Mercurial > octave
annotate libinterp/corefcn/rand.cc @ 20801:a542a9bf177e
eliminate return statements after calls to print_usage
* graphics.cc, hess.cc, input.cc, kron.cc, load-save.cc, lsode.cc,
luinc.cc, matrix_type.cc, md5sum.cc, nproc.cc, pager.cc, pr-output.cc,
psi.cc, quadcc.cc, rand.cc, regexp.cc, sighandlers.cc, sqrtm.cc,
strfind.cc, sub2ind.cc, sylvester.cc:
Eliminate return statements after calls to print_usage.
author | John W. Eaton <jwe@octave.org> |
---|---|
date | Thu, 03 Dec 2015 19:22:54 -0500 |
parents | b6f2909e7f94 |
children | 35241c4b696c |
rev | line source |
---|---|
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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1 |
2928 | 2 /* |
3 | |
19697
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4 Copyright (C) 1996-2015 John W. Eaton |
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optimize & extend randperm
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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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Use a hash map to store permutations in randperm's truncated Knuth shuffle
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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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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; |
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80 nargin--; |
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81 } |
43db83eff9db
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82 else if (s_arg == "double") |
43db83eff9db
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83 nargin--; |
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Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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84 } |
43db83eff9db
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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: at least one argument is required", 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).xarray_value ("%s: dimension must be a scalar integer", fcn); |
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98 |
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99 idx++; |
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100 nargin--; |
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101 } |
5730 | 102 } |
2928 | 103 |
4543 | 104 switch (nargin) |
2928 | 105 { |
4543 | 106 case 0: |
107 { | |
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108 if (additional_arg) |
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109 dims = a.dims (); |
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110 else |
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111 { |
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112 dims.resize (2); |
4543 | 113 |
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114 dims(0) = 1; |
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115 dims(1) = 1; |
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116 } |
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117 goto gen_matrix; |
4543 | 118 } |
119 break; | |
2928 | 120 |
4543 | 121 case 1: |
122 { | |
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123 octave_value tmp = args(idx); |
4543 | 124 |
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125 if (tmp.is_string ()) |
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126 { |
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127 std::string s_arg = tmp.string_value (); |
2928 | 128 |
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129 if (s_arg == "dist") |
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130 { |
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131 retval = octave_rand::distribution (); |
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132 } |
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133 else if (s_arg == "seed") |
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134 { |
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135 retval = octave_rand::seed (); |
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136 } |
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137 else if (s_arg == "state" || s_arg == "twister") |
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138 { |
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139 retval = octave_rand::state (fcn); |
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140 } |
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141 else if (s_arg == "uniform") |
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142 { |
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143 octave_rand::uniform_distribution (); |
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144 } |
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145 else if (s_arg == "normal") |
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146 { |
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147 octave_rand::normal_distribution (); |
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148 } |
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149 else if (s_arg == "exponential") |
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150 { |
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151 octave_rand::exponential_distribution (); |
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152 } |
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153 else if (s_arg == "poisson") |
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154 { |
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155 octave_rand::poisson_distribution (); |
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156 } |
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157 else if (s_arg == "gamma") |
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158 { |
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159 octave_rand::gamma_distribution (); |
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160 } |
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161 else |
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162 error ("%s: unrecognized string argument", fcn); |
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163 } |
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164 else if (tmp.is_scalar_type ()) |
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165 { |
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166 double dval = tmp.double_value (); |
2928 | 167 |
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168 if (xisnan (dval)) |
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169 { |
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170 error ("%s: NaN is invalid matrix dimension", fcn); |
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171 } |
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172 else |
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173 { |
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174 dims.resize (2); |
4543 | 175 |
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176 dims(0) = NINTbig (tmp.double_value ()); |
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177 dims(1) = NINTbig (tmp.double_value ()); |
2928 | 178 |
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179 goto gen_matrix; |
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180 } |
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181 } |
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182 else if (tmp.is_range ()) |
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183 { |
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184 Range r = tmp.range_value (); |
4543 | 185 |
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186 if (r.all_elements_are_ints ()) |
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187 { |
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Deprecate Array::nelem() and Range::nelem() in favour of ::numel().
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188 octave_idx_type n = r.numel (); |
4543 | 189 |
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190 dims.resize (n); |
4543 | 191 |
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192 octave_idx_type base = NINTbig (r.base ()); |
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193 octave_idx_type incr = NINTbig (r.inc ()); |
2928 | 194 |
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195 for (octave_idx_type i = 0; i < n; i++) |
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196 { |
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197 // Negative dimensions are treated as zero for Matlab |
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198 // compatibility |
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199 dims(i) = base >= 0 ? base : 0; |
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200 base += incr; |
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201 } |
2928 | 202 |
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Let rand accept negative dimensions (bug #33301)
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203 goto gen_matrix; |
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204 |
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205 } |
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206 else |
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207 error ("%s: all elements of range must be integers", fcn); |
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208 } |
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209 else if (tmp.is_matrix_type ()) |
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210 { |
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|
211 Array<int> iv; |
4543 | 212 |
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213 try |
10154
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214 { |
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215 iv = tmp.int_vector_value (true); |
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216 } |
20785
b6f2909e7f94
always throw exception after debugging with debug_on_error
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|
217 catch (octave_execution_exception& e) |
20743
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218 { |
20756
200ae1d650b7
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219 error (e, "%s: dimensions must be a scalar or array of integers", fcn); |
20743
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220 } |
4543 | 221 |
20743
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222 octave_idx_type len = iv.numel (); |
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|
223 |
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|
224 dims.resize (len); |
2928 | 225 |
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226 for (octave_idx_type i = 0; i < len; i++) |
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|
227 { |
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228 // Negative dimensions are treated as zero for Matlab |
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229 // compatibility |
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|
230 octave_idx_type elt = iv(i); |
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|
231 dims(i) = elt >=0 ? elt : 0; |
10154
40dfc0c99116
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|
232 } |
20743
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|
233 |
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|
234 goto gen_matrix; |
10154
40dfc0c99116
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|
235 } |
40dfc0c99116
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|
236 else |
40dfc0c99116
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|
237 { |
40dfc0c99116
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changeset
|
238 gripe_wrong_type_arg ("rand", tmp); |
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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239 return retval; |
40dfc0c99116
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|
240 } |
4543 | 241 } |
242 break; | |
243 | |
244 default: | |
245 { | |
10154
40dfc0c99116
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|
246 octave_value tmp = args(idx); |
4543 | 247 |
10154
40dfc0c99116
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|
248 if (nargin == 2 && tmp.is_string ()) |
40dfc0c99116
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|
249 { |
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changeset
|
250 std::string ts = tmp.string_value (); |
5164 | 251 |
10154
40dfc0c99116
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|
252 if (ts == "seed") |
40dfc0c99116
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|
253 { |
40dfc0c99116
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changeset
|
254 if (args(idx+1).is_real_scalar ()) |
40dfc0c99116
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|
255 { |
40dfc0c99116
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diff
changeset
|
256 double d = args(idx+1).double_value (); |
2928 | 257 |
20678
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diff
changeset
|
258 octave_rand::seed (d); |
10154
40dfc0c99116
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diff
changeset
|
259 } |
11586
12df7854fa7c
strip trailing whitespace from source files
John W. Eaton <jwe@octave.org>
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11553
diff
changeset
|
260 else if (args(idx+1).is_string () |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
Rik <octave@nomad.inbox5.com>
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diff
changeset
|
261 && 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
|
262 octave_rand::reset (); |
10154
40dfc0c99116
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changeset
|
263 else |
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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diff
changeset
|
264 error ("%s: seed must be a real scalar", fcn); |
40dfc0c99116
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changeset
|
265 } |
40dfc0c99116
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changeset
|
266 else if (ts == "state" || ts == "twister") |
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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changeset
|
267 { |
10709
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
|
268 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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changeset
|
269 && 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
|
270 octave_rand::reset (fcn); |
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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diff
changeset
|
271 else |
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
|
272 { |
20687
5f04bfc7c17a
backout changeset 18f38ed43962
John W. Eaton <jwe@octave.org>
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20682
diff
changeset
|
273 ColumnVector s = |
5f04bfc7c17a
backout changeset 18f38ed43962
John W. Eaton <jwe@octave.org>
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20682
diff
changeset
|
274 ColumnVector (args(idx+1).vector_value(false, true)); |
5730 | 275 |
20678
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changeset
|
276 octave_rand::state (s, fcn); |
10709
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Don't special case color_property type when emitting factory default (bug #30118)
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diff
changeset
|
277 } |
10154
40dfc0c99116
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changeset
|
278 } |
40dfc0c99116
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diff
changeset
|
279 else |
40dfc0c99116
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diff
changeset
|
280 error ("%s: unrecognized string argument", fcn); |
40dfc0c99116
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changeset
|
281 } |
40dfc0c99116
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changeset
|
282 else |
40dfc0c99116
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10066
diff
changeset
|
283 { |
40dfc0c99116
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diff
changeset
|
284 dims.resize (nargin); |
4543 | 285 |
10154
40dfc0c99116
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|
286 for (int i = 0; i < nargin; i++) |
40dfc0c99116
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changeset
|
287 { |
20711
7b608fadc663
Make error messages more specific about the variable and problem encountered.
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20704
diff
changeset
|
288 octave_idx_type elt = args(idx+i).xint_value ("%s: dimension must be a scalar or array of integers", fcn); |
20704
571508c1ed06
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changeset
|
289 |
20711
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Make error messages more specific about the variable and problem encountered.
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20704
diff
changeset
|
290 // Negative is zero for Matlab compatibility |
12905
f7a8d1dafda3
Let rand accept negative dimensions (bug #33301)
Jordi Gutiérrez Hermoso <jordigh@gmail.com>
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12639
diff
changeset
|
291 dims(i) = elt >= 0 ? elt : 0; |
10154
40dfc0c99116
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|
292 } |
4543 | 293 |
10154
40dfc0c99116
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|
294 goto gen_matrix; |
40dfc0c99116
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changeset
|
295 } |
4543 | 296 } |
297 break; | |
2928 | 298 } |
299 | |
17787
175b392e91fe
Use GNU style coding conventions for code in libinterp/
Rik <rik@octave.org>
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17744
diff
changeset
|
300 done: |
2928 | 301 |
302 return retval; | |
303 | |
17787
175b392e91fe
Use GNU style coding conventions for code in libinterp/
Rik <rik@octave.org>
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17744
diff
changeset
|
304 gen_matrix: |
2928 | 305 |
5355 | 306 dims.chop_trailing_singletons (); |
307 | |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
308 if (is_single) |
5730 | 309 { |
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
|
310 if (additional_arg) |
10154
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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10066
diff
changeset
|
311 { |
20232
a9574e3c6e9e
Deprecate Array::length() and Sparse::length() in favour of ::numel().
Carnë Draug <carandraug@octave.org>
parents:
20228
diff
changeset
|
312 if (a.numel () == 1) |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
313 return octave_rand::float_nd_array (dims, a(0)); |
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
|
314 else |
10154
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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diff
changeset
|
315 { |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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14706
diff
changeset
|
316 if (a.dims () != dims) |
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317 { |
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318 error ("%s: mismatch in argument size", fcn); |
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319 return retval; |
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320 } |
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321 octave_idx_type len = a.numel (); |
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322 FloatNDArray m (dims); |
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323 float *v = m.fortran_vec (); |
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324 for (octave_idx_type i = 0; i < len; i++) |
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325 v[i] = octave_rand::float_scalar (a(i)); |
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326 return m; |
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327 } |
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328 } |
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329 else |
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330 return octave_rand::float_nd_array (dims); |
5730 | 331 } |
332 else | |
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333 { |
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334 if (additional_arg) |
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335 { |
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336 if (a.numel () == 1) |
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337 return octave_rand::nd_array (dims, a(0)); |
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338 else |
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339 { |
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340 if (a.dims () != dims) |
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341 { |
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342 error ("%s: mismatch in argument size", fcn); |
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343 return retval; |
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344 } |
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345 octave_idx_type len = a.numel (); |
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346 NDArray m (dims); |
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347 double *v = m.fortran_vec (); |
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348 for (octave_idx_type i = 0; i < len; i++) |
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349 v[i] = octave_rand::scalar (a(i)); |
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350 return m; |
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351 } |
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352 } |
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353 else |
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354 return octave_rand::nd_array (dims); |
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355 } |
2928 | 356 } |
357 | |
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358 DEFUN (rand, args, , |
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359 "-*- texinfo -*-\n\ |
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360 @deftypefn {Built-in Function} {} rand (@var{n})\n\ |
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361 @deftypefnx {Built-in Function} {} rand (@var{m}, @var{n}, @dots{})\n\ |
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362 @deftypefnx {Built-in Function} {} rand ([@var{m} @var{n} @dots{}])\n\ |
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363 @deftypefnx {Built-in Function} {@var{v} =} rand (\"state\")\n\ |
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364 @deftypefnx {Built-in Function} {} rand (\"state\", @var{v})\n\ |
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365 @deftypefnx {Built-in Function} {} rand (\"state\", \"reset\")\n\ |
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366 @deftypefnx {Built-in Function} {@var{v} =} rand (\"seed\")\n\ |
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367 @deftypefnx {Built-in Function} {} rand (\"seed\", @var{v})\n\ |
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368 @deftypefnx {Built-in Function} {} rand (\"seed\", \"reset\")\n\ |
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369 @deftypefnx {Built-in Function} {} rand (@dots{}, \"single\")\n\ |
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370 @deftypefnx {Built-in Function} {} rand (@dots{}, \"double\")\n\ |
3369 | 371 Return a matrix with random elements uniformly distributed on the\n\ |
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372 interval (0, 1).\n\ |
5730 | 373 \n\ |
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374 The arguments are handled the same as the arguments for @code{eye}.\n\ |
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375 \n\ |
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376 You can query the state of the random number generator using the form\n\ |
2928 | 377 \n\ |
3369 | 378 @example\n\ |
5730 | 379 v = rand (\"state\")\n\ |
380 @end example\n\ | |
381 \n\ | |
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382 This returns a column vector @var{v} of length 625. Later, you can restore\n\ |
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383 the random number generator to the state @var{v} using the form\n\ |
5730 | 384 \n\ |
385 @example\n\ | |
386 rand (\"state\", v)\n\ | |
3369 | 387 @end example\n\ |
388 \n\ | |
389 @noindent\n\ | |
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390 You may also initialize the state vector from an arbitrary vector of length\n\ |
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391 @leq{} 625 for @var{v}. This new state will be a hash based on the value of\n\ |
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392 @var{v}, not @var{v} itself.\n\ |
5730 | 393 \n\ |
394 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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395 available, otherwise from CPU time, wall clock time, and the current\n\ |
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396 fraction of a second. Note that this differs from @sc{matlab}, which\n\ |
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397 always initializes the state to the same state at startup. To obtain\n\ |
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398 behavior comparable to @sc{matlab}, initialize with a deterministic state\n\ |
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399 vector in Octave's startup files (@pxref{Startup Files}).\n\ |
5730 | 400 \n\ |
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401 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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402 Twister with a period of @math{2^{19937}-1}\n\ |
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403 (See @nospell{M. Matsumoto and T. Nishimura},\n\ |
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404 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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405 pseudorandom number generator},\n\ |
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406 ACM Trans. on Modeling and Computer Simulation Vol. 8, No. 1, pp. 3--30,\n\ |
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407 January 1998,\n\ |
7171 | 408 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
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409 Do @strong{not} use for cryptography without securely hashing several\n\ |
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410 returned values together, otherwise the generator state can be learned after\n\ |
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411 reading 624 consecutive values.\n\ |
5730 | 412 \n\ |
7096 | 413 Older versions of Octave used a different random number generator.\n\ |
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414 The new generator is used by default as it is significantly faster than the\n\ |
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415 old generator, and produces random numbers with a significantly longer cycle\n\ |
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416 time. However, in some circumstances it might be desirable to obtain the\n\ |
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417 same random sequences as produced by the old generators. To do this the\n\ |
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418 keyword @qcode{\"seed\"} is used to specify that the old generators should\n\ |
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419 be used, as in\n\ |
2928 | 420 \n\ |
3369 | 421 @example\n\ |
5730 | 422 rand (\"seed\", val)\n\ |
3369 | 423 @end example\n\ |
424 \n\ | |
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425 @noindent\n\ |
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426 which sets the seed of the generator to @var{val}. The seed of the\n\ |
5730 | 427 generator can be queried with\n\ |
428 \n\ | |
429 @example\n\ | |
430 s = rand (\"seed\")\n\ | |
431 @end example\n\ | |
432 \n\ | |
433 However, it should be noted that querying the seed will not cause\n\ | |
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434 @code{rand} to use the old generators, only setting the seed will. To cause\n\ |
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435 @code{rand} to once again use the new generators, the keyword\n\ |
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436 @qcode{\"state\"} should be used to reset the state of the @code{rand}.\n\ |
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437 \n\ |
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438 The state or seed of the generator can be reset to a new random value using\n\ |
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439 the @qcode{\"reset\"} keyword.\n\ |
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440 \n\ |
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441 The class of the value returned can be controlled by a trailing\n\ |
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442 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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443 classes.\n\ |
5798 | 444 @seealso{randn, rande, randg, randp}\n\ |
3369 | 445 @end deftypefn") |
2928 | 446 { |
4307 | 447 octave_value retval; |
2928 | 448 |
449 int nargin = args.length (); | |
450 | |
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451 retval = do_rand (args, nargin, "rand", "uniform"); |
2928 | 452 |
453 return retval; | |
454 } | |
455 | |
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456 // FIXME: The old generator (selected when "seed" is set) will not |
8871 | 457 // work properly if compiled to use 64-bit integers. |
458 | |
5730 | 459 /* |
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460 %!test # "state" can be a scalar |
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461 %! rand ("state", 12); x = rand (1,4); |
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462 %! rand ("state", 12); y = rand (1,4); |
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463 %! assert (x, y); |
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464 %!test # "state" can be a vector |
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465 %! rand ("state", [12,13]); x = rand (1,4); |
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466 %! rand ("state", [12;13]); y = rand (1,4); |
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467 %! assert (x, y); |
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468 %!test # querying "state" doesn't disturb sequence |
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469 %! rand ("state", 12); rand (1,2); x = rand (1,2); |
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470 %! rand ("state", 12); rand (1,2); |
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471 %! s = rand ("state"); y = rand (1,2); |
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472 %! assert (x, y); |
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473 %! rand ("state", s); z = rand (1,2); |
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474 %! assert (x, z); |
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475 %!test # "seed" must be a scalar |
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476 %! rand ("seed", 12); x = rand (1,4); |
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477 %! rand ("seed", 12); y = rand (1,4); |
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478 %! assert (x, y); |
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479 %!error <seed must be a real scalar> rand ("seed", [12,13]) |
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480 %!test # querying "seed" returns a value which can be used later |
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481 %! s = rand ("seed"); x = rand (1,2); |
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482 %! rand ("seed", s); y = rand (1,2); |
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483 %! assert (x, y); |
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484 %!test # querying "seed" doesn't disturb sequence |
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485 %! rand ("seed", 12); rand (1,2); x = rand (1,2); |
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486 %! rand ("seed", 12); rand (1,2); |
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487 %! s = rand ("seed"); y = rand (1,2); |
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488 %! assert (x, y); |
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489 %! rand ("seed", s); z = rand (1,2); |
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490 %! assert (x, z); |
5730 | 491 */ |
492 | |
493 /* | |
494 %!test | |
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495 %! ## Test fixed state |
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496 %! rand ("state", 1); |
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497 %! assert (rand (1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382], 1e-6); |
6437 | 498 %!test |
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499 %! ## Test fixed seed |
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500 %! rand ("seed", 1); |
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501 %! assert (rand (1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759], 1e-6); |
5730 | 502 %!test |
6437 | 503 %! if (__random_statistical_tests__) |
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504 %! ## statistical tests may fail occasionally. |
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505 %! rand ("state", 12); |
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506 %! x = rand (100000, 1); |
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507 %! assert (max (x) < 1); #*** Please report this!!! *** |
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508 %! assert (min (x) > 0); #*** Please report this!!! *** |
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509 %! assert (mean (x), 0.5, 0.0024); |
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510 %! assert (var (x), 1/48, 0.0632); |
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511 %! assert (skewness (x), 0, 0.012); |
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512 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 513 %! endif |
514 %!test | |
515 %! if (__random_statistical_tests__) | |
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516 %! ## statistical tests may fail occasionally. |
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517 %! rand ("seed", 12); |
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518 %! x = rand (100000, 1); |
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519 %! assert (max (x) < 1); #*** Please report this!!! *** |
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520 %! assert (min (x) > 0); #*** Please report this!!! *** |
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521 %! assert (mean (x), 0.5, 0.0024); |
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522 %! assert (var (x), 1/48, 0.0632); |
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523 %! assert (skewness (x), 0, 0.012); |
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524 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 525 %! endif |
5730 | 526 */ |
527 | |
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528 /* |
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529 %!# Test out-of-range values as rand() seeds. See oct-rand.cc: double2uint32(). |
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530 %!function v = __rand_sample__ (initval) |
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531 %! rand ("state", initval); |
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532 %! v = rand (1, 6); |
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533 %!endfunction |
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534 %! |
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535 %!assert (__rand_sample__ (0), __rand_sample__ (2^32)) |
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536 %!assert (__rand_sample__ (-2), __rand_sample__ (2^32-2)) |
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537 %!assert (__rand_sample__ (Inf), __rand_sample__ (NaN)) |
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538 %!assert (! isequal (__rand_sample__ (-1), __rand_sample__ (-2))) |
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539 */ |
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540 |
4307 | 541 static std::string current_distribution = octave_rand::distribution (); |
542 | |
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543 DEFUN (randn, args, , |
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544 "-*- texinfo -*-\n\ |
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545 @deftypefn {Built-in Function} {} randn (@var{n})\n\ |
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546 @deftypefnx {Built-in Function} {} randn (@var{m}, @var{n}, @dots{})\n\ |
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547 @deftypefnx {Built-in Function} {} randn ([@var{m} @var{n} @dots{}])\n\ |
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548 @deftypefnx {Built-in Function} {@var{v} =} randn (\"state\")\n\ |
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549 @deftypefnx {Built-in Function} {} randn (\"state\", @var{v})\n\ |
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550 @deftypefnx {Built-in Function} {} randn (\"state\", \"reset\")\n\ |
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551 @deftypefnx {Built-in Function} {@var{v} =} randn (\"seed\")\n\ |
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552 @deftypefnx {Built-in Function} {} randn (\"seed\", @var{v})\n\ |
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553 @deftypefnx {Built-in Function} {} randn (\"seed\", \"reset\")\n\ |
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554 @deftypefnx {Built-in Function} {} randn (@dots{}, \"single\")\n\ |
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555 @deftypefnx {Built-in Function} {} randn (@dots{}, \"double\")\n\ |
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556 Return a matrix with normally distributed random elements having zero mean\n\ |
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557 and variance one.\n\ |
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558 \n\ |
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559 The arguments are handled the same as the arguments for @code{rand}.\n\ |
3369 | 560 \n\ |
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561 By default, @code{randn} uses the @nospell{Marsaglia and Tsang}\n\ |
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562 ``Ziggurat technique'' to transform from a uniform to a normal distribution.\n\ |
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563 \n\ |
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564 The class of the value returned can be controlled by a trailing\n\ |
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565 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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566 classes.\n\ |
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567 \n\ |
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568 Reference: @nospell{G. Marsaglia and W.W. Tsang},\n\ |
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569 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 570 J. Statistical Software, vol 5, 2000,\n\ |
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571 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
2928 | 572 \n\ |
6547 | 573 @seealso{rand, rande, randg, randp}\n\ |
3369 | 574 @end deftypefn") |
2928 | 575 { |
4307 | 576 octave_value retval; |
2928 | 577 |
578 int nargin = args.length (); | |
579 | |
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580 retval = do_rand (args, nargin, "randn", "normal"); |
2928 | 581 |
582 return retval; | |
583 } | |
584 | |
585 /* | |
5730 | 586 %!test |
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587 %! ## Test fixed state |
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588 %! randn ("state", 1); |
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589 %! assert (randn (1, 6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116], 1e-6); |
6437 | 590 %!test |
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591 %! ## Test fixed seed |
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592 %! randn ("seed", 1); |
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593 %! assert (randn (1, 6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133], 1e-6); |
5730 | 594 %!test |
6437 | 595 %! if (__random_statistical_tests__) |
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596 %! ## statistical tests may fail occasionally. |
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597 %! randn ("state", 12); |
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598 %! x = randn (100000, 1); |
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599 %! assert (mean (x), 0, 0.01); |
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600 %! assert (var (x), 1, 0.02); |
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601 %! assert (skewness (x), 0, 0.02); |
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602 %! assert (kurtosis (x), 0, 0.04); |
6437 | 603 %! endif |
604 %!test | |
605 %! if (__random_statistical_tests__) | |
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606 %! ## statistical tests may fail occasionally. |
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607 %! randn ("seed", 12); |
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608 %! x = randn (100000, 1); |
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609 %! assert (mean (x), 0, 0.01); |
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610 %! assert (var (x), 1, 0.02); |
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611 %! assert (skewness (x), 0, 0.02); |
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612 %! assert (kurtosis (x), 0, 0.04); |
6437 | 613 %! endif |
5730 | 614 */ |
615 | |
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616 DEFUN (rande, args, , |
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617 "-*- texinfo -*-\n\ |
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618 @deftypefn {Built-in Function} {} rande (@var{n})\n\ |
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619 @deftypefnx {Built-in Function} {} rande (@var{m}, @var{n}, @dots{})\n\ |
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620 @deftypefnx {Built-in Function} {} rande ([@var{m} @var{n} @dots{}])\n\ |
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621 @deftypefnx {Built-in Function} {@var{v} =} rande (\"state\")\n\ |
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622 @deftypefnx {Built-in Function} {} rande (\"state\", @var{v})\n\ |
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623 @deftypefnx {Built-in Function} {} rande (\"state\", \"reset\")\n\ |
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624 @deftypefnx {Built-in Function} {@var{v} =} rande (\"seed\")\n\ |
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625 @deftypefnx {Built-in Function} {} rande (\"seed\", @var{v})\n\ |
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626 @deftypefnx {Built-in Function} {} rande (\"seed\", \"reset\")\n\ |
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627 @deftypefnx {Built-in Function} {} rande (@dots{}, \"single\")\n\ |
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628 @deftypefnx {Built-in Function} {} rande (@dots{}, \"double\")\n\ |
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629 Return a matrix with exponentially distributed random elements.\n\ |
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630 \n\ |
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631 The arguments are handled the same as the arguments for @code{rand}.\n\ |
5730 | 632 \n\ |
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633 By default, @code{randn} uses the @nospell{Marsaglia and Tsang}\n\ |
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634 ``Ziggurat technique'' to transform from a uniform to a normal distribution.\n\ |
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635 \n\ |
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636 The class of the value returned can be controlled by a trailing\n\ |
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637 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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638 classes.\n\ |
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639 \n\ |
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640 Reference: @nospell{G. Marsaglia and W.W. Tsang},\n\ |
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641 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 642 J. Statistical Software, vol 5, 2000,\n\ |
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643 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
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644 \n\ |
6547 | 645 @seealso{rand, randn, randg, randp}\n\ |
5730 | 646 @end deftypefn") |
647 { | |
648 octave_value retval; | |
649 | |
650 int nargin = args.length (); | |
651 | |
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652 retval = do_rand (args, nargin, "rande", "exponential"); |
5730 | 653 |
654 return retval; | |
655 } | |
656 | |
657 /* | |
658 %!test | |
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659 %! ## Test fixed state |
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660 %! rande ("state", 1); |
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661 %! assert (rande (1, 6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291], 1e-6); |
6437 | 662 %!test |
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663 %! ## Test fixed seed |
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664 %! rande ("seed", 1); |
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665 %! assert (rande (1, 6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087], 1e-6); |
5730 | 666 %!test |
6437 | 667 %! if (__random_statistical_tests__) |
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668 %! ## statistical tests may fail occasionally |
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669 %! rande ("state", 1); |
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670 %! x = rande (100000, 1); |
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671 %! assert (min (x) > 0); # *** Please report this!!! *** |
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672 %! assert (mean (x), 1, 0.01); |
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673 %! assert (var (x), 1, 0.03); |
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674 %! assert (skewness (x), 2, 0.06); |
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675 %! assert (kurtosis (x), 6, 0.7); |
6437 | 676 %! endif |
677 %!test | |
678 %! if (__random_statistical_tests__) | |
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679 %! ## statistical tests may fail occasionally |
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680 %! rande ("seed", 1); |
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681 %! x = rande (100000, 1); |
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682 %! assert (min (x)>0); # *** Please report this!!! *** |
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683 %! assert (mean (x), 1, 0.01); |
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684 %! assert (var (x), 1, 0.03); |
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685 %! assert (skewness (x), 2, 0.06); |
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686 %! assert (kurtosis (x), 6, 0.7); |
6437 | 687 %! endif |
5730 | 688 */ |
689 | |
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690 DEFUN (randg, args, , |
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691 "-*- texinfo -*-\n\ |
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692 @deftypefn {Built-in Function} {} randg (@var{n})\n\ |
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693 @deftypefnx {Built-in Function} {} randg (@var{m}, @var{n}, @dots{})\n\ |
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694 @deftypefnx {Built-in Function} {} randg ([@var{m} @var{n} @dots{}])\n\ |
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695 @deftypefnx {Built-in Function} {@var{v} =} randg (\"state\")\n\ |
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696 @deftypefnx {Built-in Function} {} randg (\"state\", @var{v})\n\ |
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697 @deftypefnx {Built-in Function} {} randg (\"state\", \"reset\")\n\ |
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698 @deftypefnx {Built-in Function} {@var{v} =} randg (\"seed\")\n\ |
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699 @deftypefnx {Built-in Function} {} randg (\"seed\", @var{v})\n\ |
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700 @deftypefnx {Built-in Function} {} randg (\"seed\", \"reset\")\n\ |
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701 @deftypefnx {Built-in Function} {} randg (@dots{}, \"single\")\n\ |
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702 @deftypefnx {Built-in Function} {} randg (@dots{}, \"double\")\n\ |
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703 Return a matrix with @code{gamma (@var{a},1)} distributed random elements.\n\ |
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704 \n\ |
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705 The arguments are handled the same as the arguments for @code{rand}, except\n\ |
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706 for the argument @var{a}.\n\ |
5730 | 707 \n\ |
708 This can be used to generate many distributions:\n\ | |
709 \n\ | |
710 @table @asis\n\ | |
6547 | 711 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
10840 | 712 \n\ |
5730 | 713 @example\n\ |
6547 | 714 r = b * randg (a)\n\ |
5730 | 715 @end example\n\ |
10840 | 716 \n\ |
6547 | 717 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
10840 | 718 \n\ |
5730 | 719 @example\n\ |
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720 @group\n\ |
6547 | 721 r1 = randg (a, 1)\n\ |
722 r = r1 / (r1 + randg (b, 1))\n\ | |
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723 @end group\n\ |
5730 | 724 @end example\n\ |
10840 | 725 \n\ |
6547 | 726 @item @code{Erlang (a, n)}\n\ |
10840 | 727 \n\ |
5730 | 728 @example\n\ |
6547 | 729 r = a * randg (n)\n\ |
5730 | 730 @end example\n\ |
10840 | 731 \n\ |
6547 | 732 @item @code{chisq (df)} for @code{df > 0}\n\ |
10840 | 733 \n\ |
5730 | 734 @example\n\ |
6547 | 735 r = 2 * randg (df / 2)\n\ |
5730 | 736 @end example\n\ |
10840 | 737 \n\ |
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738 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
10840 | 739 \n\ |
5730 | 740 @example\n\ |
6547 | 741 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
5730 | 742 @end example\n\ |
10840 | 743 \n\ |
6547 | 744 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
10840 | 745 \n\ |
5730 | 746 @example\n\ |
7096 | 747 @group\n\ |
748 ## r1 equals 1 if n1 is infinite\n\ | |
749 r1 = 2 * randg (n1 / 2) / n1\n\ | |
750 ## r2 equals 1 if n2 is infinite\n\ | |
751 r2 = 2 * randg (n2 / 2) / n2\n\ | |
5730 | 752 r = r1 / r2\n\n\ |
7096 | 753 @end group\n\ |
5730 | 754 @end example\n\ |
10840 | 755 \n\ |
5730 | 756 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
10840 | 757 \n\ |
5730 | 758 @example\n\ |
6547 | 759 r = randp ((1 - p) / p * randg (n))\n\ |
5730 | 760 @end example\n\ |
10840 | 761 \n\ |
6547 | 762 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
5730 | 763 (use chisq if @code{L = 0})\n\ |
10840 | 764 \n\ |
5730 | 765 @example\n\ |
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766 @group\n\ |
6547 | 767 r = randp (L / 2)\n\ |
768 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
769 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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770 @end group\n\ |
5730 | 771 @end example\n\ |
10840 | 772 \n\ |
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773 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
10840 | 774 \n\ |
5730 | 775 @example\n\ |
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776 @group\n\ |
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777 r = (randg (a1), @dots{}, randg (ak))\n\ |
6547 | 778 r = r / sum (r)\n\ |
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779 @end group\n\ |
5730 | 780 @end example\n\ |
10840 | 781 \n\ |
5730 | 782 @end table\n\ |
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783 \n\ |
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784 The class of the value returned can be controlled by a trailing\n\ |
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785 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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786 classes.\n\ |
6547 | 787 @seealso{rand, randn, rande, randp}\n\ |
5730 | 788 @end deftypefn") |
789 { | |
790 octave_value retval; | |
791 | |
792 int nargin = args.length (); | |
793 | |
794 if (nargin < 1) | |
795 error ("randg: insufficient arguments"); | |
796 else | |
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797 retval = do_rand (args, nargin, "randg", "gamma", true); |
5730 | 798 |
799 return retval; | |
800 } | |
801 | |
802 /* | |
803 %!test | |
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804 %! randg ("state", 12) |
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805 %! assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]); # *** Please report |
6437 | 806 |
807 %!test | |
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808 %! ## Test fixed state |
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809 %! randg ("state", 1); |
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810 %! assert (randg (0.1, 1, 6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12], 1e-6); |
6437 | 811 %!test |
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812 %! ## Test fixed state |
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813 %! randg ("state", 1); |
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814 %! assert (randg (0.95, 1, 6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939], 1e-6); |
6437 | 815 %!test |
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816 %! ## Test fixed state |
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817 %! randg ("state", 1); |
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818 %! assert (randg (1, 1, 6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533], 1e-6); |
6437 | 819 %!test |
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820 %! ## Test fixed state |
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821 %! randg ("state", 1); |
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822 %! assert (randg (10, 1, 6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059], 1e-5); |
6437 | 823 %!test |
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824 %! ## Test fixed state |
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825 %! randg ("state", 1); |
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826 %! assert (randg (100, 1, 6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118], 1e-4); |
6437 | 827 %!test |
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828 %! ## Test fixed seed |
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829 %! randg ("seed", 1); |
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830 %! assert (randg (0.1, 1, 6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12], 1e-6); |
6437 | 831 %!test |
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832 %! ## Test fixed seed |
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833 %! randg ("seed", 1); |
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834 %! assert (randg (0.95, 1, 6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648], 1e-6); |
6437 | 835 %!test |
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836 %! ## Test fixed seed |
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837 %! randg ("seed", 1); |
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838 %! assert (randg (1, 1, 6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303], 1e-6); |
6437 | 839 %!test |
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840 %! ## Test fixed seed |
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841 %! randg ("seed", 1); |
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842 %! assert (randg (10, 1, 6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186], 1e-5); |
6437 | 843 %!test |
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844 %! ## Test fixed seed |
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845 %! randg ("seed", 1); |
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846 %! assert (randg (100, 1, 6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625], 1e-4); |
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847 %!test |
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848 %! ## Test out-of-bounds values produce NaN w/old-style generators & floats |
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849 %! randg ("seed", 1); |
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850 %! result = randg ([-2 Inf], "single"); |
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851 %! assert (result, single ([NaN NaN])); |
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852 |
6437 | 853 %!test |
854 %! if (__random_statistical_tests__) | |
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855 %! ## statistical tests may fail occasionally. |
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856 %! randg ("state", 12); |
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857 %! a = 0.1; |
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858 %! x = randg (a, 100000, 1); |
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859 %! assert (mean (x), a, 0.01); |
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860 %! assert (var (x), a, 0.01); |
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861 %! assert (skewness (x), 2/sqrt (a), 1); |
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862 %! assert (kurtosis (x), 6/a, 50); |
6437 | 863 %! endif |
864 %!test | |
865 %! if (__random_statistical_tests__) | |
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866 %! ## statistical tests may fail occasionally. |
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867 %! randg ("state", 12); |
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868 %! a = 0.95; |
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869 %! x = randg (a, 100000, 1); |
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870 %! assert (mean (x), a, 0.01); |
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871 %! assert (var (x), a, 0.04); |
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872 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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873 %! assert (kurtosis (x), 6/a, 2); |
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874 %! endif |
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875 %!test |
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876 %! if (__random_statistical_tests__) |
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877 %! ## statistical tests may fail occasionally. |
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878 %! randg ("state", 12); |
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879 %! a = 1; |
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880 %! x = randg (a, 100000, 1); |
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881 %! assert (mean (x), a, 0.01); |
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882 %! assert (var (x), a, 0.04); |
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883 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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884 %! assert (kurtosis (x), 6/a, 2); |
6437 | 885 %! endif |
886 %!test | |
887 %! if (__random_statistical_tests__) | |
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888 %! ## statistical tests may fail occasionally. |
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889 %! randg ("state", 12); |
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890 %! a = 10; |
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891 %! x = randg (a, 100000, 1); |
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892 %! assert (mean (x), a, 0.1); |
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893 %! assert (var (x), a, 0.5); |
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894 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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895 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 896 %! endif |
897 %!test | |
898 %! if (__random_statistical_tests__) | |
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899 %! ## statistical tests may fail occasionally. |
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900 %! randg ("state", 12); |
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901 %! a = 100; |
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902 %! x = randg (a, 100000, 1); |
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903 %! assert (mean (x), a, 0.2); |
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904 %! assert (var (x), a, 2); |
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905 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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906 %! assert (kurtosis (x), 6/a, 0.2); |
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907 %! endif |
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908 %!test |
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909 %! randg ("seed", 12); |
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910 %!assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]) # *** Please report |
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911 %!test |
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912 %! if (__random_statistical_tests__) |
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913 %! ## statistical tests may fail occasionally. |
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914 %! randg ("seed", 12); |
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915 %! a = 0.1; |
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916 %! x = randg (a, 100000, 1); |
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917 %! assert (mean (x), a, 0.01); |
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918 %! assert (var (x), a, 0.01); |
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919 %! assert (skewness (x), 2/sqrt (a), 1); |
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920 %! assert (kurtosis (x), 6/a, 50); |
6437 | 921 %! endif |
922 %!test | |
923 %! if (__random_statistical_tests__) | |
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924 %! ## statistical tests may fail occasionally. |
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925 %! randg ("seed", 12); |
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926 %! a = 0.95; |
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927 %! x = randg (a, 100000, 1); |
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928 %! assert (mean (x), a, 0.01); |
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929 %! assert (var (x), a, 0.04); |
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930 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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931 %! assert (kurtosis (x), 6/a, 2); |
6437 | 932 %! endif |
933 %!test | |
934 %! if (__random_statistical_tests__) | |
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935 %! ## statistical tests may fail occasionally. |
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936 %! randg ("seed", 12); |
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937 %! a = 1; |
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938 %! x = randg (a, 100000, 1); |
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939 %! assert (mean (x), a, 0.01); |
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940 %! assert (var (x), a, 0.04); |
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941 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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942 %! assert (kurtosis (x), 6/a, 2); |
6437 | 943 %! endif |
5730 | 944 %!test |
6437 | 945 %! if (__random_statistical_tests__) |
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946 %! ## statistical tests may fail occasionally. |
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947 %! randg ("seed", 12); |
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948 %! a = 10; |
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949 %! x = randg (a, 100000, 1); |
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950 %! assert (mean (x), a, 0.1); |
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951 %! assert (var (x), a, 0.5); |
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952 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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953 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 954 %! endif |
5730 | 955 %!test |
6437 | 956 %! if (__random_statistical_tests__) |
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957 %! ## statistical tests may fail occasionally. |
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958 %! randg ("seed", 12); |
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959 %! a = 100; |
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960 %! x = randg (a, 100000, 1); |
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961 %! assert (mean (x), a, 0.2); |
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962 %! assert (var (x), a, 2); |
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963 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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964 %! assert (kurtosis (x), 6/a, 0.2); |
6437 | 965 %! endif |
5730 | 966 */ |
967 | |
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968 DEFUN (randp, args, , |
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969 "-*- texinfo -*-\n\ |
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970 @deftypefn {Built-in Function} {} randp (@var{l}, @var{n})\n\ |
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971 @deftypefnx {Built-in Function} {} randp (@var{l}, @var{m}, @var{n}, @dots{})\n\ |
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972 @deftypefnx {Built-in Function} {} randp (@var{l}, [@var{m} @var{n} @dots{}])\n\ |
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973 @deftypefnx {Built-in Function} {@var{v} =} randp (\"state\")\n\ |
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974 @deftypefnx {Built-in Function} {} randp (\"state\", @var{v})\n\ |
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975 @deftypefnx {Built-in Function} {} randp (\"state\", \"reset\")\n\ |
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976 @deftypefnx {Built-in Function} {@var{v} =} randp (\"seed\")\n\ |
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977 @deftypefnx {Built-in Function} {} randp (\"seed\", @var{v})\n\ |
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978 @deftypefnx {Built-in Function} {} randp (\"seed\", \"reset\")\n\ |
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979 @deftypefnx {Built-in Function} {} randp (@dots{}, \"single\")\n\ |
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980 @deftypefnx {Built-in Function} {} randp (@dots{}, \"double\")\n\ |
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981 Return a matrix with Poisson distributed random elements with mean value\n\ |
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982 parameter given by the first argument, @var{l}.\n\ |
5730 | 983 \n\ |
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984 The arguments are handled the same as the arguments for @code{rand}, except\n\ |
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985 for the argument @var{l}.\n\ |
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986 \n\ |
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987 Five different algorithms are used depending on the range of @var{l} and\n\ |
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988 whether or not @var{l} is a scalar or a matrix.\n\ |
5730 | 989 \n\ |
990 @table @asis\n\ | |
10840 | 991 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
992 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
993 Cambridge University Press, 1992.\n\ | |
994 \n\ | |
5730 | 995 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
10840 | 996 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
997 Cambridge University Press, 1992.\n\ | |
998 \n\ | |
999 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
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1000 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP.\n\ |
10840 | 1001 \n\ |
5730 | 1002 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
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1003 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP, or\n\ |
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1004 @nospell{H. Zechner}, @cite{Efficient sampling from continuous and discrete\n\ |
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1005 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
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1006 University @nospell{Graz}, Austria, 1994.\n\ |
10840 | 1007 \n\ |
5730 | 1008 @item For @var{l} > 1e8, use normal approximation.\n\ |
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1009 @nospell{L. Montanet}, et al., @cite{Review of Particle Properties},\n\ |
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1010 Physical Review D 50 p1284, 1994.\n\ |
5730 | 1011 @end table\n\ |
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1012 \n\ |
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1013 The class of the value returned can be controlled by a trailing\n\ |
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|
1014 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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1015 classes.\n\ |
6547 | 1016 @seealso{rand, randn, rande, randg}\n\ |
5730 | 1017 @end deftypefn") |
1018 { | |
1019 octave_value retval; | |
1020 | |
1021 int nargin = args.length (); | |
1022 | |
1023 if (nargin < 1) | |
1024 error ("randp: insufficient arguments"); | |
1025 else | |
10782
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1026 retval = do_rand (args, nargin, "randp", "poisson", true); |
5730 | 1027 |
1028 return retval; | |
1029 } | |
1030 | |
1031 /* | |
1032 %!test | |
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1033 %! randp ("state", 12); |
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1034 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
6437 | 1035 %!test |
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1036 %! ## Test fixed state |
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1037 %! randp ("state", 1); |
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1038 %! assert (randp (5, 1, 6), [5 5 3 7 7 3]) |
6437 | 1039 %!test |
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1040 %! ## Test fixed state |
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1041 %! randp ("state", 1); |
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1042 %! assert (randp (15, 1, 6), [13 15 8 18 18 15]) |
6437 | 1043 %!test |
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1044 %! ## Test fixed state |
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1045 %! randp ("state", 1); |
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1046 %! assert (randp (1e9, 1, 6), [999915677 999976657 1000047684 1000019035 999985749 999977692], -1e-6) |
6437 | 1047 %!test |
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1048 %! ## Test fixed state |
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1049 %! randp ("seed", 1); |
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1050 %! %%assert (randp (5, 1, 6), [8 2 3 6 6 8]) |
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1051 %! assert (randp (5, 1, 5), [8 2 3 6 6]) |
6437 | 1052 %!test |
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1053 %! ## Test fixed state |
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1054 %! randp ("seed", 1); |
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1055 %! assert (randp (15, 1, 6), [15 16 12 10 10 12]) |
6437 | 1056 %!test |
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1057 %! ## Test fixed state |
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1058 %! randp ("seed", 1); |
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1059 %! assert (randp (1e9, 1, 6), [1000006208 1000012224 999981120 999963520 999963072 999981440], -1e-6) |
6437 | 1060 %!test |
1061 %! if (__random_statistical_tests__) | |
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1062 %! ## statistical tests may fail occasionally. |
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1063 %! randp ("state", 12); |
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1064 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1065 %! x = randp (a (1), 100000, 1); |
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1066 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1067 %! assert (mean (x), a(1), a(2)); |
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1068 %! assert (var (x), a(1), 0.02*a(1)); |
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1069 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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|
1070 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1071 %! endfor |
1072 %! endif | |
1073 %!test | |
1074 %! if (__random_statistical_tests__) | |
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1075 %! ## statistical tests may fail occasionally. |
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1076 %! randp ("state", 12); |
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1077 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1078 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1079 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1080 %! assert (mean (x), a(1), a(2)); |
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1081 %! assert (var (x), a(1), 0.02*a(1)); |
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1082 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1083 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1084 %! endfor |
1085 %! endif | |
1086 %!test | |
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1087 %! randp ("seed", 12); |
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1088 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
5730 | 1089 %!test |
6449 | 1090 %! if (__random_statistical_tests__) |
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1091 %! ## statistical tests may fail occasionally. |
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1092 %! randp ("seed", 12); |
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1093 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1094 %! x = randp (a(1), 100000, 1); |
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1095 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1096 %! assert (mean (x), a(1), a(2)); |
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1097 %! assert (var (x), a(1), 0.02*a(1)); |
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1098 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1099 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1100 %! endfor |
1101 %! endif | |
5730 | 1102 %!test |
6449 | 1103 %! if (__random_statistical_tests__) |
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1104 %! ## statistical tests may fail occasionally. |
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1105 %! randp ("seed", 12); |
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1106 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1107 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1108 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1109 %! assert (mean (x), a(1), a(2)); |
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1110 %! assert (var (x), a(1), 0.02*a(1)); |
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1111 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1112 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1113 %! endfor |
1114 %! endif | |
5730 | 1115 */ |
1116 | |
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1117 DEFUN (randperm, args, , |
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1118 "-*- texinfo -*-\n\ |
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1119 @deftypefn {Built-in Function} {} randperm (@var{n})\n\ |
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1120 @deftypefnx {Built-in Function} {} randperm (@var{n}, @var{m})\n\ |
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1121 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1122 \n\ |
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1123 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
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1124 replacement from @code{1:@var{n}}.\n\ |
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1125 \n\ |
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1126 The complexity is O(@var{n}) in memory and O(@var{m}) in time, unless\n\ |
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1127 @var{m} < @var{n}/5, in which case O(@var{m}) memory is used as well. The\n\ |
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1128 randomization is performed using rand(). All permutations are equally\n\ |
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1129 likely.\n\ |
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1130 @seealso{perms}\n\ |
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1131 @end deftypefn") |
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1132 { |
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1133 octave_value retval; |
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1134 |
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1135 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
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1136 using std::tr1::unordered_map; |
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1137 #else |
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1138 using std::unordered_map; |
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1139 #endif |
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1140 |
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1141 int nargin = args.length (); |
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1142 |
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1143 if (nargin < 1 || nargin > 2) |
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1144 print_usage (); |
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1145 |
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1146 octave_idx_type n = args(0).idx_type_value (true); |
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1147 octave_idx_type m = (nargin == 2) ? args(1).idx_type_value (true) : n; |
11586
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1148 |
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1149 if (m < 0 || n < 0) |
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1150 error ("randperm: M and N must be non-negative"); |
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1151 |
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1152 if (m > n) |
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1153 error ("randperm: M must be less than or equal to N"); |
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1154 |
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1155 // Quick and dirty heuristic to decide if we allocate or not the |
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1156 // whole vector for tracking the truncated shuffle. |
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1157 bool short_shuffle = m < n/5; |
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1158 |
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1159 // Generate random numbers. |
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1160 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
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1161 double *rvec = r.fortran_vec (); |
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1162 |
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1163 octave_idx_type idx_len = short_shuffle ? m : n; |
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1164 Array<octave_idx_type> idx; |
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1165 try |
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1166 { |
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1167 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1168 } |
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1169 catch (const std::bad_alloc&) |
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1170 { |
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1171 // Looks like n is too big and short_shuffle is false. |
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1172 // Let's try again, but this time with the alternative. |
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1173 idx_len = m; |
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1174 short_shuffle = true; |
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1175 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1176 } |
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1177 |
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1178 octave_idx_type *ivec = idx.fortran_vec (); |
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1179 |
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1180 for (octave_idx_type i = 0; i < idx_len; i++) |
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1181 ivec[i] = i; |
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1182 |
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1183 if (short_shuffle) |
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1184 { |
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1185 unordered_map<octave_idx_type, octave_idx_type> map (m); |
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1186 |
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1187 // Perform the Knuth shuffle only keeping track of moved |
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1188 // entries in the map |
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1189 for (octave_idx_type i = 0; i < m; i++) |
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1190 { |
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1191 octave_idx_type k = i + |
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1192 gnulib::floor (rvec[i] * (n - i)); |
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1193 |
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1194 // For shuffling first m entries, no need to use extra |
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1195 // storage |
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1196 if (k < m) |
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1197 { |
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1198 std::swap (ivec[i], ivec[k]); |
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1199 } |
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1200 else |
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1201 { |
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1202 if (map.find (k) == map.end ()) |
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1203 map[k] = k; |
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1204 |
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1205 std::swap (ivec[i], map[k]); |
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1206 } |
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1207 } |
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1208 } |
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1209 else |
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1210 { |
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1211 |
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1212 // Perform the Knuth shuffle of the first m entries |
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1213 for (octave_idx_type i = 0; i < m; i++) |
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1214 { |
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1215 octave_idx_type k = i + |
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1216 gnulib::floor (rvec[i] * (n - i)); |
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1217 std::swap (ivec[i], ivec[k]); |
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1218 } |
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1219 } |
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1220 |
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1221 // Convert to doubles, reusing r. |
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1222 for (octave_idx_type i = 0; i < m; i++) |
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1223 rvec[i] = ivec[i] + 1; |
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1224 |
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1225 if (m < n) |
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1226 idx.resize (dim_vector (1, m)); |
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1227 |
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1228 // Now create an array object with a cached idx_vector. |
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1229 return octave_value (new octave_matrix (r, idx_vector (idx))); |
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1230 } |
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1231 |
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1232 /* |
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1233 %!assert (sort (randperm (20)), 1:20) |
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1234 %!assert (length (randperm (20,10)), 10) |
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1235 |
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|
1236 ## Test biggish N (bug #39378) |
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1237 %!assert (length (randperm (30000^2, 100000)), 100000) |
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1238 |
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1239 %!test |
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1240 %! rand ("seed", 0); |
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1241 %! for i = 1:100 |
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1242 %! p = randperm (305, 30); |
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1243 %! assert (length (unique (p)), 30); |
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1244 %! endfor |
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1245 */ |