Mercurial > octave
annotate libinterp/corefcn/rand.cc @ 29358:0a5b15007766 stable
update Octave Project Developers copyright for the new year
In files that have the "Octave Project Developers" copyright notice,
update for 2021.
author | John W. Eaton <jwe@octave.org> |
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date | Wed, 10 Feb 2021 09:52:15 -0500 |
parents | bd51beb6205e |
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1 /////////////////////////////////////////////////////////////////////////* |
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2 // |
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3 // Copyright (C) 1996-2021 The Octave Project Developers |
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4 // |
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5 // See the file COPYRIGHT.md in the top-level directory of this |
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6 // distribution or <https://octave.org/copyright/>. |
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7 // |
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8 // This file is part of Octave. |
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9 // |
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10 // Octave is free software: you can redistribute it and/or modify it |
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11 // under the terms of the GNU General Public License as published by |
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12 // the Free Software Foundation, either version 3 of the License, or |
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13 // (at your option) any later version. |
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14 // |
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15 // Octave is distributed in the hope that it will be useful, but |
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16 // WITHOUT ANY WARRANTY; without even the implied warranty of |
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17 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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18 // GNU General Public License for more details. |
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19 // |
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20 // You should have received a copy of the GNU General Public License |
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21 // along with Octave; see the file COPYING. If not, see |
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22 // <https://www.gnu.org/licenses/>. |
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23 // |
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24 //////////////////////////////////////////////////////////////////////// |
2928 | 25 |
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26 #if defined (HAVE_CONFIG_H) |
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27 # include "config.h" |
2928 | 28 #endif |
29 | |
30 #include <ctime> | |
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31 #include <unordered_map> |
2928 | 32 #include <string> |
33 | |
34 #include "f77-fcn.h" | |
35 #include "lo-mappers.h" | |
4307 | 36 #include "oct-rand.h" |
4153 | 37 #include "quit.h" |
2928 | 38 |
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39 #include "defun.h" |
2928 | 40 #include "error.h" |
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41 #include "errwarn.h" |
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42 #include "ovl.h" |
2928 | 43 #include "unwind-prot.h" |
44 #include "utils.h" | |
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45 #include "ov-re-mat.h" |
2928 | 46 |
6437 | 47 /* |
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48 %% Restore all rand* "seed" and "state" values in order, so that the |
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49 %% new "state" algorithm remains active after these tests complete. |
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50 %!function restore_rand_states (seed, state) |
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51 %! rand ("seed", seed.rand); |
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52 %! rande ("seed", seed.rande); |
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53 %! randg ("seed", seed.randg); |
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54 %! randn ("seed", seed.randn); |
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55 %! randp ("seed", seed.randp); |
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56 %! rand ("state", state.rand); |
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57 %! rande ("state", state.rande); |
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58 %! randg ("state", state.randg); |
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59 %! randn ("state", state.randn); |
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60 %! randp ("state", state.randp); |
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61 %!endfunction |
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62 |
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63 %!shared __random_statistical_tests__, old_seed, old_state, restore_state |
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64 %! ## Flag whether the statistical tests should be run in "make check" or not |
6437 | 65 %! __random_statistical_tests__ = 0; |
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66 %! ## Save and restore the states of each of the random number generators |
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67 %! ## that are tested by the unit tests in this file. |
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68 %! old_seed.rand = rand ("seed"); |
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69 %! old_seed.rande = rande ("seed"); |
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70 %! old_seed.randg = randg ("seed"); |
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71 %! old_seed.randn = randn ("seed"); |
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72 %! old_seed.randp = randp ("seed"); |
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73 %! old_state.rand = rand ("state"); |
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74 %! old_state.rande = rande ("state"); |
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75 %! old_state.randg = randg ("state"); |
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76 %! old_state.randn = randn ("state"); |
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77 %! old_state.randp = randp ("state"); |
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78 %! restore_state = onCleanup (@() restore_rand_states (old_seed, old_state)); |
6437 | 79 */ |
80 | |
4307 | 81 static octave_value |
5730 | 82 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
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83 const std::string& distribution, bool additional_arg = false) |
2928 | 84 { |
5730 | 85 NDArray a; |
86 int idx = 0; | |
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87 bool is_single = false; |
2928 | 88 |
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89 if (nargin > 0 && args(nargin-1).is_string ()) |
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90 { |
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91 std::string s_arg = args(nargin-1).string_value (); |
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92 |
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93 if (s_arg == "single") |
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94 { |
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95 is_single = true; |
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96 nargin--; |
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97 } |
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98 else if (s_arg == "double") |
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99 nargin--; |
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100 } |
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101 |
5730 | 102 if (additional_arg) |
103 { | |
104 if (nargin == 0) | |
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105 error ("%s: at least one argument is required", fcn); |
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106 else if (args(0).is_string ()) |
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107 additional_arg = false; |
5730 | 108 else |
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109 { |
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110 a = args(0).xarray_value ("%s: dimension must be a scalar integer", fcn); |
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111 |
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112 idx++; |
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113 nargin--; |
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114 } |
5730 | 115 } |
2928 | 116 |
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117 octave_value retval; |
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118 dim_vector dims; |
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119 |
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120 octave::unwind_protect frame; |
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121 // Restore current distribution on any exit. |
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122 frame.add_fcn (octave::rand::distribution, |
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123 octave::rand::distribution ()); |
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124 |
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125 octave::rand::distribution (distribution); |
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126 |
4543 | 127 switch (nargin) |
2928 | 128 { |
4543 | 129 case 0: |
130 { | |
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131 if (additional_arg) |
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132 dims = a.dims (); |
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133 else |
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134 { |
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135 dims.resize (2); |
4543 | 136 |
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137 dims(0) = 1; |
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138 dims(1) = 1; |
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139 } |
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140 |
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141 goto gen_matrix; |
4543 | 142 } |
143 break; | |
2928 | 144 |
4543 | 145 case 1: |
146 { | |
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147 octave_value tmp = args(idx); |
4543 | 148 |
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149 if (tmp.is_string ()) |
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150 { |
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151 std::string s_arg = tmp.string_value (); |
2928 | 152 |
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153 if (s_arg == "dist") |
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|
154 retval = octave::rand::distribution (); |
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155 else if (s_arg == "seed") |
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|
156 retval = octave::rand::seed (); |
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157 else if (s_arg == "state" || s_arg == "twister") |
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158 retval = octave::rand::state (fcn); |
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159 else if (s_arg == "uniform") |
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|
160 octave::rand::uniform_distribution (); |
10154
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161 else if (s_arg == "normal") |
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162 octave::rand::normal_distribution (); |
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163 else if (s_arg == "exponential") |
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164 octave::rand::exponential_distribution (); |
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165 else if (s_arg == "poisson") |
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|
166 octave::rand::poisson_distribution (); |
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167 else if (s_arg == "gamma") |
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168 octave::rand::gamma_distribution (); |
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169 else |
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170 error ("%s: unrecognized string argument", fcn); |
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171 } |
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172 else if (tmp.is_scalar_type ()) |
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173 { |
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Accept only integers for single input form of rand (bug #56609).
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174 octave_idx_type n = tmp.idx_type_value (true); |
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175 |
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176 dims.resize (2); |
4543 | 177 |
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178 dims(0) = dims(1) = n; |
2928 | 179 |
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180 goto gen_matrix; |
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181 } |
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182 else if (tmp.is_range ()) |
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183 { |
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184 Range r = tmp.range_value (); |
4543 | 185 |
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186 if (! r.all_elements_are_ints ()) |
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187 error ("%s: all elements of range must be integers", fcn); |
4543 | 188 |
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189 octave_idx_type n = r.numel (); |
4543 | 190 |
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191 dims.resize (n); |
2928 | 192 |
21782
2aef506f3fec
use namespace for lo-mappers.h functions
John W. Eaton <jwe@octave.org>
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|
193 octave_idx_type base = octave::math::nint_big (r.base ()); |
2aef506f3fec
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diff
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194 octave_idx_type incr = octave::math::nint_big (r.inc ()); |
2928 | 195 |
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196 for (octave_idx_type i = 0; i < n; i++) |
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|
197 { |
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|
198 // Negative dimensions treated as zero for Matlab compatibility |
23450
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199 dims(i) = (base >= 0 ? base : 0); |
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200 base += incr; |
10154
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|
201 } |
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|
202 |
e528d7ab1cad
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|
203 goto gen_matrix; |
10154
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|
204 } |
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205 else if (tmp.is_matrix_type ()) |
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|
206 { |
25927
d6581134daaa
Emit an error when utility matrices (zeros, ones, rand, etc.) are given a fractional dimension in a dimension vector (bug #54781).
Rik <rik@octave.org>
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25926
diff
changeset
|
207 Array<octave_idx_type> iv; |
4543 | 208 |
20743
b6408331bfa2
eliminate more uses of error_state
John W. Eaton <jwe@octave.org>
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diff
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|
209 try |
10154
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|
210 { |
25927
d6581134daaa
Emit an error when utility matrices (zeros, ones, rand, etc.) are given a fractional dimension in a dimension vector (bug #54781).
Rik <rik@octave.org>
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25926
diff
changeset
|
211 iv = tmp.octave_idx_type_vector_value (true); |
20743
b6408331bfa2
eliminate more uses of error_state
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|
212 } |
22327
d0562b3159c7
move more classes inside octave namespace
John W. Eaton <jwe@octave.org>
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22323
diff
changeset
|
213 catch (octave::execution_exception& e) |
20743
b6408331bfa2
eliminate more uses of error_state
John W. Eaton <jwe@octave.org>
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diff
changeset
|
214 { |
20756
200ae1d650b7
propagate octave_execution_exception objects through try/catch blocks
John W. Eaton <jwe@octave.org>
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diff
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|
215 error (e, "%s: dimensions must be a scalar or array of integers", fcn); |
20743
b6408331bfa2
eliminate more uses of error_state
John W. Eaton <jwe@octave.org>
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diff
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|
216 } |
4543 | 217 |
20743
b6408331bfa2
eliminate more uses of error_state
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diff
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|
218 octave_idx_type len = iv.numel (); |
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eliminate more uses of error_state
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|
219 |
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eliminate more uses of error_state
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diff
changeset
|
220 dims.resize (len); |
2928 | 221 |
20743
b6408331bfa2
eliminate more uses of error_state
John W. Eaton <jwe@octave.org>
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diff
changeset
|
222 for (octave_idx_type i = 0; i < len; i++) |
b6408331bfa2
eliminate more uses of error_state
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|
223 { |
20894
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rand.cc: Overhaul file to make use of new C++ archetype.
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diff
changeset
|
224 // Negative dimensions treated as zero for Matlab compatibility |
20743
b6408331bfa2
eliminate more uses of error_state
John W. Eaton <jwe@octave.org>
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20711
diff
changeset
|
225 octave_idx_type elt = iv(i); |
23450
855122b993da
maint: Wrap tertiary operator in parentheses "(COND ? x : y)".
Rik <rik@octave.org>
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23220
diff
changeset
|
226 dims(i) = (elt >=0 ? elt : 0); |
10154
40dfc0c99116
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|
227 } |
20743
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eliminate more uses of error_state
John W. Eaton <jwe@octave.org>
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diff
changeset
|
228 |
b6408331bfa2
eliminate more uses of error_state
John W. Eaton <jwe@octave.org>
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diff
changeset
|
229 goto gen_matrix; |
10154
40dfc0c99116
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|
230 } |
40dfc0c99116
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diff
changeset
|
231 else |
21129
228b65504557
maint: Eliminate useless statements after err_XXX.
Rik <rik@octave.org>
parents:
21127
diff
changeset
|
232 err_wrong_type_arg ("rand", tmp); |
4543 | 233 } |
234 break; | |
235 | |
236 default: | |
237 { | |
10154
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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|
238 octave_value tmp = args(idx); |
4543 | 239 |
10154
40dfc0c99116
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|
240 if (nargin == 2 && tmp.is_string ()) |
40dfc0c99116
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diff
changeset
|
241 { |
40dfc0c99116
DLD-FUNCTIONS/*.cc: untabify
John W. Eaton <jwe@octave.org>
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changeset
|
242 std::string ts = tmp.string_value (); |
5164 | 243 |
10154
40dfc0c99116
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|
244 if (ts == "seed") |
40dfc0c99116
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diff
changeset
|
245 { |
40dfc0c99116
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diff
changeset
|
246 if (args(idx+1).is_real_scalar ()) |
40dfc0c99116
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|
247 { |
40dfc0c99116
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changeset
|
248 double d = args(idx+1).double_value (); |
2928 | 249 |
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diff
changeset
|
250 octave::rand::seed (d); |
10154
40dfc0c99116
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|
251 } |
11586
12df7854fa7c
strip trailing whitespace from source files
John W. Eaton <jwe@octave.org>
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11553
diff
changeset
|
252 else if (args(idx+1).is_string () |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
Rik <octave@nomad.inbox5.com>
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diff
changeset
|
253 && args(idx+1).string_value () == "reset") |
25433
49e0447413ad
use templates and move rand functions inside octave namespace
John W. Eaton <jwe@octave.org>
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diff
changeset
|
254 octave::rand::reset (); |
10154
40dfc0c99116
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|
255 else |
40dfc0c99116
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changeset
|
256 error ("%s: seed must be a real scalar", fcn); |
40dfc0c99116
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|
257 } |
40dfc0c99116
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|
258 else if (ts == "state" || ts == "twister") |
40dfc0c99116
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changeset
|
259 { |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
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10687
diff
changeset
|
260 if (args(idx+1).is_string () |
14846
460a3c6d8bf1
maint: Use Octave coding convention for cuddled parenthis in function calls with empty argument lists.
Rik <octave@nomad.inbox5.com>
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diff
changeset
|
261 && args(idx+1).string_value () == "reset") |
25433
49e0447413ad
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diff
changeset
|
262 octave::rand::reset (fcn); |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
parents:
10687
diff
changeset
|
263 else |
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
parents:
10687
diff
changeset
|
264 { |
27277
db687716fed6
style fixes: generally aim to break long lines before operators, not after
John W. Eaton <jwe@octave.org>
parents:
27249
diff
changeset
|
265 ColumnVector s |
db687716fed6
style fixes: generally aim to break long lines before operators, not after
John W. Eaton <jwe@octave.org>
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27249
diff
changeset
|
266 = ColumnVector (args(idx+1).vector_value (false, true)); |
25946
85f499b88c43
maint: Strip trailing whitespace from source files.
John W. Eaton <jwe@octave.org>
parents:
25651
diff
changeset
|
267 |
25651
c759aa39c23a
Produce same random number stream for "state" initialized to Inf (bug #54342).
Rik <rik@octave.org>
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25143
diff
changeset
|
268 // Backwards compatibility with previous versions of |
c759aa39c23a
Produce same random number stream for "state" initialized to Inf (bug #54342).
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25143
diff
changeset
|
269 // Octave which mapped Inf to 0. |
c759aa39c23a
Produce same random number stream for "state" initialized to Inf (bug #54342).
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diff
changeset
|
270 for (octave_idx_type i = 0; i < s.numel (); i++) |
c759aa39c23a
Produce same random number stream for "state" initialized to Inf (bug #54342).
Rik <rik@octave.org>
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25143
diff
changeset
|
271 if (octave::math::isinf (s.xelem (i))) |
c759aa39c23a
Produce same random number stream for "state" initialized to Inf (bug #54342).
Rik <rik@octave.org>
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25143
diff
changeset
|
272 s.xelem (i) = 0.0; |
5730 | 273 |
25433
49e0447413ad
use templates and move rand functions inside octave namespace
John W. Eaton <jwe@octave.org>
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25143
diff
changeset
|
274 octave::rand::state (s, fcn); |
10709
92a85ed5b86e
Don't special case color_property type when emitting factory default (bug #30118)
David Bateman <dbateman@free.fr>
parents:
10687
diff
changeset
|
275 } |
10154
40dfc0c99116
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changeset
|
276 } |
40dfc0c99116
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diff
changeset
|
277 else |
40dfc0c99116
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changeset
|
278 error ("%s: unrecognized string argument", fcn); |
40dfc0c99116
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changeset
|
279 } |
40dfc0c99116
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changeset
|
280 else |
40dfc0c99116
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John W. Eaton <jwe@octave.org>
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281 { |
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282 dims.resize (nargin); |
4543 | 283 |
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284 for (int i = 0; i < nargin; i++) |
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285 { |
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286 octave_idx_type elt = args(idx+i).idx_type_value (true); |
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287 |
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288 // Negative dimensions treated as zero for Matlab compatibility |
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289 dims(i) = (elt >= 0 ? elt : 0); |
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290 } |
4543 | 291 |
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292 goto gen_matrix; |
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293 } |
4543 | 294 } |
295 break; | |
2928 | 296 } |
297 | |
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298 // No "goto gen_matrix" in code path. Must be done processing. |
2928 | 299 return retval; |
300 | |
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301 gen_matrix: |
2928 | 302 |
5355 | 303 dims.chop_trailing_singletons (); |
304 | |
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305 if (is_single) |
5730 | 306 { |
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307 if (additional_arg) |
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308 { |
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309 if (a.numel () == 1) |
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310 return octave::rand::float_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 FloatNDArray m (dims); |
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318 float *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::float_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::float_nd_array (dims); |
5730 | 328 } |
329 else | |
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330 { |
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331 if (additional_arg) |
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332 { |
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333 if (a.numel () == 1) |
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334 return octave::rand::nd_array (dims, a(0)); |
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335 else |
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336 { |
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337 if (a.dims () != dims) |
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338 error ("%s: mismatch in argument size", fcn); |
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339 |
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340 octave_idx_type len = a.numel (); |
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341 NDArray m (dims); |
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342 double *v = m.fortran_vec (); |
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343 |
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344 for (octave_idx_type i = 0; i < len; i++) |
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345 v[i] = octave::rand::scalar (a(i)); |
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346 |
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347 return m; |
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348 } |
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349 } |
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350 else |
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351 return octave::rand::nd_array (dims); |
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352 } |
2928 | 353 } |
354 | |
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355 DEFUN (rand, args, , |
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356 doc: /* -*- texinfo -*- |
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357 @deftypefn {} {} rand (@var{n}) |
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358 @deftypefnx {} {} rand (@var{m}, @var{n}, @dots{}) |
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359 @deftypefnx {} {} rand ([@var{m} @var{n} @dots{}]) |
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360 @deftypefnx {} {@var{v} =} rand ("state") |
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361 @deftypefnx {} {} rand ("state", @var{v}) |
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362 @deftypefnx {} {} rand ("state", "reset") |
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363 @deftypefnx {} {@var{v} =} rand ("seed") |
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364 @deftypefnx {} {} rand ("seed", @var{v}) |
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365 @deftypefnx {} {} rand ("seed", "reset") |
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366 @deftypefnx {} {} rand (@dots{}, "single") |
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367 @deftypefnx {} {} rand (@dots{}, "double") |
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368 Return a matrix with random elements uniformly distributed on the |
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369 interval (0, 1). |
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370 |
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371 The arguments are handled the same as the arguments for @code{eye}. |
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372 |
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373 You can query the state of the random number generator using the form |
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374 |
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375 @example |
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376 v = rand ("state") |
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377 @end example |
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378 |
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379 This returns a column vector @var{v} of length 625. Later, you can restore |
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380 the random number generator to the state @var{v} using the form |
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381 |
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382 @example |
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383 rand ("state", v) |
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384 @end example |
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385 |
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386 @noindent |
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387 You may also initialize the state vector from an arbitrary vector of length |
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388 @leq{} 625 for @var{v}. This new state will be a hash based on the value of |
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389 @var{v}, not @var{v} itself. |
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390 |
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391 By default, the generator is initialized from @code{/dev/urandom} if it is |
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392 available, otherwise from CPU time, wall clock time, and the current |
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393 fraction of a second. Note that this differs from @sc{matlab}, which |
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394 always initializes the state to the same state at startup. To obtain |
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395 behavior comparable to @sc{matlab}, initialize with a deterministic state |
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396 vector in Octave's startup files (@pxref{Startup Files}). |
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397 |
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398 To compute the pseudo-random sequence, @code{rand} uses the Mersenne |
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399 Twister with a period of @math{2^{19937}-1} |
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400 (See @nospell{M. Matsumoto and T. Nishimura}, |
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401 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform |
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402 pseudorandom number generator}, |
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403 @nospell{ACM} Trans.@: on Modeling and Computer Simulation Vol.@: 8, No.@: 1, |
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404 pp.@: 3--30, January 1998, |
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405 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}). |
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406 Do @strong{not} use for cryptography without securely hashing several |
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407 returned values together, otherwise the generator state can be learned after |
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408 reading 624 consecutive values. |
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409 |
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410 Older versions of Octave used a different random number generator. |
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411 The new generator is used by default as it is significantly faster than the |
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412 old generator, and produces random numbers with a significantly longer cycle |
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413 time. However, in some circumstances it might be desirable to obtain the |
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414 same random sequences as produced by the old generators. To do this the |
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415 keyword @qcode{"seed"} is used to specify that the old generators should |
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416 be used, as in |
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417 |
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418 @example |
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419 rand ("seed", val) |
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420 @end example |
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421 |
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422 @noindent |
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423 which sets the seed of the generator to @var{val}. The seed of the |
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424 generator can be queried with |
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425 |
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426 @example |
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427 s = rand ("seed") |
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428 @end example |
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429 |
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430 However, it should be noted that querying the seed will not cause |
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431 @code{rand} to use the old generators, only setting the seed will. To cause |
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432 @code{rand} to once again use the new generators, the keyword |
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433 @qcode{"state"} should be used to reset the state of the @code{rand}. |
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434 |
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435 The state or seed of the generator can be reset to a new random value using |
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436 the @qcode{"reset"} keyword. |
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437 |
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438 The class of the value returned can be controlled by a trailing |
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439 @qcode{"double"} or @qcode{"single"} argument. These are the only valid |
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440 classes. |
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441 @seealso{randn, rande, randg, randp} |
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442 @end deftypefn */) |
2928 | 443 { |
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444 return do_rand (args, args.length (), "rand", "uniform"); |
2928 | 445 } |
446 | |
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447 // FIXME: The old generator (selected when "seed" is set) will not |
8871 | 448 // work properly if compiled to use 64-bit integers. |
449 | |
5730 | 450 /* |
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451 %!test # "state" can be a scalar |
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452 %! rand ("state", 12); x = rand (1,4); |
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453 %! rand ("state", 12); y = rand (1,4); |
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454 %! assert (x, y); |
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455 %!test # "state" can be a vector |
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456 %! rand ("state", [12,13]); x = rand (1,4); |
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457 %! rand ("state", [12;13]); y = rand (1,4); |
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458 %! assert (x, y); |
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459 %!test # querying "state" returns a value which can be used later |
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460 %! s = rand ("state"); x = rand (1,2); |
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461 %! rand ("state", s); y = rand (1,2); |
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462 %! assert (x, y); |
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463 %!test # querying "state" doesn't disturb sequence |
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464 %! rand ("state", 12); rand (1,2); x = rand (1,2); |
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465 %! rand ("state", 12); rand (1,2); s = rand ("state"); y = rand (1,2); |
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466 %! assert (x, y); |
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467 %! rand ("state", s); z = rand (1,2); |
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468 %! assert (x, z); |
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469 %!test # "seed" must be a scalar |
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470 %! rand ("seed", 12); x = rand (1,4); |
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471 %! rand ("seed", 12); y = rand (1,4); |
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472 %! assert (x, y); |
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473 %!error <seed must be a real scalar> rand ("seed", [12,13]) |
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474 %!test # querying "seed" returns a value which can be used later |
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475 %! s = rand ("seed"); x = rand (1,2); |
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476 %! rand ("seed", s); y = rand (1,2); |
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477 %! assert (x, y); |
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478 %!test # querying "seed" doesn't disturb sequence |
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479 %! rand ("seed", 12); rand (1,2); x = rand (1,2); |
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480 %! rand ("seed", 12); rand (1,2); s = rand ("seed"); y = rand (1,2); |
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481 %! assert (x, y); |
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482 %! rand ("seed", s); z = rand (1,2); |
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483 %! assert (x, z); |
5730 | 484 */ |
485 | |
486 /* | |
487 %!test | |
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488 %! ## Test a known fixed state |
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489 %! rand ("state", 1); |
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490 %! assert (rand (1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382], eps); |
6437 | 491 %!test |
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492 %! ## Test a known fixed seed |
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493 %! rand ("seed", 1); |
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494 %! assert (rand (1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759], 1e-6); |
5730 | 495 %!test |
6437 | 496 %! if (__random_statistical_tests__) |
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497 %! ## statistical tests may fail occasionally. |
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498 %! rand ("state", 12); |
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499 %! x = rand (100_000, 1); |
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500 %! assert (min (x) > 0); #*** Please report this!!! *** |
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501 %! assert (max (x) < 1); #*** Please report this!!! *** |
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502 %! assert (mean (x), 0.5, 0.0024); |
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503 %! assert (var (x), 1/48, 0.0632); |
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504 %! assert (skewness (x), 0, 0.012); |
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505 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 506 %! endif |
507 %!test | |
508 %! if (__random_statistical_tests__) | |
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509 %! ## statistical tests may fail occasionally. |
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510 %! rand ("seed", 12); |
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511 %! x = rand (100_000, 1); |
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512 %! assert (max (x) < 1); #*** Please report this!!! *** |
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513 %! assert (min (x) > 0); #*** Please report this!!! *** |
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514 %! assert (mean (x), 0.5, 0.0024); |
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515 %! assert (var (x), 1/48, 0.0632); |
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516 %! assert (skewness (x), 0, 0.012); |
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517 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 518 %! endif |
5730 | 519 */ |
520 | |
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521 /* |
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522 ## Test out-of-range values as rand() seeds. |
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523 %!function v = __rand_sample__ (initval) |
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524 %! rand ("state", initval); |
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525 %! v = rand (1, 6); |
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526 %!endfunction |
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527 %! |
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528 %!assert (__rand_sample__ (-1), __rand_sample__ (0)) |
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529 %!assert (__rand_sample__ (-Inf), __rand_sample__ (0)) |
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530 %!assert (__rand_sample__ (2^33), __rand_sample__ (intmax ("uint32"))) |
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531 %!assert (__rand_sample__ (Inf), __rand_sample__ (0)) |
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532 %!assert (__rand_sample__ (NaN), __rand_sample__ (0)) |
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533 */ |
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534 |
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535 /* |
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536 ## Check that negative dimensions are treated as zero for Matlab compatibility |
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537 %!assert (size (rand (1, -1, 2)), [1, 0, 2]) |
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538 |
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539 ## Test input validation |
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540 %!error <conversion of 1.1 to.* failed> rand (1, 1.1) |
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541 %!error <dimensions must be .* array of integers> rand ([1, 1.1]) |
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542 */ |
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543 |
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544 static std::string current_distribution = octave::rand::distribution (); |
4307 | 545 |
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546 DEFUN (randn, args, , |
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547 doc: /* -*- texinfo -*- |
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548 @deftypefn {} {} randn (@var{n}) |
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549 @deftypefnx {} {} randn (@var{m}, @var{n}, @dots{}) |
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550 @deftypefnx {} {} randn ([@var{m} @var{n} @dots{}]) |
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551 @deftypefnx {} {@var{v} =} randn ("state") |
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552 @deftypefnx {} {} randn ("state", @var{v}) |
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553 @deftypefnx {} {} randn ("state", "reset") |
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554 @deftypefnx {} {@var{v} =} randn ("seed") |
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555 @deftypefnx {} {} randn ("seed", @var{v}) |
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556 @deftypefnx {} {} randn ("seed", "reset") |
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557 @deftypefnx {} {} randn (@dots{}, "single") |
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558 @deftypefnx {} {} randn (@dots{}, "double") |
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559 Return a matrix with normally distributed random elements having zero mean |
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560 and variance one. |
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561 |
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562 The arguments are handled the same as the arguments for @code{rand}. |
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563 |
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564 By default, @code{randn} uses the @nospell{Marsaglia and Tsang} |
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565 ``Ziggurat technique'' to transform from a uniform to a normal distribution. |
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566 |
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567 The class of the value returned can be controlled by a trailing |
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568 @qcode{"double"} or @qcode{"single"} argument. These are the only valid |
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569 classes. |
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570 |
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571 Reference: @nospell{G. Marsaglia and W.W. Tsang}, |
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572 @cite{Ziggurat Method for Generating Random Variables}, |
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573 J. Statistical Software, vol 5, 2000, |
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574 @url{https://www.jstatsoft.org/v05/i08/} |
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575 |
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576 @seealso{rand, rande, randg, randp} |
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577 @end deftypefn */) |
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579 return do_rand (args, args.length (), "randn", "normal"); |
2928 | 580 } |
581 | |
582 /* | |
5730 | 583 %!test |
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584 %! ## Test a known fixed state |
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585 %! randn ("state", 1); |
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586 %! assert (randn (1, 6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116], 14*eps); |
6437 | 587 %!test |
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588 %! ## Test a known fixed seed |
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589 %! randn ("seed", 1); |
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590 %! assert (randn (1, 6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133], 1e-6); |
5730 | 591 %!test |
6437 | 592 %! if (__random_statistical_tests__) |
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593 %! ## statistical tests may fail occasionally. |
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594 %! randn ("state", 12); |
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595 %! x = randn (100_000, 1); |
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596 %! assert (mean (x), 0, 0.01); |
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597 %! assert (var (x), 1, 0.02); |
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598 %! assert (skewness (x), 0, 0.02); |
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599 %! assert (kurtosis (x), 0, 0.04); |
6437 | 600 %! endif |
601 %!test | |
602 %! if (__random_statistical_tests__) | |
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603 %! ## statistical tests may fail occasionally. |
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604 %! randn ("seed", 12); |
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605 %! x = randn (100_000, 1); |
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606 %! assert (mean (x), 0, 0.01); |
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607 %! assert (var (x), 1, 0.02); |
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608 %! assert (skewness (x), 0, 0.02); |
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609 %! assert (kurtosis (x), 0, 0.04); |
6437 | 610 %! endif |
5730 | 611 */ |
612 | |
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613 DEFUN (rande, args, , |
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614 doc: /* -*- texinfo -*- |
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615 @deftypefn {} {} rande (@var{n}) |
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616 @deftypefnx {} {} rande (@var{m}, @var{n}, @dots{}) |
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617 @deftypefnx {} {} rande ([@var{m} @var{n} @dots{}]) |
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618 @deftypefnx {} {@var{v} =} rande ("state") |
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619 @deftypefnx {} {} rande ("state", @var{v}) |
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620 @deftypefnx {} {} rande ("state", "reset") |
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621 @deftypefnx {} {@var{v} =} rande ("seed") |
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622 @deftypefnx {} {} rande ("seed", @var{v}) |
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623 @deftypefnx {} {} rande ("seed", "reset") |
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624 @deftypefnx {} {} rande (@dots{}, "single") |
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625 @deftypefnx {} {} rande (@dots{}, "double") |
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626 Return a matrix with exponentially distributed random elements. |
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627 |
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628 The arguments are handled the same as the arguments for @code{rand}. |
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629 |
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630 By default, @code{rande} uses the @nospell{Marsaglia and Tsang} |
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631 ``Ziggurat technique'' to transform from a uniform to an exponential |
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632 distribution. |
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633 |
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634 The class of the value returned can be controlled by a trailing |
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635 @qcode{"double"} or @qcode{"single"} argument. These are the only valid |
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636 classes. |
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637 |
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638 Reference: @nospell{G. Marsaglia and W.W. Tsang}, |
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639 @cite{Ziggurat Method for Generating Random Variables}, |
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640 J. Statistical Software, vol 5, 2000, |
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641 @url{https://www.jstatsoft.org/v05/i08/} |
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642 |
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643 @seealso{rand, randn, randg, randp} |
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644 @end deftypefn */) |
5730 | 645 { |
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646 return do_rand (args, args.length (), "rande", "exponential"); |
5730 | 647 } |
648 | |
649 /* | |
650 %!test | |
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651 %! ## Test a known fixed state |
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652 %! rande ("state", 1); |
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653 %! assert (rande (1, 6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291], 2*eps); |
6437 | 654 %!test |
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655 %! ## Test a known fixed seed |
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656 %! rande ("seed", 1); |
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657 %! assert (rande (1, 6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087], 1e-6); |
5730 | 658 %!test |
6437 | 659 %! if (__random_statistical_tests__) |
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660 %! ## statistical tests may fail occasionally |
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661 %! rande ("state", 1); |
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662 %! x = rande (100_000, 1); |
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663 %! assert (min (x) > 0); # *** Please report this!!! *** |
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664 %! assert (mean (x), 1, 0.01); |
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665 %! assert (var (x), 1, 0.03); |
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666 %! assert (skewness (x), 2, 0.06); |
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667 %! assert (kurtosis (x), 6, 0.7); |
6437 | 668 %! endif |
669 %!test | |
670 %! if (__random_statistical_tests__) | |
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671 %! ## statistical tests may fail occasionally |
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672 %! rande ("seed", 1); |
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673 %! x = rande (100_000, 1); |
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674 %! assert (min (x)>0); # *** Please report this!!! *** |
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675 %! assert (mean (x), 1, 0.01); |
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676 %! assert (var (x), 1, 0.03); |
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677 %! assert (skewness (x), 2, 0.06); |
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678 %! assert (kurtosis (x), 6, 0.7); |
6437 | 679 %! endif |
5730 | 680 */ |
681 | |
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682 DEFUN (randg, args, , |
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683 doc: /* -*- texinfo -*- |
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684 @deftypefn {} {} randg (@var{a}, @var{n}) |
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685 @deftypefnx {} {} randg (@var{a}, @var{m}, @var{n}, @dots{}) |
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686 @deftypefnx {} {} randg (@var{a}, [@var{m} @var{n} @dots{}]) |
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687 @deftypefnx {} {@var{v} =} randg ("state") |
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688 @deftypefnx {} {} randg ("state", @var{v}) |
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689 @deftypefnx {} {} randg ("state", "reset") |
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690 @deftypefnx {} {@var{v} =} randg ("seed") |
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691 @deftypefnx {} {} randg ("seed", @var{v}) |
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692 @deftypefnx {} {} randg ("seed", "reset") |
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693 @deftypefnx {} {} randg (@dots{}, "single") |
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694 @deftypefnx {} {} randg (@dots{}, "double") |
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695 |
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696 Return a matrix with @code{gamma (@var{a},1)} distributed random elements. |
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697 |
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698 The arguments are handled the same as the arguments for @code{rand}, except |
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699 for the argument @var{a}. |
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700 |
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701 This can be used to generate many distributions: |
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702 |
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703 @table @asis |
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704 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0} |
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705 |
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706 @example |
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707 r = b * randg (a) |
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708 @end example |
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709 |
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710 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1} |
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711 |
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712 @example |
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713 @group |
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714 r1 = randg (a, 1) |
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715 r = r1 / (r1 + randg (b, 1)) |
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716 @end group |
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717 @end example |
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718 |
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719 @item @code{Erlang (a, n)} |
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720 |
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721 @example |
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722 r = a * randg (n) |
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723 @end example |
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724 |
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725 @item @code{chisq (df)} for @code{df > 0} |
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726 |
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727 @example |
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728 r = 2 * randg (df / 2) |
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729 @end example |
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730 |
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731 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite) |
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732 |
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733 @example |
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734 r = randn () / sqrt (2 * randg (df / 2) / df) |
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735 @end example |
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736 |
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737 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2} |
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738 |
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739 @example |
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740 @group |
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741 ## r1 equals 1 if n1 is infinite |
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742 r1 = 2 * randg (n1 / 2) / n1 |
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743 ## r2 equals 1 if n2 is infinite |
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744 r2 = 2 * randg (n2 / 2) / n2 |
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745 r = r1 / r2 |
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746 @end group |
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747 @end example |
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748 |
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749 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1} |
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750 |
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751 @example |
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752 r = randp ((1 - p) / p * randg (n)) |
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753 @end example |
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754 |
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755 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0} |
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756 (use chisq if @code{L = 0}) |
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757 |
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758 @example |
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759 @group |
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760 r = randp (L / 2) |
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761 r(r > 0) = 2 * randg (r(r > 0)) |
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762 r(df > 0) += 2 * randg (df(df > 0)/2) |
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763 @end group |
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764 @end example |
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765 |
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766 @item @code{Dirichlet (a1, @dots{} ak)} |
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767 |
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768 @example |
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769 @group |
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770 r = (randg (a1), @dots{}, randg (ak)) |
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771 r = r / sum (r) |
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772 @end group |
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773 @end example |
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774 |
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775 @end table |
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776 |
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777 The class of the value returned can be controlled by a trailing |
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778 @qcode{"double"} or @qcode{"single"} argument. These are the only valid |
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779 classes. |
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780 @seealso{rand, randn, rande, randp} |
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781 @end deftypefn */) |
5730 | 782 { |
783 int nargin = args.length (); | |
784 | |
785 if (nargin < 1) | |
786 error ("randg: insufficient arguments"); | |
787 | |
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788 return do_rand (args, nargin, "randg", "gamma", true); |
5730 | 789 } |
790 | |
791 /* | |
792 %!test | |
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793 %! randg ("state", 12); |
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794 %! assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]); # *** Please report |
6437 | 795 |
796 %!test | |
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797 %! ## Test a known fixed state |
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798 %! randg ("state", 1); |
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799 %! assert (randg (0.1, 1, 6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12], eps); |
6437 | 800 %!test |
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801 %! ## Test a known fixed state |
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802 %! randg ("state", 1); |
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803 %! assert (randg (0.95, 1, 6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939], 12*eps); |
6437 | 804 %!test |
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805 %! ## Test a known fixed state |
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806 %! randg ("state", 1); |
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807 %! assert (randg (1, 1, 6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533], 40*eps); |
6437 | 808 %!test |
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809 %! ## Test a known fixed state |
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810 %! randg ("state", 1); |
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811 %! assert (randg (10, 1, 6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059], 56*eps); |
6437 | 812 %!test |
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813 %! ## Test a known fixed state |
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814 %! randg ("state", 1); |
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815 %! assert (randg (100, 1, 6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118], 256*eps); |
6437 | 816 %!test |
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817 %! ## Test a known fixed seed |
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818 %! randg ("seed", 1); |
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819 %! assert (randg (0.1, 1, 6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12], 1e-6); |
6437 | 820 %!test |
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821 %! ## Test a known fixed seed |
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822 %! randg ("seed", 1); |
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823 %! assert (randg (0.95, 1, 6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648], 1e-6); |
6437 | 824 %!test |
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825 %! ## Test a known fixed seed |
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826 %! randg ("seed", 1); |
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827 %! assert (randg (1, 1, 6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303], 1e-6); |
6437 | 828 %!test |
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829 %! ## Test a known fixed seed |
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830 %! randg ("seed", 1); |
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831 %! assert (randg (10, 1, 6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186], 1e-5); |
6437 | 832 %!test |
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833 %! ## Test a known fixed seed |
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834 %! randg ("seed", 1); |
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835 %! assert (randg (100, 1, 6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625], 1e-4); |
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836 %!test |
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837 %! ## Test out-of-bounds values produce NaN w/old-style generators & floats |
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838 %! randg ("seed", 1); |
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839 %! result = randg ([-2 Inf], "single"); |
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840 %! assert (result, single ([NaN NaN])); |
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841 |
6437 | 842 %!test |
843 %! if (__random_statistical_tests__) | |
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844 %! ## statistical tests may fail occasionally. |
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845 %! randg ("state", 12); |
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846 %! a = 0.1; |
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847 %! x = randg (a, 100_000, 1); |
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848 %! assert (mean (x), a, 0.01); |
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849 %! assert (var (x), a, 0.01); |
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850 %! assert (skewness (x), 2/sqrt (a), 1); |
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851 %! assert (kurtosis (x), 6/a, 50); |
6437 | 852 %! endif |
853 %!test | |
854 %! if (__random_statistical_tests__) | |
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855 %! ## statistical tests may fail occasionally. |
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856 %! randg ("state", 12); |
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857 %! a = 0.95; |
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858 %! x = randg (a, 100_000, 1); |
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859 %! assert (mean (x), a, 0.01); |
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860 %! assert (var (x), a, 0.04); |
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861 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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862 %! assert (kurtosis (x), 6/a, 2); |
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863 %! endif |
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864 %!test |
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865 %! if (__random_statistical_tests__) |
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866 %! ## statistical tests may fail occasionally. |
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867 %! randg ("state", 12); |
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868 %! a = 1; |
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869 %! x = randg (a, 100_000, 1); |
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870 %! assert (mean (x), a, 0.01); |
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871 %! assert (var (x), a, 0.04); |
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872 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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873 %! assert (kurtosis (x), 6/a, 2); |
6437 | 874 %! endif |
875 %!test | |
876 %! if (__random_statistical_tests__) | |
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877 %! ## statistical tests may fail occasionally. |
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878 %! randg ("state", 12); |
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879 %! a = 10; |
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880 %! x = randg (a, 100_000, 1); |
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881 %! assert (mean (x), a, 0.1); |
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882 %! assert (var (x), a, 0.5); |
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883 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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884 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 885 %! endif |
886 %!test | |
887 %! if (__random_statistical_tests__) | |
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888 %! ## statistical tests may fail occasionally. |
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889 %! randg ("state", 12); |
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890 %! a = 100; |
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891 %! x = randg (a, 100_000, 1); |
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892 %! assert (mean (x), a, 0.2); |
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893 %! assert (var (x), a, 2); |
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894 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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895 %! assert (kurtosis (x), 6/a, 0.2); |
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896 %! endif |
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897 %!test |
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898 %! randg ("seed", 12); |
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899 %!assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]) # *** Please report |
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900 %!test |
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901 %! if (__random_statistical_tests__) |
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902 %! ## statistical tests may fail occasionally. |
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903 %! randg ("seed", 12); |
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904 %! a = 0.1; |
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905 %! x = randg (a, 100_000, 1); |
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906 %! assert (mean (x), a, 0.01); |
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907 %! assert (var (x), a, 0.01); |
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908 %! assert (skewness (x), 2/sqrt (a), 1); |
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909 %! assert (kurtosis (x), 6/a, 50); |
6437 | 910 %! endif |
911 %!test | |
912 %! if (__random_statistical_tests__) | |
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913 %! ## statistical tests may fail occasionally. |
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914 %! randg ("seed", 12); |
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915 %! a = 0.95; |
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916 %! x = randg (a, 100_000, 1); |
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917 %! assert (mean (x), a, 0.01); |
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918 %! assert (var (x), a, 0.04); |
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919 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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920 %! assert (kurtosis (x), 6/a, 2); |
6437 | 921 %! endif |
922 %!test | |
923 %! if (__random_statistical_tests__) | |
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924 %! ## statistical tests may fail occasionally. |
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925 %! randg ("seed", 12); |
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926 %! a = 1; |
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927 %! x = randg (a, 100_000, 1); |
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928 %! assert (mean (x), a, 0.01); |
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929 %! assert (var (x), a, 0.04); |
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930 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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931 %! assert (kurtosis (x), 6/a, 2); |
6437 | 932 %! endif |
5730 | 933 %!test |
6437 | 934 %! if (__random_statistical_tests__) |
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935 %! ## statistical tests may fail occasionally. |
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936 %! randg ("seed", 12); |
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937 %! a = 10; |
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938 %! x = randg (a, 100_000, 1); |
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939 %! assert (mean (x), a, 0.1); |
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940 %! assert (var (x), a, 0.5); |
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941 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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942 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 943 %! endif |
5730 | 944 %!test |
6437 | 945 %! if (__random_statistical_tests__) |
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946 %! ## statistical tests may fail occasionally. |
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947 %! randg ("seed", 12); |
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948 %! a = 100; |
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949 %! x = randg (a, 100_000, 1); |
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950 %! assert (mean (x), a, 0.2); |
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951 %! assert (var (x), a, 2); |
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952 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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953 %! assert (kurtosis (x), 6/a, 0.2); |
6437 | 954 %! endif |
5730 | 955 */ |
956 | |
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957 DEFUN (randp, args, , |
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958 doc: /* -*- texinfo -*- |
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959 @deftypefn {} {} randp (@var{l}, @var{n}) |
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960 @deftypefnx {} {} randp (@var{l}, @var{m}, @var{n}, @dots{}) |
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961 @deftypefnx {} {} randp (@var{l}, [@var{m} @var{n} @dots{}]) |
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962 @deftypefnx {} {@var{v} =} randp ("state") |
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963 @deftypefnx {} {} randp ("state", @var{v}) |
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964 @deftypefnx {} {} randp ("state", "reset") |
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965 @deftypefnx {} {@var{v} =} randp ("seed") |
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966 @deftypefnx {} {} randp ("seed", @var{v}) |
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967 @deftypefnx {} {} randp ("seed", "reset") |
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968 @deftypefnx {} {} randp (@dots{}, "single") |
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969 @deftypefnx {} {} randp (@dots{}, "double") |
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970 Return a matrix with Poisson distributed random elements with mean value |
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971 parameter given by the first argument, @var{l}. |
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972 |
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973 The arguments are handled the same as the arguments for @code{rand}, except |
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974 for the argument @var{l}. |
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975 |
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976 Five different algorithms are used depending on the range of @var{l} and |
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977 whether or not @var{l} is a scalar or a matrix. |
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978 |
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979 @table @asis |
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980 @item For scalar @var{l} @leq{} 12, use direct method. |
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981 W.H. Press, et al., @cite{Numerical Recipes in C}, |
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982 Cambridge University Press, 1992. |
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983 |
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984 @item For scalar @var{l} > 12, use rejection method.[1] |
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985 W.H. Press, et al., @cite{Numerical Recipes in C}, |
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986 Cambridge University Press, 1992. |
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987 |
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988 @item For matrix @var{l} @leq{} 10, use inversion method.[2] |
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989 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP. |
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990 |
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991 @item For matrix @var{l} > 10, use patchwork rejection method. |
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992 @nospell{E. Stadlober, et al., WinRand source code}, available via FTP, or |
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993 @nospell{H. Zechner}, @cite{Efficient sampling from continuous and discrete |
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994 unimodal distributions}, Doctoral Dissertation, 156pp., Technical |
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995 University @nospell{Graz}, Austria, 1994. |
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996 |
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997 @item For @var{l} > 1e8, use normal approximation. |
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998 @nospell{L. Montanet}, et al., @cite{Review of Particle Properties}, |
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999 Physical Review D 50 p1284, 1994. |
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1000 @end table |
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1001 |
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1002 The class of the value returned can be controlled by a trailing |
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1003 @qcode{"double"} or @qcode{"single"} argument. These are the only valid |
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1004 classes. |
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1005 @seealso{rand, randn, rande, randg} |
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1006 @end deftypefn */) |
5730 | 1007 { |
1008 int nargin = args.length (); | |
1009 | |
1010 if (nargin < 1) | |
1011 error ("randp: insufficient arguments"); | |
1012 | |
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1013 return do_rand (args, nargin, "randp", "poisson", true); |
5730 | 1014 } |
1015 | |
1016 /* | |
1017 %!test | |
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1018 %! randp ("state", 12); |
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1019 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
6437 | 1020 %!test |
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1021 %! ## Test a known fixed state |
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1022 %! randp ("state", 1); |
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1023 %! assert (randp (5, 1, 6), [5 5 3 7 7 3]); |
6437 | 1024 %!test |
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1025 %! ## Test a known fixed state |
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1026 %! randp ("state", 1); |
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1027 %! assert (randp (15, 1, 6), [13 15 8 18 18 15]); |
6437 | 1028 %!test |
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1029 %! ## Test a known fixed state |
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1030 %! randp ("state", 1); |
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1031 %! assert (randp (1e9, 1, 6), [999915677 999976657 1000047684 1000019035 999985749 999977692], -1e-6); |
6437 | 1032 %!test |
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1033 %! ## Test a known fixed seed |
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1034 %! randp ("seed", 1); |
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1035 %! %%assert (randp (5, 1, 6), [8 2 3 6 6 8]) |
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1036 %! assert (randp (5, 1, 5), [8 2 3 6 6]); |
6437 | 1037 %!test |
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1038 %! ## Test a known fixed seed |
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1039 %! randp ("seed", 1); |
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1040 %! assert (randp (15, 1, 6), [15 16 12 10 10 12]); |
6437 | 1041 %!test |
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1042 %! ## Test a known fixed seed |
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1043 %! randp ("seed", 1); |
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1044 %! assert (randp (1e9, 1, 6), [1000006208 1000012224 999981120 999963520 999963072 999981440], -1e-6); |
6437 | 1045 %!test |
1046 %! if (__random_statistical_tests__) | |
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1047 %! ## statistical tests may fail occasionally. |
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1048 %! randp ("state", 12); |
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1049 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1050 %! x = randp (a (1), 100_000, 1); |
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1051 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1052 %! assert (mean (x), a(1), a(2)); |
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1053 %! assert (var (x), a(1), 0.02*a(1)); |
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1054 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1055 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1056 %! endfor |
1057 %! endif | |
1058 %!test | |
1059 %! if (__random_statistical_tests__) | |
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1060 %! ## statistical tests may fail occasionally. |
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1061 %! randp ("state", 12); |
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1062 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1063 %! x = randp (a(1)*ones (100_000, 1), 100_000, 1); |
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1064 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1065 %! assert (mean (x), a(1), a(2)); |
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1066 %! assert (var (x), a(1), 0.02*a(1)); |
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1067 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1068 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 1069 %! endfor |
1070 %! endif | |
1071 %!test | |
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1072 %! randp ("seed", 12); |
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1073 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
5730 | 1074 %!test |
6449 | 1075 %! if (__random_statistical_tests__) |
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1076 %! ## statistical tests may fail occasionally. |
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1077 %! randp ("seed", 12); |
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1078 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1079 %! x = randp (a(1), 100_000, 1); |
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1080 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1081 %! assert (mean (x), a(1), a(2)); |
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1082 %! assert (var (x), a(1), 0.02*a(1)); |
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1083 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1084 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1085 %! endfor |
1086 %! endif | |
5730 | 1087 %!test |
6449 | 1088 %! if (__random_statistical_tests__) |
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1089 %! ## statistical tests may fail occasionally. |
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1090 %! randp ("seed", 12); |
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1091 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1092 %! x = randp (a(1)*ones (100_000, 1), 100_000, 1); |
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1093 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1094 %! assert (mean (x), a(1), a(2)); |
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1095 %! assert (var (x), a(1), 0.02*a(1)); |
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1096 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1097 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1098 %! endfor |
1099 %! endif | |
5730 | 1100 */ |
1101 | |
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1102 DEFUN (randperm, args, , |
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1103 doc: /* -*- texinfo -*- |
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1104 @deftypefn {} {} randperm (@var{n}) |
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1105 @deftypefnx {} {} randperm (@var{n}, @var{m}) |
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1106 Return a row vector containing a random permutation of @code{1:@var{n}}. |
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1107 |
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1108 If @var{m} is supplied, return @var{m} unique entries, sampled without |
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1109 replacement from @code{1:@var{n}}. |
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1110 |
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1111 The complexity is O(@var{n}) in memory and O(@var{m}) in time, unless |
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1112 @var{m} < @var{n}/5, in which case O(@var{m}) memory is used as well. The |
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1113 randomization is performed using rand(). All permutations are equally |
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1114 likely. |
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1115 @seealso{perms} |
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1116 @end deftypefn */) |
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1117 { |
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1118 int nargin = args.length (); |
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1119 |
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1120 if (nargin < 1 || nargin > 2) |
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1121 print_usage (); |
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1122 |
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1123 octave_idx_type n = args(0).idx_type_value (true); |
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1124 octave_idx_type m = (nargin == 2) ? args(1).idx_type_value (true) : n; |
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1125 |
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1126 if (m < 0 || n < 0) |
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1127 error ("randperm: M and N must be non-negative"); |
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1128 |
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1129 if (m > n) |
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1130 error ("randperm: M must be less than or equal to N"); |
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1131 |
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1132 // Quick and dirty heuristic to decide if we allocate or not the |
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1133 // whole vector for tracking the truncated shuffle. |
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1134 bool short_shuffle = m < n/5; |
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1135 |
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1136 // Generate random numbers. |
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1137 NDArray r = octave::rand::nd_array (dim_vector (1, m)); |
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1138 double *rvec = r.fortran_vec (); |
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1139 |
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1140 octave_idx_type idx_len = (short_shuffle ? m : n); |
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1141 Array<octave_idx_type> idx; |
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1142 try |
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1143 { |
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1144 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1145 } |
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1146 catch (const std::bad_alloc&) |
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1147 { |
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1148 // Looks like n is too big and short_shuffle is false. |
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1149 // Let's try again, but this time with the alternative. |
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1150 idx_len = m; |
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1151 short_shuffle = true; |
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1152 idx = Array<octave_idx_type> (dim_vector (1, idx_len)); |
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1153 } |
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1154 |
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1155 octave_idx_type *ivec = idx.fortran_vec (); |
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1156 |
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1157 for (octave_idx_type i = 0; i < idx_len; i++) |
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1158 ivec[i] = i; |
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1159 |
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1160 if (short_shuffle) |
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1161 { |
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1162 std::unordered_map<octave_idx_type, octave_idx_type> map (m); |
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1163 |
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1164 // Perform the Knuth shuffle only keeping track of moved |
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1165 // entries in the map |
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1166 for (octave_idx_type i = 0; i < m; i++) |
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1167 { |
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1168 octave_idx_type k = i + std::floor (rvec[i] * (n - i)); |
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1169 |
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1170 // For shuffling first m entries, no need to use extra storage |
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1171 if (k < m) |
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1172 { |
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1173 std::swap (ivec[i], ivec[k]); |
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1174 } |
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1175 else |
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1176 { |
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1177 if (map.find (k) == map.end ()) |
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1178 map[k] = k; |
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1179 |
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1180 std::swap (ivec[i], map[k]); |
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1181 } |
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1182 } |
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1183 } |
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1184 else |
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1185 { |
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1186 // Perform the Knuth shuffle of the first m entries |
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1187 for (octave_idx_type i = 0; i < m; i++) |
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1188 { |
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1189 octave_idx_type k = i + std::floor (rvec[i] * (n - i)); |
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1190 std::swap (ivec[i], ivec[k]); |
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1191 } |
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1192 } |
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1193 |
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1194 // Convert to doubles, reusing r. |
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1195 for (octave_idx_type i = 0; i < m; i++) |
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1196 rvec[i] = ivec[i] + 1; |
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1197 |
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1198 if (m < n) |
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1199 idx.resize (dim_vector (1, m)); |
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1200 |
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1201 // Now create an array object with a cached idx_vector. |
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1202 return ovl (new octave_matrix (r, idx_vector (idx))); |
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1203 } |
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1204 |
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1205 /* |
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1206 %!assert (sort (randperm (20)), 1:20) |
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1207 %!assert (length (randperm (20,10)), 10) |
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1208 |
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1209 ## Test biggish N |
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1210 %!assert <*39378> (length (randperm (30_000^2, 100_000)), 100_000) |
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1211 |
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1212 %!test |
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1213 %! rand ("seed", 0); |
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1214 %! for i = 1:100 |
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1215 %! p = randperm (305, 30); |
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1216 %! assert (length (unique (p)), 30); |
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1217 %! endfor |
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1218 */ |