Mercurial > hg > octave-lyh
annotate src/DLD-FUNCTIONS/rand.cc @ 14501:60e5cf354d80
Update %!tests in DLD-FUNCTIONS/ directory with Octave coding conventions.
* __contourc__.cc, __delaunayn__.cc, __dispatch__.cc, __dsearchn__.cc,
__fltk_uigetfile__.cc, __glpk__.cc, __lin_interpn__.cc, __magick_read__.cc,
__pchip_deriv__.cc, __qp__.cc, __voronoi__.cc, besselj.cc, betainc.cc,
bsxfun.cc, cellfun.cc, chol.cc, conv2.cc, convhulln.cc, dassl.cc, det.cc,
dlmread.cc, dmperm.cc, dot.cc, eig.cc, eigs.cc, fft.cc, fft2.cc, filter.cc,
find.cc, gammainc.cc, gcd.cc, givens.cc, hess.cc, hex2num.cc, inv.cc, kron.cc,
lookup.cc, lsode.cc, lu.cc, luinc.cc, matrix_type.cc, max.cc, mgorth.cc,
nproc.cc, qr.cc, quad.cc, quadcc.cc, qz.cc, rand.cc, rcond.cc, regexp.cc,
schur.cc, spparms.cc, sqrtm.cc, str2double.cc, strfind.cc, sub2ind.cc, svd.cc,
syl.cc, time.cc, tril.cc, tsearch.cc: Update %!tests in DLD-FUNCTIONS/
directory with Octave coding conventions.
author | Rik <octave@nomad.inbox5.com> |
---|---|
date | Tue, 27 Mar 2012 22:46:45 -0700 |
parents | 72c96de7a403 |
children | 41d7e23f5734 |
rev | line source |
---|---|
2928 | 1 /* |
2 | |
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3 Copyright (C) 1996-2012 John W. Eaton |
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4 Copyright (C) 2009 VZLU Prague |
2928 | 5 |
6 This file is part of Octave. | |
7 | |
8 Octave is free software; you can redistribute it and/or modify it | |
9 under the terms of the GNU General Public License as published by the | |
7016 | 10 Free Software Foundation; either version 3 of the License, or (at your |
11 option) any later version. | |
2928 | 12 |
13 Octave is distributed in the hope that it will be useful, but WITHOUT | |
14 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
15 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
16 for more details. | |
17 | |
18 You should have received a copy of the GNU General Public License | |
7016 | 19 along with Octave; see the file COPYING. If not, see |
20 <http://www.gnu.org/licenses/>. | |
2928 | 21 |
22 */ | |
23 | |
24 #ifdef HAVE_CONFIG_H | |
25 #include <config.h> | |
26 #endif | |
27 | |
28 #include <ctime> | |
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29 #if defined (HAVE_UNORDERED_MAP) |
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30 #include <unordered_map> |
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31 #elif defined (HAVE_TR1_UNORDERED_MAP) |
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32 #include <tr1/unordered_map> |
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33 #endif |
2928 | 34 #include <string> |
35 | |
36 #include "f77-fcn.h" | |
37 #include "lo-mappers.h" | |
4307 | 38 #include "oct-rand.h" |
4153 | 39 #include "quit.h" |
2928 | 40 |
41 #include "defun-dld.h" | |
42 #include "error.h" | |
43 #include "gripes.h" | |
44 #include "oct-obj.h" | |
45 #include "unwind-prot.h" | |
46 #include "utils.h" | |
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47 #include "ov-re-mat.h" |
2928 | 48 |
6437 | 49 /* |
50 %!shared __random_statistical_tests__ | |
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51 %! # Flag whether the statistical tests should be run in "make check" or not |
6437 | 52 %! __random_statistical_tests__ = 0; |
53 */ | |
54 | |
4307 | 55 static octave_value |
5730 | 56 do_rand (const octave_value_list& args, int nargin, const char *fcn, |
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57 const std::string& distribution, bool additional_arg = false) |
2928 | 58 { |
4307 | 59 octave_value retval; |
5730 | 60 NDArray a; |
61 int idx = 0; | |
62 dim_vector dims; | |
2928 | 63 |
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64 unwind_protect frame; |
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65 // Restore current distribution on any exit. |
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66 frame.add_fcn (octave_rand::distribution, |
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67 octave_rand::distribution ()); |
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68 |
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69 octave_rand::distribution (distribution); |
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70 |
5730 | 71 if (additional_arg) |
72 { | |
73 if (nargin == 0) | |
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74 { |
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75 error ("%s: expecting at least one argument", fcn); |
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76 goto done; |
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77 } |
5730 | 78 else if (args(0).is_string()) |
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79 additional_arg = false; |
5730 | 80 else |
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81 { |
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82 a = args(0).array_value (); |
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83 if (error_state) |
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84 { |
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85 error ("%s: expecting scalar or matrix arguments", fcn); |
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86 goto done; |
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87 } |
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88 idx++; |
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89 nargin--; |
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90 } |
5730 | 91 } |
2928 | 92 |
4543 | 93 switch (nargin) |
2928 | 94 { |
4543 | 95 case 0: |
96 { | |
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97 if (additional_arg) |
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98 dims = a.dims (); |
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99 else |
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100 { |
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101 dims.resize (2); |
4543 | 102 |
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103 dims(0) = 1; |
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104 dims(1) = 1; |
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105 } |
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106 goto gen_matrix; |
4543 | 107 } |
108 break; | |
2928 | 109 |
4543 | 110 case 1: |
111 { | |
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112 octave_value tmp = args(idx); |
4543 | 113 |
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114 if (tmp.is_string ()) |
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115 { |
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116 std::string s_arg = tmp.string_value (); |
2928 | 117 |
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118 if (s_arg == "dist") |
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119 { |
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120 retval = octave_rand::distribution (); |
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121 } |
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122 else if (s_arg == "seed") |
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123 { |
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124 retval = octave_rand::seed (); |
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125 } |
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126 else if (s_arg == "state" || s_arg == "twister") |
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127 { |
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128 retval = octave_rand::state (fcn); |
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129 } |
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130 else if (s_arg == "uniform") |
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131 { |
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132 octave_rand::uniform_distribution (); |
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133 } |
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134 else if (s_arg == "normal") |
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135 { |
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136 octave_rand::normal_distribution (); |
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137 } |
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138 else if (s_arg == "exponential") |
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139 { |
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140 octave_rand::exponential_distribution (); |
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141 } |
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142 else if (s_arg == "poisson") |
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143 { |
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144 octave_rand::poisson_distribution (); |
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145 } |
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146 else if (s_arg == "gamma") |
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147 { |
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148 octave_rand::gamma_distribution (); |
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149 } |
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150 else |
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151 error ("%s: unrecognized string argument", fcn); |
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152 } |
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153 else if (tmp.is_scalar_type ()) |
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154 { |
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155 double dval = tmp.double_value (); |
2928 | 156 |
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157 if (xisnan (dval)) |
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158 { |
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159 error ("%s: NaN is invalid matrix dimension", fcn); |
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160 } |
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161 else |
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162 { |
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163 dims.resize (2); |
4543 | 164 |
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165 dims(0) = NINTbig (tmp.double_value ()); |
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166 dims(1) = NINTbig (tmp.double_value ()); |
2928 | 167 |
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168 if (! error_state) |
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169 goto gen_matrix; |
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170 } |
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171 } |
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172 else if (tmp.is_range ()) |
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173 { |
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174 Range r = tmp.range_value (); |
4543 | 175 |
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176 if (r.all_elements_are_ints ()) |
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177 { |
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178 octave_idx_type n = r.nelem (); |
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180 dims.resize (n); |
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182 octave_idx_type base = NINTbig (r.base ()); |
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183 octave_idx_type incr = NINTbig (r.inc ()); |
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185 for (octave_idx_type i = 0; i < n; i++) |
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186 { |
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187 //Negative dimensions are treated as zero for Matlab |
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188 //compatibility |
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189 dims(i) = base >= 0 ? base : 0; |
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190 base += incr; |
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191 } |
2928 | 192 |
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193 goto gen_matrix; |
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194 |
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195 } |
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196 else |
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197 error ("%s: all elements of range must be integers", |
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198 fcn); |
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199 } |
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200 else if (tmp.is_matrix_type ()) |
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201 { |
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202 Array<int> iv = tmp.int_vector_value (true); |
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204 if (! error_state) |
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205 { |
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206 octave_idx_type len = iv.length (); |
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208 dims.resize (len); |
4543 | 209 |
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210 for (octave_idx_type i = 0; i < len; i++) |
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211 { |
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212 //Negative dimensions are treated as zero for Matlab |
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213 //compatibility |
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214 octave_idx_type elt = iv(i); |
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215 dims(i) = elt >=0 ? elt : 0; |
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216 } |
2928 | 217 |
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218 goto gen_matrix; |
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219 } |
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220 else |
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221 error ("%s: expecting integer vector", fcn); |
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222 } |
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223 else |
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224 { |
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225 gripe_wrong_type_arg ("rand", tmp); |
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226 return retval; |
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227 } |
4543 | 228 } |
229 break; | |
230 | |
231 default: | |
232 { | |
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233 octave_value tmp = args(idx); |
4543 | 234 |
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235 if (nargin == 2 && tmp.is_string ()) |
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236 { |
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237 std::string ts = tmp.string_value (); |
5164 | 238 |
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239 if (ts == "seed") |
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240 { |
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241 if (args(idx+1).is_real_scalar ()) |
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242 { |
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243 double d = args(idx+1).double_value (); |
2928 | 244 |
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245 if (! error_state) |
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246 octave_rand::seed (d); |
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247 } |
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248 else if (args(idx+1).is_string () |
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92a85ed5b86e
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249 && args(idx+1).string_value() == "reset") |
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250 octave_rand::reset (); |
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251 else |
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252 error ("%s: seed must be a real scalar", fcn); |
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253 } |
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254 else if (ts == "state" || ts == "twister") |
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255 { |
10709
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256 if (args(idx+1).is_string () |
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257 && args(idx+1).string_value() == "reset") |
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258 octave_rand::reset (fcn); |
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259 else |
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260 { |
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|
261 ColumnVector s = |
10709
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262 ColumnVector (args(idx+1).vector_value(false, true)); |
5730 | 263 |
10709
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264 if (! error_state) |
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265 octave_rand::state (s, fcn); |
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266 } |
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267 } |
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268 else |
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269 error ("%s: unrecognized string argument", fcn); |
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270 } |
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271 else |
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272 { |
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273 dims.resize (nargin); |
4543 | 274 |
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275 for (int i = 0; i < nargin; i++) |
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276 { |
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277 octave_idx_type elt = args(idx+i).int_value (); |
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278 if (error_state) |
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279 { |
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280 error ("%s: expecting integer arguments", fcn); |
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281 goto done; |
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|
282 } |
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283 //Negative is zero for Matlab compatibility |
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284 dims(i) = elt >= 0 ? elt : 0; |
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285 } |
4543 | 286 |
10154
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287 goto gen_matrix; |
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|
288 } |
4543 | 289 } |
290 break; | |
2928 | 291 } |
292 | |
4543 | 293 done: |
2928 | 294 |
295 return retval; | |
296 | |
297 gen_matrix: | |
298 | |
5355 | 299 dims.chop_trailing_singletons (); |
300 | |
5730 | 301 if (additional_arg) |
302 { | |
303 if (a.length() == 1) | |
10154
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304 return octave_rand::nd_array (dims, a(0)); |
5730 | 305 else |
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|
306 { |
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307 if (a.dims() != dims) |
40dfc0c99116
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|
308 { |
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|
309 error ("%s: mismatch in argument size", fcn); |
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310 return retval; |
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|
311 } |
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312 octave_idx_type len = a.length (); |
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313 NDArray m (dims); |
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314 double *v = m.fortran_vec (); |
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315 for (octave_idx_type i = 0; i < len; i++) |
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316 v[i] = octave_rand::scalar (a(i)); |
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317 return m; |
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|
318 } |
5730 | 319 } |
320 else | |
321 return octave_rand::nd_array (dims); | |
2928 | 322 } |
323 | |
4665 | 324 DEFUN_DLD (rand, args, , |
3369 | 325 "-*- texinfo -*-\n\ |
12639
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326 @deftypefn {Loadable Function} {} rand (@var{n})\n\ |
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327 @deftypefnx {Loadable Function} {} rand (@var{n}, @var{m}, @dots{})\n\ |
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328 @deftypefnx {Loadable Function} {} rand ([@var{n} @var{m} @dots{}])\n\ |
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329 @deftypefnx {Loadable Function} {@var{v} =} rand (\"state\")\n\ |
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330 @deftypefnx {Loadable Function} {} rand (\"state\", @var{v})\n\ |
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331 @deftypefnx {Loadable Function} {} rand (\"state\", \"reset\")\n\ |
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332 @deftypefnx {Loadable Function} {@var{v} =} rand (\"seed\")\n\ |
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333 @deftypefnx {Loadable Function} {} rand (\"seed\", @var{v})\n\ |
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334 @deftypefnx {Loadable Function} {} rand (\"seed\", \"reset\")\n\ |
3369 | 335 Return a matrix with random elements uniformly distributed on the\n\ |
336 interval (0, 1). The arguments are handled the same as the arguments\n\ | |
5730 | 337 for @code{eye}.\n\ |
338 \n\ | |
339 You can query the state of the random number generator using the\n\ | |
3369 | 340 form\n\ |
2928 | 341 \n\ |
3369 | 342 @example\n\ |
5730 | 343 v = rand (\"state\")\n\ |
344 @end example\n\ | |
345 \n\ | |
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346 This returns a column vector @var{v} of length 625. Later, you can\n\ |
5730 | 347 restore the random number generator to the state @var{v}\n\ |
348 using the form\n\ | |
349 \n\ | |
350 @example\n\ | |
351 rand (\"state\", v)\n\ | |
3369 | 352 @end example\n\ |
353 \n\ | |
354 @noindent\n\ | |
5730 | 355 You may also initialize the state vector from an arbitrary vector of\n\ |
10840 | 356 length @leq{} 625 for @var{v}. This new state will be a hash based on the\n\ |
5798 | 357 value of @var{v}, not @var{v} itself.\n\ |
5730 | 358 \n\ |
359 By default, the generator is initialized from @code{/dev/urandom} if it is\n\ | |
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360 available, otherwise from CPU time, wall clock time, and the current\n\ |
5730 | 361 fraction of a second.\n\ |
362 \n\ | |
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363 To compute the pseudo-random sequence, @code{rand} uses the Mersenne\n\ |
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364 Twister with a period of @math{2^{19937}-1} (See M. Matsumoto and\n\ |
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365 T. Nishimura,\n\ |
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366 @cite{Mersenne Twister: A 623-dimensionally equidistributed uniform\n\ |
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367 pseudorandom number generator}, ACM Trans. on\n\ |
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368 Modeling and Computer Simulation Vol. 8, No. 1, pp. 3-30, January 1998,\n\ |
7171 | 369 @url{http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html}).\n\ |
6547 | 370 Do @strong{not} use for cryptography without securely hashing\n\ |
371 several returned values together, otherwise the generator state\n\ | |
372 can be learned after reading 624 consecutive values.\n\ | |
5730 | 373 \n\ |
7096 | 374 Older versions of Octave used a different random number generator.\n\ |
375 The new generator is used by default\n\ | |
5730 | 376 as it is significantly faster than the old generator, and produces\n\ |
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377 random numbers with a significantly longer cycle time. However, in\n\ |
5798 | 378 some circumstances it might be desirable to obtain the same random\n\ |
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379 sequences as used by the old generators. To do this the keyword\n\ |
5730 | 380 \"seed\" is used to specify that the old generators should be use,\n\ |
381 as in\n\ | |
2928 | 382 \n\ |
3369 | 383 @example\n\ |
5730 | 384 rand (\"seed\", val)\n\ |
3369 | 385 @end example\n\ |
386 \n\ | |
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387 @noindent\n\ |
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388 which sets the seed of the generator to @var{val}. The seed of the\n\ |
5730 | 389 generator can be queried with\n\ |
390 \n\ | |
391 @example\n\ | |
392 s = rand (\"seed\")\n\ | |
393 @end example\n\ | |
394 \n\ | |
395 However, it should be noted that querying the seed will not cause\n\ | |
396 @code{rand} to use the old generators, only setting the seed will.\n\ | |
397 To cause @code{rand} to once again use the new generators, the\n\ | |
398 keyword \"state\" should be used to reset the state of the @code{rand}.\n\ | |
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399 \n\ |
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400 The state or seed of the generator can be reset to a new random value\n\ |
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401 using the \"reset\" keyword.\n\ |
5798 | 402 @seealso{randn, rande, randg, randp}\n\ |
3369 | 403 @end deftypefn") |
2928 | 404 { |
4307 | 405 octave_value retval; |
2928 | 406 |
407 int nargin = args.length (); | |
408 | |
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409 retval = do_rand (args, nargin, "rand", "uniform"); |
2928 | 410 |
411 return retval; | |
412 } | |
413 | |
8871 | 414 // FIXME -- The old generator (selected when "seed" is set) will not |
415 // work properly if compiled to use 64-bit integers. | |
416 | |
5730 | 417 /* |
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418 %!test # "state" can be a scalar |
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419 %! rand ("state", 12); x = rand (1,4); |
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420 %! rand ("state", 12); y = rand (1,4); |
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421 %! assert (x, y); |
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422 %!test # "state" can be a vector |
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423 %! rand ("state", [12,13]); x = rand (1,4); |
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424 %! rand ("state", [12;13]); y = rand (1,4); |
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425 %! assert (x, y); |
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426 %!test # querying "state" doesn't disturb sequence |
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427 %! rand ("state", 12); rand (1,2); x = rand (1,2); |
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428 %! rand ("state", 12); rand (1,2); |
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429 %! s = rand ("state"); y = rand (1,2); |
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430 %! assert (x, y); |
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431 %! rand ("state", s); z = rand (1,2); |
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432 %! assert (x, z); |
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433 %!test # "seed" must be a scalar |
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434 %! rand ("seed", 12); x = rand (1,4); |
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435 %! rand ("seed", 12); y = rand (1,4); |
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436 %! assert (x, y); |
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437 %!error <seed must be a real scalar> rand ("seed", [12,13]) |
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438 %!test # querying "seed" returns a value which can be used later |
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439 %! s = rand ("seed"); x = rand (1,2); |
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440 %! rand ("seed", s); y = rand (1,2); |
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441 %! assert (x, y); |
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442 %!test # querying "seed" doesn't disturb sequence |
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443 %! rand ("seed", 12); rand (1,2); x = rand (1,2); |
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444 %! rand ("seed", 12); rand (1,2); |
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445 %! s = rand ("seed"); y = rand (1,2); |
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446 %! assert (x, y); |
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447 %! rand ("seed", s); z = rand (1,2); |
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448 %! assert (x, z); |
5730 | 449 */ |
450 | |
451 /* | |
452 %!test | |
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453 %! # Test fixed state |
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454 %! rand ("state", 1); |
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455 %! assert (rand (1,6), [0.1343642441124013 0.8474337369372327 0.763774618976614 0.2550690257394218 0.495435087091941 0.4494910647887382], 1e-6); |
6437 | 456 %!test |
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457 %! # Test fixed seed |
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458 %! rand ("seed", 1); |
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459 %! assert (rand (1,6), [0.8668024251237512 0.9126510815694928 0.09366085007786751 0.1664607301354408 0.7408077004365623 0.7615650338120759], 1e-6); |
5730 | 460 %!test |
6437 | 461 %! if (__random_statistical_tests__) |
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462 %! # statistical tests may fail occasionally. |
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463 %! rand ("state", 12); |
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464 %! x = rand (100000, 1); |
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465 %! assert (max (x) < 1); #*** Please report this!!! *** |
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466 %! assert (min (x) > 0); #*** Please report this!!! *** |
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467 %! assert (mean (x), 0.5, 0.0024); |
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468 %! assert (var (x), 1/48, 0.0632); |
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469 %! assert (skewness (x), 0, 0.012); |
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470 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 471 %! endif |
472 %!test | |
473 %! if (__random_statistical_tests__) | |
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474 %! # statistical tests may fail occasionally. |
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475 %! rand ("seed", 12); |
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476 %! x = rand (100000, 1); |
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477 %! assert (max (x) < 1); #*** Please report this!!! *** |
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478 %! assert (min (x) > 0); #*** Please report this!!! *** |
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479 %! assert (mean (x), 0.5, 0.0024); |
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480 %! assert (var (x), 1/48, 0.0632); |
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481 %! assert (skewness (x), 0, 0.012); |
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482 %! assert (kurtosis (x), -6/5, 0.0094); |
6437 | 483 %! endif |
5730 | 484 */ |
485 | |
4307 | 486 static std::string current_distribution = octave_rand::distribution (); |
487 | |
4665 | 488 DEFUN_DLD (randn, args, , |
3369 | 489 "-*- texinfo -*-\n\ |
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490 @deftypefn {Loadable Function} {} randn (@var{n})\n\ |
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491 @deftypefnx {Loadable Function} {} randn (@var{n}, @var{m}, @dots{})\n\ |
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492 @deftypefnx {Loadable Function} {} randn ([@var{n} @var{m} @dots{}])\n\ |
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493 @deftypefnx {Loadable Function} {@var{v} =} randn (\"state\")\n\ |
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494 @deftypefnx {Loadable Function} {} randn (\"state\", @var{v})\n\ |
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495 @deftypefnx {Loadable Function} {} randn (\"state\", \"reset\")\n\ |
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496 @deftypefnx {Loadable Function} {@var{v} =} randn (\"seed\")\n\ |
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497 @deftypefnx {Loadable Function} {} randn (\"seed\", @var{v})\n\ |
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498 @deftypefnx {Loadable Function} {} randn (\"seed\", \"reset\")\n\ |
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499 Return a matrix with normally distributed random\n\ |
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500 elements having zero mean and variance one. The arguments are\n\ |
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501 handled the same as the arguments for @code{rand}.\n\ |
3369 | 502 \n\ |
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503 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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504 to transform from a uniform to a normal distribution.\n\ |
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505 \n\ |
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506 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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507 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 508 J. Statistical Software, vol 5, 2000,\n\ |
509 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
2928 | 510 \n\ |
6547 | 511 @seealso{rand, rande, randg, randp}\n\ |
3369 | 512 @end deftypefn") |
2928 | 513 { |
4307 | 514 octave_value retval; |
2928 | 515 |
516 int nargin = args.length (); | |
517 | |
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518 retval = do_rand (args, nargin, "randn", "normal"); |
2928 | 519 |
520 return retval; | |
521 } | |
522 | |
523 /* | |
5730 | 524 %!test |
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525 %! # Test fixed state |
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526 %! randn ("state", 1); |
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527 %! assert (randn (1, 6), [-2.666521678978671 -0.7381719971724564 1.507903992673601 0.6019427189162239 -0.450661261143348 -0.7054431351574116], 1e-6); |
6437 | 528 %!test |
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529 %! # Test fixed seed |
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530 %! randn ("seed", 1); |
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531 %! assert (randn (1, 6), [-1.039402365684509 -1.25938892364502 0.1968704611063004 0.3874166905879974 -0.5976632833480835 -0.6615074276924133], 1e-6); |
5730 | 532 %!test |
6437 | 533 %! if (__random_statistical_tests__) |
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534 %! # statistical tests may fail occasionally. |
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535 %! randn ("state", 12); |
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536 %! x = randn (100000, 1); |
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537 %! assert (mean (x), 0, 0.01); |
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538 %! assert (var (x), 1, 0.02); |
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539 %! assert (skewness (x), 0, 0.02); |
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540 %! assert (kurtosis (x), 0, 0.04); |
6437 | 541 %! endif |
542 %!test | |
543 %! if (__random_statistical_tests__) | |
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544 %! # statistical tests may fail occasionally. |
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545 %! randn ("seed", 12); |
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546 %! x = randn (100000, 1); |
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547 %! assert (mean (x), 0, 0.01); |
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548 %! assert (var (x), 1, 0.02); |
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549 %! assert (skewness (x), 0, 0.02); |
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550 %! assert (kurtosis (x), 0, 0.04); |
6437 | 551 %! endif |
5730 | 552 */ |
553 | |
554 DEFUN_DLD (rande, args, , | |
555 "-*- texinfo -*-\n\ | |
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556 @deftypefn {Loadable Function} {} rande (@var{n})\n\ |
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557 @deftypefnx {Loadable Function} {} rande (@var{n}, @var{m}, @dots{})\n\ |
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558 @deftypefnx {Loadable Function} {} rande ([@var{n} @var{m} @dots{}])\n\ |
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559 @deftypefnx {Loadable Function} {@var{v} =} rande (\"state\")\n\ |
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560 @deftypefnx {Loadable Function} {} rande (\"state\", @var{v})\n\ |
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561 @deftypefnx {Loadable Function} {} rande (\"state\", \"reset\")\n\ |
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562 @deftypefnx {Loadable Function} {@var{v} =} rande (\"seed\")\n\ |
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563 @deftypefnx {Loadable Function} {} rande (\"seed\", @var{v})\n\ |
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564 @deftypefnx {Loadable Function} {} rande (\"seed\", \"reset\")\n\ |
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565 Return a matrix with exponentially distributed random elements. The\n\ |
5730 | 566 arguments are handled the same as the arguments for @code{rand}.\n\ |
567 \n\ | |
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568 By default, @code{randn} uses the Marsaglia and Tsang ``Ziggurat technique''\n\ |
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569 to transform from a uniform to an exponential distribution.\n\ |
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570 \n\ |
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571 Reference: G. Marsaglia and W.W. Tsang,\n\ |
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572 @cite{Ziggurat Method for Generating Random Variables},\n\ |
5730 | 573 J. Statistical Software, vol 5, 2000,\n\ |
574 @url{http://www.jstatsoft.org/v05/i08/})\n\ | |
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575 \n\ |
6547 | 576 @seealso{rand, randn, randg, randp}\n\ |
5730 | 577 @end deftypefn") |
578 { | |
579 octave_value retval; | |
580 | |
581 int nargin = args.length (); | |
582 | |
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583 retval = do_rand (args, nargin, "rande", "exponential"); |
5730 | 584 |
585 return retval; | |
586 } | |
587 | |
588 /* | |
589 %!test | |
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590 %! # Test fixed state |
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591 %! rande ("state", 1); |
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592 %! assert (rande (1, 6), [3.602973885835625 0.1386190677555021 0.6743112889616958 0.4512830847258422 0.7255744741233175 0.3415969205292291], 1e-6); |
6437 | 593 %!test |
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594 %! # Test fixed seed |
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595 %! rande ("seed", 1); |
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596 %! assert (rande (1, 6), [0.06492075175653866 1.717980206012726 0.4816154008731246 0.5231300676241517 0.103910739364359 1.668931916356087], 1e-6); |
5730 | 597 %!test |
6437 | 598 %! if (__random_statistical_tests__) |
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599 %! # statistical tests may fail occasionally |
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600 %! rande ("state", 1); |
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601 %! x = rande (100000, 1); |
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602 %! assert (min (x) > 0); # *** Please report this!!! *** |
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603 %! assert (mean (x), 1, 0.01); |
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604 %! assert (var (x), 1, 0.03); |
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605 %! assert (skewness (x), 2, 0.06); |
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606 %! assert (kurtosis (x), 6, 0.7); |
6437 | 607 %! endif |
608 %!test | |
609 %! if (__random_statistical_tests__) | |
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610 %! # statistical tests may fail occasionally |
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611 %! rande ("seed", 1); |
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612 %! x = rande (100000, 1); |
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613 %! assert (min (x)>0); # *** Please report this!!! *** |
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614 %! assert (mean (x), 1, 0.01); |
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615 %! assert (var (x), 1, 0.03); |
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616 %! assert (skewness (x), 2, 0.06); |
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617 %! assert (kurtosis (x), 6, 0.7); |
6437 | 618 %! endif |
5730 | 619 */ |
620 | |
621 DEFUN_DLD (randg, args, , | |
622 "-*- texinfo -*-\n\ | |
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623 @deftypefn {Loadable Function} {} randg (@var{n})\n\ |
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624 @deftypefnx {Loadable Function} {} randg (@var{n}, @var{m}, @dots{})\n\ |
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625 @deftypefnx {Loadable Function} {} randg ([@var{n} @var{m} @dots{}])\n\ |
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626 @deftypefnx {Loadable Function} {@var{v} =} randg (\"state\")\n\ |
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627 @deftypefnx {Loadable Function} {} randg (\"state\", @var{v})\n\ |
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628 @deftypefnx {Loadable Function} {} randg (\"state\", \"reset\")\n\ |
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629 @deftypefnx {Loadable Function} {@var{v} =} randg (\"seed\")\n\ |
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630 @deftypefnx {Loadable Function} {} randg (\"seed\", @var{v})\n\ |
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631 @deftypefnx {Loadable Function} {} randg (\"seed\", \"reset\")\n\ |
5730 | 632 Return a matrix with @code{gamma(@var{a},1)} distributed random elements.\n\ |
633 The arguments are handled the same as the arguments for @code{rand},\n\ | |
634 except for the argument @var{a}.\n\ | |
635 \n\ | |
636 This can be used to generate many distributions:\n\ | |
637 \n\ | |
638 @table @asis\n\ | |
6547 | 639 @item @code{gamma (a, b)} for @code{a > -1}, @code{b > 0}\n\ |
10840 | 640 \n\ |
5730 | 641 @example\n\ |
6547 | 642 r = b * randg (a)\n\ |
5730 | 643 @end example\n\ |
10840 | 644 \n\ |
6547 | 645 @item @code{beta (a, b)} for @code{a > -1}, @code{b > -1}\n\ |
10840 | 646 \n\ |
5730 | 647 @example\n\ |
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648 @group\n\ |
6547 | 649 r1 = randg (a, 1)\n\ |
650 r = r1 / (r1 + randg (b, 1))\n\ | |
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651 @end group\n\ |
5730 | 652 @end example\n\ |
10840 | 653 \n\ |
6547 | 654 @item @code{Erlang (a, n)}\n\ |
10840 | 655 \n\ |
5730 | 656 @example\n\ |
6547 | 657 r = a * randg (n)\n\ |
5730 | 658 @end example\n\ |
10840 | 659 \n\ |
6547 | 660 @item @code{chisq (df)} for @code{df > 0}\n\ |
10840 | 661 \n\ |
5730 | 662 @example\n\ |
6547 | 663 r = 2 * randg (df / 2)\n\ |
5730 | 664 @end example\n\ |
10840 | 665 \n\ |
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666 @item @code{t (df)} for @code{0 < df < inf} (use randn if df is infinite)\n\ |
10840 | 667 \n\ |
5730 | 668 @example\n\ |
6547 | 669 r = randn () / sqrt (2 * randg (df / 2) / df)\n\ |
5730 | 670 @end example\n\ |
10840 | 671 \n\ |
6547 | 672 @item @code{F (n1, n2)} for @code{0 < n1}, @code{0 < n2}\n\ |
10840 | 673 \n\ |
5730 | 674 @example\n\ |
7096 | 675 @group\n\ |
676 ## r1 equals 1 if n1 is infinite\n\ | |
677 r1 = 2 * randg (n1 / 2) / n1\n\ | |
678 ## r2 equals 1 if n2 is infinite\n\ | |
679 r2 = 2 * randg (n2 / 2) / n2\n\ | |
5730 | 680 r = r1 / r2\n\n\ |
7096 | 681 @end group\n\ |
5730 | 682 @end example\n\ |
10840 | 683 \n\ |
5730 | 684 @item negative @code{binomial (n, p)} for @code{n > 0}, @code{0 < p <= 1}\n\ |
10840 | 685 \n\ |
5730 | 686 @example\n\ |
6547 | 687 r = randp ((1 - p) / p * randg (n))\n\ |
5730 | 688 @end example\n\ |
10840 | 689 \n\ |
6547 | 690 @item non-central @code{chisq (df, L)}, for @code{df >= 0} and @code{L > 0}\n\ |
5730 | 691 (use chisq if @code{L = 0})\n\ |
10840 | 692 \n\ |
5730 | 693 @example\n\ |
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694 @group\n\ |
6547 | 695 r = randp (L / 2)\n\ |
696 r(r > 0) = 2 * randg (r(r > 0))\n\ | |
697 r(df > 0) += 2 * randg (df(df > 0)/2)\n\ | |
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698 @end group\n\ |
5730 | 699 @end example\n\ |
10840 | 700 \n\ |
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701 @item @code{Dirichlet (a1, @dots{} ak)}\n\ |
10840 | 702 \n\ |
5730 | 703 @example\n\ |
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704 @group\n\ |
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705 r = (randg (a1), @dots{}, randg (ak))\n\ |
6547 | 706 r = r / sum (r)\n\ |
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707 @end group\n\ |
5730 | 708 @end example\n\ |
10840 | 709 \n\ |
5730 | 710 @end table\n\ |
6547 | 711 @seealso{rand, randn, rande, randp}\n\ |
5730 | 712 @end deftypefn") |
713 { | |
714 octave_value retval; | |
715 | |
716 int nargin = args.length (); | |
717 | |
718 if (nargin < 1) | |
719 error ("randg: insufficient arguments"); | |
720 else | |
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721 retval = do_rand (args, nargin, "randg", "gamma", true); |
5730 | 722 |
723 return retval; | |
724 } | |
725 | |
726 /* | |
727 %!test | |
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728 %! randg ("state", 12) |
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729 %! assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]); # *** Please report |
6437 | 730 |
731 %!test | |
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732 %! # Test fixed state |
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733 %! randg ("state", 1); |
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734 %! assert (randg (0.1, 1, 6), [0.0103951513331241 8.335671459898252e-05 0.00138691397249762 0.000587308416993855 0.495590518784736 2.3921917414795e-12], 1e-6); |
6437 | 735 %!test |
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736 %! # Test fixed state |
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737 %! randg ("state", 1); |
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738 %! assert (randg (0.95, 1, 6), [3.099382433255327 0.3974529788871218 0.644367450750855 1.143261091802246 1.964111762696822 0.04011915547957939], 1e-6); |
6437 | 739 %!test |
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740 %! # Test fixed state |
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741 %! randg ("state", 1); |
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742 %! assert (randg (1, 1, 6), [0.2273389379645993 1.288822625058359 0.2406335209340746 1.218869553370733 1.024649860162554 0.09631230343599533], 1e-6); |
6437 | 743 %!test |
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744 %! # Test fixed state |
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745 %! randg ("state", 1); |
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746 %! assert (randg (10, 1, 6), [3.520369644331133 15.15369864472106 8.332112081991205 8.406211067432674 11.81193475187611 10.88792728177059], 1e-5); |
6437 | 747 %!test |
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748 %! # Test fixed state |
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749 %! randg ("state", 1); |
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750 %! assert (randg (100, 1, 6), [75.34570255262264 115.4911985594699 95.23493031356388 95.48926019250911 106.2397448229803 103.4813150404118], 1e-4); |
6437 | 751 %!test |
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752 %! # Test fixed seed |
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753 %! randg ("seed", 1); |
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754 %! assert (randg (0.1, 1, 6), [0.07144210487604141 0.460641473531723 0.4749028384685516 0.06823389977216721 0.000293838675133884 1.802567535340305e-12], 1e-6); |
6437 | 755 %!test |
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756 %! # Test fixed seed |
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757 %! randg ("seed", 1); |
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758 %! assert (randg (0.95, 1, 6), [1.664905071258545 1.879976987838745 1.905677795410156 0.9948706030845642 0.5606933236122131 0.0766092911362648], 1e-6); |
6437 | 759 %!test |
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760 %! # Test fixed seed |
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761 %! randg ("seed", 1); |
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762 %! assert (randg (1, 1, 6), [0.03512085229158401 0.6488978862762451 0.8114678859710693 0.1666885763406754 1.60791552066803 1.90356981754303], 1e-6); |
6437 | 763 %!test |
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764 %! # Test fixed seed |
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765 %! randg ("seed", 1); |
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766 %! assert (randg (10, 1, 6), [6.566435813903809 10.11648464202881 10.73162078857422 7.747178077697754 6.278522491455078 6.240195751190186], 1e-5); |
6437 | 767 %!test |
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768 %! # Test fixed seed |
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769 %! randg ("seed", 1); |
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770 %! assert (randg (100, 1, 6), [89.40208435058594 101.4734725952148 103.4020004272461 93.62763214111328 88.33104705810547 88.1871337890625], 1e-4); |
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771 |
6437 | 772 %!test |
773 %! if (__random_statistical_tests__) | |
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774 %! # statistical tests may fail occasionally. |
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775 %! randg ("state", 12); |
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776 %! a = 0.1; |
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777 %! x = randg (a, 100000, 1); |
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778 %! assert (mean (x), a, 0.01); |
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779 %! assert (var (x), a, 0.01); |
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780 %! assert (skewness (x), 2/sqrt (a), 1); |
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781 %! assert (kurtosis (x), 6/a, 50); |
6437 | 782 %! endif |
783 %!test | |
784 %! if (__random_statistical_tests__) | |
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785 %! # statistical tests may fail occasionally. |
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786 %! randg ("state", 12); |
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787 %! a = 0.95; |
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788 %! x = randg (a, 100000, 1); |
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789 %! assert (mean (x), a, 0.01); |
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790 %! assert (var (x), a, 0.04); |
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791 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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792 %! assert (kurtosis (x), 6/a, 2); |
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793 %! endif |
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794 %!test |
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795 %! if (__random_statistical_tests__) |
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796 %! # statistical tests may fail occasionally. |
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797 %! randg ("state", 12); |
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798 %! a = 1; |
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799 %! x = randg (a, 100000, 1); |
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800 %! assert (mean (x), a, 0.01); |
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801 %! assert (var (x), a, 0.04); |
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802 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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803 %! assert (kurtosis (x), 6/a, 2); |
6437 | 804 %! endif |
805 %!test | |
806 %! if (__random_statistical_tests__) | |
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807 %! # statistical tests may fail occasionally. |
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808 %! randg ("state", 12); |
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809 %! a = 10; |
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810 %! x = randg (a, 100000, 1); |
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811 %! assert (mean (x), a, 0.1); |
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812 %! assert (var (x), a, 0.5); |
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813 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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814 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 815 %! endif |
816 %!test | |
817 %! if (__random_statistical_tests__) | |
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818 %! # statistical tests may fail occasionally. |
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819 %! randg ("state", 12); |
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820 %! a = 100; |
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821 %! x = randg (a, 100000, 1); |
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822 %! assert (mean (x), a, 0.2); |
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823 %! assert (var (x), a, 2); |
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824 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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825 %! assert (kurtosis (x), 6/a, 0.2); |
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826 %! endif |
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827 %!test |
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828 %! randg ("seed", 12); |
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829 %!assert (randg ([-inf, -1, 0, inf, nan]), [nan, nan, nan, nan, nan]) # *** Please report |
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830 %!test |
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831 %! if (__random_statistical_tests__) |
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832 %! # statistical tests may fail occasionally. |
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833 %! randg ("seed", 12); |
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834 %! a = 0.1; |
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835 %! x = randg (a, 100000, 1); |
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836 %! assert (mean (x), a, 0.01); |
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837 %! assert (var (x), a, 0.01); |
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838 %! assert (skewness (x), 2/sqrt (a), 1); |
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839 %! assert (kurtosis (x), 6/a, 50); |
6437 | 840 %! endif |
841 %!test | |
842 %! if (__random_statistical_tests__) | |
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843 %! # statistical tests may fail occasionally. |
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844 %! randg ("seed", 12); |
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845 %! a = 0.95; |
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846 %! x = randg (a, 100000, 1); |
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847 %! assert (mean (x), a, 0.01); |
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848 %! assert (var (x), a, 0.04); |
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849 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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850 %! assert (kurtosis (x), 6/a, 2); |
6437 | 851 %! endif |
852 %!test | |
853 %! if (__random_statistical_tests__) | |
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854 %! # statistical tests may fail occasionally. |
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855 %! randg ("seed", 12); |
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856 %! a = 1; |
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857 %! x = randg (a, 100000, 1); |
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858 %! assert (mean (x), a, 0.01); |
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859 %! assert (var (x), a, 0.04); |
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860 %! assert (skewness (x), 2/sqrt (a), 0.2); |
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861 %! assert (kurtosis (x), 6/a, 2); |
6437 | 862 %! endif |
5730 | 863 %!test |
6437 | 864 %! if (__random_statistical_tests__) |
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865 %! # statistical tests may fail occasionally. |
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866 %! randg ("seed", 12); |
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867 %! a = 10; |
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868 %! x = randg (a, 100000, 1); |
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869 %! assert (mean (x), a, 0.1); |
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870 %! assert (var (x), a, 0.5); |
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871 %! assert (skewness (x), 2/sqrt (a), 0.1); |
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872 %! assert (kurtosis (x), 6/a, 0.5); |
6437 | 873 %! endif |
5730 | 874 %!test |
6437 | 875 %! if (__random_statistical_tests__) |
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876 %! # statistical tests may fail occasionally. |
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877 %! randg ("seed", 12); |
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878 %! a = 100; |
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879 %! x = randg (a, 100000, 1); |
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880 %! assert (mean (x), a, 0.2); |
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881 %! assert (var (x), a, 2); |
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882 %! assert (skewness (x), 2/sqrt (a), 0.05); |
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883 %! assert (kurtosis (x), 6/a, 0.2); |
6437 | 884 %! endif |
5730 | 885 */ |
886 | |
887 DEFUN_DLD (randp, args, , | |
888 "-*- texinfo -*-\n\ | |
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889 @deftypefn {Loadable Function} {} randp (@var{l}, @var{n})\n\ |
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890 @deftypefnx {Loadable Function} {} randp (@var{l}, @var{n}, @var{m}, @dots{})\n\ |
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891 @deftypefnx {Loadable Function} {} randp (@var{l}, [@var{n} @var{m} @dots{}])\n\ |
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892 @deftypefnx {Loadable Function} {@var{v} =} randp (\"state\")\n\ |
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893 @deftypefnx {Loadable Function} {} randp (\"state\", @var{v})\n\ |
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894 @deftypefnx {Loadable Function} {} randp (\"state\", \"reset\")\n\ |
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895 @deftypefnx {Loadable Function} {@var{v} =} randp (\"seed\")\n\ |
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896 @deftypefnx {Loadable Function} {} randp (\"seed\", @var{v})\n\ |
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897 @deftypefnx {Loadable Function} {} randp (\"seed\", \"reset\")\n\ |
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898 Return a matrix with Poisson distributed random elements with mean value\n\ |
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899 parameter given by the first argument, @var{l}. The arguments\n\ |
5730 | 900 are handled the same as the arguments for @code{rand}, except for the\n\ |
901 argument @var{l}.\n\ | |
902 \n\ | |
903 Five different algorithms are used depending on the range of @var{l}\n\ | |
904 and whether or not @var{l} is a scalar or a matrix.\n\ | |
905 \n\ | |
906 @table @asis\n\ | |
10840 | 907 @item For scalar @var{l} @leq{} 12, use direct method.\n\ |
908 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ | |
909 Cambridge University Press, 1992.\n\ | |
910 \n\ | |
5730 | 911 @item For scalar @var{l} > 12, use rejection method.[1]\n\ |
10840 | 912 W.H. Press, et al., @cite{Numerical Recipes in C},\n\ |
913 Cambridge University Press, 1992.\n\ | |
914 \n\ | |
915 @item For matrix @var{l} @leq{} 10, use inversion method.[2]\n\ | |
10791
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916 E. Stadlober, et al., WinRand source code, available via FTP.\n\ |
10840 | 917 \n\ |
5730 | 918 @item For matrix @var{l} > 10, use patchwork rejection method.\n\ |
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919 E. Stadlober, et al., WinRand source code, available via FTP, or\n\ |
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920 H. Zechner, @cite{Efficient sampling from continuous and discrete\n\ |
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921 unimodal distributions}, Doctoral Dissertation, 156pp., Technical\n\ |
5730 | 922 University Graz, Austria, 1994.\n\ |
10840 | 923 \n\ |
5730 | 924 @item For @var{l} > 1e8, use normal approximation.\n\ |
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925 L. Montanet, et al., @cite{Review of Particle Properties}, Physical Review\n\ |
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926 D 50 p1284, 1994.\n\ |
5730 | 927 @end table\n\ |
6547 | 928 @seealso{rand, randn, rande, randg}\n\ |
5730 | 929 @end deftypefn") |
930 { | |
931 octave_value retval; | |
932 | |
933 int nargin = args.length (); | |
934 | |
935 if (nargin < 1) | |
936 error ("randp: insufficient arguments"); | |
937 else | |
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938 retval = do_rand (args, nargin, "randp", "poisson", true); |
5730 | 939 |
940 return retval; | |
941 } | |
942 | |
943 /* | |
944 %!test | |
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945 %! randp ("state", 12); |
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946 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
6437 | 947 %!test |
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948 %! # Test fixed state |
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949 %! randp ("state", 1); |
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950 %! assert (randp (5, 1, 6), [5 5 3 7 7 3]) |
6437 | 951 %!test |
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952 %! # Test fixed state |
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953 %! randp ("state", 1); |
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954 %! assert (randp (15, 1, 6), [13 15 8 18 18 15]) |
6437 | 955 %!test |
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956 %! # Test fixed state |
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957 %! randp ("state", 1); |
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958 %! assert (randp (1e9, 1, 6), [999915677 999976657 1000047684 1000019035 999985749 999977692], -1e-6) |
6437 | 959 %!test |
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960 %! # Test fixed state |
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961 %! randp ("seed", 1); |
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962 %! %%assert (randp (5, 1, 6), [8 2 3 6 6 8]) |
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963 %! assert (randp (5, 1, 5), [8 2 3 6 6]) |
6437 | 964 %!test |
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965 %! # Test fixed state |
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966 %! randp ("seed", 1); |
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967 %! assert (randp (15, 1, 6), [15 16 12 10 10 12]) |
6437 | 968 %!test |
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969 %! # Test fixed state |
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970 %! randp ("seed", 1); |
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971 %! assert (randp (1e9, 1, 6), [1000006208 1000012224 999981120 999963520 999963072 999981440], -1e-6) |
6437 | 972 %!test |
973 %! if (__random_statistical_tests__) | |
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974 %! # statistical tests may fail occasionally. |
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975 %! randp ("state", 12); |
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976 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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977 %! x = randp (a (1), 100000, 1); |
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978 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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979 %! assert (mean (x), a(1), a(2)); |
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980 %! assert (var (x), a(1), 0.02*a(1)); |
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981 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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982 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 983 %! endfor |
984 %! endif | |
985 %!test | |
986 %! if (__random_statistical_tests__) | |
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987 %! # statistical tests may fail occasionally. |
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988 %! randp ("state", 12); |
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989 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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990 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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991 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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992 %! assert (mean (x), a(1), a(2)); |
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993 %! assert (var (x), a(1), 0.02*a(1)); |
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994 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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995 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6437 | 996 %! endfor |
997 %! endif | |
998 %!test | |
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999 %! randp ("seed", 12); |
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1000 %! assert (randp ([-inf, -1, 0, inf, nan]), [nan, nan, 0, nan, nan]); # *** Please report |
5730 | 1001 %!test |
6449 | 1002 %! if (__random_statistical_tests__) |
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1003 %! # statistical tests may fail occasionally. |
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1004 %! randp ("seed", 12); |
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1005 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1006 %! x = randp (a(1), 100000, 1); |
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1007 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1008 %! assert (mean (x), a(1), a(2)); |
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1009 %! assert (var (x), a(1), 0.02*a(1)); |
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1010 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1011 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1012 %! endfor |
1013 %! endif | |
5730 | 1014 %!test |
6449 | 1015 %! if (__random_statistical_tests__) |
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1016 %! # statistical tests may fail occasionally. |
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1017 %! randp ("seed", 12); |
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1018 %! for a = [5, 15, 1e9; 0.03, 0.03, -5e-3; 0.03, 0.03, 0.03] |
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1019 %! x = randp (a(1)*ones (100000, 1), 100000, 1); |
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1020 %! assert (min (x) >= 0); # *** Please report this!!! *** |
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1021 %! assert (mean (x), a(1), a(2)); |
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1022 %! assert (var (x), a(1), 0.02*a(1)); |
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1023 %! assert (skewness (x), 1/sqrt (a(1)), a(3)); |
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1024 %! assert (kurtosis (x), 1/a(1), 3*a(3)); |
6449 | 1025 %! endfor |
1026 %! endif | |
5730 | 1027 */ |
1028 | |
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1029 DEFUN_DLD (randperm, args, , |
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1030 "-*- texinfo -*-\n\ |
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1031 @deftypefn {Loadable Function} {} randperm (@var{n})\n\ |
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1032 @deftypefnx {Loadable Function} {} randperm (@var{n}, @var{m})\n\ |
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1033 Return a row vector containing a random permutation of @code{1:@var{n}}.\n\ |
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1034 If @var{m} is supplied, return @var{m} unique entries, sampled without\n\ |
14038
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1035 replacement from @code{1:@var{n}}. The complexity is O(@var{n}) in\n\ |
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1036 memory and O(@var{m}) in time, unless @var{m} < @var{n}/5, in which case\n\ |
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1037 O(@var{m}) memory is used as well. The randomization is performed using\n\ |
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1038 rand(). All permutations are equally likely.\n\ |
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1039 @seealso{perms}\n\ |
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1040 @end deftypefn") |
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1041 { |
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1042 |
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1043 #ifdef USE_UNORDERED_MAP_WITH_TR1 |
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1044 using std::tr1::unordered_map; |
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1045 #else |
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1046 using std::unordered_map; |
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1047 #endif |
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1048 |
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1049 int nargin = args.length (); |
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1050 octave_value retval; |
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1051 |
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1052 if (nargin == 1 || nargin == 2) |
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1053 { |
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1054 octave_idx_type n, m; |
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1055 |
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1056 n = args(0).idx_type_value (true); |
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1057 |
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1058 if (nargin == 2) |
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1059 m = args(1).idx_type_value (true); |
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1060 else |
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1061 m = n; |
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1062 |
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1063 if (m < 0 || n < 0) |
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1064 error ("randperm: M and N must be non-negative"); |
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1065 |
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1066 if (m > n) |
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1067 error ("randperm: M must be less than or equal to N"); |
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1068 |
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1069 // Quick and dirty heuristic to decide if we allocate or not the |
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1070 // whole vector for tracking the truncated shuffle. |
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1071 bool short_shuffle = m < n/5 && m < 1e5; |
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1072 |
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1073 if (! error_state) |
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1074 { |
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1075 // Generate random numbers. |
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1076 NDArray r = octave_rand::nd_array (dim_vector (1, m)); |
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1077 double *rvec = r.fortran_vec (); |
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1078 |
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1079 octave_idx_type idx_len = short_shuffle ? m : n; |
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1080 Array<octave_idx_type> idx (dim_vector (1, idx_len)); |
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1081 octave_idx_type *ivec = idx.fortran_vec (); |
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1082 |
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1083 for (octave_idx_type i = 0; i < idx_len; i++) |
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1084 ivec[i] = i; |
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1085 |
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1086 if (short_shuffle) |
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1087 { |
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1088 unordered_map<octave_idx_type, octave_idx_type> map (m); |
13255
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1089 |
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1090 // Perform the Knuth shuffle only keeping track of moved |
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1091 // entries in the map |
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1092 for (octave_idx_type i = 0; i < m; i++) |
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1093 { |
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1094 octave_idx_type k = i + |
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1095 gnulib::floor (rvec[i] * (n - i)); |
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1096 |
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1097 if (map.find(k) == map.end()) |
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1098 { |
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1099 map[k] = ivec[i]; |
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1100 ivec[i] = k; |
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1101 } |
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1102 else |
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1103 std::swap (ivec[i], map[k]); |
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1104 |
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1105 } |
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1106 } |
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1107 else |
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1108 { |
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1109 |
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1110 // Perform the Knuth shuffle of the first m entries |
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1111 for (octave_idx_type i = 0; i < m; i++) |
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1112 { |
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1113 octave_idx_type k = i + |
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1114 gnulib::floor (rvec[i] * (n - i)); |
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1115 std::swap (ivec[i], ivec[k]); |
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1116 } |
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1117 } |
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1118 |
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1119 // Convert to doubles, reusing r. |
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1120 for (octave_idx_type i = 0; i < m; i++) |
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1121 rvec[i] = ivec[i] + 1; |
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1122 |
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1123 if (m < n) |
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1124 idx.resize (dim_vector (1, m)); |
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1125 |
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1126 // Now create an array object with a cached idx_vector. |
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1127 retval = new octave_matrix (r, idx_vector (idx)); |
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1128 } |
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1129 } |
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1130 else |
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1131 print_usage (); |
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1132 |
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1133 return retval; |
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1134 } |
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1135 |
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1136 /* |
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1137 %!assert (sort (randperm (20)), 1:20) |
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1138 %!assert (length (randperm (20,10)), 10) |
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1139 */ |