annotate scripts/sparse/sprandsym.m @ 13205:abf1e00111dd

Completely new implementation of sprandsym
author Jordi Gutiérrez Hermoso <jordigh@octave.org>
date Sat, 24 Sep 2011 03:46:36 -0500
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1 ## Copyright (C) 2004-2011 David Bateman and Andy Adler
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2 ## Copyright (C) 2011 Jordi GutiƩrrez Hermoso
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3 ##
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4 ## This file is part of Octave.
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5 ##
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6 ## Octave is free software; you can redistribute it and/or modify it
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7 ## under the terms of the GNU General Public License as published by
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8 ## the Free Software Foundation; either version 3 of the License, or (at
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9 ## your option) any later version.
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10 ##
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11 ## Octave is distributed in the hope that it will be useful, but
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12 ## WITHOUT ANY WARRANTY; without even the implied warranty of
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13 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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14 ## General Public License for more details.
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15 ##
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16 ## You should have received a copy of the GNU General Public License
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17 ## along with Octave; see the file COPYING. If not, see
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18 ## <http://www.gnu.org/licenses/>.
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19
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20 ## -*- texinfo -*-
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21 ## @deftypefn {Function File} {} sprandsym (@var{n}, @var{d})
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22 ## @deftypefnx {Function File} {} sprandsym (@var{s})
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23 ## Generate a symmetric random sparse matrix. The size of the matrix will be
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24 ## @var{n} by @var{n}, with a density of values given by @var{d}.
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25 ## @var{d} should be between 0 and 1. Values will be normally
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26 ## distributed with mean of zero and variance 1.
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27 ##
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28 ## If called with a single matrix argument, a random sparse matrix is
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29 ## generated wherever the matrix @var{S} is non-zero in its lower
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30 ## triangular part.
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31 ## @seealso{sprand, sprandn}
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32 ## @end deftypefn
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33
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34 function S = sprandsym (n, d)
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35
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36 if (nargin != 1 && nargin != 2)
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37 print_usage ();
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38 endif
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39
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40 if (nargin == 1)
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41 [i, j] = find (tril (n));
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42 [nr, nc] = size (n);
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43 S = sparse (i, j, randn (size (i)), nr, nc);
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44 S = S + tril (S, -1)';
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45 return;
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46 endif
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47
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48 if (!(isscalar (n) && n == fix (n) && n > 0))
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49 error ("sprand: N must be an integer greater than 0");
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50 endif
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51
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52 if (d < 0 || d > 1)
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53 error ("sprand: density D must be between 0 and 1");
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54 endif
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55
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56 ## Actual number of nonzero entries
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57 k = round (n^2*d);
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58
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59 ## Diagonal nonzero entries, same parity as k
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60 r = pick_rand_diag (n, k);
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61
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62 ## Off diagonal nonzero entries
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63 m = (k - r)/2;
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64
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65 ondiag = randperm (n, r);
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66 offdiag = randperm (n*(n - 1)/2, m);
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67
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68 ## Do five Newton iterations to solve n(n - 1)/2 = offdiag (this is the
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69 ## row index)
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70 x = sqrt (offdiag);
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71 for ii = 1:5
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72 x = x - (x.^2 - x - 2*offdiag)./(2*x - 1);
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73 endfor
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74 i = floor(x);
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75 i(i.^2 - i - 2*offdiag != 0) += 1;
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76
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77 ## Column index
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78 j = offdiag - (i - 1).*(i - 2)/2;
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79
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80 diagvals = randn (1, r);
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81 offdiagvals = randn (1, m);
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82
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83 S = sparse ([ondiag, i, j], [ondiag, j, i],
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84 [diagvals, offdiagvals, offdiagvals], n, n);
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85
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86 endfunction
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87
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88 function r = pick_rand_diag (n, k)
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89 ## Pick a random number R of entries for the diagonal of a sparse NxN
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90 ## square matrix with exactly K nonzero entries, ensuring that this R
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91 ## is chosen uniformly over all such matrices.
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92 ##
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93 ## Let D be the number of diagonal entries and M the number of
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94 ## off-diagonal entries. Then K = D + 2*M. Let A = N*(N-1)/2 be the
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95 ## number of available entries in the upper triangle of the matrix.
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96 ## Then, by a simple counting argument, there is a total of
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97 ##
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98 ## T = nchoosek (N, D) * nchoosek (A, M)
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99 ##
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100 ## symmetric NxN matrices with a total of K nonzero entries and D on
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101 ## the diagonal. Letting D range from mod (K,2) through min (N,K), and
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102 ## dividing by this sum, we obtain the probability P for D to be each
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103 ## of those values.
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104 ##
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105 ## However, we cannot use this form for computation, as the binomial
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106 ## coefficients become unmanageably large. Instead, we use the
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107 ## successive quotients Q(i) = T(i+1)/T(i), which we easily compute to
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108 ## be
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109 ##
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110 ## (N - D)*(N - D - 1)*M
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111 ## Q = -------------------------------
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112 ## (D + 2)*(D + 1)*(A - M + 1)
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113 ##
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114 ## Then the cumprod of these quotients is
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115 ##
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116 ## C = [ T(2)/T(1), T(3)/T(1), ..., T(N)/T(1) ]
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117 ##
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118 ## Their sum + 1 is thus S = sum (T)/T(1), and then C(i)/S is the
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119 ## desired probability P(i) for i = 2:N. The first P(1) can be
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120 ## obtained by the condition that sum (P) = 1, and the cumsum will
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121 ## give the distribution function for computing the random number of
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122 ## entries on the diagonal R.
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123
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124 ## Compute the stuff described above
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125 a = n*(n - 1)/2;
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126 d = [mod(k,2):2:min(n,k)-2];
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127 m = (k - d)/2;
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128 q = (n - d).*(n - d - 1).*m ./ (d + 2)./(d + 1)./(a - m + 1);
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129 c = cumprod (q);
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130 s = sum (c) + 1;
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131 p = c/s;
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132
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133 ## Add missing entries
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134 p = [1 - sum(p), p];
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135 d(end+1) = d(end) + 2;
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136
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137 ## Pick a random r using this distribution
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138 r = d(sum (cumsum (p) < rand) + 1);
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139
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140 endfunction
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141
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142 %!test
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143 %! s = sprandsym (10, 0.1);
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144 %! assert (issparse (s));
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145 %! assert (issymmetric (s));
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146 %! assert (size (s), [10, 10]);
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147 %! assert (nnz (s) / numel (s), 0.1, .01);
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148
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149 %% Test 1-input calling form
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150 %!test
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151 %! s = sprandsym (sparse ([1 2 3], [3 2 3], [2 2 2]));
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152 %! [i, j] = find (s);
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153 %! assert (sort (i), [2 3]');
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154 %! assert (sort (j), [2 3]');
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155
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156 %% Test input validation
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157 %!error sprandsym ()
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158 %!error sprandsym (1, 2, 3)
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159 %!error sprandsym (ones(3), 0.5)
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160 %!error sprandsym (3.5, 0.5)
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161 %!error sprandsym (0, 0.5)
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162 %!error sprandsym (3, -1)
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163 %!error sprandsym (3, 2)
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164