Mercurial > octave
annotate libinterp/corefcn/rand.cc @ 18794:03c2671493f9
maint: Periodic merge of gui-release to default.
author | Rik <rik@octave.org> |
---|---|
date | Mon, 02 Jun 2014 11:17:59 -0700 |
parents | a142f35f3cb6 |
children | 0850b5212619 |
rev | line source |
---|---|
14655
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Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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1 |
2928 | 2 /* |
3 | |
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4 Copyright (C) 1996-2013 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" | |
45 #include "oct-obj.h" | |
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 { |
4307 | 60 octave_value retval; |
5730 | 61 NDArray a; |
62 int idx = 0; | |
63 dim_vector dims; | |
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43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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64 bool is_single = false; |
2928 | 65 |
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66 unwind_protect frame; |
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67 // Restore current distribution on any exit. |
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68 frame.add_fcn (octave_rand::distribution, |
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69 octave_rand::distribution ()); |
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70 |
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71 octave_rand::distribution (distribution); |
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72 |
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73 if (nargin > 0 && args(nargin-1).is_string ()) |
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74 { |
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75 std::string s_arg = args(nargin-1).string_value (); |
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76 |
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77 if (s_arg == "single") |
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78 { |
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79 is_single = true; |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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80 nargin--; |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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81 } |
43db83eff9db
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82 else if (s_arg == "double") |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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83 nargin--; |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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84 } |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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parents:
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85 |
5730 | 86 if (additional_arg) |
87 { | |
88 if (nargin == 0) | |
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89 { |
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90 error ("%s: expecting at least one argument", fcn); |
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91 goto done; |
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92 } |
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93 else if (args(0).is_string ()) |
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94 additional_arg = false; |
5730 | 95 else |
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96 { |
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97 a = args(0).array_value (); |
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98 if (error_state) |
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99 { |
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100 error ("%s: expecting scalar or matrix arguments", fcn); |
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101 goto done; |
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102 } |
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103 idx++; |
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104 nargin--; |
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105 } |
5730 | 106 } |
2928 | 107 |
4543 | 108 switch (nargin) |
2928 | 109 { |
4543 | 110 case 0: |
111 { | |
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112 if (additional_arg) |
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113 dims = a.dims (); |
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114 else |
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115 { |
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116 dims.resize (2); |
4543 | 117 |
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118 dims(0) = 1; |
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119 dims(1) = 1; |
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120 } |
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121 goto gen_matrix; |
4543 | 122 } |
123 break; | |
2928 | 124 |
4543 | 125 case 1: |
126 { | |
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127 octave_value tmp = args(idx); |
4543 | 128 |
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129 if (tmp.is_string ()) |
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130 { |
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131 std::string s_arg = tmp.string_value (); |
2928 | 132 |
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133 if (s_arg == "dist") |
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134 { |
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135 retval = octave_rand::distribution (); |
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136 } |
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137 else if (s_arg == "seed") |
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138 { |
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139 retval = octave_rand::seed (); |
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140 } |
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141 else if (s_arg == "state" || s_arg == "twister") |
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142 { |
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143 retval = octave_rand::state (fcn); |
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144 } |
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145 else if (s_arg == "uniform") |
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146 { |
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147 octave_rand::uniform_distribution (); |
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148 } |
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149 else if (s_arg == "normal") |
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150 { |
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151 octave_rand::normal_distribution (); |
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152 } |
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153 else if (s_arg == "exponential") |
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154 { |
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155 octave_rand::exponential_distribution (); |
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156 } |
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157 else if (s_arg == "poisson") |
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158 { |
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159 octave_rand::poisson_distribution (); |
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160 } |
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161 else if (s_arg == "gamma") |
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162 { |
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163 octave_rand::gamma_distribution (); |
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164 } |
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165 else |
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166 error ("%s: unrecognized string argument", fcn); |
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167 } |
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168 else if (tmp.is_scalar_type ()) |
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169 { |
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170 double dval = tmp.double_value (); |
2928 | 171 |
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172 if (xisnan (dval)) |
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173 { |
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174 error ("%s: NaN is invalid matrix dimension", fcn); |
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|
175 } |
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|
176 else |
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|
177 { |
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|
178 dims.resize (2); |
4543 | 179 |
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|
180 dims(0) = NINTbig (tmp.double_value ()); |
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181 dims(1) = NINTbig (tmp.double_value ()); |
2928 | 182 |
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183 if (! error_state) |
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184 goto gen_matrix; |
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|
185 } |
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|
186 } |
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187 else if (tmp.is_range ()) |
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|
188 { |
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189 Range r = tmp.range_value (); |
4543 | 190 |
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191 if (r.all_elements_are_ints ()) |
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192 { |
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193 octave_idx_type n = r.nelem (); |
4543 | 194 |
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195 dims.resize (n); |
4543 | 196 |
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197 octave_idx_type base = NINTbig (r.base ()); |
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198 octave_idx_type incr = NINTbig (r.inc ()); |
2928 | 199 |
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200 for (octave_idx_type i = 0; i < n; i++) |
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201 { |
12905
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Let rand accept negative dimensions (bug #33301)
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202 //Negative dimensions are treated as zero for Matlab |
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203 //compatibility |
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Let rand accept negative dimensions (bug #33301)
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204 dims(i) = base >= 0 ? base : 0; |
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Let rand accept negative dimensions (bug #33301)
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205 base += incr; |
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206 } |
2928 | 207 |
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Let rand accept negative dimensions (bug #33301)
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208 goto gen_matrix; |
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209 |
10154
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210 } |
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211 else |
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Improve docstrings for functions in DLD-FUNCTIONS directory.
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212 error ("%s: all elements of range must be integers", |
10154
40dfc0c99116
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|
213 fcn); |
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214 } |
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215 else if (tmp.is_matrix_type ()) |
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216 { |
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217 Array<int> iv = tmp.int_vector_value (true); |
4543 | 218 |
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219 if (! error_state) |
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220 { |
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221 octave_idx_type len = iv.length (); |
2928 | 222 |
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223 dims.resize (len); |
4543 | 224 |
10154
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225 for (octave_idx_type i = 0; i < len; i++) |
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226 { |
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227 //Negative dimensions are treated as zero for Matlab |
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228 //compatibility |
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229 octave_idx_type elt = iv(i); |
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230 dims(i) = elt >=0 ? elt : 0; |
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231 } |
2928 | 232 |
10154
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233 goto gen_matrix; |
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|
234 } |
40dfc0c99116
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235 else |
40dfc0c99116
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236 error ("%s: expecting integer vector", fcn); |
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|
237 } |
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|
238 else |
40dfc0c99116
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|
239 { |
40dfc0c99116
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|
240 gripe_wrong_type_arg ("rand", tmp); |
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241 return retval; |
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242 } |
4543 | 243 } |
244 break; | |
245 | |
246 default: | |
247 { | |
10154
40dfc0c99116
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|
248 octave_value tmp = args(idx); |
4543 | 249 |
10154
40dfc0c99116
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|
250 if (nargin == 2 && tmp.is_string ()) |
40dfc0c99116
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|
251 { |
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|
252 std::string ts = tmp.string_value (); |
5164 | 253 |
10154
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|
254 if (ts == "seed") |
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|
255 { |
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|
256 if (args(idx+1).is_real_scalar ()) |
40dfc0c99116
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|
257 { |
40dfc0c99116
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changeset
|
258 double d = args(idx+1).double_value (); |
2928 | 259 |
10154
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|
260 if (! error_state) |
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|
261 octave_rand::seed (d); |
40dfc0c99116
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|
262 } |
11586
12df7854fa7c
strip trailing whitespace from source files
John W. Eaton <jwe@octave.org>
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11553
diff
changeset
|
263 else if (args(idx+1).is_string () |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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14706
diff
changeset
|
264 && 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>
parents:
10687
diff
changeset
|
265 octave_rand::reset (); |
10154
40dfc0c99116
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|
266 else |
40dfc0c99116
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changeset
|
267 error ("%s: seed must be a real scalar", fcn); |
40dfc0c99116
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changeset
|
268 } |
40dfc0c99116
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changeset
|
269 else if (ts == "state" || ts == "twister") |
40dfc0c99116
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|
270 { |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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10687
diff
changeset
|
271 if (args(idx+1).is_string () |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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|
272 && 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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10687
diff
changeset
|
273 octave_rand::reset (fcn); |
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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diff
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|
274 else |
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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10687
diff
changeset
|
275 { |
11586
12df7854fa7c
strip trailing whitespace from source files
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11553
diff
changeset
|
276 ColumnVector s = |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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10687
diff
changeset
|
277 ColumnVector (args(idx+1).vector_value(false, true)); |
5730 | 278 |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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changeset
|
279 if (! error_state) |
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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diff
changeset
|
280 octave_rand::state (s, fcn); |
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
281 } |
10154
40dfc0c99116
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diff
changeset
|
282 } |
40dfc0c99116
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diff
changeset
|
283 else |
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
284 error ("%s: unrecognized string argument", fcn); |
40dfc0c99116
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diff
changeset
|
285 } |
40dfc0c99116
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diff
changeset
|
286 else |
40dfc0c99116
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diff
changeset
|
287 { |
40dfc0c99116
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diff
changeset
|
288 dims.resize (nargin); |
4543 | 289 |
10154
40dfc0c99116
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changeset
|
290 for (int i = 0; i < nargin; i++) |
40dfc0c99116
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|
291 { |
12905
f7a8d1dafda3
Let rand accept negative dimensions (bug #33301)
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12639
diff
changeset
|
292 octave_idx_type elt = args(idx+i).int_value (); |
10154
40dfc0c99116
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|
293 if (error_state) |
40dfc0c99116
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changeset
|
294 { |
40dfc0c99116
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changeset
|
295 error ("%s: expecting integer arguments", fcn); |
40dfc0c99116
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changeset
|
296 goto done; |
40dfc0c99116
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diff
changeset
|
297 } |
12905
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Let rand accept negative dimensions (bug #33301)
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12639
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changeset
|
298 //Negative is zero for Matlab compatibility |
f7a8d1dafda3
Let rand accept negative dimensions (bug #33301)
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12639
diff
changeset
|
299 dims(i) = elt >= 0 ? elt : 0; |
10154
40dfc0c99116
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|
300 } |
4543 | 301 |
10154
40dfc0c99116
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changeset
|
302 goto gen_matrix; |
40dfc0c99116
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changeset
|
303 } |
4543 | 304 } |
305 break; | |
2928 | 306 } |
307 | |
17787
175b392e91fe
Use GNU style coding conventions for code in libinterp/
Rik <rik@octave.org>
parents:
17744
diff
changeset
|
308 done: |
2928 | 309 |
310 return retval; | |
311 | |
17787
175b392e91fe
Use GNU style coding conventions for code in libinterp/
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17744
diff
changeset
|
312 gen_matrix: |
2928 | 313 |
5355 | 314 dims.chop_trailing_singletons (); |
315 | |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
316 if (is_single) |
5730 | 317 { |
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
|
318 if (additional_arg) |
10154
40dfc0c99116
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diff
changeset
|
319 { |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
Rik <octave@nomad.inbox5.com>
parents:
14706
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320 if (a.length () == 1) |
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321 return octave_rand::float_nd_array (dims, a(0)); |
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322 else |
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323 { |
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324 if (a.dims () != dims) |
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325 { |
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326 error ("%s: mismatch in argument size", fcn); |
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327 return retval; |
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328 } |
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329 octave_idx_type len = a.length (); |
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330 FloatNDArray m (dims); |
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331 float *v = m.fortran_vec (); |
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332 for (octave_idx_type i = 0; i < len; i++) |
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333 v[i] = octave_rand::float_scalar (a(i)); |
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334 return m; |
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335 } |
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336 } |
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337 else |
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338 return octave_rand::float_nd_array (dims); |
5730 | 339 } |
340 else | |
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341 { |
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342 if (additional_arg) |
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343 { |
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344 if (a.length () == 1) |
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345 return octave_rand::nd_array (dims, a(0)); |
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346 else |
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347 { |
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348 if (a.dims () != dims) |
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349 { |
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350 error ("%s: mismatch in argument size", fcn); |
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351 return retval; |
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352 } |
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353 octave_idx_type len = a.length (); |
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354 NDArray m (dims); |
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355 double *v = m.fortran_vec (); |
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356 for (octave_idx_type i = 0; i < len; i++) |
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357 v[i] = octave_rand::scalar (a(i)); |
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358 return m; |
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359 } |
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360 } |
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361 else |
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362 return octave_rand::nd_array (dims); |
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363 } |
2928 | 364 } |
365 | |
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366 DEFUN (rand, args, , |
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367 "-*- texinfo -*-\n\ |
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368 @deftypefn {Built-in Function} {} rand (@var{n})\n\ |
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369 @deftypefnx {Built-in Function} {} rand (@var{m}, @var{n}, @dots{})\n\ |
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370 @deftypefnx {Built-in Function} {} rand ([@var{m} @var{n} @dots{}])\n\ |
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371 @deftypefnx {Built-in Function} {@var{v} =} rand (\"state\")\n\ |
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372 @deftypefnx {Built-in Function} {} rand (\"state\", @var{v})\n\ |
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373 @deftypefnx {Built-in Function} {} rand (\"state\", \"reset\")\n\ |
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374 @deftypefnx {Built-in Function} {@var{v} =} rand (\"seed\")\n\ |
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375 @deftypefnx {Built-in Function} {} rand (\"seed\", @var{v})\n\ |
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376 @deftypefnx {Built-in Function} {} rand (\"seed\", \"reset\")\n\ |
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377 @deftypefnx {Built-in Function} {} rand (@dots{}, \"single\")\n\ |
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378 @deftypefnx {Built-in Function} {} rand (@dots{}, \"double\")\n\ |
3369 | 379 Return a matrix with random elements uniformly distributed on the\n\ |
380 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
5730 | 381 for @code{eye}.\n\ |
382 \n\ | |
383 You can query the state of the random number generator using the\n\ | |
3369 | 384 form\n\ |
2928 | 385 \n\ |
3369 | 386 @example\n\ |
5730 | 387 v = rand (\"state\")\n\ |
388 @end example\n\ | |
389 \n\ | |
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390 This returns a column vector @var{v} of length 625. Later, you can\n\ |
5730 | 391 restore the random number generator to the state @var{v}\n\ |
392 using the form\n\ | |
393 \n\ | |
394 @example\n\ | |
395 rand (\"state\", v)\n\ | |
3369 | 396 @end example\n\ |
397 \n\ | |
398 @noindent\n\ | |
5730 | 399 You may also initialize the state vector from an arbitrary vector of\n\ |
10840 | 400 length @leq{} 625 for @var{v}. This new state will be a hash based on the\n\ |
5798 | 401 value of @var{v}, not @var{v} itself.\n\ |
5730 | 402 \n\ |
403 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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404 available, otherwise from CPU time, wall clock time, and the current\n\ |
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405 fraction of a second. Note that this differs from @sc{matlab}, which\n\ |
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406 always initializes the state to the same state at startup. To obtain\n\ |
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407 behavior comparable to @sc{matlab}, initialize with a deterministic state\n\ |
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408 vector in Octave's startup files (@pxref{Startup Files}).\n\ |
5730 | 409 \n\ |
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410 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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411 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and\n\ |
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412 T. Nishimura,\n\ |
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413 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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414 pseudorandom number generator}, ACM Trans. on\n\ |
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415 Modeling and Computer Simulation Vol. 8, No. 1, pp. 3-30, January 1998,\n\ |
7171 | 416 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
6547 | 417 Do @strong{not} use for cryptography without securely hashing\n\ |
418 several returned values together, otherwise the generator state\n\ | |
419 can be learned after reading 624 consecutive values.\n\ | |
5730 | 420 \n\ |
7096 | 421 Older versions of Octave used a different random number generator.\n\ |
422 The new generator is used by default\n\ | |
5730 | 423 as it is significantly faster than the old generator, and produces\n\ |
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424 random numbers with a significantly longer cycle time. However, in\n\ |
5798 | 425 some circumstances it might be desirable to obtain the same random\n\ |
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426 sequences as used by the old generators. To do this the keyword\n\ |
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427 @qcode{\"seed\"} is used to specify that the old generators should be use,\n\ |
5730 | 428 as in\n\ |
2928 | 429 \n\ |
3369 | 430 @example\n\ |
5730 | 431 rand (\"seed\", val)\n\ |
3369 | 432 @end example\n\ |
433 \n\ | |
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434 @noindent\n\ |
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435 which sets the seed of the generator to @var{val}. The seed of the\n\ |
5730 | 436 generator can be queried with\n\ |
437 \n\ | |
438 @example\n\ | |
439 s = rand (\"seed\")\n\ | |
440 @end example\n\ | |
441 \n\ | |
442 However, it should be noted that querying the seed will not cause\n\ | |
443 @code{rand} to use the old generators, only setting the seed will.\n\ | |
444 To cause @code{rand} to once again use the new generators, the\n\ | |
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445 keyword @qcode{\"state\"} should be used to reset the state of the\n\ |
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446 @code{rand}.\n\ |
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447 \n\ |
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448 The state or seed of the generator can be reset to a new random value\n\ |
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449 using the @qcode{\"reset\"} keyword.\n\ |
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450 \n\ |
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451 The class of the value returned can be controlled by a trailing\n\ |
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452 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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453 classes.\n\ |
5798 | 454 @seealso{randn, rande, randg, randp}\n\ |
3369 | 455 @end deftypefn") |
2928 | 456 { |
4307 | 457 octave_value retval; |
2928 | 458 |
459 int nargin = args.length (); | |
460 | |
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461 retval = do_rand (args, nargin, "rand", "uniform"); |
2928 | 462 |
463 return retval; | |
464 } | |
465 | |
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466 // FIXME: The old generator (selected when "seed" is set) will not |
8871 | 467 // work properly if compiled to use 64-bit integers. |
468 | |
5730 | 469 /* |
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470 %!test # "state" can be a scalar |
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471 %! rand ("state", 12); x = rand (1,4); |
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472 %! rand ("state", 12); y = rand (1,4); |
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473 %! assert (x, y); |
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474 %!test # "state" can be a vector |
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475 %! rand ("state", [12,13]); x = rand (1,4); |
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476 %! rand ("state", [12;13]); y = rand (1,4); |
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477 %! assert (x, y); |
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478 %!test # querying "state" doesn't disturb sequence |
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479 %! rand ("state", 12); rand (1,2); x = rand (1,2); |
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480 %! rand ("state", 12); rand (1,2); |
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481 %! s = rand ("state"); y = rand (1,2); |
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482 %! assert (x, y); |
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483 %! rand ("state", s); z = rand (1,2); |
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484 %! assert (x, z); |
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485 %!test # "seed" must be a scalar |
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486 %! rand ("seed", 12); x = rand (1,4); |
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487 %! rand ("seed", 12); y = rand (1,4); |
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488 %! assert (x, y); |
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489 %!error <seed must be a real scalar> rand ("seed", [12,13]) |
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490 %!test # querying "seed" returns a value which can be used later |
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491 %! s = rand ("seed"); x = rand (1,2); |
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492 %! rand ("seed", s); y = rand (1,2); |
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493 %! assert (x, y); |
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494 %!test # querying "seed" doesn't disturb sequence |
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495 %! rand ("seed", 12); rand (1,2); x = rand (1,2); |
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496 %! rand ("seed", 12); rand (1,2); |
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497 %! s = rand ("seed"); y = rand (1,2); |
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498 %! assert (x, y); |
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499 %! rand ("seed", s); z = rand (1,2); |
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500 %! assert (x, z); |
5730 | 501 */ |
502 | |
503 /* | |
504 %!test | |
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505 %! ## Test fixed state |
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506 %! rand ("state", 1); |
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507 %! assert (rand (1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382], 1e-6); |
6437 | 508 %!test |
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509 %! ## Test fixed seed |
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510 %! rand ("seed", 1); |
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511 %! assert (rand (1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759], 1e-6); |
5730 | 512 %!test |
6437 | 513 %! if (__random_statistical_tests__) |
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514 %! ## statistical tests may fail occasionally. |
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515 %! rand ("state", 12); |
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516 %! x = rand (100000, 1); |
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517 %! assert (max (x) < 1); #*** Please report this!!! *** |
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518 %! assert (min (x) > 0); #*** Please report this!!! *** |
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519 %! assert (mean (x), 0.5, 0.0024); |
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520 %! assert (var (x), 1/48, 0.0632); |
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521 %! assert (skewness (x), 0, 0.012); |
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522 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 523 %! endif |
524 %!test | |
525 %! if (__random_statistical_tests__) | |
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526 %! ## statistical tests may fail occasionally. |
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527 %! rand ("seed", 12); |
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528 %! x = rand (100000, 1); |
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529 %! assert (max (x) < 1); #*** Please report this!!! *** |
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530 %! assert (min (x) > 0); #*** Please report this!!! *** |
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531 %! assert (mean (x), 0.5, 0.0024); |
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532 %! assert (var (x), 1/48, 0.0632); |
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533 %! assert (skewness (x), 0, 0.012); |
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534 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 535 %! endif |
5730 | 536 */ |
537 | |
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538 /* |
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539 %!# Test out-of-range values as rand() seeds. See oct-rand.cc: double2uint32(). |
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540 %!function v = __rand_sample__ (initval) |
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541 %! rand ("state", initval); |
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542 %! v = rand (1, 6); |
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543 %!endfunction |
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544 %! |
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545 %!assert (__rand_sample__ (0), __rand_sample__ (2^32)) |
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546 %!assert (__rand_sample__ (-2), __rand_sample__ (2^32-2)) |
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547 %!assert (__rand_sample__ (Inf), __rand_sample__ (NaN)) |
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548 %!assert (! isequal (__rand_sample__ (-1), __rand_sample__ (-2))) |
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549 */ |
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550 |
4307 | 551 static std::string current_distribution = octave_rand::distribution (); |
552 | |
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553 DEFUN (randn, args, , |
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554 "-*- texinfo -*-\n\ |
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555 @deftypefn {Built-in Function} {} randn (@var{n})\n\ |
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556 @deftypefnx {Built-in Function} {} randn (@var{m}, @var{n}, @dots{})\n\ |
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557 @deftypefnx {Built-in Function} {} randn ([@var{m} @var{n} @dots{}])\n\ |
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558 @deftypefnx {Built-in Function} {@var{v} =} randn (\"state\")\n\ |
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559 @deftypefnx {Built-in Function} {} randn (\"state\", @var{v})\n\ |
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560 @deftypefnx {Built-in Function} {} randn (\"state\", \"reset\")\n\ |
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561 @deftypefnx {Built-in Function} {@var{v} =} randn (\"seed\")\n\ |
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562 @deftypefnx {Built-in Function} {} randn (\"seed\", @var{v})\n\ |
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563 @deftypefnx {Built-in Function} {} randn (\"seed\", \"reset\")\n\ |
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564 @deftypefnx {Built-in Function} {} randn (@dots{}, \"single\")\n\ |
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565 @deftypefnx {Built-in Function} {} randn (@dots{}, \"double\")\n\ |
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566 Return a matrix with normally distributed random\n\ |
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567 elements having zero mean and variance one. The arguments are\n\ |
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568 handled the same as the arguments for @code{rand}.\n\ |
3369 | 569 \n\ |
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570 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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571 to transform from a uniform to a normal distribution.\n\ |
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572 \n\ |
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573 The class of the value returned can be controlled by a trailing\n\ |
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574 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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575 classes.\n\ |
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576 \n\ |
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577 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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578 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 579 J. Statistical Software, vol 5, 2000,\n\ |
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580 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
2928 | 581 \n\ |
6547 | 582 @seealso{rand, rande, randg, randp}\n\ |
3369 | 583 @end deftypefn") |
2928 | 584 { |
4307 | 585 octave_value retval; |
2928 | 586 |
587 int nargin = args.length (); | |
588 | |
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589 retval = do_rand (args, nargin, "randn", "normal"); |
2928 | 590 |
591 return retval; | |
592 } | |
593 | |
594 /* | |
5730 | 595 %!test |
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596 %! ## Test fixed state |
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597 %! randn ("state", 1); |
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598 %! assert (randn (1, 6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116], 1e-6); |
6437 | 599 %!test |
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600 %! ## Test fixed seed |
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601 %! randn ("seed", 1); |
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602 %! assert (randn (1, 6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133], 1e-6); |
5730 | 603 %!test |
6437 | 604 %! if (__random_statistical_tests__) |
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605 %! ## statistical tests may fail occasionally. |
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606 %! randn ("state", 12); |
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607 %! x = randn (100000, 1); |
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608 %! assert (mean (x), 0, 0.01); |
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609 %! assert (var (x), 1, 0.02); |
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610 %! assert (skewness (x), 0, 0.02); |
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611 %! assert (kurtosis (x), 0, 0.04); |
6437 | 612 %! endif |
613 %!test | |
614 %! if (__random_statistical_tests__) | |
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615 %! ## statistical tests may fail occasionally. |
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616 %! randn ("seed", 12); |
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617 %! x = randn (100000, 1); |
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618 %! assert (mean (x), 0, 0.01); |
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619 %! assert (var (x), 1, 0.02); |
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620 %! assert (skewness (x), 0, 0.02); |
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621 %! assert (kurtosis (x), 0, 0.04); |
6437 | 622 %! endif |
5730 | 623 */ |
624 | |
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625 DEFUN (rande, args, , |
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626 "-*- texinfo -*-\n\ |
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627 @deftypefn {Built-in Function} {} rande (@var{n})\n\ |
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628 @deftypefnx {Built-in Function} {} rande (@var{m}, @var{n}, @dots{})\n\ |
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629 @deftypefnx {Built-in Function} {} rande ([@var{m} @var{n} @dots{}])\n\ |
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630 @deftypefnx {Built-in Function} {@var{v} =} rande (\"state\")\n\ |
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631 @deftypefnx {Built-in Function} {} rande (\"state\", @var{v})\n\ |
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632 @deftypefnx {Built-in Function} {} rande (\"state\", \"reset\")\n\ |
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633 @deftypefnx {Built-in Function} {@var{v} =} rande (\"seed\")\n\ |
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634 @deftypefnx {Built-in Function} {} rande (\"seed\", @var{v})\n\ |
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635 @deftypefnx {Built-in Function} {} rande (\"seed\", \"reset\")\n\ |
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636 @deftypefnx {Built-in Function} {} rande (@dots{}, \"single\")\n\ |
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637 @deftypefnx {Built-in Function} {} rande (@dots{}, \"double\")\n\ |
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638 Return a matrix with exponentially distributed random elements. The\n\ |
5730 | 639 arguments are handled the same as the arguments for @code{rand}.\n\ |
640 \n\ | |
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641 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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642 to transform from a uniform to an exponential distribution.\n\ |
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643 \n\ |
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644 The class of the value returned can be controlled by a trailing\n\ |
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645 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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646 classes.\n\ |
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647 \n\ |
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648 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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649 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 650 J. Statistical Software, vol 5, 2000,\n\ |
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651 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
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652 \n\ |
6547 | 653 @seealso{rand, randn, randg, randp}\n\ |
5730 | 654 @end deftypefn") |
655 { | |
656 octave_value retval; | |
657 | |
658 int nargin = args.length (); | |
659 | |
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660 retval = do_rand (args, nargin, "rande", "exponential"); |
5730 | 661 |
662 return retval; | |
663 } | |
664 | |
665 /* | |
666 %!test | |
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667 %! ## Test fixed state |
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668 %! rande ("state", 1); |
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669 %! assert (rande (1, 6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291], 1e-6); |
6437 | 670 %!test |
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671 %! ## Test fixed seed |
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672 %! rande ("seed", 1); |
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673 %! assert (rande (1, 6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087], 1e-6); |
5730 | 674 %!test |
6437 | 675 %! if (__random_statistical_tests__) |
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676 %! ## statistical tests may fail occasionally |
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677 %! rande ("state", 1); |
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678 %! x = rande (100000, 1); |
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679 %! assert (min (x) > 0); # *** Please report this!!! *** |
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680 %! assert (mean (x), 1, 0.01); |
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681 %! assert (var (x), 1, 0.03); |
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682 %! assert (skewness (x), 2, 0.06); |
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683 %! assert (kurtosis (x), 6, 0.7); |
6437 | 684 %! endif |
685 %!test | |
686 %! if (__random_statistical_tests__) | |
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687 %! ## statistical tests may fail occasionally |
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688 %! rande ("seed", 1); |
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689 %! x = rande (100000, 1); |
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690 %! assert (min (x)>0); # *** Please report this!!! *** |
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691 %! assert (mean (x), 1, 0.01); |
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692 %! assert (var (x), 1, 0.03); |
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693 %! assert (skewness (x), 2, 0.06); |
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694 %! assert (kurtosis (x), 6, 0.7); |
6437 | 695 %! endif |
5730 | 696 */ |
697 | |
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698 DEFUN (randg, args, , |
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699 "-*- texinfo -*-\n\ |
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700 @deftypefn {Built-in Function} {} randg (@var{n})\n\ |
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701 @deftypefnx {Built-in Function} {} randg (@var{m}, @var{n}, @dots{})\n\ |
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702 @deftypefnx {Built-in Function} {} randg ([@var{m} @var{n} @dots{}])\n\ |
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703 @deftypefnx {Built-in Function} {@var{v} =} randg (\"state\")\n\ |
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704 @deftypefnx {Built-in Function} {} randg (\"state\", @var{v})\n\ |
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705 @deftypefnx {Built-in Function} {} randg (\"state\", \"reset\")\n\ |
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706 @deftypefnx {Built-in Function} {@var{v} =} randg (\"seed\")\n\ |
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707 @deftypefnx {Built-in Function} {} randg (\"seed\", @var{v})\n\ |
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708 @deftypefnx {Built-in Function} {} randg (\"seed\", \"reset\")\n\ |
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709 @deftypefnx {Built-in Function} {} randg (@dots{}, \"single\")\n\ |
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710 @deftypefnx {Built-in Function} {} randg (@dots{}, \"double\")\n\ |
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711 Return a matrix with @code{gamma (@var{a},1)} distributed random elements.\n\ |
5730 | 712 The arguments are handled the same as the arguments for @code{rand},\n\ |
713 except for the argument @var{a}.\n\ | |
714 \n\ | |
715 This can be used to generate many distributions:\n\ | |
716 \n\ | |
717 @table @asis\n\ | |
6547 | 718 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
10840 | 719 \n\ |
5730 | 720 @example\n\ |
6547 | 721 r = b * randg (a)\n\ |
5730 | 722 @end example\n\ |
10840 | 723 \n\ |
6547 | 724 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
10840 | 725 \n\ |
5730 | 726 @example\n\ |
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727 @group\n\ |
6547 | 728 r1 = randg (a, 1)\n\ |
729 r = r1 / (r1 + randg (b, 1))\n\ | |
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730 @end group\n\ |
5730 | 731 @end example\n\ |
10840 | 732 \n\ |
6547 | 733 @item @code{Erlang (a, n)}\n\ |
10840 | 734 \n\ |
5730 | 735 @example\n\ |
6547 | 736 r = a * randg (n)\n\ |
5730 | 737 @end example\n\ |
10840 | 738 \n\ |
6547 | 739 @item @code{chisq (df)} for @code{df > 0}\n\ |
10840 | 740 \n\ |
5730 | 741 @example\n\ |
6547 | 742 r = 2 * randg (df / 2)\n\ |
5730 | 743 @end example\n\ |
10840 | 744 \n\ |
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745 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
10840 | 746 \n\ |
5730 | 747 @example\n\ |
6547 | 748 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
5730 | 749 @end example\n\ |
10840 | 750 \n\ |
6547 | 751 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
10840 | 752 \n\ |
5730 | 753 @example\n\ |
7096 | 754 @group\n\ |
755 ## r1 equals 1 if n1 is infinite\n\ | |
756 r1 = 2 * randg (n1 / 2) / n1\n\ | |
757 ## r2 equals 1 if n2 is infinite\n\ | |
758 r2 = 2 * randg (n2 / 2) / n2\n\ | |
5730 | 759 r = r1 / r2\n\n\ |
7096 | 760 @end group\n\ |
5730 | 761 @end example\n\ |
10840 | 762 \n\ |
5730 | 763 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
10840 | 764 \n\ |
5730 | 765 @example\n\ |
6547 | 766 r = randp ((1 - p) / p * randg (n))\n\ |
5730 | 767 @end example\n\ |
10840 | 768 \n\ |
6547 | 769 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
5730 | 770 (use chisq if @code{L = 0})\n\ |
10840 | 771 \n\ |
5730 | 772 @example\n\ |
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773 @group\n\ |
6547 | 774 r = randp (L / 2)\n\ |
775 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
776 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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777 @end group\n\ |
5730 | 778 @end example\n\ |
10840 | 779 \n\ |
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780 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
10840 | 781 \n\ |
5730 | 782 @example\n\ |
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783 @group\n\ |
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784 r = (randg (a1), @dots{}, randg (ak))\n\ |
6547 | 785 r = r / sum (r)\n\ |
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786 @end group\n\ |
5730 | 787 @end example\n\ |
10840 | 788 \n\ |
5730 | 789 @end table\n\ |
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790 \n\ |
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791 The class of the value returned can be controlled by a trailing\n\ |
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792 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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793 classes.\n\ |
6547 | 794 @seealso{rand, randn, rande, randp}\n\ |
5730 | 795 @end deftypefn") |
796 { | |
797 octave_value retval; | |
798 | |
799 int nargin = args.length (); | |
800 | |
801 if (nargin < 1) | |
802 error ("randg: insufficient arguments"); | |
803 else | |
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804 retval = do_rand (args, nargin, "randg", "gamma", true); |
5730 | 805 |
806 return retval; | |
807 } | |
808 | |
809 /* | |
810 %!test | |
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811 %! randg ("state", 12) |
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812 %! assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]); # *** Please report |
6437 | 813 |
814 %!test | |
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815 %! ## Test fixed state |
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816 %! randg ("state", 1); |
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817 %! assert (randg (0.1, 1, 6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12], 1e-6); |
6437 | 818 %!test |
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819 %! ## Test fixed state |
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820 %! randg ("state", 1); |
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821 %! assert (randg (0.95, 1, 6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939], 1e-6); |
6437 | 822 %!test |
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823 %! ## Test fixed state |
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824 %! randg ("state", 1); |
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825 %! assert (randg (1, 1, 6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533], 1e-6); |
6437 | 826 %!test |
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827 %! ## Test fixed state |
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828 %! randg ("state", 1); |
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829 %! assert (randg (10, 1, 6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059], 1e-5); |
6437 | 830 %!test |
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831 %! ## Test fixed state |
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832 %! randg ("state", 1); |
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833 %! assert (randg (100, 1, 6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118], 1e-4); |
6437 | 834 %!test |
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835 %! ## Test fixed seed |
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836 %! randg ("seed", 1); |
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837 %! assert (randg (0.1, 1, 6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12], 1e-6); |
6437 | 838 %!test |
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839 %! ## Test fixed seed |
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840 %! randg ("seed", 1); |
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841 %! assert (randg (0.95, 1, 6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648], 1e-6); |
6437 | 842 %!test |
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843 %! ## Test fixed seed |
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844 %! randg ("seed", 1); |
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845 %! assert (randg (1, 1, 6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303], 1e-6); |
6437 | 846 %!test |
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847 %! ## Test fixed seed |
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848 %! randg ("seed", 1); |
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849 %! assert (randg (10, 1, 6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186], 1e-5); |
6437 | 850 %!test |
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851 %! ## Test fixed seed |
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852 %! randg ("seed", 1); |
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853 %! assert (randg (100, 1, 6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625], 1e-4); |
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854 %!test |
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855 %! ## Test out-of-bounds values produce NaN w/old-style generators & floats |
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856 %! randg ("seed", 1); |
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857 %! result = randg ([-2 Inf], "single"); |
18574
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858 %! assert (result, single ([NaN NaN])); |
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859 |
6437 | 860 %!test |
861 %! if (__random_statistical_tests__) | |
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862 %! ## statistical tests may fail occasionally. |
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863 %! randg ("state", 12); |
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864 %! a = 0.1; |
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865 %! x = randg (a, 100000, 1); |
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866 %! assert (mean (x), a, 0.01); |
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867 %! assert (var (x), a, 0.01); |
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868 %! assert (skewness (x), 2/sqrt (a), 1); |
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869 %! assert (kurtosis (x), 6/a, 50); |
6437 | 870 %! endif |
871 %!test | |
872 %! if (__random_statistical_tests__) | |
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873 %! ## statistical tests may fail occasionally. |
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874 %! randg ("state", 12); |
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875 %! a = 0.95; |
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876 %! x = randg (a, 100000, 1); |
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877 %! assert (mean (x), a, 0.01); |
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878 %! assert (var (x), a, 0.04); |
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879 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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880 %! assert (kurtosis (x), 6/a, 2); |
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881 %! endif |
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882 %!test |
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883 %! if (__random_statistical_tests__) |
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884 %! ## statistical tests may fail occasionally. |
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885 %! randg ("state", 12); |
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886 %! a = 1; |
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887 %! x = randg (a, 100000, 1); |
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888 %! assert (mean (x), a, 0.01); |
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889 %! assert (var (x), a, 0.04); |
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890 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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891 %! assert (kurtosis (x), 6/a, 2); |
6437 | 892 %! endif |
893 %!test | |
894 %! if (__random_statistical_tests__) | |
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895 %! ## statistical tests may fail occasionally. |
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896 %! randg ("state", 12); |
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897 %! a = 10; |
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898 %! x = randg (a, 100000, 1); |
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899 %! assert (mean (x), a, 0.1); |
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900 %! assert (var (x), a, 0.5); |
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901 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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902 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 903 %! endif |
904 %!test | |
905 %! if (__random_statistical_tests__) | |
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906 %! ## statistical tests may fail occasionally. |
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907 %! randg ("state", 12); |
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908 %! a = 100; |
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909 %! x = randg (a, 100000, 1); |
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910 %! assert (mean (x), a, 0.2); |
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911 %! assert (var (x), a, 2); |
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912 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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913 %! assert (kurtosis (x), 6/a, 0.2); |
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914 %! endif |
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915 %!test |
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916 %! randg ("seed", 12); |
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917 %!assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]) # *** Please report |
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918 %!test |
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919 %! if (__random_statistical_tests__) |
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920 %! ## statistical tests may fail occasionally. |
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921 %! randg ("seed", 12); |
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922 %! a = 0.1; |
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923 %! x = randg (a, 100000, 1); |
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924 %! assert (mean (x), a, 0.01); |
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925 %! assert (var (x), a, 0.01); |
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926 %! assert (skewness (x), 2/sqrt (a), 1); |
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927 %! assert (kurtosis (x), 6/a, 50); |
6437 | 928 %! endif |
929 %!test | |
930 %! if (__random_statistical_tests__) | |
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931 %! ## statistical tests may fail occasionally. |
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932 %! randg ("seed", 12); |
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933 %! a = 0.95; |
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934 %! x = randg (a, 100000, 1); |
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935 %! assert (mean (x), a, 0.01); |
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936 %! assert (var (x), a, 0.04); |
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937 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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938 %! assert (kurtosis (x), 6/a, 2); |
6437 | 939 %! endif |
940 %!test | |
941 %! if (__random_statistical_tests__) | |
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942 %! ## statistical tests may fail occasionally. |
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943 %! randg ("seed", 12); |
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944 %! a = 1; |
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945 %! x = randg (a, 100000, 1); |
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946 %! assert (mean (x), a, 0.01); |
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947 %! assert (var (x), a, 0.04); |
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948 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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949 %! assert (kurtosis (x), 6/a, 2); |
6437 | 950 %! endif |
5730 | 951 %!test |
6437 | 952 %! if (__random_statistical_tests__) |
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953 %! ## statistical tests may fail occasionally. |
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954 %! randg ("seed", 12); |
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955 %! a = 10; |
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956 %! x = randg (a, 100000, 1); |
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957 %! assert (mean (x), a, 0.1); |
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958 %! assert (var (x), a, 0.5); |
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959 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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960 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 961 %! endif |
5730 | 962 %!test |
6437 | 963 %! if (__random_statistical_tests__) |
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964 %! ## statistical tests may fail occasionally. |
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965 %! randg ("seed", 12); |
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966 %! a = 100; |
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967 %! x = randg (a, 100000, 1); |
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968 %! assert (mean (x), a, 0.2); |
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969 %! assert (var (x), a, 2); |
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970 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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971 %! assert (kurtosis (x), 6/a, 0.2); |
6437 | 972 %! endif |
5730 | 973 */ |
974 | |
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975 DEFUN (randp, args, , |
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976 "-*- texinfo -*-\n\ |
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977 @deftypefn {Built-in Function} {} randp (@var{l}, @var{n})\n\ |
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978 @deftypefnx {Built-in Function} {} randp (@var{l}, @var{m}, @var{n}, @dots{})\n\ |
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979 @deftypefnx {Built-in Function} {} randp (@var{l}, [@var{m} @var{n} @dots{}])\n\ |
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980 @deftypefnx {Built-in Function} {@var{v} =} randp (\"state\")\n\ |
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981 @deftypefnx {Built-in Function} {} randp (\"state\", @var{v})\n\ |
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982 @deftypefnx {Built-in Function} {} randp (\"state\", \"reset\")\n\ |
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983 @deftypefnx {Built-in Function} {@var{v} =} randp (\"seed\")\n\ |
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984 @deftypefnx {Built-in Function} {} randp (\"seed\", @var{v})\n\ |
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985 @deftypefnx {Built-in Function} {} randp (\"seed\", \"reset\")\n\ |
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986 @deftypefnx {Built-in Function} {} randp (@dots{}, \"single\")\n\ |
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987 @deftypefnx {Built-in Function} {} randp (@dots{}, \"double\")\n\ |
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988 Return a matrix with Poisson distributed random elements with mean value\n\ |
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989 parameter given by the first argument, @var{l}. The arguments\n\ |
5730 | 990 are handled the same as the arguments for @code{rand}, except for the\n\ |
991 argument @var{l}.\n\ | |
992 \n\ | |
993 Five different algorithms are used depending on the range of @var{l}\n\ | |
994 and whether or not @var{l} is a scalar or a matrix.\n\ | |
995 \n\ | |
996 @table @asis\n\ | |
10840 | 997 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
998 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
999 Cambridge University Press, 1992.\n\ | |
1000 \n\ | |
5730 | 1001 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
10840 | 1002 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
1003 Cambridge University Press, 1992.\n\ | |
1004 \n\ | |
1005 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
10791
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1006 E. Stadlober, et al., WinRand source code, available via FTP.\n\ |
10840 | 1007 \n\ |
5730 | 1008 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
10791
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1009 E. Stadlober, et al., WinRand source code, available via FTP, or\n\ |
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1010 H. Zechner, @cite{Efficient sampling from continuous and discrete\n\ |
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1011 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
5730 | 1012 University Graz, Austria, 1994.\n\ |
10840 | 1013 \n\ |
5730 | 1014 @item For @var{l} > 1e8, use normal approximation.\n\ |
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1015 L. Montanet, et al., @cite{Review of Particle Properties}, Physical Review\n\ |
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1016 D 50 p1284, 1994.\n\ |
5730 | 1017 @end table\n\ |
14655
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1018 \n\ |
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1019 The class of the value returned can be controlled by a trailing\n\ |
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1020 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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1021 classes.\n\ |
6547 | 1022 @seealso{rand, randn, rande, randg}\n\ |
5730 | 1023 @end deftypefn") |
1024 { | |
1025 octave_value retval; | |
1026 | |
1027 int nargin = args.length (); | |
1028 | |
1029 if (nargin < 1) | |
1030 error ("randp: insufficient arguments"); | |
1031 else | |
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1032 retval = do_rand (args, nargin, "randp", "poisson", true); |
5730 | 1033 |
1034 return retval; | |
1035 } | |
1036 | |
1037 /* | |
1038 %!test | |
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1039 %! randp ("state", 12); |
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1040 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
6437 | 1041 %!test |
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1042 %! ## Test fixed state |
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1043 %! randp ("state", 1); |
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1044 %! assert (randp (5, 1, 6), [5 5 3 7 7 3]) |
6437 | 1045 %!test |
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1046 %! ## Test fixed state |
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1047 %! randp ("state", 1); |
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1048 %! assert (randp (15, 1, 6), [13 15 8 18 18 15]) |
6437 | 1049 %!test |
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1050 %! ## Test fixed state |
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1051 %! randp ("state", 1); |
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1052 %! assert (randp (1e9, 1, 6), [999915677 999976657 1000047684 1000019035 999985749 999977692], -1e-6) |
6437 | 1053 %!test |
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1054 %! ## Test fixed state |
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1055 %! randp ("seed", 1); |
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1056 %! %%assert (randp (5, 1, 6), [8 2 3 6 6 8]) |
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1057 %! assert (randp (5, 1, 5), [8 2 3 6 6]) |
6437 | 1058 %!test |
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1059 %! ## Test fixed state |
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1060 %! randp ("seed", 1); |
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1061 %! assert (randp (15, 1, 6), [15 16 12 10 10 12]) |
6437 | 1062 %!test |
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1063 %! ## Test fixed state |
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1064 %! randp ("seed", 1); |
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1065 %! assert (randp (1e9, 1, 6), [1000006208 1000012224 999981120 999963520 999963072 999981440], -1e-6) |
6437 | 1066 %!test |
1067 %! if (__random_statistical_tests__) | |
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1068 %! ## statistical tests may fail occasionally. |
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1069 %! randp ("state", 12); |
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1070 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1071 %! x = randp (a (1), 100000, 1); |
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1072 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1073 %! assert (mean (x), a(1), a(2)); |
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1074 %! assert (var (x), a(1), 0.02*a(1)); |
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1075 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1076 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1077 %! endfor |
1078 %! endif | |
1079 %!test | |
1080 %! if (__random_statistical_tests__) | |
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1081 %! ## statistical tests may fail occasionally. |
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1082 %! randp ("state", 12); |
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1083 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1084 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1085 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1086 %! assert (mean (x), a(1), a(2)); |
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1087 %! assert (var (x), a(1), 0.02*a(1)); |
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1088 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1089 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1090 %! endfor |
1091 %! endif | |
1092 %!test | |
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1093 %! randp ("seed", 12); |
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1094 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
5730 | 1095 %!test |
6449 | 1096 %! if (__random_statistical_tests__) |
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1097 %! ## statistical tests may fail occasionally. |
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1098 %! randp ("seed", 12); |
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1099 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1100 %! x = randp (a(1), 100000, 1); |
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1101 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1102 %! assert (mean (x), a(1), a(2)); |
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1103 %! assert (var (x), a(1), 0.02*a(1)); |
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1104 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1105 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1106 %! endfor |
1107 %! endif | |
5730 | 1108 %!test |
6449 | 1109 %! if (__random_statistical_tests__) |
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1110 %! ## statistical tests may fail occasionally. |
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1111 %! randp ("seed", 12); |
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1112 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1113 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1114 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1115 %! assert (mean (x), a(1), a(2)); |
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1116 %! assert (var (x), a(1), 0.02*a(1)); |
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1117 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1118 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1119 %! endfor |
1120 %! endif | |
5730 | 1121 */ |
1122 | |
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1123 DEFUN (randperm, args, , |
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1124 "-*- texinfo -*-\n\ |
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1125 @deftypefn {Built-in Function} {} randperm (@var{n})\n\ |
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1126 @deftypefnx {Built-in Function} {} randperm (@var{n}, @var{m})\n\ |
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1127 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1128 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
14038
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1129 replacement from @code{1:@var{n}}. The complexity is O(@var{n}) in\n\ |
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1130 memory and O(@var{m}) in time, unless @var{m} < @var{n}/5, in which case\n\ |
14038
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1131 O(@var{m}) memory is used as well. The randomization is performed using\n\ |
14853
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1132 rand(). All permutations are equally likely.\n\ |
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1133 @seealso{perms}\n\ |
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1134 @end deftypefn") |
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1135 { |
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1136 |
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1137 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
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1138 using std::tr1::unordered_map; |
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1139 #else |
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1140 using std::unordered_map; |
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1141 #endif |
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1142 |
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1143 int nargin = args.length (); |
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1144 octave_value retval; |
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1145 |
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1146 if (nargin == 1 || nargin == 2) |
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1147 { |
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1148 octave_idx_type n, m; |
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1149 |
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1150 n = args(0).idx_type_value (true); |
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1151 |
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1152 if (nargin == 2) |
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1153 m = args(1).idx_type_value (true); |
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1154 else |
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1155 m = n; |
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1156 |
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1157 if (m < 0 || n < 0) |
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1158 error ("randperm: M and N must be non-negative"); |
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1159 |
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1160 if (m > n) |
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1161 error ("randperm: M must be less than or equal to N"); |
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1162 |
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1163 // Quick and dirty heuristic to decide if we allocate or not the |
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1164 // whole vector for tracking the truncated shuffle. |
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1165 bool short_shuffle = m < n/5; |
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1166 |
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1167 if (! error_state) |
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1168 { |
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1169 // Generate random numbers. |
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1170 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
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1171 double *rvec = r.fortran_vec (); |
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1172 |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1173 octave_idx_type idx_len = short_shuffle ? m : n; |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1174 Array<octave_idx_type> idx; |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1175 try |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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1176 { |
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|
1177 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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|
1178 } |
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175b392e91fe
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|
1179 catch (std::bad_alloc) |
17662
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|
1180 { |
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|
1181 // Looks like n is too big and short_shuffle is false. |
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1182 // Let's try again, but this time with the alternative. |
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|
1183 idx_len = m; |
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|
1184 short_shuffle = true; |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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1185 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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|
1186 } |
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|
1187 |
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1188 octave_idx_type *ivec = idx.fortran_vec (); |
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1189 |
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1190 for (octave_idx_type i = 0; i < idx_len; i++) |
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1191 ivec[i] = i; |
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1192 |
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1193 if (short_shuffle) |
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1194 { |
13727
478efc95cb7a
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Michael Goffioul <michael.goffioul@gmail.com>
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13255
diff
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|
1195 unordered_map<octave_idx_type, octave_idx_type> map (m); |
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1196 |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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1197 // Perform the Knuth shuffle only keeping track of moved |
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1198 // entries in the map |
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1199 for (octave_idx_type i = 0; i < m; i++) |
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1200 { |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1201 octave_idx_type k = i + |
17787
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Rik <rik@octave.org>
parents:
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|
1202 gnulib::floor (rvec[i] * (n - i)); |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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1203 |
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1204 //For shuffling first m entries, no need to use extra |
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1205 //storage |
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1206 if (k < m) |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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1207 { |
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|
1208 std::swap (ivec[i], ivec[k]); |
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|
1209 } |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1210 else |
14688
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1211 { |
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|
1212 if (map.find (k) == map.end ()) |
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|
1213 map[k] = k; |
13255
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1214 |
14688
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1215 std::swap (ivec[i], map[k]); |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1216 } |
13255
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1217 } |
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|
1218 } |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1219 else |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1220 { |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1221 |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1222 // Perform the Knuth shuffle of the first m entries |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1223 for (octave_idx_type i = 0; i < m; i++) |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1224 { |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1225 octave_idx_type k = i + |
17787
175b392e91fe
Use GNU style coding conventions for code in libinterp/
Rik <rik@octave.org>
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diff
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|
1226 gnulib::floor (rvec[i] * (n - i)); |
13255
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1227 std::swap (ivec[i], ivec[k]); |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1228 } |
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|
1229 } |
54f45f883a53
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|
1230 |
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|
1231 // Convert to doubles, reusing r. |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1232 for (octave_idx_type i = 0; i < m; i++) |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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diff
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|
1233 rvec[i] = ivec[i] + 1; |
9647
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|
1234 |
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|
1235 if (m < n) |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1236 idx.resize (dim_vector (1, m)); |
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|
1237 |
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diff
changeset
|
1238 // Now create an array object with a cached idx_vector. |
11586
12df7854fa7c
strip trailing whitespace from source files
John W. Eaton <jwe@octave.org>
parents:
11553
diff
changeset
|
1239 retval = new octave_matrix (r, idx_vector (idx)); |
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|
1240 } |
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|
1241 } |
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|
1242 else |
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|
1243 print_usage (); |
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|
1244 |
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|
1245 return retval; |
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|
1246 } |
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|
1247 |
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|
1248 /* |
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|
1249 %!assert (sort (randperm (20)), 1:20) |
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|
1250 %!assert (length (randperm (20,10)), 10) |
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|
1251 |
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1252 ## Test biggish N (bug #39378) |
17812
fffd0c0ca2dc
maint: A few Octave coding convention tweaks
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17787
diff
changeset
|
1253 %!assert (length (randperm (30000^2, 100000)), 100000) |
17662
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Jordi Gutiérrez Hermoso <jordigh@octave.org>
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|
1254 |
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|
1255 %!test |
42edbe6f6a8d
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|
1256 %! rand ("seed", 0); |
42edbe6f6a8d
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|
1257 %! for i = 1:100 |
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|
1258 %! p = randperm (305, 30); |
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|
1259 %! assert (length (unique (p)), 30); |
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|
1260 %! endfor |
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|
1261 */ |