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1 ## Copyright (C) 2004 David Bateman & Andy Adler |
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2 ## |
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3 ## This program is free software; you can redistribute it and/or modify |
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4 ## it under the terms of the GNU General Public License as published by |
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5 ## the Free Software Foundation; either version 2 of the License, or |
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6 ## (at your option) any later version. |
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7 ## |
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8 ## This program is distributed in the hope that it will be useful, |
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9 ## but WITHOUT ANY WARRANTY; without even the implied warranty of |
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10 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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11 ## GNU General Public License for more details. |
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12 ## |
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13 ## You should have received a copy of the GNU General Public License |
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14 ## along with this program; if not, write to the Free Software |
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15 ## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA |
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16 ## 02110-1301 USA |
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17 |
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18 ## -*- texinfo -*- |
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19 ## @deftypefn {Function File} {} sprandsym (@var{n}, @var{d}) |
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20 ## @deftypefnx {Function File} {} sprandsym (@var{s}) |
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21 ## Generate a symmetric random sparse matrix. The size of the matrix will be |
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22 ## @var{n} by @var{n}, with a density of values given by @var{d}. |
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23 ## @var{d} should be between 0 and 1. Values will be normally |
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24 ## distributed with mean of zero and variance 1. |
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25 ## |
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26 ## Note: sometimes the actual density may be a bit smaller than @var{d}. |
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27 ## This is unlikely to happen for large really sparse matrices. |
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28 ## |
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29 ## If called with a single matrix argument, a random sparse matrix is |
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30 ## generated wherever the matrix @var{S} is non-zero in its lower |
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31 ## triangular part. |
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32 ## @seealso{sprand, sprandn} |
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33 ## @end deftypefn |
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34 |
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35 function S = sprandsym (n, d) |
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36 if (nargin == 1) |
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37 [i, j, v, nr, nc] = spfind (tril (n)); |
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38 S = sparse (i, j, randn (size (v)), nr, nc); |
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39 S = S + tril (S, -1)'; |
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40 elseif (nargin == 2) |
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41 m1 = floor (n/2); |
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42 n1 = m1 + rem (n, 2); |
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43 mn1 = m1*n1; |
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44 k1 = round (d*mn1); |
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45 idx1 = unique (fix(rand(min(k1*1.01,k1+10),1)*mn1))+1; |
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46 ## idx contains random numbers in [1,mn] generate 1% or 10 more |
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47 ## random values than necessary in order to reduce the probability |
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48 ## that there are less than k distinct values; maybe a better |
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49 ## strategy could be used but I don't think it's worth the price. |
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50 k1 = min (length (idx1), k1); # actual number of entries in S |
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51 j1 = floor ((idx1(1:k1)-1)/m1); |
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52 i1 = idx1(1:k1) - j1*m1; |
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53 |
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54 n2 = ceil (n/2); |
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55 nn2 = n2*n2; |
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56 k2 = round (d*nn2); |
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57 idx2 = unique (fix (rand (min (k2*1.01, k1+10), 1)*nn2)) + 1; |
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58 k2 = min (length (idx2), k2); |
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59 j2 = floor ((idx2(1:k2)-1)/n2); |
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60 i2 = idx2(1:k2) - j2*n2; |
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61 |
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62 if (isempty (i1) && isempty (i2)) |
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63 S = sparse (n, n); |
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64 else |
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65 S1 = sparse (i1, j1+1, randn (k1, 1), m1, n1); |
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66 S = [tril(S1), sparse(m1,m1); ... |
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67 sparse(i2,j2+1,randn(k2,1),n2,n2), triu(S1,1)']; |
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68 S = S + tril (S, -1)'; |
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69 endif |
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70 else |
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71 print_usage (); |
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72 endif |
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73 endfunction |
