Mercurial > octave
annotate libinterp/corefcn/rand.cc @ 21041:63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
for large matrices in 64 bit builds, e.g. calls of "size" and large sparse
matrix creation.
* libinterp/corefcn/daspk.cc(daspk_user_function): Use idx_type_value ().
* libinterp/corefcn/data.cc(diag,size): Use idx_type_value ().
* libinterp/corefcn/rand.cc(do_rand): Use idx_type_value ().
* libinterp/corefcn/tril.cc(do_trilu): Use idx_type_value ().
* libinterp/corefcn/utils.cc(get_dimensions): Use idx_type_value ().
* libinterp/dldfcn/qr.cc(qrshift): Use idx_type_value ().
author | Kai T. Ohlhus <k.ohlhus@gmail.com> |
---|---|
date | Wed, 06 Jan 2016 18:04:02 +0100 |
parents | 48b2ad5ee801 |
children | e39e05d90788 |
rev | line source |
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1 |
2928 | 2 /* |
3 | |
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4 Copyright (C) 1996-2015 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> | |
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30 #if defined (HAVE_UNORDERED_MAP) |
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31 #include <unordered_map> |
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32 #elif defined (HAVE_TR1_UNORDERED_MAP) |
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33 #include <tr1/unordered_map> |
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34 #endif |
2928 | 35 #include <string> |
36 | |
37 #include "f77-fcn.h" | |
38 #include "lo-mappers.h" | |
4307 | 39 #include "oct-rand.h" |
4153 | 40 #include "quit.h" |
2928 | 41 |
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42 #include "defun.h" |
2928 | 43 #include "error.h" |
44 #include "gripes.h" | |
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45 #include "ovl.h" |
2928 | 46 #include "unwind-prot.h" |
47 #include "utils.h" | |
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48 #include "ov-re-mat.h" |
2928 | 49 |
6437 | 50 /* |
51 %!shared __random_statistical_tests__ | |
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52 %! ## Flag whether the statistical tests should be run in "make check" or not |
6437 | 53 %! __random_statistical_tests__ = 0; |
54 */ | |
55 | |
4307 | 56 static octave_value |
5730 | 57 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
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58 const std::string& distribution, bool additional_arg = false) |
2928 | 59 { |
5730 | 60 NDArray a; |
61 int idx = 0; | |
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62 bool is_single = false; |
2928 | 63 |
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64 if (nargin > 0 && args(nargin-1).is_string ()) |
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65 { |
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66 std::string s_arg = args(nargin-1).string_value (); |
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67 |
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68 if (s_arg == "single") |
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69 { |
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70 is_single = true; |
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71 nargin--; |
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72 } |
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73 else if (s_arg == "double") |
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74 nargin--; |
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75 } |
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76 |
5730 | 77 if (additional_arg) |
78 { | |
79 if (nargin == 0) | |
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80 error ("%s: at least one argument is required", fcn); |
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81 else if (args(0).is_string ()) |
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82 additional_arg = false; |
5730 | 83 else |
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84 { |
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85 a = args(0).xarray_value ("%s: dimension must be a scalar integer", fcn); |
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86 |
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87 idx++; |
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88 nargin--; |
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89 } |
5730 | 90 } |
2928 | 91 |
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92 octave_value retval; |
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93 dim_vector dims; |
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94 |
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95 unwind_protect frame; |
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96 // Restore current distribution on any exit. |
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97 frame.add_fcn (octave_rand::distribution, |
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98 octave_rand::distribution ()); |
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99 |
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100 octave_rand::distribution (distribution); |
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101 |
4543 | 102 switch (nargin) |
2928 | 103 { |
4543 | 104 case 0: |
105 { | |
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106 if (additional_arg) |
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107 dims = a.dims (); |
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108 else |
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109 { |
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110 dims.resize (2); |
4543 | 111 |
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112 dims(0) = 1; |
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113 dims(1) = 1; |
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114 } |
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115 |
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116 goto gen_matrix; |
4543 | 117 } |
118 break; | |
2928 | 119 |
4543 | 120 case 1: |
121 { | |
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122 octave_value tmp = args(idx); |
4543 | 123 |
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124 if (tmp.is_string ()) |
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125 { |
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126 std::string s_arg = tmp.string_value (); |
2928 | 127 |
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128 if (s_arg == "dist") |
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129 retval = octave_rand::distribution (); |
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130 else if (s_arg == "seed") |
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131 retval = octave_rand::seed (); |
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132 else if (s_arg == "state" || s_arg == "twister") |
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133 retval = octave_rand::state (fcn); |
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134 else if (s_arg == "uniform") |
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135 octave_rand::uniform_distribution (); |
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136 else if (s_arg == "normal") |
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137 octave_rand::normal_distribution (); |
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138 else if (s_arg == "exponential") |
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139 octave_rand::exponential_distribution (); |
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140 else if (s_arg == "poisson") |
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141 octave_rand::poisson_distribution (); |
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142 else if (s_arg == "gamma") |
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143 octave_rand::gamma_distribution (); |
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144 else |
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145 error ("%s: unrecognized string argument", fcn); |
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146 } |
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147 else if (tmp.is_scalar_type ()) |
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148 { |
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149 double dval = tmp.double_value (); |
2928 | 150 |
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151 if (xisnan (dval)) |
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152 error ("%s: NaN is invalid matrix dimension", fcn); |
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153 |
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154 dims.resize (2); |
4543 | 155 |
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156 dims(0) = NINTbig (tmp.double_value ()); |
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157 dims(1) = NINTbig (tmp.double_value ()); |
2928 | 158 |
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159 goto gen_matrix; |
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160 } |
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161 else if (tmp.is_range ()) |
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162 { |
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163 Range r = tmp.range_value (); |
4543 | 164 |
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165 if (! r.all_elements_are_ints ()) |
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166 error ("%s: all elements of range must be integers", fcn); |
4543 | 167 |
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168 octave_idx_type n = r.numel (); |
4543 | 169 |
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170 dims.resize (n); |
2928 | 171 |
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172 octave_idx_type base = NINTbig (r.base ()); |
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173 octave_idx_type incr = NINTbig (r.inc ()); |
2928 | 174 |
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175 for (octave_idx_type i = 0; i < n; i++) |
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176 { |
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177 // Negative dimensions treated as zero for Matlab compatibility |
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178 dims(i) = base >= 0 ? base : 0; |
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179 base += incr; |
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180 } |
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181 |
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182 goto gen_matrix; |
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183 } |
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184 else if (tmp.is_matrix_type ()) |
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185 { |
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186 Array<int> iv; |
4543 | 187 |
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188 try |
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189 { |
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190 iv = tmp.int_vector_value (true); |
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191 } |
20785
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always throw exception after debugging with debug_on_error
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192 catch (octave_execution_exception& e) |
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193 { |
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194 error (e, "%s: dimensions must be a scalar or array of integers", fcn); |
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195 } |
4543 | 196 |
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197 octave_idx_type len = iv.numel (); |
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198 |
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199 dims.resize (len); |
2928 | 200 |
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201 for (octave_idx_type i = 0; i < len; i++) |
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202 { |
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203 // Negative dimensions treated as zero for Matlab compatibility |
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204 octave_idx_type elt = iv(i); |
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205 dims(i) = elt >=0 ? elt : 0; |
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206 } |
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207 |
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208 goto gen_matrix; |
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209 } |
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|
210 else |
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211 { |
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212 gripe_wrong_type_arg ("rand", tmp); |
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213 return retval; |
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214 } |
4543 | 215 } |
216 break; | |
217 | |
218 default: | |
219 { | |
10154
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220 octave_value tmp = args(idx); |
4543 | 221 |
10154
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222 if (nargin == 2 && tmp.is_string ()) |
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|
223 { |
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224 std::string ts = tmp.string_value (); |
5164 | 225 |
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226 if (ts == "seed") |
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227 { |
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228 if (args(idx+1).is_real_scalar ()) |
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229 { |
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230 double d = args(idx+1).double_value (); |
2928 | 231 |
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|
232 octave_rand::seed (d); |
10154
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|
233 } |
11586
12df7854fa7c
strip trailing whitespace from source files
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diff
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|
234 else if (args(idx+1).is_string () |
14846
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maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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|
235 && args(idx+1).string_value () == "reset") |
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
|
236 octave_rand::reset (); |
10154
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|
237 else |
40dfc0c99116
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|
238 error ("%s: seed must be a real scalar", fcn); |
40dfc0c99116
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|
239 } |
40dfc0c99116
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|
240 else if (ts == "state" || ts == "twister") |
40dfc0c99116
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|
241 { |
10709
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Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
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|
242 if (args(idx+1).is_string () |
14846
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maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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|
243 && args(idx+1).string_value () == "reset") |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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|
244 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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|
245 else |
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Don't special case color_property type when emitting factory default (bug #30118)
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diff
changeset
|
246 { |
20687
5f04bfc7c17a
backout changeset 18f38ed43962
John W. Eaton <jwe@octave.org>
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20682
diff
changeset
|
247 ColumnVector s = |
5f04bfc7c17a
backout changeset 18f38ed43962
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20682
diff
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|
248 ColumnVector (args(idx+1).vector_value(false, true)); |
5730 | 249 |
20678
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|
250 octave_rand::state (s, fcn); |
10709
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Don't special case color_property type when emitting factory default (bug #30118)
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|
251 } |
10154
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|
252 } |
40dfc0c99116
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changeset
|
253 else |
40dfc0c99116
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|
254 error ("%s: unrecognized string argument", fcn); |
40dfc0c99116
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|
255 } |
40dfc0c99116
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|
256 else |
40dfc0c99116
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diff
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|
257 { |
40dfc0c99116
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diff
changeset
|
258 dims.resize (nargin); |
4543 | 259 |
10154
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|
260 for (int i = 0; i < nargin; i++) |
40dfc0c99116
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|
261 { |
21041
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
Kai T. Ohlhus <k.ohlhus@gmail.com>
parents:
20940
diff
changeset
|
262 octave_idx_type elt = |
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
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diff
changeset
|
263 args(idx+i).xidx_type_value ( |
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
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20940
diff
changeset
|
264 "%s: dimension must be a scalar or array of integers", |
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
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20940
diff
changeset
|
265 fcn); |
20704
571508c1ed06
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changeset
|
266 |
20894
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diff
changeset
|
267 // Negative dimensions treated as zero for Matlab compatibility |
12905
f7a8d1dafda3
Let rand accept negative dimensions (bug #33301)
Jordi Gutiérrez Hermoso <jordigh@gmail.com>
parents:
12639
diff
changeset
|
268 dims(i) = elt >= 0 ? elt : 0; |
10154
40dfc0c99116
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|
269 } |
4543 | 270 |
10154
40dfc0c99116
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diff
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|
271 goto gen_matrix; |
40dfc0c99116
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diff
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|
272 } |
4543 | 273 } |
274 break; | |
2928 | 275 } |
276 | |
20894
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diff
changeset
|
277 // No "goto gen_matrix" in code path. Must be done. |
2928 | 278 return retval; |
279 | |
17787
175b392e91fe
Use GNU style coding conventions for code in libinterp/
Rik <rik@octave.org>
parents:
17744
diff
changeset
|
280 gen_matrix: |
2928 | 281 |
5355 | 282 dims.chop_trailing_singletons (); |
283 | |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
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14620
diff
changeset
|
284 if (is_single) |
5730 | 285 { |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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14620
diff
changeset
|
286 if (additional_arg) |
10154
40dfc0c99116
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diff
changeset
|
287 { |
20232
a9574e3c6e9e
Deprecate Array::length() and Sparse::length() in favour of ::numel().
Carnë Draug <carandraug@octave.org>
parents:
20228
diff
changeset
|
288 if (a.numel () == 1) |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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14620
diff
changeset
|
289 return octave_rand::float_nd_array (dims, a(0)); |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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changeset
|
290 else |
10154
40dfc0c99116
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diff
changeset
|
291 { |
14846
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maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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14706
diff
changeset
|
292 if (a.dims () != dims) |
20831
35241c4b696c
eliminate return statements after calls to error
John W. Eaton <jwe@octave.org>
parents:
20801
diff
changeset
|
293 error ("%s: mismatch in argument size", fcn); |
35241c4b696c
eliminate return statements after calls to error
John W. Eaton <jwe@octave.org>
parents:
20801
diff
changeset
|
294 |
20232
a9574e3c6e9e
Deprecate Array::length() and Sparse::length() in favour of ::numel().
Carnë Draug <carandraug@octave.org>
parents:
20228
diff
changeset
|
295 octave_idx_type len = a.numel (); |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
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14620
diff
changeset
|
296 FloatNDArray m (dims); |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
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14620
diff
changeset
|
297 float *v = m.fortran_vec (); |
20894
e528d7ab1cad
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20853
diff
changeset
|
298 |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
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14620
diff
changeset
|
299 for (octave_idx_type i = 0; i < len; i++) |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
300 v[i] = octave_rand::float_scalar (a(i)); |
20894
e528d7ab1cad
rand.cc: Overhaul file to make use of new C++ archetype.
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20853
diff
changeset
|
301 |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
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14620
diff
changeset
|
302 return m; |
10154
40dfc0c99116
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10066
diff
changeset
|
303 } |
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
304 } |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
305 else |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
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306 return octave_rand::float_nd_array (dims); |
5730 | 307 } |
308 else | |
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309 { |
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310 if (additional_arg) |
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311 { |
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312 if (a.numel () == 1) |
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313 return octave_rand::nd_array (dims, a(0)); |
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314 else |
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315 { |
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316 if (a.dims () != dims) |
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317 error ("%s: mismatch in argument size", fcn); |
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318 |
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319 octave_idx_type len = a.numel (); |
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320 NDArray m (dims); |
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321 double *v = m.fortran_vec (); |
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322 |
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323 for (octave_idx_type i = 0; i < len; i++) |
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324 v[i] = octave_rand::scalar (a(i)); |
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325 |
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326 return m; |
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327 } |
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328 } |
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329 else |
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330 return octave_rand::nd_array (dims); |
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331 } |
2928 | 332 } |
333 | |
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334 DEFUN (rand, args, , |
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335 "-*- texinfo -*-\n\ |
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336 @deftypefn {} {} rand (@var{n})\n\ |
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337 @deftypefnx {} {} rand (@var{m}, @var{n}, @dots{})\n\ |
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338 @deftypefnx {} {} rand ([@var{m} @var{n} @dots{}])\n\ |
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339 @deftypefnx {} {@var{v} =} rand (\"state\")\n\ |
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340 @deftypefnx {} {} rand (\"state\", @var{v})\n\ |
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341 @deftypefnx {} {} rand (\"state\", \"reset\")\n\ |
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342 @deftypefnx {} {@var{v} =} rand (\"seed\")\n\ |
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343 @deftypefnx {} {} rand (\"seed\", @var{v})\n\ |
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344 @deftypefnx {} {} rand (\"seed\", \"reset\")\n\ |
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345 @deftypefnx {} {} rand (@dots{}, \"single\")\n\ |
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346 @deftypefnx {} {} rand (@dots{}, \"double\")\n\ |
3369 | 347 Return a matrix with random elements uniformly distributed on the\n\ |
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348 interval (0, 1).\n\ |
5730 | 349 \n\ |
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350 The arguments are handled the same as the arguments for @code{eye}.\n\ |
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351 \n\ |
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352 You can query the state of the random number generator using the form\n\ |
2928 | 353 \n\ |
3369 | 354 @example\n\ |
5730 | 355 v = rand (\"state\")\n\ |
356 @end example\n\ | |
357 \n\ | |
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358 This returns a column vector @var{v} of length 625. Later, you can restore\n\ |
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359 the random number generator to the state @var{v} using the form\n\ |
5730 | 360 \n\ |
361 @example\n\ | |
362 rand (\"state\", v)\n\ | |
3369 | 363 @end example\n\ |
364 \n\ | |
365 @noindent\n\ | |
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366 You may also initialize the state vector from an arbitrary vector of length\n\ |
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367 @leq{} 625 for @var{v}. This new state will be a hash based on the value of\n\ |
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368 @var{v}, not @var{v} itself.\n\ |
5730 | 369 \n\ |
370 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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371 available, otherwise from CPU time, wall clock time, and the current\n\ |
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372 fraction of a second. Note that this differs from @sc{matlab}, which\n\ |
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373 always initializes the state to the same state at startup. To obtain\n\ |
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374 behavior comparable to @sc{matlab}, initialize with a deterministic state\n\ |
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375 vector in Octave's startup files (@pxref{Startup Files}).\n\ |
5730 | 376 \n\ |
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377 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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378 Twister with a period of @math{2^{19937}-1}\n\ |
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379 (See @nospell{M. Matsumoto and T. Nishimura},\n\ |
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380 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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381 pseudorandom number generator},\n\ |
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382 ACM Trans. on Modeling and Computer Simulation Vol. 8, No. 1, pp. 3--30,\n\ |
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383 January 1998,\n\ |
7171 | 384 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
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385 Do @strong{not} use for cryptography without securely hashing several\n\ |
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386 returned values together, otherwise the generator state can be learned after\n\ |
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387 reading 624 consecutive values.\n\ |
5730 | 388 \n\ |
7096 | 389 Older versions of Octave used a different random number generator.\n\ |
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390 The new generator is used by default as it is significantly faster than the\n\ |
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391 old generator, and produces random numbers with a significantly longer cycle\n\ |
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392 time. However, in some circumstances it might be desirable to obtain the\n\ |
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393 same random sequences as produced by the old generators. To do this the\n\ |
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394 keyword @qcode{\"seed\"} is used to specify that the old generators should\n\ |
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395 be used, as in\n\ |
2928 | 396 \n\ |
3369 | 397 @example\n\ |
5730 | 398 rand (\"seed\", val)\n\ |
3369 | 399 @end example\n\ |
400 \n\ | |
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401 @noindent\n\ |
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402 which sets the seed of the generator to @var{val}. The seed of the\n\ |
5730 | 403 generator can be queried with\n\ |
404 \n\ | |
405 @example\n\ | |
406 s = rand (\"seed\")\n\ | |
407 @end example\n\ | |
408 \n\ | |
409 However, it should be noted that querying the seed will not cause\n\ | |
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410 @code{rand} to use the old generators, only setting the seed will. To cause\n\ |
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411 @code{rand} to once again use the new generators, the keyword\n\ |
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412 @qcode{\"state\"} should be used to reset the state of the @code{rand}.\n\ |
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413 \n\ |
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414 The state or seed of the generator can be reset to a new random value using\n\ |
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415 the @qcode{\"reset\"} keyword.\n\ |
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416 \n\ |
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417 The class of the value returned can be controlled by a trailing\n\ |
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418 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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419 classes.\n\ |
5798 | 420 @seealso{randn, rande, randg, randp}\n\ |
3369 | 421 @end deftypefn") |
2928 | 422 { |
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423 return do_rand (args, args.length (), "rand", "uniform"); |
2928 | 424 } |
425 | |
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426 // FIXME: The old generator (selected when "seed" is set) will not |
8871 | 427 // work properly if compiled to use 64-bit integers. |
428 | |
5730 | 429 /* |
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430 %!test # "state" can be a scalar |
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431 %! rand ("state", 12); x = rand (1,4); |
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432 %! rand ("state", 12); y = rand (1,4); |
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433 %! assert (x, y); |
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434 %!test # "state" can be a vector |
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435 %! rand ("state", [12,13]); x = rand (1,4); |
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436 %! rand ("state", [12;13]); y = rand (1,4); |
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437 %! assert (x, y); |
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438 %!test # querying "state" doesn't disturb sequence |
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439 %! rand ("state", 12); rand (1,2); x = rand (1,2); |
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440 %! rand ("state", 12); rand (1,2); |
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441 %! s = rand ("state"); y = rand (1,2); |
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442 %! assert (x, y); |
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443 %! rand ("state", s); z = rand (1,2); |
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444 %! assert (x, z); |
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445 %!test # "seed" must be a scalar |
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446 %! rand ("seed", 12); x = rand (1,4); |
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447 %! rand ("seed", 12); y = rand (1,4); |
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448 %! assert (x, y); |
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449 %!error <seed must be a real scalar> rand ("seed", [12,13]) |
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450 %!test # querying "seed" returns a value which can be used later |
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451 %! s = rand ("seed"); x = rand (1,2); |
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452 %! rand ("seed", s); y = rand (1,2); |
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453 %! assert (x, y); |
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454 %!test # querying "seed" doesn't disturb sequence |
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455 %! rand ("seed", 12); rand (1,2); x = rand (1,2); |
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456 %! rand ("seed", 12); rand (1,2); |
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457 %! s = rand ("seed"); y = rand (1,2); |
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458 %! assert (x, y); |
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459 %! rand ("seed", s); z = rand (1,2); |
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460 %! assert (x, z); |
5730 | 461 */ |
462 | |
463 /* | |
464 %!test | |
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465 %! ## Test fixed state |
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466 %! rand ("state", 1); |
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467 %! assert (rand (1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382], 1e-6); |
6437 | 468 %!test |
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469 %! ## Test fixed seed |
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470 %! rand ("seed", 1); |
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471 %! assert (rand (1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759], 1e-6); |
5730 | 472 %!test |
6437 | 473 %! if (__random_statistical_tests__) |
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474 %! ## statistical tests may fail occasionally. |
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475 %! rand ("state", 12); |
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476 %! x = rand (100000, 1); |
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477 %! assert (max (x) < 1); #*** Please report this!!! *** |
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478 %! assert (min (x) > 0); #*** Please report this!!! *** |
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479 %! assert (mean (x), 0.5, 0.0024); |
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480 %! assert (var (x), 1/48, 0.0632); |
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481 %! assert (skewness (x), 0, 0.012); |
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482 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 483 %! endif |
484 %!test | |
485 %! if (__random_statistical_tests__) | |
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486 %! ## statistical tests may fail occasionally. |
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487 %! rand ("seed", 12); |
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488 %! x = rand (100000, 1); |
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489 %! assert (max (x) < 1); #*** Please report this!!! *** |
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490 %! assert (min (x) > 0); #*** Please report this!!! *** |
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491 %! assert (mean (x), 0.5, 0.0024); |
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492 %! assert (var (x), 1/48, 0.0632); |
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493 %! assert (skewness (x), 0, 0.012); |
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494 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 495 %! endif |
5730 | 496 */ |
497 | |
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498 /* |
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499 %!# Test out-of-range values as rand() seeds. See oct-rand.cc: double2uint32(). |
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500 %!function v = __rand_sample__ (initval) |
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501 %! rand ("state", initval); |
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502 %! v = rand (1, 6); |
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503 %!endfunction |
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504 %! |
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505 %!assert (__rand_sample__ (0), __rand_sample__ (2^32)) |
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506 %!assert (__rand_sample__ (-2), __rand_sample__ (2^32-2)) |
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507 %!assert (__rand_sample__ (Inf), __rand_sample__ (NaN)) |
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508 %!assert (! isequal (__rand_sample__ (-1), __rand_sample__ (-2))) |
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509 */ |
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510 |
4307 | 511 static std::string current_distribution = octave_rand::distribution (); |
512 | |
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513 DEFUN (randn, args, , |
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514 "-*- texinfo -*-\n\ |
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515 @deftypefn {} {} randn (@var{n})\n\ |
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516 @deftypefnx {} {} randn (@var{m}, @var{n}, @dots{})\n\ |
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517 @deftypefnx {} {} randn ([@var{m} @var{n} @dots{}])\n\ |
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518 @deftypefnx {} {@var{v} =} randn (\"state\")\n\ |
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519 @deftypefnx {} {} randn (\"state\", @var{v})\n\ |
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520 @deftypefnx {} {} randn (\"state\", \"reset\")\n\ |
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521 @deftypefnx {} {@var{v} =} randn (\"seed\")\n\ |
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522 @deftypefnx {} {} randn (\"seed\", @var{v})\n\ |
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523 @deftypefnx {} {} randn (\"seed\", \"reset\")\n\ |
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524 @deftypefnx {} {} randn (@dots{}, \"single\")\n\ |
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525 @deftypefnx {} {} randn (@dots{}, \"double\")\n\ |
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526 Return a matrix with normally distributed random elements having zero mean\n\ |
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527 and variance one.\n\ |
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528 \n\ |
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529 The arguments are handled the same as the arguments for @code{rand}.\n\ |
3369 | 530 \n\ |
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531 By default, @code{randn} uses the @nospell{Marsaglia and Tsang}\n\ |
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532 ``Ziggurat technique'' to transform from a uniform to a normal distribution.\n\ |
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533 \n\ |
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534 The class of the value returned can be controlled by a trailing\n\ |
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535 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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536 classes.\n\ |
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537 \n\ |
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538 Reference: @nospell{G. Marsaglia and W.W. Tsang},\n\ |
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539 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 540 J. Statistical Software, vol 5, 2000,\n\ |
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541 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
2928 | 542 \n\ |
6547 | 543 @seealso{rand, rande, randg, randp}\n\ |
3369 | 544 @end deftypefn") |
2928 | 545 { |
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546 return do_rand (args, args.length (), "randn", "normal"); |
2928 | 547 } |
548 | |
549 /* | |
5730 | 550 %!test |
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551 %! ## Test fixed state |
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552 %! randn ("state", 1); |
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553 %! assert (randn (1, 6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116], 1e-6); |
6437 | 554 %!test |
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555 %! ## Test fixed seed |
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556 %! randn ("seed", 1); |
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557 %! assert (randn (1, 6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133], 1e-6); |
5730 | 558 %!test |
6437 | 559 %! if (__random_statistical_tests__) |
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560 %! ## statistical tests may fail occasionally. |
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561 %! randn ("state", 12); |
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562 %! x = randn (100000, 1); |
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563 %! assert (mean (x), 0, 0.01); |
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564 %! assert (var (x), 1, 0.02); |
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565 %! assert (skewness (x), 0, 0.02); |
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566 %! assert (kurtosis (x), 0, 0.04); |
6437 | 567 %! endif |
568 %!test | |
569 %! if (__random_statistical_tests__) | |
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570 %! ## statistical tests may fail occasionally. |
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571 %! randn ("seed", 12); |
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572 %! x = randn (100000, 1); |
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573 %! assert (mean (x), 0, 0.01); |
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574 %! assert (var (x), 1, 0.02); |
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575 %! assert (skewness (x), 0, 0.02); |
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576 %! assert (kurtosis (x), 0, 0.04); |
6437 | 577 %! endif |
5730 | 578 */ |
579 | |
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580 DEFUN (rande, args, , |
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581 "-*- texinfo -*-\n\ |
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582 @deftypefn {} {} rande (@var{n})\n\ |
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583 @deftypefnx {} {} rande (@var{m}, @var{n}, @dots{})\n\ |
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584 @deftypefnx {} {} rande ([@var{m} @var{n} @dots{}])\n\ |
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585 @deftypefnx {} {@var{v} =} rande (\"state\")\n\ |
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586 @deftypefnx {} {} rande (\"state\", @var{v})\n\ |
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587 @deftypefnx {} {} rande (\"state\", \"reset\")\n\ |
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588 @deftypefnx {} {@var{v} =} rande (\"seed\")\n\ |
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589 @deftypefnx {} {} rande (\"seed\", @var{v})\n\ |
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590 @deftypefnx {} {} rande (\"seed\", \"reset\")\n\ |
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591 @deftypefnx {} {} rande (@dots{}, \"single\")\n\ |
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592 @deftypefnx {} {} rande (@dots{}, \"double\")\n\ |
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593 Return a matrix with exponentially distributed random elements.\n\ |
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594 \n\ |
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595 The arguments are handled the same as the arguments for @code{rand}.\n\ |
5730 | 596 \n\ |
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597 By default, @code{randn} uses the @nospell{Marsaglia and Tsang}\n\ |
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598 ``Ziggurat technique'' to transform from a uniform to a normal distribution.\n\ |
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599 \n\ |
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600 The class of the value returned can be controlled by a trailing\n\ |
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601 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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602 classes.\n\ |
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603 \n\ |
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604 Reference: @nospell{G. Marsaglia and W.W. Tsang},\n\ |
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605 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 606 J. Statistical Software, vol 5, 2000,\n\ |
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607 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
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608 \n\ |
6547 | 609 @seealso{rand, randn, randg, randp}\n\ |
5730 | 610 @end deftypefn") |
611 { | |
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612 return do_rand (args, args.length (), "rande", "exponential"); |
5730 | 613 } |
614 | |
615 /* | |
616 %!test | |
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617 %! ## Test fixed state |
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618 %! rande ("state", 1); |
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619 %! assert (rande (1, 6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291], 1e-6); |
6437 | 620 %!test |
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621 %! ## Test fixed seed |
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622 %! rande ("seed", 1); |
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623 %! assert (rande (1, 6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087], 1e-6); |
5730 | 624 %!test |
6437 | 625 %! if (__random_statistical_tests__) |
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626 %! ## statistical tests may fail occasionally |
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627 %! rande ("state", 1); |
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628 %! x = rande (100000, 1); |
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629 %! assert (min (x) > 0); # *** Please report this!!! *** |
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630 %! assert (mean (x), 1, 0.01); |
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631 %! assert (var (x), 1, 0.03); |
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632 %! assert (skewness (x), 2, 0.06); |
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633 %! assert (kurtosis (x), 6, 0.7); |
6437 | 634 %! endif |
635 %!test | |
636 %! if (__random_statistical_tests__) | |
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637 %! ## statistical tests may fail occasionally |
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638 %! rande ("seed", 1); |
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639 %! x = rande (100000, 1); |
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640 %! assert (min (x)>0); # *** Please report this!!! *** |
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641 %! assert (mean (x), 1, 0.01); |
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642 %! assert (var (x), 1, 0.03); |
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643 %! assert (skewness (x), 2, 0.06); |
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644 %! assert (kurtosis (x), 6, 0.7); |
6437 | 645 %! endif |
5730 | 646 */ |
647 | |
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648 DEFUN (randg, args, , |
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649 "-*- texinfo -*-\n\ |
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650 @deftypefn {} {} randg (@var{n})\n\ |
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651 @deftypefnx {} {} randg (@var{m}, @var{n}, @dots{})\n\ |
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652 @deftypefnx {} {} randg ([@var{m} @var{n} @dots{}])\n\ |
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653 @deftypefnx {} {@var{v} =} randg (\"state\")\n\ |
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654 @deftypefnx {} {} randg (\"state\", @var{v})\n\ |
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655 @deftypefnx {} {} randg (\"state\", \"reset\")\n\ |
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656 @deftypefnx {} {@var{v} =} randg (\"seed\")\n\ |
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657 @deftypefnx {} {} randg (\"seed\", @var{v})\n\ |
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658 @deftypefnx {} {} randg (\"seed\", \"reset\")\n\ |
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659 @deftypefnx {} {} randg (@dots{}, \"single\")\n\ |
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660 @deftypefnx {} {} randg (@dots{}, \"double\")\n\ |
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661 Return a matrix with @code{gamma (@var{a},1)} distributed random elements.\n\ |
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662 \n\ |
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663 The arguments are handled the same as the arguments for @code{rand}, except\n\ |
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664 for the argument @var{a}.\n\ |
5730 | 665 \n\ |
666 This can be used to generate many distributions:\n\ | |
667 \n\ | |
668 @table @asis\n\ | |
6547 | 669 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
10840 | 670 \n\ |
5730 | 671 @example\n\ |
6547 | 672 r = b * randg (a)\n\ |
5730 | 673 @end example\n\ |
10840 | 674 \n\ |
6547 | 675 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
10840 | 676 \n\ |
5730 | 677 @example\n\ |
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678 @group\n\ |
6547 | 679 r1 = randg (a, 1)\n\ |
680 r = r1 / (r1 + randg (b, 1))\n\ | |
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681 @end group\n\ |
5730 | 682 @end example\n\ |
10840 | 683 \n\ |
6547 | 684 @item @code{Erlang (a, n)}\n\ |
10840 | 685 \n\ |
5730 | 686 @example\n\ |
6547 | 687 r = a * randg (n)\n\ |
5730 | 688 @end example\n\ |
10840 | 689 \n\ |
6547 | 690 @item @code{chisq (df)} for @code{df > 0}\n\ |
10840 | 691 \n\ |
5730 | 692 @example\n\ |
6547 | 693 r = 2 * randg (df / 2)\n\ |
5730 | 694 @end example\n\ |
10840 | 695 \n\ |
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696 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
10840 | 697 \n\ |
5730 | 698 @example\n\ |
6547 | 699 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
5730 | 700 @end example\n\ |
10840 | 701 \n\ |
6547 | 702 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
10840 | 703 \n\ |
5730 | 704 @example\n\ |
7096 | 705 @group\n\ |
706 ## r1 equals 1 if n1 is infinite\n\ | |
707 r1 = 2 * randg (n1 / 2) / n1\n\ | |
708 ## r2 equals 1 if n2 is infinite\n\ | |
709 r2 = 2 * randg (n2 / 2) / n2\n\ | |
5730 | 710 r = r1 / r2\n\n\ |
7096 | 711 @end group\n\ |
5730 | 712 @end example\n\ |
10840 | 713 \n\ |
5730 | 714 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
10840 | 715 \n\ |
5730 | 716 @example\n\ |
6547 | 717 r = randp ((1 - p) / p * randg (n))\n\ |
5730 | 718 @end example\n\ |
10840 | 719 \n\ |
6547 | 720 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
5730 | 721 (use chisq if @code{L = 0})\n\ |
10840 | 722 \n\ |
5730 | 723 @example\n\ |
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724 @group\n\ |
6547 | 725 r = randp (L / 2)\n\ |
726 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
727 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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728 @end group\n\ |
5730 | 729 @end example\n\ |
10840 | 730 \n\ |
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731 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
10840 | 732 \n\ |
5730 | 733 @example\n\ |
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734 @group\n\ |
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735 r = (randg (a1), @dots{}, randg (ak))\n\ |
6547 | 736 r = r / sum (r)\n\ |
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737 @end group\n\ |
5730 | 738 @end example\n\ |
10840 | 739 \n\ |
5730 | 740 @end table\n\ |
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741 \n\ |
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742 The class of the value returned can be controlled by a trailing\n\ |
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743 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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744 classes.\n\ |
6547 | 745 @seealso{rand, randn, rande, randp}\n\ |
5730 | 746 @end deftypefn") |
747 { | |
748 int nargin = args.length (); | |
749 | |
750 if (nargin < 1) | |
751 error ("randg: insufficient arguments"); | |
752 | |
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753 return do_rand (args, nargin, "randg", "gamma", true); |
5730 | 754 } |
755 | |
756 /* | |
757 %!test | |
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758 %! randg ("state", 12) |
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759 %! assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]); # *** Please report |
6437 | 760 |
761 %!test | |
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762 %! ## Test fixed state |
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763 %! randg ("state", 1); |
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764 %! assert (randg (0.1, 1, 6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12], 1e-6); |
6437 | 765 %!test |
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766 %! ## Test fixed state |
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767 %! randg ("state", 1); |
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768 %! assert (randg (0.95, 1, 6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939], 1e-6); |
6437 | 769 %!test |
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770 %! ## Test fixed state |
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771 %! randg ("state", 1); |
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772 %! assert (randg (1, 1, 6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533], 1e-6); |
6437 | 773 %!test |
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774 %! ## Test fixed state |
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775 %! randg ("state", 1); |
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776 %! assert (randg (10, 1, 6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059], 1e-5); |
6437 | 777 %!test |
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778 %! ## Test fixed state |
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779 %! randg ("state", 1); |
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780 %! assert (randg (100, 1, 6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118], 1e-4); |
6437 | 781 %!test |
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782 %! ## Test fixed seed |
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783 %! randg ("seed", 1); |
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784 %! assert (randg (0.1, 1, 6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12], 1e-6); |
6437 | 785 %!test |
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786 %! ## Test fixed seed |
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787 %! randg ("seed", 1); |
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788 %! assert (randg (0.95, 1, 6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648], 1e-6); |
6437 | 789 %!test |
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790 %! ## Test fixed seed |
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791 %! randg ("seed", 1); |
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792 %! assert (randg (1, 1, 6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303], 1e-6); |
6437 | 793 %!test |
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794 %! ## Test fixed seed |
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795 %! randg ("seed", 1); |
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796 %! assert (randg (10, 1, 6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186], 1e-5); |
6437 | 797 %!test |
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798 %! ## Test fixed seed |
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799 %! randg ("seed", 1); |
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800 %! assert (randg (100, 1, 6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625], 1e-4); |
18573
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801 %!test |
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802 %! ## Test out-of-bounds values produce NaN w/old-style generators & floats |
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803 %! randg ("seed", 1); |
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804 %! result = randg ([-2 Inf], "single"); |
18574
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805 %! assert (result, single ([NaN NaN])); |
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806 |
6437 | 807 %!test |
808 %! if (__random_statistical_tests__) | |
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809 %! ## statistical tests may fail occasionally. |
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810 %! randg ("state", 12); |
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811 %! a = 0.1; |
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812 %! x = randg (a, 100000, 1); |
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813 %! assert (mean (x), a, 0.01); |
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814 %! assert (var (x), a, 0.01); |
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815 %! assert (skewness (x), 2/sqrt (a), 1); |
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816 %! assert (kurtosis (x), 6/a, 50); |
6437 | 817 %! endif |
818 %!test | |
819 %! if (__random_statistical_tests__) | |
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820 %! ## statistical tests may fail occasionally. |
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821 %! randg ("state", 12); |
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822 %! a = 0.95; |
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823 %! x = randg (a, 100000, 1); |
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824 %! assert (mean (x), a, 0.01); |
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825 %! assert (var (x), a, 0.04); |
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826 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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827 %! assert (kurtosis (x), 6/a, 2); |
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828 %! endif |
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829 %!test |
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830 %! if (__random_statistical_tests__) |
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831 %! ## statistical tests may fail occasionally. |
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832 %! randg ("state", 12); |
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833 %! a = 1; |
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834 %! x = randg (a, 100000, 1); |
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835 %! assert (mean (x), a, 0.01); |
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836 %! assert (var (x), a, 0.04); |
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837 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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838 %! assert (kurtosis (x), 6/a, 2); |
6437 | 839 %! endif |
840 %!test | |
841 %! if (__random_statistical_tests__) | |
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842 %! ## statistical tests may fail occasionally. |
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843 %! randg ("state", 12); |
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844 %! a = 10; |
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845 %! x = randg (a, 100000, 1); |
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846 %! assert (mean (x), a, 0.1); |
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847 %! assert (var (x), a, 0.5); |
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848 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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849 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 850 %! endif |
851 %!test | |
852 %! if (__random_statistical_tests__) | |
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853 %! ## statistical tests may fail occasionally. |
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854 %! randg ("state", 12); |
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855 %! a = 100; |
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856 %! x = randg (a, 100000, 1); |
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857 %! assert (mean (x), a, 0.2); |
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858 %! assert (var (x), a, 2); |
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859 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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860 %! assert (kurtosis (x), 6/a, 0.2); |
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861 %! endif |
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862 %!test |
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863 %! randg ("seed", 12); |
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864 %!assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]) # *** Please report |
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865 %!test |
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866 %! if (__random_statistical_tests__) |
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867 %! ## statistical tests may fail occasionally. |
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868 %! randg ("seed", 12); |
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869 %! a = 0.1; |
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870 %! x = randg (a, 100000, 1); |
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871 %! assert (mean (x), a, 0.01); |
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872 %! assert (var (x), a, 0.01); |
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873 %! assert (skewness (x), 2/sqrt (a), 1); |
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874 %! assert (kurtosis (x), 6/a, 50); |
6437 | 875 %! endif |
876 %!test | |
877 %! if (__random_statistical_tests__) | |
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878 %! ## statistical tests may fail occasionally. |
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879 %! randg ("seed", 12); |
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880 %! a = 0.95; |
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881 %! x = randg (a, 100000, 1); |
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882 %! assert (mean (x), a, 0.01); |
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883 %! assert (var (x), a, 0.04); |
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884 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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885 %! assert (kurtosis (x), 6/a, 2); |
6437 | 886 %! endif |
887 %!test | |
888 %! if (__random_statistical_tests__) | |
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889 %! ## statistical tests may fail occasionally. |
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890 %! randg ("seed", 12); |
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891 %! a = 1; |
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892 %! x = randg (a, 100000, 1); |
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893 %! assert (mean (x), a, 0.01); |
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894 %! assert (var (x), a, 0.04); |
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895 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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896 %! assert (kurtosis (x), 6/a, 2); |
6437 | 897 %! endif |
5730 | 898 %!test |
6437 | 899 %! if (__random_statistical_tests__) |
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900 %! ## statistical tests may fail occasionally. |
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901 %! randg ("seed", 12); |
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902 %! a = 10; |
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903 %! x = randg (a, 100000, 1); |
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904 %! assert (mean (x), a, 0.1); |
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905 %! assert (var (x), a, 0.5); |
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906 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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907 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 908 %! endif |
5730 | 909 %!test |
6437 | 910 %! if (__random_statistical_tests__) |
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911 %! ## statistical tests may fail occasionally. |
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912 %! randg ("seed", 12); |
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913 %! a = 100; |
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914 %! x = randg (a, 100000, 1); |
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915 %! assert (mean (x), a, 0.2); |
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916 %! assert (var (x), a, 2); |
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917 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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918 %! assert (kurtosis (x), 6/a, 0.2); |
6437 | 919 %! endif |
5730 | 920 */ |
921 | |
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922 DEFUN (randp, args, , |
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923 "-*- texinfo -*-\n\ |
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924 @deftypefn {} {} randp (@var{l}, @var{n})\n\ |
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925 @deftypefnx {} {} randp (@var{l}, @var{m}, @var{n}, @dots{})\n\ |
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926 @deftypefnx {} {} randp (@var{l}, [@var{m} @var{n} @dots{}])\n\ |
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927 @deftypefnx {} {@var{v} =} randp (\"state\")\n\ |
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928 @deftypefnx {} {} randp (\"state\", @var{v})\n\ |
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929 @deftypefnx {} {} randp (\"state\", \"reset\")\n\ |
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930 @deftypefnx {} {@var{v} =} randp (\"seed\")\n\ |
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931 @deftypefnx {} {} randp (\"seed\", @var{v})\n\ |
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932 @deftypefnx {} {} randp (\"seed\", \"reset\")\n\ |
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933 @deftypefnx {} {} randp (@dots{}, \"single\")\n\ |
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934 @deftypefnx {} {} randp (@dots{}, \"double\")\n\ |
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935 Return a matrix with Poisson distributed random elements with mean value\n\ |
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936 parameter given by the first argument, @var{l}.\n\ |
5730 | 937 \n\ |
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938 The arguments are handled the same as the arguments for @code{rand}, except\n\ |
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939 for the argument @var{l}.\n\ |
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940 \n\ |
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941 Five different algorithms are used depending on the range of @var{l} and\n\ |
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942 whether or not @var{l} is a scalar or a matrix.\n\ |
5730 | 943 \n\ |
944 @table @asis\n\ | |
10840 | 945 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
946 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
947 Cambridge University Press, 1992.\n\ | |
948 \n\ | |
5730 | 949 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
10840 | 950 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
951 Cambridge University Press, 1992.\n\ | |
952 \n\ | |
953 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
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954 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP.\n\ |
10840 | 955 \n\ |
5730 | 956 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
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957 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP, or\n\ |
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958 @nospell{H. Zechner}, @cite{Efficient sampling from continuous and discrete\n\ |
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959 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
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960 University @nospell{Graz}, Austria, 1994.\n\ |
10840 | 961 \n\ |
5730 | 962 @item For @var{l} > 1e8, use normal approximation.\n\ |
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963 @nospell{L. Montanet}, et al., @cite{Review of Particle Properties},\n\ |
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964 Physical Review D 50 p1284, 1994.\n\ |
5730 | 965 @end table\n\ |
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966 \n\ |
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967 The class of the value returned can be controlled by a trailing\n\ |
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968 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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969 classes.\n\ |
6547 | 970 @seealso{rand, randn, rande, randg}\n\ |
5730 | 971 @end deftypefn") |
972 { | |
973 int nargin = args.length (); | |
974 | |
975 if (nargin < 1) | |
976 error ("randp: insufficient arguments"); | |
977 | |
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978 return do_rand (args, nargin, "randp", "poisson", true); |
5730 | 979 } |
980 | |
981 /* | |
982 %!test | |
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983 %! randp ("state", 12); |
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984 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
6437 | 985 %!test |
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986 %! ## Test fixed state |
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987 %! randp ("state", 1); |
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988 %! assert (randp (5, 1, 6), [5 5 3 7 7 3]) |
6437 | 989 %!test |
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990 %! ## Test fixed state |
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991 %! randp ("state", 1); |
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992 %! assert (randp (15, 1, 6), [13 15 8 18 18 15]) |
6437 | 993 %!test |
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994 %! ## Test fixed state |
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995 %! randp ("state", 1); |
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996 %! assert (randp (1e9, 1, 6), [999915677 999976657 1000047684 1000019035 999985749 999977692], -1e-6) |
6437 | 997 %!test |
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998 %! ## Test fixed state |
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999 %! randp ("seed", 1); |
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1000 %! %%assert (randp (5, 1, 6), [8 2 3 6 6 8]) |
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1001 %! assert (randp (5, 1, 5), [8 2 3 6 6]) |
6437 | 1002 %!test |
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1003 %! ## Test fixed state |
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1004 %! randp ("seed", 1); |
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1005 %! assert (randp (15, 1, 6), [15 16 12 10 10 12]) |
6437 | 1006 %!test |
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1007 %! ## Test fixed state |
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1008 %! randp ("seed", 1); |
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1009 %! assert (randp (1e9, 1, 6), [1000006208 1000012224 999981120 999963520 999963072 999981440], -1e-6) |
6437 | 1010 %!test |
1011 %! if (__random_statistical_tests__) | |
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1012 %! ## statistical tests may fail occasionally. |
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1013 %! randp ("state", 12); |
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1014 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1015 %! x = randp (a (1), 100000, 1); |
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1016 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1017 %! assert (mean (x), a(1), a(2)); |
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1018 %! assert (var (x), a(1), 0.02*a(1)); |
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1019 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1020 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1021 %! endfor |
1022 %! endif | |
1023 %!test | |
1024 %! if (__random_statistical_tests__) | |
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1025 %! ## statistical tests may fail occasionally. |
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1026 %! randp ("state", 12); |
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1027 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1028 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1029 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1030 %! assert (mean (x), a(1), a(2)); |
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1031 %! assert (var (x), a(1), 0.02*a(1)); |
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1032 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1033 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1034 %! endfor |
1035 %! endif | |
1036 %!test | |
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1037 %! randp ("seed", 12); |
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1038 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
5730 | 1039 %!test |
6449 | 1040 %! if (__random_statistical_tests__) |
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1041 %! ## statistical tests may fail occasionally. |
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1042 %! randp ("seed", 12); |
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1043 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1044 %! x = randp (a(1), 100000, 1); |
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1045 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1046 %! assert (mean (x), a(1), a(2)); |
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1047 %! assert (var (x), a(1), 0.02*a(1)); |
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1048 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1049 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1050 %! endfor |
1051 %! endif | |
5730 | 1052 %!test |
6449 | 1053 %! if (__random_statistical_tests__) |
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1054 %! ## statistical tests may fail occasionally. |
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1055 %! randp ("seed", 12); |
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1056 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1057 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1058 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1059 %! assert (mean (x), a(1), a(2)); |
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1060 %! assert (var (x), a(1), 0.02*a(1)); |
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1061 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1062 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1063 %! endfor |
1064 %! endif | |
5730 | 1065 */ |
1066 | |
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1067 DEFUN (randperm, args, , |
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1068 "-*- texinfo -*-\n\ |
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1069 @deftypefn {} {} randperm (@var{n})\n\ |
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1070 @deftypefnx {} {} randperm (@var{n}, @var{m})\n\ |
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1071 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
20172
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1072 \n\ |
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1073 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
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1074 replacement from @code{1:@var{n}}.\n\ |
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1075 \n\ |
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1076 The complexity is O(@var{n}) in memory and O(@var{m}) in time, unless\n\ |
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1077 @var{m} < @var{n}/5, in which case O(@var{m}) memory is used as well. The\n\ |
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1078 randomization is performed using rand(). All permutations are equally\n\ |
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1079 likely.\n\ |
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1080 @seealso{perms}\n\ |
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1081 @end deftypefn") |
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1082 { |
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1083 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
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1084 using std::tr1::unordered_map; |
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1085 #else |
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1086 using std::unordered_map; |
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1087 #endif |
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1088 |
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1089 int nargin = args.length (); |
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1090 |
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1091 if (nargin < 1 || nargin > 2) |
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1092 print_usage (); |
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1093 |
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1094 octave_idx_type n = args(0).idx_type_value (true); |
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1095 octave_idx_type m = (nargin == 2) ? args(1).idx_type_value (true) : n; |
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1096 |
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1097 if (m < 0 || n < 0) |
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1098 error ("randperm: M and N must be non-negative"); |
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1099 |
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1100 if (m > n) |
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1101 error ("randperm: M must be less than or equal to N"); |
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1102 |
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1103 // Quick and dirty heuristic to decide if we allocate or not the |
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1104 // whole vector for tracking the truncated shuffle. |
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1105 bool short_shuffle = m < n/5; |
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1106 |
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1107 // Generate random numbers. |
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1108 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
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1109 double *rvec = r.fortran_vec (); |
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1110 |
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1111 octave_idx_type idx_len = short_shuffle ? m : n; |
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1112 Array<octave_idx_type> idx; |
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1113 try |
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1114 { |
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1115 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1116 } |
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1117 catch (const std::bad_alloc&) |
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1118 { |
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1119 // Looks like n is too big and short_shuffle is false. |
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1120 // Let's try again, but this time with the alternative. |
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1121 idx_len = m; |
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1122 short_shuffle = true; |
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1123 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1124 } |
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1125 |
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1126 octave_idx_type *ivec = idx.fortran_vec (); |
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1127 |
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1128 for (octave_idx_type i = 0; i < idx_len; i++) |
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1129 ivec[i] = i; |
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1130 |
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1131 if (short_shuffle) |
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1132 { |
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1133 unordered_map<octave_idx_type, octave_idx_type> map (m); |
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1134 |
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1135 // Perform the Knuth shuffle only keeping track of moved |
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1136 // entries in the map |
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1137 for (octave_idx_type i = 0; i < m; i++) |
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1138 { |
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1139 octave_idx_type k = i + |
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1140 gnulib::floor (rvec[i] * (n - i)); |
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1141 |
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1142 // For shuffling first m entries, no need to use extra |
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1143 // storage |
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1144 if (k < m) |
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1145 { |
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1146 std::swap (ivec[i], ivec[k]); |
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1147 } |
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1148 else |
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1149 { |
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1150 if (map.find (k) == map.end ()) |
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1151 map[k] = k; |
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1152 |
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1153 std::swap (ivec[i], map[k]); |
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1154 } |
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1155 } |
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1156 } |
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1157 else |
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1158 { |
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1159 // Perform the Knuth shuffle of the first m entries |
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1160 for (octave_idx_type i = 0; i < m; i++) |
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1161 { |
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1162 octave_idx_type k = i + |
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1163 gnulib::floor (rvec[i] * (n - i)); |
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1164 std::swap (ivec[i], ivec[k]); |
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1165 } |
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1166 } |
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1167 |
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1168 // Convert to doubles, reusing r. |
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1169 for (octave_idx_type i = 0; i < m; i++) |
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1170 rvec[i] = ivec[i] + 1; |
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1171 |
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1172 if (m < n) |
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1173 idx.resize (dim_vector (1, m)); |
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1174 |
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1175 // Now create an array object with a cached idx_vector. |
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1176 return octave_value (new octave_matrix (r, idx_vector (idx))); |
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1177 } |
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1178 |
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1179 /* |
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1180 %!assert (sort (randperm (20)), 1:20) |
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1181 %!assert (length (randperm (20,10)), 10) |
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1182 |
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1183 ## Test biggish N (bug #39378) |
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1184 %!assert (length (randperm (30000^2, 100000)), 100000) |
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1185 |
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1186 %!test |
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1187 %! rand ("seed", 0); |
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1188 %! for i = 1:100 |
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1189 %! p = randperm (305, 30); |
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1190 %! assert (length (unique (p)), 30); |
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1191 %! endfor |
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1192 */ |