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226 return retval; |
|
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227 } |
| 4543 | 228 } |
| 229 break; | |
| 230 | |
| 231 default: | |
| 232 { | |
|
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233 octave_value tmp = args(idx); |
| 4543 | 234 |
|
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235 if (nargin == 2 && tmp.is_string ()) |
|
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236 { |
|
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237 std::string ts = tmp.string_value (); |
| 5164 | 238 |
|
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239 if (ts == "seed") |
|
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240 { |
|
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241 if (args(idx+1).is_real_scalar ()) |
|
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242 { |
|
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243 double d = args(idx+1).double_value (); |
| 2928 | 244 |
|
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245 if (! error_state) |
|
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246 octave_rand::seed (d); |
|
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247 } |
|
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248 else if (args(idx+1).is_string () |
|
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92a85ed5b86e
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249 && args(idx+1).string_value() == "reset") |
|
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|
250 octave_rand::reset (); |
|
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251 else |
|
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252 error ("%s: seed must be a real scalar", fcn); |
|
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253 } |
|
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254 else if (ts == "state" || ts == "twister") |
|
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|
255 { |
|
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|
256 if (args(idx+1).is_string () |
|
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|
257 && args(idx+1).string_value() == "reset") |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
258 octave_rand::reset (fcn); |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
259 else |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
260 { |
|
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|
261 ColumnVector s = |
|
10709
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Don't special case color_property type when emitting factory default (bug #30118)
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|
262 ColumnVector (args(idx+1).vector_value(false, true)); |
| 5730 | 263 |
|
10709
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|
264 if (! error_state) |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
265 octave_rand::state (s, fcn); |
|
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Don't special case color_property type when emitting factory default (bug #30118)
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|
266 } |
|
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|
267 } |
|
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|
268 else |
|
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|
269 error ("%s: unrecognized string argument", fcn); |
|
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270 } |
|
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271 else |
|
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272 { |
|
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273 dims.resize (nargin); |
| 4543 | 274 |
|
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275 for (int i = 0; i < nargin; i++) |
|
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276 { |
|
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|
277 octave_idx_type elt = args(idx+i).int_value (); |
|
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278 if (error_state) |
|
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279 { |
|
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280 error ("%s: expecting integer arguments", fcn); |
|
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281 goto done; |
|
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|
282 } |
|
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|
283 //Negative is zero for Matlab compatibility |
|
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|
284 dims(i) = elt >= 0 ? elt : 0; |
|
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|
285 } |
| 4543 | 286 |
|
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|
287 goto gen_matrix; |
|
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|
288 } |
| 4543 | 289 } |
| 290 break; | |
| 2928 | 291 } |
| 292 | |
| 4543 | 293 done: |
| 2928 | 294 |
| 295 return retval; | |
| 296 | |
| 297 gen_matrix: | |
| 298 | |
| 5355 | 299 dims.chop_trailing_singletons (); |
| 300 | |
| 5730 | 301 if (additional_arg) |
| 302 { | |
| 303 if (a.length() == 1) | |
|
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|
304 return octave_rand::nd_array (dims, a(0)); |
| 5730 | 305 else |
|
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|
306 { |
|
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|
307 if (a.dims() != dims) |
|
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|
308 { |
|
40dfc0c99116
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|
309 error ("%s: mismatch in argument size", fcn); |
|
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|
310 return retval; |
|
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|
311 } |
|
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|
312 octave_idx_type len = a.length (); |
|
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changeset
|
313 NDArray m (dims); |
|
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changeset
|
314 double *v = m.fortran_vec (); |
|
40dfc0c99116
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changeset
|
315 for (octave_idx_type i = 0; i < len; i++) |
|
40dfc0c99116
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changeset
|
316 v[i] = octave_rand::scalar (a(i)); |
|
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|
317 return m; |
|
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changeset
|
318 } |
| 5730 | 319 } |
| 320 else | |
| 321 return octave_rand::nd_array (dims); | |
| 2928 | 322 } |
| 323 | |
| 4665 | 324 DEFUN_DLD (rand, args, , |
| 3369 | 325 "-*- texinfo -*-\n\ |
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326 @deftypefn {Loadable Function} {} rand (@var{n})\n\ |
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327 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m}, @dots{})\n\ |
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328 @deftypefnx {Loadable Function} {} rand ([@var{n} @var{m} @dots{}])\n\ |
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329 @deftypefnx {Loadable Function} {@var{v} =} rand (\"state\")\n\ |
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330 @deftypefnx {Loadable Function} {} rand (\"state\", @var{v})\n\ |
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331 @deftypefnx {Loadable Function} {} rand (\"state\", \"reset\")\n\ |
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332 @deftypefnx {Loadable Function} {@var{v} =} rand (\"seed\")\n\ |
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333 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{v})\n\ |
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334 @deftypefnx {Loadable Function} {} rand (\"seed\", \"reset\")\n\ |
| 3369 | 335 Return a matrix with random elements uniformly distributed on the\n\ |
| 336 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
| 5730 | 337 for @code{eye}.\n\ |
| 338 \n\ | |
| 339 You can query the state of the random number generator using the\n\ | |
| 3369 | 340 form\n\ |
| 2928 | 341 \n\ |
| 3369 | 342 @example\n\ |
| 5730 | 343 v = rand (\"state\")\n\ |
| 344 @end example\n\ | |
| 345 \n\ | |
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346 This returns a column vector @var{v} of length 625. Later, you can\n\ |
| 5730 | 347 restore the random number generator to the state @var{v}\n\ |
| 348 using the form\n\ | |
| 349 \n\ | |
| 350 @example\n\ | |
| 351 rand (\"state\", v)\n\ | |
| 3369 | 352 @end example\n\ |
| 353 \n\ | |
| 354 @noindent\n\ | |
| 5730 | 355 You may also initialize the state vector from an arbitrary vector of\n\ |
| 10840 | 356 length @leq{} 625 for @var{v}. This new state will be a hash based on the\n\ |
| 5798 | 357 value of @var{v}, not @var{v} itself.\n\ |
| 5730 | 358 \n\ |
| 359 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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360 available, otherwise from CPU time, wall clock time, and the current\n\ |
| 5730 | 361 fraction of a second.\n\ |
| 362 \n\ | |
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363 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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364 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and\n\ |
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365 T. Nishimura,\n\ |
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366 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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367 pseudorandom number generator}, ACM Trans. on\n\ |
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368 Modeling and Computer Simulation Vol. 8, No. 1, pp. 3-30, January 1998,\n\ |
| 7171 | 369 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
| 6547 | 370 Do @strong{not} use for cryptography without securely hashing\n\ |
| 371 several returned values together, otherwise the generator state\n\ | |
| 372 can be learned after reading 624 consecutive values.\n\ | |
| 5730 | 373 \n\ |
| 7096 | 374 Older versions of Octave used a different random number generator.\n\ |
| 375 The new generator is used by default\n\ | |
| 5730 | 376 as it is significantly faster than the old generator, and produces\n\ |
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377 random numbers with a significantly longer cycle time. However, in\n\ |
| 5798 | 378 some circumstances it might be desirable to obtain the same random\n\ |
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379 sequences as used by the old generators. To do this the keyword\n\ |
| 5730 | 380 \"seed\" is used to specify that the old generators should be use,\n\ |
| 381 as in\n\ | |
| 2928 | 382 \n\ |
| 3369 | 383 @example\n\ |
| 5730 | 384 rand (\"seed\", val)\n\ |
| 3369 | 385 @end example\n\ |
| 386 \n\ | |
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387 @noindent\n\ |
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388 which sets the seed of the generator to @var{val}. The seed of the\n\ |
| 5730 | 389 generator can be queried with\n\ |
| 390 \n\ | |
| 391 @example\n\ | |
| 392 s = rand (\"seed\")\n\ | |
| 393 @end example\n\ | |
| 394 \n\ | |
| 395 However, it should be noted that querying the seed will not cause\n\ | |
| 396 @code{rand} to use the old generators, only setting the seed will.\n\ | |
| 397 To cause @code{rand} to once again use the new generators, the\n\ | |
| 398 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
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399 \n\ |
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400 The state or seed of the generator can be reset to a new random value\n\ |
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401 using the \"reset\" keyword.\n\ |
| 5798 | 402 @seealso{randn, rande, randg, randp}\n\ |
| 3369 | 403 @end deftypefn") |
| 2928 | 404 { |
| 4307 | 405 octave_value retval; |
| 2928 | 406 |
| 407 int nargin = args.length (); | |
| 408 | |
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409 retval = do_rand (args, nargin, "rand", "uniform"); |
| 2928 | 410 |
| 411 return retval; | |
| 412 } | |
| 413 | |
| 8871 | 414 // FIXME -- The old generator (selected when "seed" is set) will not |
| 415 // work properly if compiled to use 64-bit integers. | |
| 416 | |
| 5730 | 417 /* |
| 418 %!test # 'state' can be a scalar | |
| 419 %! rand('state',12); x = rand(1,4); | |
| 420 %! rand('state',12); y = rand(1,4); | |
| 421 %! assert(x,y); | |
| 422 %!test # 'state' can be a vector | |
| 423 %! rand('state',[12,13]); x=rand(1,4); | |
| 424 %! rand('state',[12;13]); y=rand(1,4); | |
| 425 %! assert(x,y); | |
| 426 %!test # querying 'state' doesn't disturb sequence | |
| 427 %! rand('state',12); rand(1,2); x=rand(1,2); | |
| 428 %! rand('state',12); rand(1,2); | |
| 429 %! s=rand('state'); y=rand(1,2); | |
| 430 %! assert(x,y); | |
| 431 %! rand('state',s); z=rand(1,2); | |
| 432 %! assert(x,z); | |
| 433 %!test # 'seed' must be a scalar | |
| 434 %! rand('seed',12); x = rand(1,4); | |
| 435 %! rand('seed',12); y = rand(1,4); | |
| 436 %! assert(x,y); | |
| 437 %!error(rand('seed',[12,13])) | |
| 438 %!test # querying 'seed' returns a value which can be used later | |
| 439 %! s=rand('seed'); x=rand(1,2); | |
| 440 %! rand('seed',s); y=rand(1,2); | |
| 441 %! assert(x,y); | |
| 442 %!test # querying 'seed' doesn't disturb sequence | |
| 443 %! rand('seed',12); rand(1,2); x=rand(1,2); | |
| 444 %! rand('seed',12); rand(1,2); | |
| 445 %! s=rand('seed'); y=rand(1,2); | |
| 446 %! assert(x,y); | |
| 447 %! rand('seed',s); z=rand(1,2); | |
| 448 %! assert(x,z); | |
| 449 */ | |
| 450 | |
| 451 /* | |
| 452 %!test | |
| 6437 | 453 %! % Test fixed state |
| 454 %! rand("state",1); | |
| 6443 | 455 %! assert (rand(1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382],1e-6); |
| 6437 | 456 %!test |
| 6443 | 457 %! % Test fixed seed |
| 6437 | 458 %! rand("seed",1); |
| 6443 | 459 %! assert (rand(1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759],1e-6); |
| 5730 | 460 %!test |
| 6437 | 461 %! if (__random_statistical_tests__) |
| 462 %! % statistical tests may fail occasionally. | |
| 463 %! rand("state",12); | |
| 464 %! x = rand(100000,1); | |
| 465 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 466 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 467 %! assert(mean(x),0.5,0.0024); | |
| 468 %! assert(var(x),1/48,0.0632); | |
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469 %! assert(skewness(x),0,0.012); |
| 6437 | 470 %! assert(kurtosis(x),-6/5,0.0094); |
| 471 %! endif | |
| 472 %!test | |
| 473 %! if (__random_statistical_tests__) | |
| 474 %! % statistical tests may fail occasionally. | |
| 475 %! rand("seed",12); | |
| 476 %! x = rand(100000,1); | |
| 477 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 478 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 479 %! assert(mean(x),0.5,0.0024); | |
| 480 %! assert(var(x),1/48,0.0632); | |
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481 %! assert(skewness(x),0,0.012); |
| 6437 | 482 %! assert(kurtosis(x),-6/5,0.0094); |
| 483 %! endif | |
| 5730 | 484 */ |
| 485 | |
| 486 | |
| 4307 | 487 static std::string current_distribution = octave_rand::distribution (); |
| 488 | |
| 4665 | 489 DEFUN_DLD (randn, args, , |
| 3369 | 490 "-*- texinfo -*-\n\ |
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491 @deftypefn {Loadable Function} {} randn (@var{n})\n\ |
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492 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m}, @dots{})\n\ |
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493 @deftypefnx {Loadable Function} {} randn ([@var{n} @var{m} @dots{}])\n\ |
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494 @deftypefnx {Loadable Function} {@var{v} =} randn (\"state\")\n\ |
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495 @deftypefnx {Loadable Function} {} randn (\"state\", @var{v})\n\ |
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496 @deftypefnx {Loadable Function} {} randn (\"state\", \"reset\")\n\ |
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497 @deftypefnx {Loadable Function} {@var{v} =} randn (\"seed\")\n\ |
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498 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{v})\n\ |
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499 @deftypefnx {Loadable Function} {} randn (\"seed\", \"reset\")\n\ |
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500 Return a matrix with normally distributed random\n\ |
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501 elements having zero mean and variance one. The arguments are\n\ |
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502 handled the same as the arguments for @code{rand}.\n\ |
| 3369 | 503 \n\ |
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504 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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505 to transform from a uniform to a normal distribution.\n\ |
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506 \n\ |
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507 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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508 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 509 J. Statistical Software, vol 5, 2000,\n\ |
| 510 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 2928 | 511 \n\ |
| 6547 | 512 @seealso{rand, rande, randg, randp}\n\ |
| 3369 | 513 @end deftypefn") |
| 2928 | 514 { |
| 4307 | 515 octave_value retval; |
| 2928 | 516 |
| 517 int nargin = args.length (); | |
| 518 | |
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519 retval = do_rand (args, nargin, "randn", "normal"); |
| 2928 | 520 |
| 521 return retval; | |
| 522 } | |
| 523 | |
| 524 /* | |
| 5730 | 525 %!test |
| 6437 | 526 %! % Test fixed state |
| 527 %! randn("state",1); | |
| 6443 | 528 %! assert (randn(1,6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116],1e-6); |
| 6437 | 529 %!test |
| 6443 | 530 %! % Test fixed seed |
| 6437 | 531 %! randn("seed",1); |
| 6443 | 532 %! assert (randn(1,6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133],1e-6); |
| 5730 | 533 %!test |
| 6437 | 534 %! if (__random_statistical_tests__) |
| 535 %! % statistical tests may fail occasionally. | |
| 536 %! randn("state",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 | |
| 543 %!test | |
| 544 %! if (__random_statistical_tests__) | |
| 545 %! % statistical tests may fail occasionally. | |
| 546 %! randn("seed",12); | |
| 547 %! x = randn(100000,1); | |
| 548 %! assert(mean(x),0,0.01); | |
| 549 %! assert(var(x),1,0.02); | |
| 550 %! assert(skewness(x),0,0.02); | |
| 551 %! assert(kurtosis(x),0,0.04); | |
| 552 %! endif | |
| 5730 | 553 */ |
| 554 | |
| 555 DEFUN_DLD (rande, args, , | |
| 556 "-*- texinfo -*-\n\ | |
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557 @deftypefn {Loadable Function} {} rande (@var{n})\n\ |
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558 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m}, @dots{})\n\ |
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559 @deftypefnx {Loadable Function} {} rande ([@var{n} @var{m} @dots{}])\n\ |
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560 @deftypefnx {Loadable Function} {@var{v} =} rande (\"state\")\n\ |
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561 @deftypefnx {Loadable Function} {} rande (\"state\", @var{v})\n\ |
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562 @deftypefnx {Loadable Function} {} rande (\"state\", \"reset\")\n\ |
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563 @deftypefnx {Loadable Function} {@var{v} =} rande (\"seed\")\n\ |
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564 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{v})\n\ |
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565 @deftypefnx {Loadable Function} {} rande (\"seed\", \"reset\")\n\ |
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566 Return a matrix with exponentially distributed random elements. The\n\ |
| 5730 | 567 arguments are handled the same as the arguments for @code{rand}.\n\ |
| 568 \n\ | |
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569 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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570 to transform from a uniform to an exponential distribution.\n\ |
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571 \n\ |
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572 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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573 @cite{Ziggurat Method for Generating Random Variables},\n\ |
| 5730 | 574 J. Statistical Software, vol 5, 2000,\n\ |
| 575 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
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576 \n\ |
| 6547 | 577 @seealso{rand, randn, randg, randp}\n\ |
| 5730 | 578 @end deftypefn") |
| 579 { | |
| 580 octave_value retval; | |
| 581 | |
| 582 int nargin = args.length (); | |
| 583 | |
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584 retval = do_rand (args, nargin, "rande", "exponential"); |
| 5730 | 585 |
| 586 return retval; | |
| 587 } | |
| 588 | |
| 589 /* | |
| 590 %!test | |
| 6437 | 591 %! % Test fixed state |
| 592 %! rande("state",1); | |
| 6443 | 593 %! assert (rande(1,6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291],1e-6); |
| 6437 | 594 %!test |
| 6443 | 595 %! % Test fixed seed |
| 6437 | 596 %! rande("seed",1); |
| 6443 | 597 %! assert (rande(1,6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087],1e-6); |
| 5730 | 598 %!test |
| 6437 | 599 %! if (__random_statistical_tests__) |
| 600 %! % statistical tests may fail occasionally | |
| 601 %! rande("state",1); | |
| 602 %! x = rande(100000,1); | |
| 603 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 604 %! assert(mean(x),1,0.01); | |
| 605 %! assert(var(x),1,0.03); | |
| 606 %! assert(skewness(x),2,0.06); | |
| 607 %! assert(kurtosis(x),6,0.7); | |
| 608 %! endif | |
| 609 %!test | |
| 610 %! if (__random_statistical_tests__) | |
| 611 %! % statistical tests may fail occasionally | |
| 612 %! rande("seed",1); | |
| 613 %! x = rande(100000,1); | |
| 614 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 615 %! assert(mean(x),1,0.01); | |
| 616 %! assert(var(x),1,0.03); | |
| 617 %! assert(skewness(x),2,0.06); | |
| 618 %! assert(kurtosis(x),6,0.7); | |
| 619 %! endif | |
| 5730 | 620 */ |
| 621 | |
| 622 DEFUN_DLD (randg, args, , | |
| 623 "-*- texinfo -*-\n\ | |
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624 @deftypefn {Loadable Function} {} randg (@var{n})\n\ |
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625 @deftypefnx {Loadable Function} {} randg (@var{n}, @var{m}, @dots{})\n\ |
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626 @deftypefnx {Loadable Function} {} randg ([@var{n} @var{m} @dots{}])\n\ |
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627 @deftypefnx {Loadable Function} {@var{v} =} randg (\"state\")\n\ |
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628 @deftypefnx {Loadable Function} {} randg (\"state\", @var{v})\n\ |
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629 @deftypefnx {Loadable Function} {} randg (\"state\", \"reset\")\n\ |
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630 @deftypefnx {Loadable Function} {@var{v} =} randg (\"seed\")\n\ |
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631 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{v})\n\ |
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632 @deftypefnx {Loadable Function} {} randg (\"seed\", \"reset\")\n\ |
| 5730 | 633 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ |
| 634 The arguments are handled the same as the arguments for @code{rand},\n\ | |
| 635 except for the argument @var{a}.\n\ | |
| 636 \n\ | |
| 637 This can be used to generate many distributions:\n\ | |
| 638 \n\ | |
| 639 @table @asis\n\ | |
| 6547 | 640 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
| 10840 | 641 \n\ |
| 5730 | 642 @example\n\ |
| 6547 | 643 r = b * randg (a)\n\ |
| 5730 | 644 @end example\n\ |
| 10840 | 645 \n\ |
| 6547 | 646 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
| 10840 | 647 \n\ |
| 5730 | 648 @example\n\ |
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649 @group\n\ |
| 6547 | 650 r1 = randg (a, 1)\n\ |
| 651 r = r1 / (r1 + randg (b, 1))\n\ | |
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652 @end group\n\ |
| 5730 | 653 @end example\n\ |
| 10840 | 654 \n\ |
| 6547 | 655 @item @code{Erlang (a, n)}\n\ |
| 10840 | 656 \n\ |
| 5730 | 657 @example\n\ |
| 6547 | 658 r = a * randg (n)\n\ |
| 5730 | 659 @end example\n\ |
| 10840 | 660 \n\ |
| 6547 | 661 @item @code{chisq (df)} for @code{df > 0}\n\ |
| 10840 | 662 \n\ |
| 5730 | 663 @example\n\ |
| 6547 | 664 r = 2 * randg (df / 2)\n\ |
| 5730 | 665 @end example\n\ |
| 10840 | 666 \n\ |
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667 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
| 10840 | 668 \n\ |
| 5730 | 669 @example\n\ |
| 6547 | 670 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
| 5730 | 671 @end example\n\ |
| 10840 | 672 \n\ |
| 6547 | 673 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
| 10840 | 674 \n\ |
| 5730 | 675 @example\n\ |
| 7096 | 676 @group\n\ |
| 677 ## r1 equals 1 if n1 is infinite\n\ | |
| 678 r1 = 2 * randg (n1 / 2) / n1\n\ | |
| 679 ## r2 equals 1 if n2 is infinite\n\ | |
| 680 r2 = 2 * randg (n2 / 2) / n2\n\ | |
| 5730 | 681 r = r1 / r2\n\n\ |
| 7096 | 682 @end group\n\ |
| 5730 | 683 @end example\n\ |
| 10840 | 684 \n\ |
| 5730 | 685 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
| 10840 | 686 \n\ |
| 5730 | 687 @example\n\ |
| 6547 | 688 r = randp ((1 - p) / p * randg (n))\n\ |
| 5730 | 689 @end example\n\ |
| 10840 | 690 \n\ |
| 6547 | 691 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
| 5730 | 692 (use chisq if @code{L = 0})\n\ |
| 10840 | 693 \n\ |
| 5730 | 694 @example\n\ |
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695 @group\n\ |
| 6547 | 696 r = randp (L / 2)\n\ |
| 697 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
| 698 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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699 @end group\n\ |
| 5730 | 700 @end example\n\ |
| 10840 | 701 \n\ |
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702 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
| 10840 | 703 \n\ |
| 5730 | 704 @example\n\ |
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705 @group\n\ |
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706 r = (randg (a1), @dots{}, randg (ak))\n\ |
| 6547 | 707 r = r / sum (r)\n\ |
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708 @end group\n\ |
| 5730 | 709 @end example\n\ |
| 10840 | 710 \n\ |
| 5730 | 711 @end table\n\ |
| 6547 | 712 @seealso{rand, randn, rande, randp}\n\ |
| 5730 | 713 @end deftypefn") |
| 714 { | |
| 715 octave_value retval; | |
| 716 | |
| 717 int nargin = args.length (); | |
| 718 | |
| 719 if (nargin < 1) | |
| 720 error ("randg: insufficient arguments"); | |
| 721 else | |
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722 retval = do_rand (args, nargin, "randg", "gamma", true); |
| 5730 | 723 |
| 724 return retval; | |
| 725 } | |
| 726 | |
| 727 /* | |
| 728 %!test | |
| 6437 | 729 %! randg("state",12) |
| 730 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report | |
| 731 | |
| 732 | |
| 733 %!test | |
| 734 %! % Test fixed state | |
| 735 %! randg("state",1); | |
| 6443 | 736 %! assert (randg(0.1,1,6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12],1e-6); |
| 6437 | 737 %!test |
| 738 %! % Test fixed state | |
| 739 %! randg("state",1); | |
| 6443 | 740 %! assert (randg(0.95,1,6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939],1e-6); |
| 6437 | 741 %!test |
| 742 %! % Test fixed state | |
| 743 %! randg("state",1); | |
| 6443 | 744 %! assert (randg(1,1,6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533],1e-6); |
| 6437 | 745 %!test |
| 746 %! % Test fixed state | |
| 747 %! randg("state",1); | |
| 6443 | 748 %! assert (randg(10,1,6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059],1e-5); |
| 6437 | 749 %!test |
| 750 %! % Test fixed state | |
| 751 %! randg("state",1); | |
| 6443 | 752 %! assert (randg(100,1,6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118],1e-4); |
| 6437 | 753 %!test |
| 754 %! % Test fixed seed | |
| 755 %! randg("seed",1); | |
| 6443 | 756 %! assert (randg(0.1,1,6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12],1e-6); |
| 6437 | 757 %!test |
| 758 %! % Test fixed seed | |
| 759 %! randg("seed",1); | |
| 6443 | 760 %! assert (randg(0.95,1,6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648],1e-6); |
| 6437 | 761 %!test |
| 762 %! % Test fixed seed | |
| 763 %! randg("seed",1); | |
| 6443 | 764 %! assert (randg(1,1,6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303],1e-6); |
| 6437 | 765 %!test |
| 766 %! % Test fixed seed | |
| 767 %! randg("seed",1); | |
| 6443 | 768 %! assert (randg(10,1,6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186],1e-5); |
| 6437 | 769 %!test |
| 770 %! % Test fixed seed | |
| 771 %! randg("seed",1); | |
| 6443 | 772 %! assert (randg(100,1,6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625],1e-4); |
| 6437 | 773 %!test |
| 774 %! if (__random_statistical_tests__) | |
| 775 %! % statistical tests may fail occasionally. | |
| 776 %! randg("state",12) | |
| 777 %! a=0.1; x = randg(a,100000,1); | |
| 778 %! assert(mean(x), a, 0.01); | |
| 779 %! assert(var(x), a, 0.01); | |
| 780 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 781 %! assert(kurtosis(x),6/a, 50.); | |
| 782 %! endif | |
| 783 %!test | |
| 784 %! if (__random_statistical_tests__) | |
| 785 %! % statistical tests may fail occasionally. | |
| 786 %! randg("state",12) | |
| 787 %! a=0.95; x = randg(a,100000,1); | |
| 788 %! assert(mean(x), a, 0.01); | |
| 789 %! assert(var(x), a, 0.04); | |
| 790 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 791 %! assert(kurtosis(x),6/a, 2.); | |
| 792 %! endif | |
| 793 %!test | |
| 794 %! if (__random_statistical_tests__) | |
| 795 %! % statistical tests may fail occasionally. | |
| 796 %! randg("state",12) | |
| 797 %! a=1; x = randg(a,100000,1); | |
| 798 %! assert(mean(x),a, 0.01); | |
| 799 %! assert(var(x),a, 0.04); | |
| 800 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 801 %! assert(kurtosis(x),6/a, 2.); | |
| 802 %! endif | |
| 803 %!test | |
| 804 %! if (__random_statistical_tests__) | |
| 805 %! % statistical tests may fail occasionally. | |
| 806 %! randg("state",12) | |
| 807 %! a=10; x = randg(a,100000,1); | |
| 808 %! assert(mean(x), a, 0.1); | |
| 809 %! assert(var(x), a, 0.5); | |
| 810 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 811 %! assert(kurtosis(x),6/a, 0.5); | |
| 812 %! endif | |
| 813 %!test | |
| 814 %! if (__random_statistical_tests__) | |
| 815 %! % statistical tests may fail occasionally. | |
| 816 %! randg("state",12) | |
| 817 %! a=100; x = randg(a,100000,1); | |
| 818 %! assert(mean(x), a, 0.2); | |
| 819 %! assert(var(x), a, 2.); | |
| 820 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 821 %! assert(kurtosis(x),6/a, 0.2); | |
| 822 %! endif | |
| 823 %!test | |
| 824 %! randg("seed",12) | |
| 5730 | 825 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report |
| 826 %!test | |
| 6437 | 827 %! if (__random_statistical_tests__) |
| 828 %! % statistical tests may fail occasionally. | |
| 829 %! randg("seed",12) | |
| 830 %! a=0.1; x = randg(a,100000,1); | |
| 831 %! assert(mean(x), a, 0.01); | |
| 832 %! assert(var(x), a, 0.01); | |
| 833 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 834 %! assert(kurtosis(x),6/a, 50.); | |
| 835 %! endif | |
| 5730 | 836 %!test |
| 6437 | 837 %! if (__random_statistical_tests__) |
| 838 %! % statistical tests may fail occasionally. | |
| 839 %! randg("seed",12) | |
| 840 %! a=0.95; x = randg(a,100000,1); | |
| 841 %! assert(mean(x), a, 0.01); | |
| 842 %! assert(var(x), a, 0.04); | |
| 843 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 844 %! assert(kurtosis(x),6/a, 2.); | |
| 845 %! endif | |
| 5730 | 846 %!test |
| 6437 | 847 %! if (__random_statistical_tests__) |
| 848 %! % statistical tests may fail occasionally. | |
| 849 %! randg("seed",12) | |
| 850 %! a=1; x = randg(a,100000,1); | |
| 851 %! assert(mean(x),a, 0.01); | |
| 852 %! assert(var(x),a, 0.04); | |
| 853 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 854 %! assert(kurtosis(x),6/a, 2.); | |
| 855 %! endif | |
| 5730 | 856 %!test |
| 6437 | 857 %! if (__random_statistical_tests__) |
| 858 %! % statistical tests may fail occasionally. | |
| 859 %! randg("seed",12) | |
| 860 %! a=10; x = randg(a,100000,1); | |
| 861 %! assert(mean(x), a, 0.1); | |
| 862 %! assert(var(x), a, 0.5); | |
| 863 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 864 %! assert(kurtosis(x),6/a, 0.5); | |
| 865 %! endif | |
| 5730 | 866 %!test |
| 6437 | 867 %! if (__random_statistical_tests__) |
| 868 %! % statistical tests may fail occasionally. | |
| 869 %! randg("seed",12) | |
| 870 %! a=100; x = randg(a,100000,1); | |
| 871 %! assert(mean(x), a, 0.2); | |
| 872 %! assert(var(x), a, 2.); | |
| 873 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 874 %! assert(kurtosis(x),6/a, 0.2); | |
| 875 %! endif | |
| 5730 | 876 */ |
| 877 | |
| 878 | |
| 879 DEFUN_DLD (randp, args, , | |
| 880 "-*- texinfo -*-\n\ | |
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881 @deftypefn {Loadable Function} {} randp (@var{l}, @var{n})\n\ |
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882 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m}, @dots{})\n\ |
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883 @deftypefnx {Loadable Function} {} randp (@var{l}, [@var{n} @var{m} @dots{}])\n\ |
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884 @deftypefnx {Loadable Function} {@var{v} =} randp (\"state\")\n\ |
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885 @deftypefnx {Loadable Function} {} randp (\"state\", @var{v})\n\ |
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886 @deftypefnx {Loadable Function} {} randp (\"state\", \"reset\")\n\ |
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887 @deftypefnx {Loadable Function} {@var{v} =} randp (\"seed\")\n\ |
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888 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{v})\n\ |
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889 @deftypefnx {Loadable Function} {} randp (\"seed\", \"reset\")\n\ |
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890 Return a matrix with Poisson distributed random elements with mean value\n\ |
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891 parameter given by the first argument, @var{l}. The arguments\n\ |
| 5730 | 892 are handled the same as the arguments for @code{rand}, except for the\n\ |
| 893 argument @var{l}.\n\ | |
| 894 \n\ | |
| 895 Five different algorithms are used depending on the range of @var{l}\n\ | |
| 896 and whether or not @var{l} is a scalar or a matrix.\n\ | |
| 897 \n\ | |
| 898 @table @asis\n\ | |
| 10840 | 899 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
| 900 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
| 901 Cambridge University Press, 1992.\n\ | |
| 902 \n\ | |
| 5730 | 903 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
| 10840 | 904 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
| 905 Cambridge University Press, 1992.\n\ | |
| 906 \n\ | |
| 907 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
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908 E. Stadlober, et al., WinRand source code, available via FTP.\n\ |
| 10840 | 909 \n\ |
| 5730 | 910 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
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911 E. Stadlober, et al., WinRand source code, available via FTP, or\n\ |
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912 H. Zechner, @cite{Efficient sampling from continuous and discrete\n\ |
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913 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
| 5730 | 914 University Graz, Austria, 1994.\n\ |
| 10840 | 915 \n\ |
| 5730 | 916 @item For @var{l} > 1e8, use normal approximation.\n\ |
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917 L. Montanet, et al., @cite{Review of Particle Properties}, Physical Review\n\ |
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918 D 50 p1284, 1994.\n\ |
| 5730 | 919 @end table\n\ |
| 6547 | 920 @seealso{rand, randn, rande, randg}\n\ |
| 5730 | 921 @end deftypefn") |
| 922 { | |
| 923 octave_value retval; | |
| 924 | |
| 925 int nargin = args.length (); | |
| 926 | |
| 927 if (nargin < 1) | |
| 928 error ("randp: insufficient arguments"); | |
| 929 else | |
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930 retval = do_rand (args, nargin, "randp", "poisson", true); |
| 5730 | 931 |
| 932 return retval; | |
| 933 } | |
| 934 | |
| 935 /* | |
| 936 %!test | |
| 6437 | 937 %! randp("state",12) |
| 938 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report | |
| 939 %!test | |
| 940 %! % Test fixed state | |
| 941 %! randp("state",1); | |
| 942 %! assert(randp(5,1,6),[5 5 3 7 7 3]) | |
| 943 %!test | |
| 944 %! % Test fixed state | |
| 945 %! randp("state",1); | |
| 946 %! assert(randp(15,1,6),[13 15 8 18 18 15]) | |
| 947 %!test | |
| 948 %! % Test fixed state | |
| 949 %! randp("state",1); | |
| 7421 | 950 %! assert(randp(1e9,1,6),[999915677 999976657 1000047684 1000019035 999985749 999977692],-1e-6) |
| 6437 | 951 %!test |
| 952 %! % Test fixed state | |
| 953 %! randp("seed",1); | |
| 6449 | 954 %! %%assert(randp(5,1,6),[8 2 3 6 6 8]) |
| 955 %! assert(randp(5,1,5),[8 2 3 6 6]) | |
| 6437 | 956 %!test |
| 957 %! % Test fixed state | |
| 958 %! randp("seed",1); | |
| 959 %! assert(randp(15,1,6),[15 16 12 10 10 12]) | |
| 960 %!test | |
| 961 %! % Test fixed state | |
| 962 %! randp("seed",1); | |
| 7421 | 963 %! assert(randp(1e9,1,6),[1000006208 1000012224 999981120 999963520 999963072 999981440],-1e-6) |
| 6437 | 964 %!test |
| 965 %! if (__random_statistical_tests__) | |
| 966 %! % statistical tests may fail occasionally. | |
| 967 %! randp("state",12) | |
| 968 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 969 %! x = randp(a(1),100000,1); | |
| 970 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 971 %! assert(mean(x),a(1),a(2)); | |
| 972 %! assert(var(x),a(1),0.02*a(1)); | |
| 973 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 974 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 975 %! endfor | |
| 976 %! endif | |
| 977 %!test | |
| 978 %! if (__random_statistical_tests__) | |
| 979 %! % statistical tests may fail occasionally. | |
| 980 %! randp("state",12) | |
| 981 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 982 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 983 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 984 %! assert(mean(x),a(1),a(2)); | |
| 985 %! assert(var(x),a(1),0.02*a(1)); | |
| 986 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 987 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 988 %! endfor | |
| 989 %! endif | |
| 990 %!test | |
| 991 %! randp("seed",12) | |
| 5730 | 992 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report |
| 993 %!test | |
| 6449 | 994 %! if (__random_statistical_tests__) |
| 995 %! % statistical tests may fail occasionally. | |
| 996 %! randp("seed",12) | |
| 997 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 998 %! x = randp(a(1),100000,1); | |
| 999 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 1000 %! assert(mean(x),a(1),a(2)); | |
| 1001 %! assert(var(x),a(1),0.02*a(1)); | |
| 1002 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 1003 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1004 %! endfor | |
| 1005 %! endif | |
| 5730 | 1006 %!test |
| 6449 | 1007 %! if (__random_statistical_tests__) |
| 1008 %! % statistical tests may fail occasionally. | |
| 1009 %! randp("seed",12) | |
| 1010 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 1011 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 1012 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 1013 %! assert(mean(x),a(1),a(2)); | |
| 1014 %! assert(var(x),a(1),0.02*a(1)); | |
| 1015 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 1016 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 1017 %! endfor | |
| 1018 %! endif | |
| 5730 | 1019 */ |
| 1020 | |
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1021 DEFUN_DLD (randperm, args, , |
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1022 "-*- texinfo -*-\n\ |
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1023 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
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1024 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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1025 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1026 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
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1027 replacement from @code{1:@var{n}}. The complexity is O(@var{n}) in\n\ |
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1028 memory and O(@var{m}) in time, unless @var{m} < @var{n}/5, in which case\n\ |
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1029 O(@var{m}) memory is used as well. The randomization is performed using\n\ |
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1030 rand(). All permutations are equally likely.\n\ |
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1031 @seealso{perms}\n\ |
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1032 @end deftypefn") |
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1033 { |
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1034 |
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1035 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
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1036 using std::tr1::unordered_map; |
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1037 #else |
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1038 using std::unordered_map; |
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1039 #endif |
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1040 |
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1041 int nargin = args.length (); |
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1042 octave_value retval; |
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1043 |
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1044 if (nargin == 1 || nargin == 2) |
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1045 { |
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1046 octave_idx_type n, m; |
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1047 |
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1048 n = args(0).idx_type_value (true); |
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1049 |
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1050 if (nargin == 2) |
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1051 m = args(1).idx_type_value (true); |
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1052 else |
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1053 m = n; |
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1054 |
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1055 if (m < 0 || n < 0) |
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1056 error ("randperm: M and N must be non-negative"); |
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1057 |
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1058 if (m > n) |
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1059 error ("randperm: M must be less than or equal to N"); |
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1060 |
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1061 // Quick and dirty heuristic to decide if we allocate or not the |
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1062 // whole vector for tracking the truncated shuffle. |
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1063 bool short_shuffle = m < n/5 && m < 1e5; |
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1064 |
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1065 if (! error_state) |
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1066 { |
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1067 // Generate random numbers. |
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1068 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
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1069 double *rvec = r.fortran_vec (); |
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1070 |
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1071 octave_idx_type idx_len = short_shuffle ? m : n; |
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1072 Array<octave_idx_type> idx (dim_vector (1, idx_len)); |
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1073 octave_idx_type *ivec = idx.fortran_vec (); |
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1074 |
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1075 for (octave_idx_type i = 0; i < idx_len; i++) |
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1076 ivec[i] = i; |
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1077 |
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1078 if (short_shuffle) |
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1079 { |
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13727
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1080 unordered_map<octave_idx_type, octave_idx_type> map (m); |
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1081 |
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1082 // Perform the Knuth shuffle only keeping track of moved |
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1083 // entries in the map |
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1084 for (octave_idx_type i = 0; i < m; i++) |
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1085 { |
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1086 octave_idx_type k = i + |
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1087 gnulib::floor (rvec[i] * (n - i)); |
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1088 |
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1089 //For shuffling first m entries, no need to use extra |
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1090 //storage |
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1091 if (k < m) |
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1092 { |
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1093 std::swap (ivec[i], ivec[k]); |
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1094 } |
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1095 else |
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1096 { |
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1097 if (map.find (k) == map.end ()) |
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1098 map[k] = k; |
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1099 |
|
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1100 std::swap (ivec[i], map[k]); |
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1101 } |
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1102 } |
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1103 } |
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1104 else |
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1105 { |
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1106 |
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1107 // Perform the Knuth shuffle of the first m entries |
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1108 for (octave_idx_type i = 0; i < m; i++) |
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1109 { |
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1110 octave_idx_type k = i + |
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1111 gnulib::floor (rvec[i] * (n - i)); |
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1112 std::swap (ivec[i], ivec[k]); |
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1113 } |
|
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1114 } |
|
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1115 |
|
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1116 // Convert to doubles, reusing r. |
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1117 for (octave_idx_type i = 0; i < m; i++) |
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1118 rvec[i] = ivec[i] + 1; |
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1119 |
|
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1120 if (m < n) |
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1121 idx.resize (dim_vector (1, m)); |
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1122 |
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1123 // Now create an array object with a cached idx_vector. |
|
11586
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1124 retval = new octave_matrix (r, idx_vector (idx)); |
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1125 } |
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1126 } |
|
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1127 else |
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1128 print_usage (); |
|
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1129 |
|
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1130 return retval; |
|
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1131 } |
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1132 |
|
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1133 /* |
|
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1134 %!assert (sort (randperm (20)), 1:20) |
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1135 %!assert (length (randperm (20,10)), 10) |
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1136 |
|
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1137 %!test |
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1138 %! rand ("seed", 0); |
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1139 %! for i = 1:100 |
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1140 %! p = randperm (305, 30); |
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1141 %! assert (length (unique (p)), 30); |
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1142 %! endfor |
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1143 */ |