Mercurial > octave
annotate libinterp/corefcn/rand.cc @ 21234:20a824cd1d9e
maint: Merge stable to default.
author | Rik <rik@octave.org> |
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date | Tue, 09 Feb 2016 10:35:28 -0800 |
parents | fcac5dbbf9ed 6b0ded11d708 |
children | 40de9f8f23a6 |
rev | line source |
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14655
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Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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1 |
2928 | 2 /* |
3 | |
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4 Copyright (C) 1996-2015 John W. Eaton |
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5 Copyright (C) 2009 VZLU Prague |
2928 | 6 |
7 This file is part of Octave. | |
8 | |
9 Octave is free software; you can redistribute it and/or modify it | |
10 under the terms of the GNU General Public License as published by the | |
7016 | 11 Free Software Foundation; either version 3 of the License, or (at your |
12 option) any later version. | |
2928 | 13 |
14 Octave is distributed in the hope that it will be useful, but WITHOUT | |
15 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
16 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
17 for more details. | |
18 | |
19 You should have received a copy of the GNU General Public License | |
7016 | 20 along with Octave; see the file COPYING. If not, see |
21 <http://www.gnu.org/licenses/>. | |
2928 | 22 |
23 */ | |
24 | |
25 #ifdef HAVE_CONFIG_H | |
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26 # include <config.h> |
2928 | 27 #endif |
28 | |
29 #include <ctime> | |
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30 #if defined (HAVE_UNORDERED_MAP) |
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31 # include <unordered_map> |
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32 #elif defined (HAVE_TR1_UNORDERED_MAP) |
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33 # include <tr1/unordered_map> |
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34 #endif |
2928 | 35 #include <string> |
36 | |
37 #include "f77-fcn.h" | |
38 #include "lo-mappers.h" | |
4307 | 39 #include "oct-rand.h" |
4153 | 40 #include "quit.h" |
2928 | 41 |
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42 #include "defun.h" |
2928 | 43 #include "error.h" |
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44 #include "errwarn.h" |
20940
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45 #include "ovl.h" |
2928 | 46 #include "unwind-prot.h" |
47 #include "utils.h" | |
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48 #include "ov-re-mat.h" |
2928 | 49 |
6437 | 50 /* |
51 %!shared __random_statistical_tests__ | |
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52 %! ## Flag whether the statistical tests should be run in "make check" or not |
6437 | 53 %! __random_statistical_tests__ = 0; |
54 */ | |
55 | |
4307 | 56 static octave_value |
5730 | 57 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
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58 const std::string& distribution, bool additional_arg = false) |
2928 | 59 { |
5730 | 60 NDArray a; |
61 int idx = 0; | |
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Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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62 bool is_single = false; |
2928 | 63 |
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64 if (nargin > 0 && args(nargin-1).is_string ()) |
14655
43db83eff9db
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65 { |
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66 std::string s_arg = args(nargin-1).string_value (); |
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67 |
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68 if (s_arg == "single") |
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69 { |
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70 is_single = true; |
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71 nargin--; |
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72 } |
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73 else if (s_arg == "double") |
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74 nargin--; |
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75 } |
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76 |
5730 | 77 if (additional_arg) |
78 { | |
79 if (nargin == 0) | |
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80 error ("%s: at least one argument is required", fcn); |
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81 else if (args(0).is_string ()) |
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82 additional_arg = false; |
5730 | 83 else |
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84 { |
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85 a = args(0).xarray_value ("%s: dimension must be a scalar integer", fcn); |
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86 |
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87 idx++; |
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88 nargin--; |
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89 } |
5730 | 90 } |
2928 | 91 |
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92 octave_value retval; |
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93 dim_vector dims; |
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94 |
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95 unwind_protect frame; |
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96 // Restore current distribution on any exit. |
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97 frame.add_fcn (octave_rand::distribution, |
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98 octave_rand::distribution ()); |
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99 |
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100 octave_rand::distribution (distribution); |
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101 |
4543 | 102 switch (nargin) |
2928 | 103 { |
4543 | 104 case 0: |
105 { | |
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106 if (additional_arg) |
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107 dims = a.dims (); |
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108 else |
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109 { |
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110 dims.resize (2); |
4543 | 111 |
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112 dims(0) = 1; |
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113 dims(1) = 1; |
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114 } |
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115 |
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116 goto gen_matrix; |
4543 | 117 } |
118 break; | |
2928 | 119 |
4543 | 120 case 1: |
121 { | |
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122 octave_value tmp = args(idx); |
4543 | 123 |
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124 if (tmp.is_string ()) |
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125 { |
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126 std::string s_arg = tmp.string_value (); |
2928 | 127 |
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128 if (s_arg == "dist") |
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129 retval = octave_rand::distribution (); |
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130 else if (s_arg == "seed") |
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131 retval = octave_rand::seed (); |
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132 else if (s_arg == "state" || s_arg == "twister") |
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133 retval = octave_rand::state (fcn); |
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134 else if (s_arg == "uniform") |
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135 octave_rand::uniform_distribution (); |
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136 else if (s_arg == "normal") |
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137 octave_rand::normal_distribution (); |
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138 else if (s_arg == "exponential") |
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139 octave_rand::exponential_distribution (); |
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140 else if (s_arg == "poisson") |
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141 octave_rand::poisson_distribution (); |
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142 else if (s_arg == "gamma") |
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143 octave_rand::gamma_distribution (); |
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144 else |
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145 error ("%s: unrecognized string argument", fcn); |
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146 } |
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147 else if (tmp.is_scalar_type ()) |
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148 { |
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149 double dval = tmp.double_value (); |
2928 | 150 |
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151 if (xisnan (dval)) |
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152 error ("%s: NaN is invalid matrix dimension", fcn); |
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153 |
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154 dims.resize (2); |
4543 | 155 |
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156 dims(0) = NINTbig (tmp.double_value ()); |
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157 dims(1) = NINTbig (tmp.double_value ()); |
2928 | 158 |
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159 goto gen_matrix; |
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160 } |
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161 else if (tmp.is_range ()) |
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162 { |
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163 Range r = tmp.range_value (); |
4543 | 164 |
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165 if (! r.all_elements_are_ints ()) |
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166 error ("%s: all elements of range must be integers", fcn); |
4543 | 167 |
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168 octave_idx_type n = r.numel (); |
4543 | 169 |
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170 dims.resize (n); |
2928 | 171 |
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172 octave_idx_type base = NINTbig (r.base ()); |
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173 octave_idx_type incr = NINTbig (r.inc ()); |
2928 | 174 |
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175 for (octave_idx_type i = 0; i < n; i++) |
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|
176 { |
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|
177 // Negative dimensions treated as zero for Matlab compatibility |
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178 dims(i) = base >= 0 ? base : 0; |
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179 base += incr; |
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180 } |
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181 |
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182 goto gen_matrix; |
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183 } |
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184 else if (tmp.is_matrix_type ()) |
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185 { |
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186 Array<int> iv; |
4543 | 187 |
20743
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188 try |
10154
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189 { |
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190 iv = tmp.int_vector_value (true); |
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|
191 } |
20785
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always throw exception after debugging with debug_on_error
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192 catch (octave_execution_exception& e) |
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193 { |
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194 error (e, "%s: dimensions must be a scalar or array of integers", fcn); |
20743
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195 } |
4543 | 196 |
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197 octave_idx_type len = iv.numel (); |
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198 |
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199 dims.resize (len); |
2928 | 200 |
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201 for (octave_idx_type i = 0; i < len; i++) |
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202 { |
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203 // Negative dimensions treated as zero for Matlab compatibility |
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204 octave_idx_type elt = iv(i); |
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205 dims(i) = elt >=0 ? elt : 0; |
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|
206 } |
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207 |
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208 goto gen_matrix; |
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|
209 } |
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|
210 else |
21129
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maint: Eliminate useless statements after err_XXX.
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21127
diff
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|
211 err_wrong_type_arg ("rand", tmp); |
4543 | 212 } |
213 break; | |
214 | |
215 default: | |
216 { | |
10154
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|
217 octave_value tmp = args(idx); |
4543 | 218 |
10154
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219 if (nargin == 2 && tmp.is_string ()) |
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|
220 { |
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221 std::string ts = tmp.string_value (); |
5164 | 222 |
10154
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|
223 if (ts == "seed") |
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|
224 { |
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|
225 if (args(idx+1).is_real_scalar ()) |
40dfc0c99116
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|
226 { |
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|
227 double d = args(idx+1).double_value (); |
2928 | 228 |
20678
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|
229 octave_rand::seed (d); |
10154
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|
230 } |
11586
12df7854fa7c
strip trailing whitespace from source files
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|
231 else if (args(idx+1).is_string () |
14846
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maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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|
232 && args(idx+1).string_value () == "reset") |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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changeset
|
233 octave_rand::reset (); |
10154
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|
234 else |
40dfc0c99116
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|
235 error ("%s: seed must be a real scalar", fcn); |
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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|
236 } |
40dfc0c99116
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|
237 else if (ts == "state" || ts == "twister") |
40dfc0c99116
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|
238 { |
10709
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Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
parents:
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diff
changeset
|
239 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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|
240 && 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
|
241 octave_rand::reset (fcn); |
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
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changeset
|
242 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
|
243 { |
11586
12df7854fa7c
strip trailing whitespace from source files
John W. Eaton <jwe@octave.org>
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11553
diff
changeset
|
244 ColumnVector s = |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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diff
changeset
|
245 ColumnVector (args(idx+1).vector_value(false, true)); |
5730 | 246 |
20678
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|
247 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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|
248 } |
10154
40dfc0c99116
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|
249 } |
40dfc0c99116
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diff
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|
250 else |
40dfc0c99116
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changeset
|
251 error ("%s: unrecognized string argument", fcn); |
40dfc0c99116
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|
252 } |
40dfc0c99116
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|
253 else |
40dfc0c99116
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changeset
|
254 { |
40dfc0c99116
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|
255 dims.resize (nargin); |
4543 | 256 |
10154
40dfc0c99116
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|
257 for (int i = 0; i < nargin; i++) |
40dfc0c99116
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changeset
|
258 { |
21041
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
Kai T. Ohlhus <k.ohlhus@gmail.com>
parents:
20940
diff
changeset
|
259 octave_idx_type elt = |
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
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diff
changeset
|
260 args(idx+i).xidx_type_value ( |
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
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20940
diff
changeset
|
261 "%s: dimension must be a scalar or array of integers", |
63374982750b
Usage of corresponding value extractor for octave_idx_type. Improves support
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20940
diff
changeset
|
262 fcn); |
20704
571508c1ed06
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diff
changeset
|
263 |
20894
e528d7ab1cad
rand.cc: Overhaul file to make use of new C++ archetype.
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parents:
20853
diff
changeset
|
264 // Negative dimensions treated as zero for Matlab compatibility |
12905
f7a8d1dafda3
Let rand accept negative dimensions (bug #33301)
Jordi Gutiérrez Hermoso <jordigh@gmail.com>
parents:
12639
diff
changeset
|
265 dims(i) = elt >= 0 ? elt : 0; |
10154
40dfc0c99116
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changeset
|
266 } |
4543 | 267 |
10154
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
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|
268 goto gen_matrix; |
40dfc0c99116
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diff
changeset
|
269 } |
4543 | 270 } |
271 break; | |
2928 | 272 } |
273 | |
21170
1edaf1ee65eb
maint: Replace some uses of goto in libinterp.
Rik <rik@octave.org>
parents:
21129
diff
changeset
|
274 // No "goto gen_matrix" in code path. Must be done processing. |
2928 | 275 return retval; |
276 | |
17787
175b392e91fe
Use GNU style coding conventions for code in libinterp/
Rik <rik@octave.org>
parents:
17744
diff
changeset
|
277 gen_matrix: |
2928 | 278 |
5355 | 279 dims.chop_trailing_singletons (); |
280 | |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
281 if (is_single) |
5730 | 282 { |
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
|
283 if (additional_arg) |
10154
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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10066
diff
changeset
|
284 { |
20232
a9574e3c6e9e
Deprecate Array::length() and Sparse::length() in favour of ::numel().
Carnë Draug <carandraug@octave.org>
parents:
20228
diff
changeset
|
285 if (a.numel () == 1) |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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14620
diff
changeset
|
286 return octave_rand::float_nd_array (dims, a(0)); |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
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14620
diff
changeset
|
287 else |
10154
40dfc0c99116
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diff
changeset
|
288 { |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
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14706
diff
changeset
|
289 if (a.dims () != dims) |
20831
35241c4b696c
eliminate return statements after calls to error
John W. Eaton <jwe@octave.org>
parents:
20801
diff
changeset
|
290 error ("%s: mismatch in argument size", fcn); |
35241c4b696c
eliminate return statements after calls to error
John W. Eaton <jwe@octave.org>
parents:
20801
diff
changeset
|
291 |
20232
a9574e3c6e9e
Deprecate Array::length() and Sparse::length() in favour of ::numel().
Carnë Draug <carandraug@octave.org>
parents:
20228
diff
changeset
|
292 octave_idx_type len = a.numel (); |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
293 FloatNDArray m (dims); |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
294 float *v = m.fortran_vec (); |
20894
e528d7ab1cad
rand.cc: Overhaul file to make use of new C++ archetype.
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parents:
20853
diff
changeset
|
295 |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
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14620
diff
changeset
|
296 for (octave_idx_type i = 0; i < len; i++) |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
297 v[i] = octave_rand::float_scalar (a(i)); |
20894
e528d7ab1cad
rand.cc: Overhaul file to make use of new C++ archetype.
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20853
diff
changeset
|
298 |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
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14620
diff
changeset
|
299 return m; |
10154
40dfc0c99116
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10066
diff
changeset
|
300 } |
40dfc0c99116
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John W. Eaton <jwe@octave.org>
parents:
10066
diff
changeset
|
301 } |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
302 else |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
303 return octave_rand::float_nd_array (dims); |
5730 | 304 } |
305 else | |
14655
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
306 { |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
307 if (additional_arg) |
43db83eff9db
Implement single precision rand, randn, rande, randg and randp generators (bug #34351, #36293)
David Bateman <dbateman@free.fr>
parents:
14620
diff
changeset
|
308 { |
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309 if (a.numel () == 1) |
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310 return octave_rand::nd_array (dims, a(0)); |
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311 else |
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312 { |
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313 if (a.dims () != dims) |
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314 error ("%s: mismatch in argument size", fcn); |
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315 |
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316 octave_idx_type len = a.numel (); |
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317 NDArray m (dims); |
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318 double *v = m.fortran_vec (); |
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319 |
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320 for (octave_idx_type i = 0; i < len; i++) |
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321 v[i] = octave_rand::scalar (a(i)); |
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322 |
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323 return m; |
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324 } |
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325 } |
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326 else |
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327 return octave_rand::nd_array (dims); |
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328 } |
2928 | 329 } |
330 | |
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331 DEFUN (rand, args, , |
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332 "-*- texinfo -*-\n\ |
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333 @deftypefn {} {} rand (@var{n})\n\ |
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334 @deftypefnx {} {} rand (@var{m}, @var{n}, @dots{})\n\ |
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335 @deftypefnx {} {} rand ([@var{m} @var{n} @dots{}])\n\ |
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336 @deftypefnx {} {@var{v} =} rand (\"state\")\n\ |
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337 @deftypefnx {} {} rand (\"state\", @var{v})\n\ |
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338 @deftypefnx {} {} rand (\"state\", \"reset\")\n\ |
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339 @deftypefnx {} {@var{v} =} rand (\"seed\")\n\ |
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340 @deftypefnx {} {} rand (\"seed\", @var{v})\n\ |
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341 @deftypefnx {} {} rand (\"seed\", \"reset\")\n\ |
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342 @deftypefnx {} {} rand (@dots{}, \"single\")\n\ |
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343 @deftypefnx {} {} rand (@dots{}, \"double\")\n\ |
3369 | 344 Return a matrix with random elements uniformly distributed on the\n\ |
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345 interval (0, 1).\n\ |
5730 | 346 \n\ |
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347 The arguments are handled the same as the arguments for @code{eye}.\n\ |
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348 \n\ |
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349 You can query the state of the random number generator using the form\n\ |
2928 | 350 \n\ |
3369 | 351 @example\n\ |
5730 | 352 v = rand (\"state\")\n\ |
353 @end example\n\ | |
354 \n\ | |
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355 This returns a column vector @var{v} of length 625. Later, you can restore\n\ |
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356 the random number generator to the state @var{v} using the form\n\ |
5730 | 357 \n\ |
358 @example\n\ | |
359 rand (\"state\", v)\n\ | |
3369 | 360 @end example\n\ |
361 \n\ | |
362 @noindent\n\ | |
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363 You may also initialize the state vector from an arbitrary vector of length\n\ |
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364 @leq{} 625 for @var{v}. This new state will be a hash based on the value of\n\ |
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365 @var{v}, not @var{v} itself.\n\ |
5730 | 366 \n\ |
367 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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368 available, otherwise from CPU time, wall clock time, and the current\n\ |
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369 fraction of a second. Note that this differs from @sc{matlab}, which\n\ |
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370 always initializes the state to the same state at startup. To obtain\n\ |
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371 behavior comparable to @sc{matlab}, initialize with a deterministic state\n\ |
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372 vector in Octave's startup files (@pxref{Startup Files}).\n\ |
5730 | 373 \n\ |
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374 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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375 Twister with a period of @math{2^{19937}-1}\n\ |
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376 (See @nospell{M. Matsumoto and T. Nishimura},\n\ |
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377 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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378 pseudorandom number generator},\n\ |
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379 ACM Trans. on Modeling and Computer Simulation Vol. 8, No. 1, pp. 3--30,\n\ |
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380 January 1998,\n\ |
7171 | 381 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
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382 Do @strong{not} use for cryptography without securely hashing several\n\ |
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383 returned values together, otherwise the generator state can be learned after\n\ |
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384 reading 624 consecutive values.\n\ |
5730 | 385 \n\ |
7096 | 386 Older versions of Octave used a different random number generator.\n\ |
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387 The new generator is used by default as it is significantly faster than the\n\ |
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388 old generator, and produces random numbers with a significantly longer cycle\n\ |
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389 time. However, in some circumstances it might be desirable to obtain the\n\ |
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390 same random sequences as produced by the old generators. To do this the\n\ |
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391 keyword @qcode{\"seed\"} is used to specify that the old generators should\n\ |
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392 be used, as in\n\ |
2928 | 393 \n\ |
3369 | 394 @example\n\ |
5730 | 395 rand (\"seed\", val)\n\ |
3369 | 396 @end example\n\ |
397 \n\ | |
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398 @noindent\n\ |
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399 which sets the seed of the generator to @var{val}. The seed of the\n\ |
5730 | 400 generator can be queried with\n\ |
401 \n\ | |
402 @example\n\ | |
403 s = rand (\"seed\")\n\ | |
404 @end example\n\ | |
405 \n\ | |
406 However, it should be noted that querying the seed will not cause\n\ | |
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407 @code{rand} to use the old generators, only setting the seed will. To cause\n\ |
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408 @code{rand} to once again use the new generators, the keyword\n\ |
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409 @qcode{\"state\"} should be used to reset the state of the @code{rand}.\n\ |
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410 \n\ |
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411 The state or seed of the generator can be reset to a new random value using\n\ |
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412 the @qcode{\"reset\"} keyword.\n\ |
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413 \n\ |
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414 The class of the value returned can be controlled by a trailing\n\ |
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415 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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416 classes.\n\ |
5798 | 417 @seealso{randn, rande, randg, randp}\n\ |
3369 | 418 @end deftypefn") |
2928 | 419 { |
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420 return do_rand (args, args.length (), "rand", "uniform"); |
2928 | 421 } |
422 | |
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423 // FIXME: The old generator (selected when "seed" is set) will not |
8871 | 424 // work properly if compiled to use 64-bit integers. |
425 | |
5730 | 426 /* |
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427 %!test # "state" can be a scalar |
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428 %! rand ("state", 12); x = rand (1,4); |
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429 %! rand ("state", 12); y = rand (1,4); |
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430 %! assert (x, y); |
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431 %!test # "state" can be a vector |
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432 %! rand ("state", [12,13]); x = rand (1,4); |
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433 %! rand ("state", [12;13]); y = rand (1,4); |
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434 %! assert (x, y); |
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435 %!test # querying "state" doesn't disturb sequence |
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436 %! rand ("state", 12); rand (1,2); x = rand (1,2); |
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437 %! rand ("state", 12); rand (1,2); |
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438 %! s = rand ("state"); y = rand (1,2); |
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439 %! assert (x, y); |
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440 %! rand ("state", s); z = rand (1,2); |
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441 %! assert (x, z); |
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442 %!test # "seed" must be a scalar |
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443 %! rand ("seed", 12); x = rand (1,4); |
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444 %! rand ("seed", 12); y = rand (1,4); |
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445 %! assert (x, y); |
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446 %!error <seed must be a real scalar> rand ("seed", [12,13]) |
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447 %!test # querying "seed" returns a value which can be used later |
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448 %! s = rand ("seed"); x = rand (1,2); |
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449 %! rand ("seed", s); y = rand (1,2); |
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450 %! assert (x, y); |
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451 %!test # querying "seed" doesn't disturb sequence |
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452 %! rand ("seed", 12); rand (1,2); x = rand (1,2); |
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453 %! rand ("seed", 12); rand (1,2); |
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454 %! s = rand ("seed"); y = rand (1,2); |
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455 %! assert (x, y); |
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456 %! rand ("seed", s); z = rand (1,2); |
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457 %! assert (x, z); |
5730 | 458 */ |
459 | |
460 /* | |
461 %!test | |
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462 %! ## Test fixed state |
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463 %! rand ("state", 1); |
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464 %! assert (rand (1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382], 1e-6); |
6437 | 465 %!test |
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466 %! ## Test fixed seed |
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467 %! rand ("seed", 1); |
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468 %! assert (rand (1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759], 1e-6); |
5730 | 469 %!test |
6437 | 470 %! if (__random_statistical_tests__) |
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471 %! ## statistical tests may fail occasionally. |
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472 %! rand ("state", 12); |
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473 %! x = rand (100000, 1); |
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474 %! assert (max (x) < 1); #*** Please report this!!! *** |
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475 %! assert (min (x) > 0); #*** Please report this!!! *** |
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476 %! assert (mean (x), 0.5, 0.0024); |
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477 %! assert (var (x), 1/48, 0.0632); |
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478 %! assert (skewness (x), 0, 0.012); |
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479 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 480 %! endif |
481 %!test | |
482 %! if (__random_statistical_tests__) | |
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483 %! ## statistical tests may fail occasionally. |
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484 %! rand ("seed", 12); |
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485 %! x = rand (100000, 1); |
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486 %! assert (max (x) < 1); #*** Please report this!!! *** |
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487 %! assert (min (x) > 0); #*** Please report this!!! *** |
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488 %! assert (mean (x), 0.5, 0.0024); |
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489 %! assert (var (x), 1/48, 0.0632); |
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490 %! assert (skewness (x), 0, 0.012); |
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491 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 492 %! endif |
5730 | 493 */ |
494 | |
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495 /* |
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496 %!# Test out-of-range values as rand() seeds. See oct-rand.cc: double2uint32(). |
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497 %!function v = __rand_sample__ (initval) |
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498 %! rand ("state", initval); |
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499 %! v = rand (1, 6); |
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500 %!endfunction |
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501 %! |
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502 %!assert (__rand_sample__ (0), __rand_sample__ (2^32)) |
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503 %!assert (__rand_sample__ (-2), __rand_sample__ (2^32-2)) |
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504 %!assert (__rand_sample__ (Inf), __rand_sample__ (NaN)) |
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505 %!assert (! isequal (__rand_sample__ (-1), __rand_sample__ (-2))) |
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506 */ |
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507 |
4307 | 508 static std::string current_distribution = octave_rand::distribution (); |
509 | |
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510 DEFUN (randn, args, , |
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511 "-*- texinfo -*-\n\ |
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512 @deftypefn {} {} randn (@var{n})\n\ |
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513 @deftypefnx {} {} randn (@var{m}, @var{n}, @dots{})\n\ |
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514 @deftypefnx {} {} randn ([@var{m} @var{n} @dots{}])\n\ |
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515 @deftypefnx {} {@var{v} =} randn (\"state\")\n\ |
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516 @deftypefnx {} {} randn (\"state\", @var{v})\n\ |
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517 @deftypefnx {} {} randn (\"state\", \"reset\")\n\ |
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518 @deftypefnx {} {@var{v} =} randn (\"seed\")\n\ |
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519 @deftypefnx {} {} randn (\"seed\", @var{v})\n\ |
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520 @deftypefnx {} {} randn (\"seed\", \"reset\")\n\ |
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521 @deftypefnx {} {} randn (@dots{}, \"single\")\n\ |
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522 @deftypefnx {} {} randn (@dots{}, \"double\")\n\ |
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523 Return a matrix with normally distributed random elements having zero mean\n\ |
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524 and variance one.\n\ |
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525 \n\ |
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526 The arguments are handled the same as the arguments for @code{rand}.\n\ |
3369 | 527 \n\ |
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528 By default, @code{randn} uses the @nospell{Marsaglia and Tsang}\n\ |
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529 ``Ziggurat technique'' to transform from a uniform to a normal distribution.\n\ |
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530 \n\ |
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531 The class of the value returned can be controlled by a trailing\n\ |
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532 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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533 classes.\n\ |
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534 \n\ |
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535 Reference: @nospell{G. Marsaglia and W.W. Tsang},\n\ |
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536 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 537 J. Statistical Software, vol 5, 2000,\n\ |
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538 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
2928 | 539 \n\ |
6547 | 540 @seealso{rand, rande, randg, randp}\n\ |
3369 | 541 @end deftypefn") |
2928 | 542 { |
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543 return do_rand (args, args.length (), "randn", "normal"); |
2928 | 544 } |
545 | |
546 /* | |
5730 | 547 %!test |
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548 %! ## Test fixed state |
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549 %! randn ("state", 1); |
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550 %! assert (randn (1, 6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116], 1e-6); |
6437 | 551 %!test |
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552 %! ## Test fixed seed |
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553 %! randn ("seed", 1); |
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554 %! assert (randn (1, 6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133], 1e-6); |
5730 | 555 %!test |
6437 | 556 %! if (__random_statistical_tests__) |
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557 %! ## statistical tests may fail occasionally. |
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558 %! randn ("state", 12); |
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559 %! x = randn (100000, 1); |
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560 %! assert (mean (x), 0, 0.01); |
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561 %! assert (var (x), 1, 0.02); |
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562 %! assert (skewness (x), 0, 0.02); |
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563 %! assert (kurtosis (x), 0, 0.04); |
6437 | 564 %! endif |
565 %!test | |
566 %! if (__random_statistical_tests__) | |
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567 %! ## statistical tests may fail occasionally. |
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568 %! randn ("seed", 12); |
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569 %! x = randn (100000, 1); |
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570 %! assert (mean (x), 0, 0.01); |
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571 %! assert (var (x), 1, 0.02); |
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572 %! assert (skewness (x), 0, 0.02); |
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573 %! assert (kurtosis (x), 0, 0.04); |
6437 | 574 %! endif |
5730 | 575 */ |
576 | |
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577 DEFUN (rande, args, , |
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578 "-*- texinfo -*-\n\ |
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579 @deftypefn {} {} rande (@var{n})\n\ |
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580 @deftypefnx {} {} rande (@var{m}, @var{n}, @dots{})\n\ |
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581 @deftypefnx {} {} rande ([@var{m} @var{n} @dots{}])\n\ |
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582 @deftypefnx {} {@var{v} =} rande (\"state\")\n\ |
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583 @deftypefnx {} {} rande (\"state\", @var{v})\n\ |
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584 @deftypefnx {} {} rande (\"state\", \"reset\")\n\ |
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585 @deftypefnx {} {@var{v} =} rande (\"seed\")\n\ |
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586 @deftypefnx {} {} rande (\"seed\", @var{v})\n\ |
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587 @deftypefnx {} {} rande (\"seed\", \"reset\")\n\ |
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588 @deftypefnx {} {} rande (@dots{}, \"single\")\n\ |
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589 @deftypefnx {} {} rande (@dots{}, \"double\")\n\ |
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590 Return a matrix with exponentially distributed random elements.\n\ |
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591 \n\ |
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592 The arguments are handled the same as the arguments for @code{rand}.\n\ |
5730 | 593 \n\ |
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594 By default, @code{rande} uses the @nospell{Marsaglia and Tsang}\n\ |
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595 ``Ziggurat technique'' to transform from a uniform to an exponential\n\ |
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596 distribution.\n\ |
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597 \n\ |
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598 The class of the value returned can be controlled by a trailing\n\ |
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599 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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600 classes.\n\ |
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601 \n\ |
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602 Reference: @nospell{G. Marsaglia and W.W. Tsang},\n\ |
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603 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 604 J. Statistical Software, vol 5, 2000,\n\ |
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605 @url{http://www.jstatsoft.org/v05/i08/}\n\ |
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606 \n\ |
6547 | 607 @seealso{rand, randn, randg, randp}\n\ |
5730 | 608 @end deftypefn") |
609 { | |
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610 return do_rand (args, args.length (), "rande", "exponential"); |
5730 | 611 } |
612 | |
613 /* | |
614 %!test | |
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615 %! ## Test fixed state |
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616 %! rande ("state", 1); |
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617 %! assert (rande (1, 6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291], 1e-6); |
6437 | 618 %!test |
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619 %! ## Test fixed seed |
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620 %! rande ("seed", 1); |
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621 %! assert (rande (1, 6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087], 1e-6); |
5730 | 622 %!test |
6437 | 623 %! if (__random_statistical_tests__) |
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624 %! ## statistical tests may fail occasionally |
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625 %! rande ("state", 1); |
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626 %! x = rande (100000, 1); |
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627 %! assert (min (x) > 0); # *** Please report this!!! *** |
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628 %! assert (mean (x), 1, 0.01); |
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629 %! assert (var (x), 1, 0.03); |
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630 %! assert (skewness (x), 2, 0.06); |
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631 %! assert (kurtosis (x), 6, 0.7); |
6437 | 632 %! endif |
633 %!test | |
634 %! if (__random_statistical_tests__) | |
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635 %! ## statistical tests may fail occasionally |
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636 %! rande ("seed", 1); |
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637 %! x = rande (100000, 1); |
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638 %! assert (min (x)>0); # *** Please report this!!! *** |
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639 %! assert (mean (x), 1, 0.01); |
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640 %! assert (var (x), 1, 0.03); |
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641 %! assert (skewness (x), 2, 0.06); |
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642 %! assert (kurtosis (x), 6, 0.7); |
6437 | 643 %! endif |
5730 | 644 */ |
645 | |
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646 DEFUN (randg, args, , |
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647 "-*- texinfo -*-\n\ |
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648 @deftypefn {} {} randg (@var{n})\n\ |
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649 @deftypefnx {} {} randg (@var{m}, @var{n}, @dots{})\n\ |
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650 @deftypefnx {} {} randg ([@var{m} @var{n} @dots{}])\n\ |
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651 @deftypefnx {} {@var{v} =} randg (\"state\")\n\ |
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652 @deftypefnx {} {} randg (\"state\", @var{v})\n\ |
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653 @deftypefnx {} {} randg (\"state\", \"reset\")\n\ |
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654 @deftypefnx {} {@var{v} =} randg (\"seed\")\n\ |
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655 @deftypefnx {} {} randg (\"seed\", @var{v})\n\ |
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656 @deftypefnx {} {} randg (\"seed\", \"reset\")\n\ |
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657 @deftypefnx {} {} randg (@dots{}, \"single\")\n\ |
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658 @deftypefnx {} {} randg (@dots{}, \"double\")\n\ |
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659 Return a matrix with @code{gamma (@var{a},1)} distributed random elements.\n\ |
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660 \n\ |
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661 The arguments are handled the same as the arguments for @code{rand}, except\n\ |
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662 for the argument @var{a}.\n\ |
5730 | 663 \n\ |
664 This can be used to generate many distributions:\n\ | |
665 \n\ | |
666 @table @asis\n\ | |
6547 | 667 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
10840 | 668 \n\ |
5730 | 669 @example\n\ |
6547 | 670 r = b * randg (a)\n\ |
5730 | 671 @end example\n\ |
10840 | 672 \n\ |
6547 | 673 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
10840 | 674 \n\ |
5730 | 675 @example\n\ |
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676 @group\n\ |
6547 | 677 r1 = randg (a, 1)\n\ |
678 r = r1 / (r1 + randg (b, 1))\n\ | |
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679 @end group\n\ |
5730 | 680 @end example\n\ |
10840 | 681 \n\ |
6547 | 682 @item @code{Erlang (a, n)}\n\ |
10840 | 683 \n\ |
5730 | 684 @example\n\ |
6547 | 685 r = a * randg (n)\n\ |
5730 | 686 @end example\n\ |
10840 | 687 \n\ |
6547 | 688 @item @code{chisq (df)} for @code{df > 0}\n\ |
10840 | 689 \n\ |
5730 | 690 @example\n\ |
6547 | 691 r = 2 * randg (df / 2)\n\ |
5730 | 692 @end example\n\ |
10840 | 693 \n\ |
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694 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
10840 | 695 \n\ |
5730 | 696 @example\n\ |
6547 | 697 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
5730 | 698 @end example\n\ |
10840 | 699 \n\ |
6547 | 700 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
10840 | 701 \n\ |
5730 | 702 @example\n\ |
7096 | 703 @group\n\ |
704 ## r1 equals 1 if n1 is infinite\n\ | |
705 r1 = 2 * randg (n1 / 2) / n1\n\ | |
706 ## r2 equals 1 if n2 is infinite\n\ | |
707 r2 = 2 * randg (n2 / 2) / n2\n\ | |
5730 | 708 r = r1 / r2\n\n\ |
7096 | 709 @end group\n\ |
5730 | 710 @end example\n\ |
10840 | 711 \n\ |
5730 | 712 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
10840 | 713 \n\ |
5730 | 714 @example\n\ |
6547 | 715 r = randp ((1 - p) / p * randg (n))\n\ |
5730 | 716 @end example\n\ |
10840 | 717 \n\ |
6547 | 718 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
5730 | 719 (use chisq if @code{L = 0})\n\ |
10840 | 720 \n\ |
5730 | 721 @example\n\ |
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722 @group\n\ |
6547 | 723 r = randp (L / 2)\n\ |
724 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
725 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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726 @end group\n\ |
5730 | 727 @end example\n\ |
10840 | 728 \n\ |
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729 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
10840 | 730 \n\ |
5730 | 731 @example\n\ |
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732 @group\n\ |
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733 r = (randg (a1), @dots{}, randg (ak))\n\ |
6547 | 734 r = r / sum (r)\n\ |
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735 @end group\n\ |
5730 | 736 @end example\n\ |
10840 | 737 \n\ |
5730 | 738 @end table\n\ |
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739 \n\ |
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740 The class of the value returned can be controlled by a trailing\n\ |
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741 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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742 classes.\n\ |
6547 | 743 @seealso{rand, randn, rande, randp}\n\ |
5730 | 744 @end deftypefn") |
745 { | |
746 int nargin = args.length (); | |
747 | |
748 if (nargin < 1) | |
749 error ("randg: insufficient arguments"); | |
750 | |
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751 return do_rand (args, nargin, "randg", "gamma", true); |
5730 | 752 } |
753 | |
754 /* | |
755 %!test | |
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756 %! randg ("state", 12) |
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757 %! assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]); # *** Please report |
6437 | 758 |
759 %!test | |
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760 %! ## Test fixed state |
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761 %! randg ("state", 1); |
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762 %! assert (randg (0.1, 1, 6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12], 1e-6); |
6437 | 763 %!test |
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764 %! ## Test fixed state |
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765 %! randg ("state", 1); |
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766 %! assert (randg (0.95, 1, 6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939], 1e-6); |
6437 | 767 %!test |
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768 %! ## Test fixed state |
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769 %! randg ("state", 1); |
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770 %! assert (randg (1, 1, 6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533], 1e-6); |
6437 | 771 %!test |
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772 %! ## Test fixed state |
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773 %! randg ("state", 1); |
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774 %! assert (randg (10, 1, 6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059], 1e-5); |
6437 | 775 %!test |
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776 %! ## Test fixed state |
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777 %! randg ("state", 1); |
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778 %! assert (randg (100, 1, 6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118], 1e-4); |
6437 | 779 %!test |
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780 %! ## Test fixed seed |
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781 %! randg ("seed", 1); |
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782 %! assert (randg (0.1, 1, 6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12], 1e-6); |
6437 | 783 %!test |
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784 %! ## Test fixed seed |
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785 %! randg ("seed", 1); |
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786 %! assert (randg (0.95, 1, 6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648], 1e-6); |
6437 | 787 %!test |
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788 %! ## Test fixed seed |
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789 %! randg ("seed", 1); |
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790 %! assert (randg (1, 1, 6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303], 1e-6); |
6437 | 791 %!test |
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792 %! ## Test fixed seed |
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793 %! randg ("seed", 1); |
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794 %! assert (randg (10, 1, 6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186], 1e-5); |
6437 | 795 %!test |
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796 %! ## Test fixed seed |
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797 %! randg ("seed", 1); |
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798 %! assert (randg (100, 1, 6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625], 1e-4); |
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799 %!test |
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800 %! ## Test out-of-bounds values produce NaN w/old-style generators & floats |
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801 %! randg ("seed", 1); |
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802 %! result = randg ([-2 Inf], "single"); |
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803 %! assert (result, single ([NaN NaN])); |
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804 |
6437 | 805 %!test |
806 %! if (__random_statistical_tests__) | |
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807 %! ## statistical tests may fail occasionally. |
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808 %! randg ("state", 12); |
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809 %! a = 0.1; |
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810 %! x = randg (a, 100000, 1); |
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811 %! assert (mean (x), a, 0.01); |
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812 %! assert (var (x), a, 0.01); |
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813 %! assert (skewness (x), 2/sqrt (a), 1); |
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814 %! assert (kurtosis (x), 6/a, 50); |
6437 | 815 %! endif |
816 %!test | |
817 %! if (__random_statistical_tests__) | |
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818 %! ## statistical tests may fail occasionally. |
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819 %! randg ("state", 12); |
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820 %! a = 0.95; |
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821 %! x = randg (a, 100000, 1); |
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822 %! assert (mean (x), a, 0.01); |
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823 %! assert (var (x), a, 0.04); |
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824 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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825 %! assert (kurtosis (x), 6/a, 2); |
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826 %! endif |
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827 %!test |
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828 %! if (__random_statistical_tests__) |
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829 %! ## statistical tests may fail occasionally. |
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830 %! randg ("state", 12); |
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831 %! a = 1; |
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832 %! x = randg (a, 100000, 1); |
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833 %! assert (mean (x), a, 0.01); |
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834 %! assert (var (x), a, 0.04); |
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835 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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836 %! assert (kurtosis (x), 6/a, 2); |
6437 | 837 %! endif |
838 %!test | |
839 %! if (__random_statistical_tests__) | |
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840 %! ## statistical tests may fail occasionally. |
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841 %! randg ("state", 12); |
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842 %! a = 10; |
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843 %! x = randg (a, 100000, 1); |
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844 %! assert (mean (x), a, 0.1); |
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845 %! assert (var (x), a, 0.5); |
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846 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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847 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 848 %! endif |
849 %!test | |
850 %! if (__random_statistical_tests__) | |
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851 %! ## statistical tests may fail occasionally. |
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852 %! randg ("state", 12); |
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853 %! a = 100; |
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854 %! x = randg (a, 100000, 1); |
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855 %! assert (mean (x), a, 0.2); |
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856 %! assert (var (x), a, 2); |
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857 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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858 %! assert (kurtosis (x), 6/a, 0.2); |
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859 %! endif |
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860 %!test |
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861 %! randg ("seed", 12); |
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862 %!assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]) # *** Please report |
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863 %!test |
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864 %! if (__random_statistical_tests__) |
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865 %! ## statistical tests may fail occasionally. |
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866 %! randg ("seed", 12); |
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867 %! a = 0.1; |
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868 %! x = randg (a, 100000, 1); |
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869 %! assert (mean (x), a, 0.01); |
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870 %! assert (var (x), a, 0.01); |
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871 %! assert (skewness (x), 2/sqrt (a), 1); |
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872 %! assert (kurtosis (x), 6/a, 50); |
6437 | 873 %! endif |
874 %!test | |
875 %! if (__random_statistical_tests__) | |
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876 %! ## statistical tests may fail occasionally. |
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877 %! randg ("seed", 12); |
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878 %! a = 0.95; |
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879 %! x = randg (a, 100000, 1); |
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880 %! assert (mean (x), a, 0.01); |
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881 %! assert (var (x), a, 0.04); |
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882 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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883 %! assert (kurtosis (x), 6/a, 2); |
6437 | 884 %! endif |
885 %!test | |
886 %! if (__random_statistical_tests__) | |
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887 %! ## statistical tests may fail occasionally. |
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888 %! randg ("seed", 12); |
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889 %! a = 1; |
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890 %! x = randg (a, 100000, 1); |
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891 %! assert (mean (x), a, 0.01); |
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892 %! assert (var (x), a, 0.04); |
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893 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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894 %! assert (kurtosis (x), 6/a, 2); |
6437 | 895 %! endif |
5730 | 896 %!test |
6437 | 897 %! if (__random_statistical_tests__) |
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898 %! ## statistical tests may fail occasionally. |
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899 %! randg ("seed", 12); |
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900 %! a = 10; |
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901 %! x = randg (a, 100000, 1); |
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902 %! assert (mean (x), a, 0.1); |
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903 %! assert (var (x), a, 0.5); |
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904 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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905 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 906 %! endif |
5730 | 907 %!test |
6437 | 908 %! if (__random_statistical_tests__) |
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909 %! ## statistical tests may fail occasionally. |
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910 %! randg ("seed", 12); |
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911 %! a = 100; |
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912 %! x = randg (a, 100000, 1); |
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913 %! assert (mean (x), a, 0.2); |
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914 %! assert (var (x), a, 2); |
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915 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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916 %! assert (kurtosis (x), 6/a, 0.2); |
6437 | 917 %! endif |
5730 | 918 */ |
919 | |
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920 DEFUN (randp, args, , |
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921 "-*- texinfo -*-\n\ |
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922 @deftypefn {} {} randp (@var{l}, @var{n})\n\ |
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923 @deftypefnx {} {} randp (@var{l}, @var{m}, @var{n}, @dots{})\n\ |
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924 @deftypefnx {} {} randp (@var{l}, [@var{m} @var{n} @dots{}])\n\ |
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925 @deftypefnx {} {@var{v} =} randp (\"state\")\n\ |
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926 @deftypefnx {} {} randp (\"state\", @var{v})\n\ |
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927 @deftypefnx {} {} randp (\"state\", \"reset\")\n\ |
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928 @deftypefnx {} {@var{v} =} randp (\"seed\")\n\ |
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929 @deftypefnx {} {} randp (\"seed\", @var{v})\n\ |
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930 @deftypefnx {} {} randp (\"seed\", \"reset\")\n\ |
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931 @deftypefnx {} {} randp (@dots{}, \"single\")\n\ |
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932 @deftypefnx {} {} randp (@dots{}, \"double\")\n\ |
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933 Return a matrix with Poisson distributed random elements with mean value\n\ |
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934 parameter given by the first argument, @var{l}.\n\ |
5730 | 935 \n\ |
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936 The arguments are handled the same as the arguments for @code{rand}, except\n\ |
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937 for the argument @var{l}.\n\ |
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938 \n\ |
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939 Five different algorithms are used depending on the range of @var{l} and\n\ |
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940 whether or not @var{l} is a scalar or a matrix.\n\ |
5730 | 941 \n\ |
942 @table @asis\n\ | |
10840 | 943 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
944 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
945 Cambridge University Press, 1992.\n\ | |
946 \n\ | |
5730 | 947 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
10840 | 948 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
949 Cambridge University Press, 1992.\n\ | |
950 \n\ | |
951 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
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952 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP.\n\ |
10840 | 953 \n\ |
5730 | 954 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
19040
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955 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP, or\n\ |
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956 @nospell{H. Zechner}, @cite{Efficient sampling from continuous and discrete\n\ |
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957 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
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958 University @nospell{Graz}, Austria, 1994.\n\ |
10840 | 959 \n\ |
5730 | 960 @item For @var{l} > 1e8, use normal approximation.\n\ |
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961 @nospell{L. Montanet}, et al., @cite{Review of Particle Properties},\n\ |
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962 Physical Review D 50 p1284, 1994.\n\ |
5730 | 963 @end table\n\ |
14655
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964 \n\ |
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965 The class of the value returned can be controlled by a trailing\n\ |
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966 @qcode{\"double\"} or @qcode{\"single\"} argument. These are the only valid\n\ |
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967 classes.\n\ |
6547 | 968 @seealso{rand, randn, rande, randg}\n\ |
5730 | 969 @end deftypefn") |
970 { | |
971 int nargin = args.length (); | |
972 | |
973 if (nargin < 1) | |
974 error ("randp: insufficient arguments"); | |
975 | |
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976 return do_rand (args, nargin, "randp", "poisson", true); |
5730 | 977 } |
978 | |
979 /* | |
980 %!test | |
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981 %! randp ("state", 12); |
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982 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
6437 | 983 %!test |
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984 %! ## Test fixed state |
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985 %! randp ("state", 1); |
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986 %! assert (randp (5, 1, 6), [5 5 3 7 7 3]) |
6437 | 987 %!test |
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988 %! ## Test fixed state |
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989 %! randp ("state", 1); |
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990 %! assert (randp (15, 1, 6), [13 15 8 18 18 15]) |
6437 | 991 %!test |
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992 %! ## Test fixed state |
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993 %! randp ("state", 1); |
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994 %! assert (randp (1e9, 1, 6), [999915677 999976657 1000047684 1000019035 999985749 999977692], -1e-6) |
6437 | 995 %!test |
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996 %! ## Test fixed state |
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997 %! randp ("seed", 1); |
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998 %! %%assert (randp (5, 1, 6), [8 2 3 6 6 8]) |
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999 %! assert (randp (5, 1, 5), [8 2 3 6 6]) |
6437 | 1000 %!test |
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1001 %! ## Test fixed state |
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1002 %! randp ("seed", 1); |
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1003 %! assert (randp (15, 1, 6), [15 16 12 10 10 12]) |
6437 | 1004 %!test |
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1005 %! ## Test fixed state |
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1006 %! randp ("seed", 1); |
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1007 %! assert (randp (1e9, 1, 6), [1000006208 1000012224 999981120 999963520 999963072 999981440], -1e-6) |
6437 | 1008 %!test |
1009 %! if (__random_statistical_tests__) | |
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1010 %! ## statistical tests may fail occasionally. |
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1011 %! randp ("state", 12); |
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1012 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1013 %! x = randp (a (1), 100000, 1); |
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1014 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1015 %! assert (mean (x), a(1), a(2)); |
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1016 %! assert (var (x), a(1), 0.02*a(1)); |
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1017 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1018 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1019 %! endfor |
1020 %! endif | |
1021 %!test | |
1022 %! if (__random_statistical_tests__) | |
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1023 %! ## statistical tests may fail occasionally. |
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1024 %! randp ("state", 12); |
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1025 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1026 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1027 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1028 %! assert (mean (x), a(1), a(2)); |
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1029 %! assert (var (x), a(1), 0.02*a(1)); |
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1030 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1031 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1032 %! endfor |
1033 %! endif | |
1034 %!test | |
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1035 %! randp ("seed", 12); |
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1036 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
5730 | 1037 %!test |
6449 | 1038 %! if (__random_statistical_tests__) |
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1039 %! ## statistical tests may fail occasionally. |
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1040 %! randp ("seed", 12); |
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1041 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1042 %! x = randp (a(1), 100000, 1); |
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1043 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1044 %! assert (mean (x), a(1), a(2)); |
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1045 %! assert (var (x), a(1), 0.02*a(1)); |
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1046 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1047 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1048 %! endfor |
1049 %! endif | |
5730 | 1050 %!test |
6449 | 1051 %! if (__random_statistical_tests__) |
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1052 %! ## statistical tests may fail occasionally. |
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1053 %! randp ("seed", 12); |
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1054 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1055 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1056 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1057 %! assert (mean (x), a(1), a(2)); |
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1058 %! assert (var (x), a(1), 0.02*a(1)); |
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1059 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1060 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1061 %! endfor |
1062 %! endif | |
5730 | 1063 */ |
1064 | |
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1065 DEFUN (randperm, args, , |
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1066 "-*- texinfo -*-\n\ |
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1067 @deftypefn {} {} randperm (@var{n})\n\ |
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1068 @deftypefnx {} {} randperm (@var{n}, @var{m})\n\ |
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1069 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
20172
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1070 \n\ |
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1071 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
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1072 replacement from @code{1:@var{n}}.\n\ |
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1073 \n\ |
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1074 The complexity is O(@var{n}) in memory and O(@var{m}) in time, unless\n\ |
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1075 @var{m} < @var{n}/5, in which case O(@var{m}) memory is used as well. The\n\ |
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1076 randomization is performed using rand(). All permutations are equally\n\ |
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1077 likely.\n\ |
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1078 @seealso{perms}\n\ |
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1079 @end deftypefn") |
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1080 { |
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1081 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
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1082 using std::tr1::unordered_map; |
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1083 #else |
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1084 using std::unordered_map; |
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1085 #endif |
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1086 |
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1087 int nargin = args.length (); |
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1088 |
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1089 if (nargin < 1 || nargin > 2) |
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1090 print_usage (); |
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1091 |
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1092 octave_idx_type n = args(0).idx_type_value (true); |
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1093 octave_idx_type m = (nargin == 2) ? args(1).idx_type_value (true) : n; |
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1094 |
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1095 if (m < 0 || n < 0) |
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1096 error ("randperm: M and N must be non-negative"); |
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1097 |
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1098 if (m > n) |
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1099 error ("randperm: M must be less than or equal to N"); |
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1100 |
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1101 // Quick and dirty heuristic to decide if we allocate or not the |
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1102 // whole vector for tracking the truncated shuffle. |
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1103 bool short_shuffle = m < n/5; |
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1104 |
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1105 // Generate random numbers. |
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1106 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
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1107 double *rvec = r.fortran_vec (); |
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1108 |
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1109 octave_idx_type idx_len = short_shuffle ? m : n; |
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1110 Array<octave_idx_type> idx; |
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1111 try |
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1112 { |
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1113 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1114 } |
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1115 catch (const std::bad_alloc&) |
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1116 { |
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1117 // Looks like n is too big and short_shuffle is false. |
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1118 // Let's try again, but this time with the alternative. |
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1119 idx_len = m; |
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1120 short_shuffle = true; |
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1121 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1122 } |
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1123 |
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1124 octave_idx_type *ivec = idx.fortran_vec (); |
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1125 |
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1126 for (octave_idx_type i = 0; i < idx_len; i++) |
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1127 ivec[i] = i; |
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1128 |
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1129 if (short_shuffle) |
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1130 { |
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1131 unordered_map<octave_idx_type, octave_idx_type> map (m); |
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1132 |
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1133 // Perform the Knuth shuffle only keeping track of moved |
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1134 // entries in the map |
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1135 for (octave_idx_type i = 0; i < m; i++) |
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1136 { |
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1137 octave_idx_type k = i + |
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1138 gnulib::floor (rvec[i] * (n - i)); |
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1139 |
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1140 // For shuffling first m entries, no need to use extra |
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1141 // storage |
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1142 if (k < m) |
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1143 { |
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1144 std::swap (ivec[i], ivec[k]); |
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1145 } |
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1146 else |
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1147 { |
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1148 if (map.find (k) == map.end ()) |
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1149 map[k] = k; |
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1150 |
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1151 std::swap (ivec[i], map[k]); |
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1152 } |
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1153 } |
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1154 } |
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1155 else |
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1156 { |
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1157 // Perform the Knuth shuffle of the first m entries |
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1158 for (octave_idx_type i = 0; i < m; i++) |
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1159 { |
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1160 octave_idx_type k = i + |
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1161 gnulib::floor (rvec[i] * (n - i)); |
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1162 std::swap (ivec[i], ivec[k]); |
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1163 } |
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1164 } |
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1165 |
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1166 // Convert to doubles, reusing r. |
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1167 for (octave_idx_type i = 0; i < m; i++) |
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1168 rvec[i] = ivec[i] + 1; |
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1169 |
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1170 if (m < n) |
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1171 idx.resize (dim_vector (1, m)); |
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1172 |
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1173 // Now create an array object with a cached idx_vector. |
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1174 return ovl (new octave_matrix (r, idx_vector (idx))); |
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1175 } |
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1176 |
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1177 /* |
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1178 %!assert (sort (randperm (20)), 1:20) |
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1179 %!assert (length (randperm (20,10)), 10) |
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1180 |
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1181 ## Test biggish N (bug #39378) |
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1182 %!assert (length (randperm (30000^2, 100000)), 100000) |
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1183 |
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1184 %!test |
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1185 %! rand ("seed", 0); |
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1186 %! for i = 1:100 |
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1187 %! p = randperm (305, 30); |
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1188 %! assert (length (unique (p)), 30); |
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1189 %! endfor |
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1190 */ |