Mercurial > hg > octave-lyh
annotate src/DLD-FUNCTIONS/rand.cc @ 10787:ac433932ce23
Correct typo in rande documentation (bug #30446).
| author | Rik <octave@nomad.inbox5.com> |
|---|---|
| date | Wed, 14 Jul 2010 09:57:32 -0700 |
| parents | d1f920d1ce0c |
| children | 3140cb7a05a1 |
| rev | line source |
|---|---|
| 2928 | 1 /* |
| 2 | |
| 7017 | 3 Copyright (C) 1996, 1997, 1998, 1999, 2000, 2002, 2003, 2005, 2006, |
| 8920 | 4 2007, 2008, 2009 John W. Eaton |
|
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5 Copyright (C) 2009 VZLU Prague |
| 2928 | 6 |
| 7 This file is part of Octave. | |
| 8 | |
| 9 Octave is free software; you can redistribute it and/or modify it | |
| 10 under the terms of the GNU General Public License as published by the | |
| 7016 | 11 Free Software Foundation; either version 3 of the License, or (at your |
| 12 option) any later version. | |
| 2928 | 13 |
| 14 Octave is distributed in the hope that it will be useful, but WITHOUT | |
| 15 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
| 16 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
| 17 for more details. | |
| 18 | |
| 19 You should have received a copy of the GNU General Public License | |
| 7016 | 20 along with Octave; see the file COPYING. If not, see |
| 21 <http://www.gnu.org/licenses/>. | |
| 2928 | 22 |
| 23 */ | |
| 24 | |
| 25 #ifdef HAVE_CONFIG_H | |
| 26 #include <config.h> | |
| 27 #endif | |
| 28 | |
| 29 #include <ctime> | |
| 30 | |
| 31 #include <string> | |
| 32 | |
| 33 #include "f77-fcn.h" | |
| 34 #include "lo-mappers.h" | |
| 4307 | 35 #include "oct-rand.h" |
| 4153 | 36 #include "quit.h" |
| 2928 | 37 |
| 38 #include "defun-dld.h" | |
| 39 #include "error.h" | |
| 40 #include "gripes.h" | |
| 41 #include "oct-obj.h" | |
| 42 #include "unwind-prot.h" | |
| 43 #include "utils.h" | |
|
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44 #include "ov-re-mat.h" |
| 2928 | 45 |
| 6437 | 46 /* |
| 47 %!shared __random_statistical_tests__ | |
| 48 %! % Flag whether the statistical tests should be run in "make check" or not | |
| 49 %! __random_statistical_tests__ = 0; | |
| 50 */ | |
| 51 | |
| 4307 | 52 static octave_value |
| 5730 | 53 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
|
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54 const std::string& distribution, bool additional_arg = false) |
| 2928 | 55 { |
| 4307 | 56 octave_value retval; |
| 5730 | 57 NDArray a; |
| 58 int idx = 0; | |
| 59 dim_vector dims; | |
| 2928 | 60 |
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61 unwind_protect frame; |
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62 // Restore current distribution on any exit. |
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63 frame.add_fcn (octave_rand::distribution, |
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64 octave_rand::distribution ()); |
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65 |
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66 octave_rand::distribution (distribution); |
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67 |
| 5730 | 68 if (additional_arg) |
| 69 { | |
| 70 if (nargin == 0) | |
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71 { |
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72 error ("%s: expecting at least one argument", fcn); |
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73 goto done; |
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74 } |
| 5730 | 75 else if (args(0).is_string()) |
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76 additional_arg = false; |
| 5730 | 77 else |
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78 { |
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79 a = args(0).array_value (); |
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80 if (error_state) |
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81 { |
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82 error ("%s: expecting scalar or matrix arguments", fcn); |
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83 goto done; |
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84 } |
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85 idx++; |
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86 nargin--; |
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87 } |
| 5730 | 88 } |
| 2928 | 89 |
| 4543 | 90 switch (nargin) |
| 2928 | 91 { |
| 4543 | 92 case 0: |
| 93 { | |
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94 if (additional_arg) |
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95 dims = a.dims (); |
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96 else |
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97 { |
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98 dims.resize (2); |
| 4543 | 99 |
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100 dims(0) = 1; |
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101 dims(1) = 1; |
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102 } |
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103 goto gen_matrix; |
| 4543 | 104 } |
| 105 break; | |
| 2928 | 106 |
| 4543 | 107 case 1: |
| 108 { | |
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109 octave_value tmp = args(idx); |
| 4543 | 110 |
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111 if (tmp.is_string ()) |
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112 { |
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113 std::string s_arg = tmp.string_value (); |
| 2928 | 114 |
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115 if (s_arg == "dist") |
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116 { |
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117 retval = octave_rand::distribution (); |
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118 } |
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119 else if (s_arg == "seed") |
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120 { |
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121 retval = octave_rand::seed (); |
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122 } |
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123 else if (s_arg == "state" || s_arg == "twister") |
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124 { |
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125 retval = octave_rand::state (fcn); |
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126 } |
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127 else if (s_arg == "uniform") |
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128 { |
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129 octave_rand::uniform_distribution (); |
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130 } |
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131 else if (s_arg == "normal") |
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132 { |
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133 octave_rand::normal_distribution (); |
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134 } |
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135 else if (s_arg == "exponential") |
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136 { |
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137 octave_rand::exponential_distribution (); |
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138 } |
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139 else if (s_arg == "poisson") |
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140 { |
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141 octave_rand::poisson_distribution (); |
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142 } |
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143 else if (s_arg == "gamma") |
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144 { |
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145 octave_rand::gamma_distribution (); |
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146 } |
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147 else |
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148 error ("%s: unrecognized string argument", fcn); |
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149 } |
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150 else if (tmp.is_scalar_type ()) |
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151 { |
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152 double dval = tmp.double_value (); |
| 2928 | 153 |
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154 if (xisnan (dval)) |
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155 { |
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156 error ("%s: NaN is invalid a matrix dimension", fcn); |
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157 } |
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158 else |
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159 { |
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160 dims.resize (2); |
| 4543 | 161 |
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162 dims(0) = NINTbig (tmp.double_value ()); |
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163 dims(1) = NINTbig (tmp.double_value ()); |
| 2928 | 164 |
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165 if (! error_state) |
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166 goto gen_matrix; |
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167 } |
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168 } |
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169 else if (tmp.is_range ()) |
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170 { |
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171 Range r = tmp.range_value (); |
| 4543 | 172 |
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173 if (r.all_elements_are_ints ()) |
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174 { |
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175 octave_idx_type n = r.nelem (); |
| 4543 | 176 |
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177 dims.resize (n); |
| 4543 | 178 |
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179 octave_idx_type base = NINTbig (r.base ()); |
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180 octave_idx_type incr = NINTbig (r.inc ()); |
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181 octave_idx_type lim = NINTbig (r.limit ()); |
| 2928 | 182 |
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|
183 if (base < 0 || lim < 0) |
|
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|
184 error ("%s: all dimensions must be nonnegative", fcn); |
|
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|
185 else |
|
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|
186 { |
|
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|
187 for (octave_idx_type i = 0; i < n; i++) |
|
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|
188 { |
|
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|
189 dims(i) = base; |
|
40dfc0c99116
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|
190 base += incr; |
|
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|
191 } |
| 2928 | 192 |
|
10154
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diff
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|
193 goto gen_matrix; |
|
40dfc0c99116
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|
194 } |
|
40dfc0c99116
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|
195 } |
|
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|
196 else |
|
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|
197 error ("%s: expecting all elements of range to 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 |
|
10154
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|
210 for (octave_idx_type i = 0; i < len; i++) |
|
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|
211 { |
|
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|
212 octave_idx_type elt = iv(i); |
| 4543 | 213 |
|
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|
214 if (elt < 0) |
|
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|
215 { |
|
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|
216 error ("%s: all dimensions must be nonnegative", fcn); |
|
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|
217 goto done; |
|
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diff
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|
218 } |
| 4543 | 219 |
|
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|
220 dims(i) = iv(i); |
|
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|
221 } |
| 2928 | 222 |
|
10154
40dfc0c99116
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|
223 goto gen_matrix; |
|
40dfc0c99116
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|
224 } |
|
40dfc0c99116
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|
225 else |
|
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|
226 error ("%s: expecting integer vector", fcn); |
|
40dfc0c99116
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|
227 } |
|
40dfc0c99116
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|
228 else |
|
40dfc0c99116
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|
229 { |
|
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diff
changeset
|
230 gripe_wrong_type_arg ("rand", tmp); |
|
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231 return retval; |
|
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diff
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|
232 } |
| 4543 | 233 } |
| 234 break; | |
| 235 | |
| 236 default: | |
| 237 { | |
|
10154
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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|
238 octave_value tmp = args(idx); |
| 4543 | 239 |
|
10154
40dfc0c99116
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changeset
|
240 if (nargin == 2 && tmp.is_string ()) |
|
40dfc0c99116
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changeset
|
241 { |
|
40dfc0c99116
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diff
changeset
|
242 std::string ts = tmp.string_value (); |
| 5164 | 243 |
|
10154
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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changeset
|
244 if (ts == "seed") |
|
40dfc0c99116
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|
245 { |
|
40dfc0c99116
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changeset
|
246 if (args(idx+1).is_real_scalar ()) |
|
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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diff
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|
247 { |
|
40dfc0c99116
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changeset
|
248 double d = args(idx+1).double_value (); |
| 2928 | 249 |
|
10154
40dfc0c99116
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|
250 if (! error_state) |
|
40dfc0c99116
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diff
changeset
|
251 octave_rand::seed (d); |
|
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|
252 } |
|
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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changeset
|
253 else if (args(idx+1).is_string () |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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diff
changeset
|
254 && args(idx+1).string_value() == "reset") |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
255 octave_rand::reset (); |
|
10154
40dfc0c99116
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changeset
|
256 else |
|
40dfc0c99116
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changeset
|
257 error ("%s: seed must be a real scalar", fcn); |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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diff
changeset
|
258 } |
|
40dfc0c99116
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|
259 else if (ts == "state" || ts == "twister") |
|
40dfc0c99116
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|
260 { |
|
10709
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Don't special case color_property type when emitting factory default (bug #30118)
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diff
changeset
|
261 if (args(idx+1).is_string () |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
262 && args(idx+1).string_value() == "reset") |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
parents:
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diff
changeset
|
263 octave_rand::reset (fcn); |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
264 else |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
parents:
10687
diff
changeset
|
265 { |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
266 ColumnVector s = |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
267 ColumnVector (args(idx+1).vector_value(false, true)); |
| 5730 | 268 |
|
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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changeset
|
269 if (! error_state) |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
270 octave_rand::state (s, fcn); |
|
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
271 } |
|
10154
40dfc0c99116
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changeset
|
272 } |
|
40dfc0c99116
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diff
changeset
|
273 else |
|
40dfc0c99116
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diff
changeset
|
274 error ("%s: unrecognized string argument", fcn); |
|
40dfc0c99116
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changeset
|
275 } |
|
40dfc0c99116
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diff
changeset
|
276 else |
|
40dfc0c99116
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diff
changeset
|
277 { |
|
40dfc0c99116
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diff
changeset
|
278 dims.resize (nargin); |
| 4543 | 279 |
|
10154
40dfc0c99116
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diff
changeset
|
280 for (int i = 0; i < nargin; i++) |
|
40dfc0c99116
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diff
changeset
|
281 { |
|
40dfc0c99116
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diff
changeset
|
282 dims(i) = args(idx+i).int_value (); |
| 4543 | 283 |
|
10154
40dfc0c99116
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changeset
|
284 if (error_state) |
|
40dfc0c99116
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changeset
|
285 { |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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diff
changeset
|
286 error ("%s: expecting integer arguments", fcn); |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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diff
changeset
|
287 goto done; |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
288 } |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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diff
changeset
|
289 } |
| 4543 | 290 |
|
10154
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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diff
changeset
|
291 goto gen_matrix; |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
292 } |
| 4543 | 293 } |
| 294 break; | |
| 2928 | 295 } |
| 296 | |
| 4543 | 297 done: |
| 2928 | 298 |
| 299 return retval; | |
| 300 | |
| 301 gen_matrix: | |
| 302 | |
| 5355 | 303 dims.chop_trailing_singletons (); |
| 304 | |
| 5730 | 305 if (additional_arg) |
| 306 { | |
| 307 if (a.length() == 1) | |
|
10154
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
308 return octave_rand::nd_array (dims, a(0)); |
| 5730 | 309 else |
|
10154
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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diff
changeset
|
310 { |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
311 if (a.dims() != dims) |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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10066
diff
changeset
|
312 { |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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10066
diff
changeset
|
313 error ("%s: mismatch in argument size", fcn); |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
314 return retval; |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
315 } |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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10066
diff
changeset
|
316 octave_idx_type len = a.length (); |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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10066
diff
changeset
|
317 NDArray m (dims); |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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10066
diff
changeset
|
318 double *v = m.fortran_vec (); |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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diff
changeset
|
319 for (octave_idx_type i = 0; i < len; i++) |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
320 v[i] = octave_rand::scalar (a(i)); |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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diff
changeset
|
321 return m; |
|
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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diff
changeset
|
322 } |
| 5730 | 323 } |
| 324 else | |
| 325 return octave_rand::nd_array (dims); | |
| 2928 | 326 } |
| 327 | |
| 4665 | 328 DEFUN_DLD (rand, args, , |
| 3369 | 329 "-*- texinfo -*-\n\ |
|
10687
a8ce6bdecce5
Improve documentation strings.
Rik <octave@nomad.inbox5.com>
parents:
10155
diff
changeset
|
330 @deftypefn {Loadable Function} {} rand (@var{x})\n\ |
| 3369 | 331 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m})\n\ |
| 5730 | 332 @deftypefnx {Loadable Function} {} rand (\"state\", @var{x})\n\ |
|
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
parents:
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diff
changeset
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333 @deftypefnx {Loadable Function} {} rand (\"state\", \"reset\")\n\ |
| 5730 | 334 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{x})\n\ |
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335 @deftypefnx {Loadable Function} {} rand (\"seed\", \"reset\")\n\ |
| 3369 | 336 Return a matrix with random elements uniformly distributed on the\n\ |
| 337 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
| 5730 | 338 for @code{eye}.\n\ |
| 339 \n\ | |
| 340 You can query the state of the random number generator using the\n\ | |
| 3369 | 341 form\n\ |
| 2928 | 342 \n\ |
| 3369 | 343 @example\n\ |
| 5730 | 344 v = rand (\"state\")\n\ |
| 345 @end example\n\ | |
| 346 \n\ | |
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347 This returns a column vector @var{v} of length 625. Later, you can\n\ |
| 5730 | 348 restore the random number generator to the state @var{v}\n\ |
| 349 using the form\n\ | |
| 350 \n\ | |
| 351 @example\n\ | |
| 352 rand (\"state\", v)\n\ | |
| 3369 | 353 @end example\n\ |
| 354 \n\ | |
| 355 @noindent\n\ | |
| 5730 | 356 You may also initialize the state vector from an arbitrary vector of\n\ |
| 357 length <= 625 for @var{v}. This new state will be a hash based on the\n\ | |
| 5798 | 358 value of @var{v}, not @var{v} itself.\n\ |
| 5730 | 359 \n\ |
| 360 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
| 361 available, otherwise from cpu time, wall clock time and the current\n\ | |
| 362 fraction of a second.\n\ | |
| 363 \n\ | |
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364 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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365 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and T. Nishimura,\n\ |
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366 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator}, ACM Trans. on\n\ |
| 7001 | 367 Modeling and Computer Simulation Vol. 8, No. 1, January pp.3-30 1998,\n\ |
| 7171 | 368 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
| 6547 | 369 Do @strong{not} use for cryptography without securely hashing\n\ |
| 370 several returned values together, otherwise the generator state\n\ | |
| 371 can be learned after reading 624 consecutive values.\n\ | |
| 5730 | 372 \n\ |
| 7096 | 373 Older versions of Octave used a different random number generator.\n\ |
| 374 The new generator is used by default\n\ | |
| 5730 | 375 as it is significantly faster than the old generator, and produces\n\ |
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376 random numbers with a significantly longer cycle time. However, in\n\ |
| 5798 | 377 some circumstances it might be desirable to obtain the same random\n\ |
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378 sequences as used by the old generators. To do this the keyword\n\ |
| 5730 | 379 \"seed\" is used to specify that the old generators should be use,\n\ |
| 380 as in\n\ | |
| 2928 | 381 \n\ |
| 3369 | 382 @example\n\ |
| 5730 | 383 rand (\"seed\", val)\n\ |
| 3369 | 384 @end example\n\ |
| 385 \n\ | |
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386 which sets the seed of the generator to @var{val}. The seed of the\n\ |
| 5730 | 387 generator can be queried with\n\ |
| 388 \n\ | |
| 389 @example\n\ | |
| 390 s = rand (\"seed\")\n\ | |
| 391 @end example\n\ | |
| 392 \n\ | |
| 393 However, it should be noted that querying the seed will not cause\n\ | |
| 394 @code{rand} to use the old generators, only setting the seed will.\n\ | |
| 395 To cause @code{rand} to once again use the new generators, the\n\ | |
| 396 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
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397 \n\ |
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398 The state or seed of the generator can be reset to a new random value\n\ |
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399 using the \"reset\" keyword.\n\ |
| 5798 | 400 @seealso{randn, rande, randg, randp}\n\ |
| 3369 | 401 @end deftypefn") |
| 2928 | 402 { |
| 4307 | 403 octave_value retval; |
| 2928 | 404 |
| 405 int nargin = args.length (); | |
| 406 | |
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407 retval = do_rand (args, nargin, "rand", "uniform"); |
| 2928 | 408 |
| 409 return retval; | |
| 410 } | |
| 411 | |
| 8871 | 412 // FIXME -- The old generator (selected when "seed" is set) will not |
| 413 // work properly if compiled to use 64-bit integers. | |
| 414 | |
| 5730 | 415 /* |
| 416 %!test # 'state' can be a scalar | |
| 417 %! rand('state',12); x = rand(1,4); | |
| 418 %! rand('state',12); y = rand(1,4); | |
| 419 %! assert(x,y); | |
| 420 %!test # 'state' can be a vector | |
| 421 %! rand('state',[12,13]); x=rand(1,4); | |
| 422 %! rand('state',[12;13]); y=rand(1,4); | |
| 423 %! assert(x,y); | |
| 424 %!test # querying 'state' doesn't disturb sequence | |
| 425 %! rand('state',12); rand(1,2); x=rand(1,2); | |
| 426 %! rand('state',12); rand(1,2); | |
| 427 %! s=rand('state'); y=rand(1,2); | |
| 428 %! assert(x,y); | |
| 429 %! rand('state',s); z=rand(1,2); | |
| 430 %! assert(x,z); | |
| 431 %!test # 'seed' must be a scalar | |
| 432 %! rand('seed',12); x = rand(1,4); | |
| 433 %! rand('seed',12); y = rand(1,4); | |
| 434 %! assert(x,y); | |
| 435 %!error(rand('seed',[12,13])) | |
| 436 %!test # querying 'seed' returns a value which can be used later | |
| 437 %! s=rand('seed'); x=rand(1,2); | |
| 438 %! rand('seed',s); y=rand(1,2); | |
| 439 %! assert(x,y); | |
| 440 %!test # querying 'seed' doesn't disturb sequence | |
| 441 %! rand('seed',12); rand(1,2); x=rand(1,2); | |
| 442 %! rand('seed',12); rand(1,2); | |
| 443 %! s=rand('seed'); y=rand(1,2); | |
| 444 %! assert(x,y); | |
| 445 %! rand('seed',s); z=rand(1,2); | |
| 446 %! assert(x,z); | |
| 447 */ | |
| 448 | |
| 449 /* | |
| 450 %!test | |
| 6437 | 451 %! % Test fixed state |
| 452 %! rand("state",1); | |
| 6443 | 453 %! assert (rand(1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382],1e-6); |
| 6437 | 454 %!test |
| 6443 | 455 %! % Test fixed seed |
| 6437 | 456 %! rand("seed",1); |
| 6443 | 457 %! assert (rand(1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759],1e-6); |
| 5730 | 458 %!test |
| 6437 | 459 %! if (__random_statistical_tests__) |
| 460 %! % statistical tests may fail occasionally. | |
| 461 %! rand("state",12); | |
| 462 %! x = rand(100000,1); | |
| 463 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 464 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 465 %! assert(mean(x),0.5,0.0024); | |
| 466 %! assert(var(x),1/48,0.0632); | |
| 467 %! assert(skewness(x),0,0.012); | |
| 468 %! assert(kurtosis(x),-6/5,0.0094); | |
| 469 %! endif | |
| 470 %!test | |
| 471 %! if (__random_statistical_tests__) | |
| 472 %! % statistical tests may fail occasionally. | |
| 473 %! rand("seed",12); | |
| 474 %! x = rand(100000,1); | |
| 475 %! assert(max(x)<1.); %*** Please report this!!! *** | |
| 476 %! assert(min(x)>0.); %*** Please report this!!! *** | |
| 477 %! assert(mean(x),0.5,0.0024); | |
| 478 %! assert(var(x),1/48,0.0632); | |
| 479 %! assert(skewness(x),0,0.012); | |
| 480 %! assert(kurtosis(x),-6/5,0.0094); | |
| 481 %! endif | |
| 5730 | 482 */ |
| 483 | |
| 484 | |
| 4307 | 485 static std::string current_distribution = octave_rand::distribution (); |
| 486 | |
| 2928 | 487 static void |
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488 reset_rand_generator (void) |
| 2928 | 489 { |
| 4307 | 490 octave_rand::distribution (current_distribution); |
| 2928 | 491 } |
| 492 | |
| 4665 | 493 DEFUN_DLD (randn, args, , |
| 3369 | 494 "-*- texinfo -*-\n\ |
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495 @deftypefn {Loadable Function} {} randn (@var{x})\n\ |
| 3369 | 496 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m})\n\ |
| 5730 | 497 @deftypefnx {Loadable Function} {} randn (\"state\", @var{x})\n\ |
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498 @deftypefnx {Loadable Function} {} randn (\"state\", \"reset\")\n\ |
| 5730 | 499 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{x})\n\ |
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500 @deftypefnx {Loadable Function} {} randn (\"seed\", \"reset\")\n\ |
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501 Return a matrix with normally distributed random\n\ |
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502 elements having zero mean and variance one. The arguments are\n\ |
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503 handled the same as the arguments for @code{rand}.\n\ |
| 3369 | 504 \n\ |
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505 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique'' to\n\ |
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506 transform from a uniform to a normal distribution. (G. Marsaglia and\n\ |
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507 W.W. Tsang, @cite{Ziggurat method for generating random variables},\n\ |
| 5730 | 508 J. Statistical Software, vol 5, 2000,\n\ |
| 509 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 2928 | 510 \n\ |
| 6547 | 511 @seealso{rand, rande, randg, randp}\n\ |
| 3369 | 512 @end deftypefn") |
| 2928 | 513 { |
| 4307 | 514 octave_value retval; |
| 2928 | 515 |
| 516 int nargin = args.length (); | |
| 517 | |
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518 retval = do_rand (args, nargin, "randn", "normal"); |
| 2928 | 519 |
| 520 return retval; | |
| 521 } | |
| 522 | |
| 523 /* | |
| 5730 | 524 %!test |
| 6437 | 525 %! % Test fixed state |
| 526 %! randn("state",1); | |
| 6443 | 527 %! assert (randn(1,6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116],1e-6); |
| 6437 | 528 %!test |
| 6443 | 529 %! % Test fixed seed |
| 6437 | 530 %! randn("seed",1); |
| 6443 | 531 %! assert (randn(1,6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133],1e-6); |
| 5730 | 532 %!test |
| 6437 | 533 %! if (__random_statistical_tests__) |
| 534 %! % statistical tests may fail occasionally. | |
| 535 %! randn("state",12); | |
| 536 %! x = randn(100000,1); | |
| 537 %! assert(mean(x),0,0.01); | |
| 538 %! assert(var(x),1,0.02); | |
| 539 %! assert(skewness(x),0,0.02); | |
| 540 %! assert(kurtosis(x),0,0.04); | |
| 541 %! endif | |
| 542 %!test | |
| 543 %! if (__random_statistical_tests__) | |
| 544 %! % statistical tests may fail occasionally. | |
| 545 %! randn("seed",12); | |
| 546 %! x = randn(100000,1); | |
| 547 %! assert(mean(x),0,0.01); | |
| 548 %! assert(var(x),1,0.02); | |
| 549 %! assert(skewness(x),0,0.02); | |
| 550 %! assert(kurtosis(x),0,0.04); | |
| 551 %! endif | |
| 5730 | 552 */ |
| 553 | |
| 554 DEFUN_DLD (rande, args, , | |
| 555 "-*- texinfo -*-\n\ | |
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556 @deftypefn {Loadable Function} {} rande (@var{x})\n\ |
| 5730 | 557 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m})\n\ |
| 558 @deftypefnx {Loadable Function} {} rande (\"state\", @var{x})\n\ | |
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559 @deftypefnx {Loadable Function} {} rande (\"state\", \"reset\")\n\ |
| 5730 | 560 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{x})\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{rande} uses the Marsaglia and Tsang ``Ziggurat technique'' to\n\ |
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566 transform from a uniform to a exponential distribution. (G. Marsaglia and\n\ |
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567 W.W. Tsang, @cite{Ziggurat method for generating random variables},\n\ |
| 5730 | 568 J. Statistical Software, vol 5, 2000,\n\ |
| 569 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
| 6547 | 570 @seealso{rand, randn, randg, randp}\n\ |
| 5730 | 571 @end deftypefn") |
| 572 { | |
| 573 octave_value retval; | |
| 574 | |
| 575 int nargin = args.length (); | |
| 576 | |
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577 retval = do_rand (args, nargin, "rande", "exponential"); |
| 5730 | 578 |
| 579 return retval; | |
| 580 } | |
| 581 | |
| 582 /* | |
| 583 %!test | |
| 6437 | 584 %! % Test fixed state |
| 585 %! rande("state",1); | |
| 6443 | 586 %! assert (rande(1,6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291],1e-6); |
| 6437 | 587 %!test |
| 6443 | 588 %! % Test fixed seed |
| 6437 | 589 %! rande("seed",1); |
| 6443 | 590 %! assert (rande(1,6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087],1e-6); |
| 5730 | 591 %!test |
| 6437 | 592 %! if (__random_statistical_tests__) |
| 593 %! % statistical tests may fail occasionally | |
| 594 %! rande("state",1); | |
| 595 %! x = rande(100000,1); | |
| 596 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 597 %! assert(mean(x),1,0.01); | |
| 598 %! assert(var(x),1,0.03); | |
| 599 %! assert(skewness(x),2,0.06); | |
| 600 %! assert(kurtosis(x),6,0.7); | |
| 601 %! endif | |
| 602 %!test | |
| 603 %! if (__random_statistical_tests__) | |
| 604 %! % statistical tests may fail occasionally | |
| 605 %! rande("seed",1); | |
| 606 %! x = rande(100000,1); | |
| 607 %! assert(min(x)>0); % *** Please report this!!! *** | |
| 608 %! assert(mean(x),1,0.01); | |
| 609 %! assert(var(x),1,0.03); | |
| 610 %! assert(skewness(x),2,0.06); | |
| 611 %! assert(kurtosis(x),6,0.7); | |
| 612 %! endif | |
| 5730 | 613 */ |
| 614 | |
| 615 DEFUN_DLD (randg, args, , | |
| 616 "-*- texinfo -*-\n\ | |
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617 @deftypefn {Loadable Function} {} randg (@var{a}, @var{x})\n\ |
| 5730 | 618 @deftypefnx {Loadable Function} {} randg (@var{a}, @var{n}, @var{m})\n\ |
| 619 @deftypefnx {Loadable Function} {} randg (\"state\", @var{x})\n\ | |
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620 @deftypefnx {Loadable Function} {} randg (\"state\", \"reset\")\n\ |
| 5730 | 621 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{x})\n\ |
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622 @deftypefnx {Loadable Function} {} randg (\"seed\", \"reset\")\n\ |
| 5730 | 623 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ |
| 624 The arguments are handled the same as the arguments for @code{rand},\n\ | |
| 625 except for the argument @var{a}.\n\ | |
| 626 \n\ | |
| 627 This can be used to generate many distributions:\n\ | |
| 628 \n\ | |
| 629 @table @asis\n\ | |
| 6547 | 630 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
| 5730 | 631 @example\n\ |
| 6547 | 632 r = b * randg (a)\n\ |
| 5730 | 633 @end example\n\ |
| 6547 | 634 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
| 5730 | 635 @example\n\ |
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636 @group\n\ |
| 6547 | 637 r1 = randg (a, 1)\n\ |
| 638 r = r1 / (r1 + randg (b, 1))\n\ | |
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639 @end group\n\ |
| 5730 | 640 @end example\n\ |
| 6547 | 641 @item @code{Erlang (a, n)}\n\ |
| 5730 | 642 @example\n\ |
| 6547 | 643 r = a * randg (n)\n\ |
| 5730 | 644 @end example\n\ |
| 6547 | 645 @item @code{chisq (df)} for @code{df > 0}\n\ |
| 5730 | 646 @example\n\ |
| 6547 | 647 r = 2 * randg (df / 2)\n\ |
| 5730 | 648 @end example\n\ |
| 649 @item @code{t(df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ | |
| 650 @example\n\ | |
| 6547 | 651 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
| 5730 | 652 @end example\n\ |
| 6547 | 653 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
| 5730 | 654 @example\n\ |
| 7096 | 655 @group\n\ |
| 656 ## r1 equals 1 if n1 is infinite\n\ | |
| 657 r1 = 2 * randg (n1 / 2) / n1\n\ | |
| 658 ## r2 equals 1 if n2 is infinite\n\ | |
| 659 r2 = 2 * randg (n2 / 2) / n2\n\ | |
| 5730 | 660 r = r1 / r2\n\n\ |
| 7096 | 661 @end group\n\ |
| 5730 | 662 @end example\n\ |
| 663 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ | |
| 664 @example\n\ | |
| 6547 | 665 r = randp ((1 - p) / p * randg (n))\n\ |
| 5730 | 666 @end example\n\ |
| 6547 | 667 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
| 5730 | 668 (use chisq if @code{L = 0})\n\ |
| 669 @example\n\ | |
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670 @group\n\ |
| 6547 | 671 r = randp (L / 2)\n\ |
| 672 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
| 673 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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674 @end group\n\ |
| 5730 | 675 @end example\n\ |
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676 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
| 5730 | 677 @example\n\ |
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678 @group\n\ |
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679 r = (randg (a1), @dots{}, randg (ak))\n\ |
| 6547 | 680 r = r / sum (r)\n\ |
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681 @end group\n\ |
| 5730 | 682 @end example\n\ |
| 683 @end table\n\ | |
| 6547 | 684 @seealso{rand, randn, rande, randp}\n\ |
| 5730 | 685 @end deftypefn") |
| 686 { | |
| 687 octave_value retval; | |
| 688 | |
| 689 int nargin = args.length (); | |
| 690 | |
| 691 if (nargin < 1) | |
| 692 error ("randg: insufficient arguments"); | |
| 693 else | |
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694 retval = do_rand (args, nargin, "randg", "gamma", true); |
| 5730 | 695 |
| 696 return retval; | |
| 697 } | |
| 698 | |
| 699 /* | |
| 700 %!test | |
| 6437 | 701 %! randg("state",12) |
| 702 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report | |
| 703 | |
| 704 | |
| 705 %!test | |
| 706 %! % Test fixed state | |
| 707 %! randg("state",1); | |
| 6443 | 708 %! assert (randg(0.1,1,6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12],1e-6); |
| 6437 | 709 %!test |
| 710 %! % Test fixed state | |
| 711 %! randg("state",1); | |
| 6443 | 712 %! assert (randg(0.95,1,6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939],1e-6); |
| 6437 | 713 %!test |
| 714 %! % Test fixed state | |
| 715 %! randg("state",1); | |
| 6443 | 716 %! assert (randg(1,1,6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533],1e-6); |
| 6437 | 717 %!test |
| 718 %! % Test fixed state | |
| 719 %! randg("state",1); | |
| 6443 | 720 %! assert (randg(10,1,6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059],1e-5); |
| 6437 | 721 %!test |
| 722 %! % Test fixed state | |
| 723 %! randg("state",1); | |
| 6443 | 724 %! assert (randg(100,1,6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118],1e-4); |
| 6437 | 725 %!test |
| 726 %! % Test fixed seed | |
| 727 %! randg("seed",1); | |
| 6443 | 728 %! assert (randg(0.1,1,6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12],1e-6); |
| 6437 | 729 %!test |
| 730 %! % Test fixed seed | |
| 731 %! randg("seed",1); | |
| 6443 | 732 %! assert (randg(0.95,1,6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648],1e-6); |
| 6437 | 733 %!test |
| 734 %! % Test fixed seed | |
| 735 %! randg("seed",1); | |
| 6443 | 736 %! assert (randg(1,1,6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303],1e-6); |
| 6437 | 737 %!test |
| 738 %! % Test fixed seed | |
| 739 %! randg("seed",1); | |
| 6443 | 740 %! assert (randg(10,1,6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186],1e-5); |
| 6437 | 741 %!test |
| 742 %! % Test fixed seed | |
| 743 %! randg("seed",1); | |
| 6443 | 744 %! assert (randg(100,1,6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625],1e-4); |
| 6437 | 745 %!test |
| 746 %! if (__random_statistical_tests__) | |
| 747 %! % statistical tests may fail occasionally. | |
| 748 %! randg("state",12) | |
| 749 %! a=0.1; x = randg(a,100000,1); | |
| 750 %! assert(mean(x), a, 0.01); | |
| 751 %! assert(var(x), a, 0.01); | |
| 752 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 753 %! assert(kurtosis(x),6/a, 50.); | |
| 754 %! endif | |
| 755 %!test | |
| 756 %! if (__random_statistical_tests__) | |
| 757 %! % statistical tests may fail occasionally. | |
| 758 %! randg("state",12) | |
| 759 %! a=0.95; x = randg(a,100000,1); | |
| 760 %! assert(mean(x), a, 0.01); | |
| 761 %! assert(var(x), a, 0.04); | |
| 762 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 763 %! assert(kurtosis(x),6/a, 2.); | |
| 764 %! endif | |
| 765 %!test | |
| 766 %! if (__random_statistical_tests__) | |
| 767 %! % statistical tests may fail occasionally. | |
| 768 %! randg("state",12) | |
| 769 %! a=1; x = randg(a,100000,1); | |
| 770 %! assert(mean(x),a, 0.01); | |
| 771 %! assert(var(x),a, 0.04); | |
| 772 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 773 %! assert(kurtosis(x),6/a, 2.); | |
| 774 %! endif | |
| 775 %!test | |
| 776 %! if (__random_statistical_tests__) | |
| 777 %! % statistical tests may fail occasionally. | |
| 778 %! randg("state",12) | |
| 779 %! a=10; x = randg(a,100000,1); | |
| 780 %! assert(mean(x), a, 0.1); | |
| 781 %! assert(var(x), a, 0.5); | |
| 782 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 783 %! assert(kurtosis(x),6/a, 0.5); | |
| 784 %! endif | |
| 785 %!test | |
| 786 %! if (__random_statistical_tests__) | |
| 787 %! % statistical tests may fail occasionally. | |
| 788 %! randg("state",12) | |
| 789 %! a=100; x = randg(a,100000,1); | |
| 790 %! assert(mean(x), a, 0.2); | |
| 791 %! assert(var(x), a, 2.); | |
| 792 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 793 %! assert(kurtosis(x),6/a, 0.2); | |
| 794 %! endif | |
| 795 %!test | |
| 796 %! randg("seed",12) | |
| 5730 | 797 %!assert(randg([-inf,-1,0,inf,nan]),[nan,nan,nan,nan,nan]) % *** Please report |
| 798 %!test | |
| 6437 | 799 %! if (__random_statistical_tests__) |
| 800 %! % statistical tests may fail occasionally. | |
| 801 %! randg("seed",12) | |
| 802 %! a=0.1; x = randg(a,100000,1); | |
| 803 %! assert(mean(x), a, 0.01); | |
| 804 %! assert(var(x), a, 0.01); | |
| 805 %! assert(skewness(x),2/sqrt(a), 1.); | |
| 806 %! assert(kurtosis(x),6/a, 50.); | |
| 807 %! endif | |
| 5730 | 808 %!test |
| 6437 | 809 %! if (__random_statistical_tests__) |
| 810 %! % statistical tests may fail occasionally. | |
| 811 %! randg("seed",12) | |
| 812 %! a=0.95; x = randg(a,100000,1); | |
| 813 %! assert(mean(x), a, 0.01); | |
| 814 %! assert(var(x), a, 0.04); | |
| 815 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 816 %! assert(kurtosis(x),6/a, 2.); | |
| 817 %! endif | |
| 5730 | 818 %!test |
| 6437 | 819 %! if (__random_statistical_tests__) |
| 820 %! % statistical tests may fail occasionally. | |
| 821 %! randg("seed",12) | |
| 822 %! a=1; x = randg(a,100000,1); | |
| 823 %! assert(mean(x),a, 0.01); | |
| 824 %! assert(var(x),a, 0.04); | |
| 825 %! assert(skewness(x),2/sqrt(a), 0.2); | |
| 826 %! assert(kurtosis(x),6/a, 2.); | |
| 827 %! endif | |
| 5730 | 828 %!test |
| 6437 | 829 %! if (__random_statistical_tests__) |
| 830 %! % statistical tests may fail occasionally. | |
| 831 %! randg("seed",12) | |
| 832 %! a=10; x = randg(a,100000,1); | |
| 833 %! assert(mean(x), a, 0.1); | |
| 834 %! assert(var(x), a, 0.5); | |
| 835 %! assert(skewness(x),2/sqrt(a), 0.1); | |
| 836 %! assert(kurtosis(x),6/a, 0.5); | |
| 837 %! endif | |
| 5730 | 838 %!test |
| 6437 | 839 %! if (__random_statistical_tests__) |
| 840 %! % statistical tests may fail occasionally. | |
| 841 %! randg("seed",12) | |
| 842 %! a=100; x = randg(a,100000,1); | |
| 843 %! assert(mean(x), a, 0.2); | |
| 844 %! assert(var(x), a, 2.); | |
| 845 %! assert(skewness(x),2/sqrt(a), 0.05); | |
| 846 %! assert(kurtosis(x),6/a, 0.2); | |
| 847 %! endif | |
| 5730 | 848 */ |
| 849 | |
| 850 | |
| 851 DEFUN_DLD (randp, args, , | |
| 852 "-*- texinfo -*-\n\ | |
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853 @deftypefn {Loadable Function} {} randp (@var{l}, @var{x})\n\ |
| 5730 | 854 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m})\n\ |
| 855 @deftypefnx {Loadable Function} {} randp (\"state\", @var{x})\n\ | |
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856 @deftypefnx {Loadable Function} {} randp (\"state\", \"reset\")\n\ |
| 5730 | 857 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{x})\n\ |
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858 @deftypefnx {Loadable Function} {} randp (\"seed\", \"reset\")\n\ |
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859 Return a matrix with Poisson distributed random elements with mean value\n\ |
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860 parameter given by the first argument, @var{l}. The arguments\n\ |
| 5730 | 861 are handled the same as the arguments for @code{rand}, except for the\n\ |
| 862 argument @var{l}.\n\ | |
| 863 \n\ | |
| 864 Five different algorithms are used depending on the range of @var{l}\n\ | |
| 865 and whether or not @var{l} is a scalar or a matrix.\n\ | |
| 866 \n\ | |
| 867 @table @asis\n\ | |
| 868 @item For scalar @var{l} <= 12, use direct method.\n\ | |
| 869 Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\ | |
| 870 @item For scalar @var{l} > 12, use rejection method.[1]\n\ | |
| 871 Press, et al., 'Numerical Recipes in C', Cambridge University Press, 1992.\n\ | |
| 872 @item For matrix @var{l} <= 10, use inversion method.[2]\n\ | |
| 873 Stadlober E., et al., WinRand source code, available via FTP.\n\ | |
| 874 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ | |
| 875 Stadlober E., et al., WinRand source code, available via FTP, or\n\ | |
| 876 H. Zechner, 'Efficient sampling from continuous and discrete\n\ | |
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877 unimodal distributions', Doctoral Dissertation, 156pp., Technical\n\ |
| 5730 | 878 University Graz, Austria, 1994.\n\ |
| 879 @item For @var{l} > 1e8, use normal approximation.\n\ | |
| 880 L. Montanet, et al., 'Review of Particle Properties', Physical Review\n\ | |
| 881 D 50 p1284, 1994\n\ | |
| 882 @end table\n\ | |
| 6547 | 883 @seealso{rand, randn, rande, randg}\n\ |
| 5730 | 884 @end deftypefn") |
| 885 { | |
| 886 octave_value retval; | |
| 887 | |
| 888 int nargin = args.length (); | |
| 889 | |
| 890 if (nargin < 1) | |
| 891 error ("randp: insufficient arguments"); | |
| 892 else | |
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893 retval = do_rand (args, nargin, "randp", "poisson", true); |
| 5730 | 894 |
| 895 return retval; | |
| 896 } | |
| 897 | |
| 898 /* | |
| 899 %!test | |
| 6437 | 900 %! randp("state",12) |
| 901 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report | |
| 902 %!test | |
| 903 %! % Test fixed state | |
| 904 %! randp("state",1); | |
| 905 %! assert(randp(5,1,6),[5 5 3 7 7 3]) | |
| 906 %!test | |
| 907 %! % Test fixed state | |
| 908 %! randp("state",1); | |
| 909 %! assert(randp(15,1,6),[13 15 8 18 18 15]) | |
| 910 %!test | |
| 911 %! % Test fixed state | |
| 912 %! randp("state",1); | |
| 7421 | 913 %! assert(randp(1e9,1,6),[999915677 999976657 1000047684 1000019035 999985749 999977692],-1e-6) |
| 6437 | 914 %!test |
| 915 %! % Test fixed state | |
| 916 %! randp("seed",1); | |
| 6449 | 917 %! %%assert(randp(5,1,6),[8 2 3 6 6 8]) |
| 918 %! assert(randp(5,1,5),[8 2 3 6 6]) | |
| 6437 | 919 %!test |
| 920 %! % Test fixed state | |
| 921 %! randp("seed",1); | |
| 922 %! assert(randp(15,1,6),[15 16 12 10 10 12]) | |
| 923 %!test | |
| 924 %! % Test fixed state | |
| 925 %! randp("seed",1); | |
| 7421 | 926 %! assert(randp(1e9,1,6),[1000006208 1000012224 999981120 999963520 999963072 999981440],-1e-6) |
| 6437 | 927 %!test |
| 928 %! if (__random_statistical_tests__) | |
| 929 %! % statistical tests may fail occasionally. | |
| 930 %! randp("state",12) | |
| 931 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 932 %! x = randp(a(1),100000,1); | |
| 933 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 934 %! assert(mean(x),a(1),a(2)); | |
| 935 %! assert(var(x),a(1),0.02*a(1)); | |
| 936 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 937 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 938 %! endfor | |
| 939 %! endif | |
| 940 %!test | |
| 941 %! if (__random_statistical_tests__) | |
| 942 %! % statistical tests may fail occasionally. | |
| 943 %! randp("state",12) | |
| 944 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 945 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 946 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 947 %! assert(mean(x),a(1),a(2)); | |
| 948 %! assert(var(x),a(1),0.02*a(1)); | |
| 949 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 950 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 951 %! endfor | |
| 952 %! endif | |
| 953 %!test | |
| 954 %! randp("seed",12) | |
| 5730 | 955 %!assert(randp([-inf,-1,0,inf,nan]),[nan,nan,0,nan,nan]); % *** Please report |
| 956 %!test | |
| 6449 | 957 %! if (__random_statistical_tests__) |
| 958 %! % statistical tests may fail occasionally. | |
| 959 %! randp("seed",12) | |
| 960 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 961 %! x = randp(a(1),100000,1); | |
| 962 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 963 %! assert(mean(x),a(1),a(2)); | |
| 964 %! assert(var(x),a(1),0.02*a(1)); | |
| 965 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 966 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 967 %! endfor | |
| 968 %! endif | |
| 5730 | 969 %!test |
| 6449 | 970 %! if (__random_statistical_tests__) |
| 971 %! % statistical tests may fail occasionally. | |
| 972 %! randp("seed",12) | |
| 973 %! for a=[5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] | |
| 974 %! x = randp(a(1)*ones(100000,1),100000,1); | |
| 975 %! assert(min(x)>=0); % *** Please report this!!! *** | |
| 976 %! assert(mean(x),a(1),a(2)); | |
| 977 %! assert(var(x),a(1),0.02*a(1)); | |
| 978 %! assert(skewness(x),1/sqrt(a(1)),a(3)); | |
| 979 %! assert(kurtosis(x),1/a(1),3*a(3)); | |
| 980 %! endfor | |
| 981 %! endif | |
| 5730 | 982 */ |
| 983 | |
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984 DEFUN_DLD (randperm, args, , |
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985 "-*- texinfo -*-\n\ |
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986 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
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987 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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988 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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989 If @var{m} is supplied, return @var{m} permutations,\n\ |
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990 one in each row of a NxM matrix. The complexity is O(M*N) in both time and\n\ |
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991 memory. The randomization is performed using rand().\n\ |
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992 All permutations are equally likely.\n\ |
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993 @seealso{perms}\n\ |
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994 @end deftypefn") |
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995 { |
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996 int nargin = args.length (); |
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997 octave_value retval; |
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998 |
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999 if (nargin == 1 || nargin == 2) |
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1000 { |
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1001 octave_idx_type n, m; |
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1002 |
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1003 if (nargin == 2) |
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1004 m = args(1).idx_type_value (true); |
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1005 else |
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1006 m = 1; |
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1007 |
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1008 n = args(0).idx_type_value (true); |
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1009 |
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1010 if (m < 0 || n < 0) |
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1011 error ("randperm: m and n must be non-negative"); |
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1012 |
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1013 if (! error_state) |
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1014 { |
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1015 // Generate random numbers. |
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1016 NDArray r = octave_rand::nd_array (dim_vector (m, n)); |
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1017 |
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1018 // Create transposed to allow faster access. |
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1019 Array<octave_idx_type> idx (dim_vector (n, m)); |
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1020 |
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1021 double *rvec = r.fortran_vec (); |
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1022 |
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1023 octave_idx_type *ivec = idx.fortran_vec (); |
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1024 |
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1025 // Perform the Knuth shuffle. |
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1026 for (octave_idx_type j = 0; j < m; j++) |
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1027 { |
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1028 for (octave_idx_type i = 0; i < n; i++) |
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1029 ivec[i] = i; |
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1030 |
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1031 for (octave_idx_type i = 0; i < n; i++) |
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1032 { |
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1033 octave_idx_type k = i + floor (rvec[i] * (n - i)); |
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1034 std::swap (ivec[i], ivec[k]); |
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1035 } |
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1036 |
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1037 ivec += n; |
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1038 rvec += n; |
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1039 } |
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1040 |
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1041 // Transpose. |
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1042 idx = idx.transpose (); |
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1043 |
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1044 // Re-fetch the pointers. |
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1045 ivec = idx.fortran_vec (); |
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1046 rvec = r.fortran_vec (); |
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1047 |
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1048 // Convert to doubles, reusing r. |
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1049 for (octave_idx_type i = 0, l = m*n; i < l; i++) |
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1050 rvec[i] = ivec[i] + 1; |
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1051 |
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1052 // Now create an array object with a cached idx_vector. |
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1053 retval = new octave_matrix (r, idx_vector (idx)); |
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1054 } |
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1055 } |
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1056 else |
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1057 print_usage (); |
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1058 |
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1059 return retval; |
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1060 } |
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1061 |
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1062 /* |
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1063 %!assert(sort(randperm(20)),1:20) |
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1064 %!assert(sort(randperm(20,50),2),repmat(1:20,50,1)) |
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1065 */ |