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1 ## Copyright (C) 2004, 2005, 2006, 2007 Paul Kienzle |
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2 ## |
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3 ## This file is part of Octave. |
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4 ## |
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5 ## Octave is free software; you can redistribute it and/or modify it |
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6 ## under the terms of the GNU General Public License as published by |
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7 ## the Free Software Foundation; either version 3 of the License, or (at |
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8 ## your option) any later version. |
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9 ## |
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10 ## Octave is distributed in the hope that it will be useful, but |
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11 ## WITHOUT ANY WARRANTY; without even the implied warranty of |
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12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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13 ## General Public License for more details. |
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14 ## |
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15 ## You should have received a copy of the GNU General Public License |
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16 ## along with Octave; see the file COPYING. If not, see |
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17 ## <http://www.gnu.org/licenses/>. |
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18 ## |
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19 ## Original version by Paul Kienzle distributed as free software in the |
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20 ## public domain. |
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21 |
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22 ## -*- texinfo -*- |
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23 ## @deftypefn {Function File} {} sprandn (@var{m}, @var{n}, @var{d}) |
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24 ## @deftypefnx {Function File} {} sprandn (@var{s}) |
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25 ## Generate a random sparse matrix. The size of the matrix will be |
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26 ## @var{m} by @var{n}, with a density of values given by @var{d}. |
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27 ## @var{d} should be between 0 and 1. Values will be normally |
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28 ## distributed with mean of zero and variance 1. |
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29 ## |
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30 ## Note: sometimes the actual density may be a bit smaller than @var{d}. |
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31 ## This is unlikely to happen for large really sparse matrices. |
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32 ## |
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33 ## If called with a single matrix argument, a random sparse matrix is |
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34 ## generated wherever the matrix @var{S} is non-zero. |
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35 ## @seealso{sprand} |
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36 ## @end deftypefn |
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37 |
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38 ## Author: Paul Kienzle <pkienzle@users.sf.net> |
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39 |
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40 function S = sprandn (m, n, d) |
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41 if (nargin == 1) |
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42 [i, j, v, nr, nc] = spfind (m); |
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43 S = sparse (i, j, randn (size (v)), nr, nc); |
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44 elseif (nargin == 3) |
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45 mn = m*n; |
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46 k = round (d*mn); |
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47 idx = unique (fix (rand (min (k*1.01, k+10), 1) * mn)) + 1; |
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48 ## idx contains random numbers in [1,mn] |
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49 ## generate 1% or 10 more random values than necessary in order to |
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50 ## reduce the probability that there are less than k distinct |
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51 ## values; maybe a better strategy could be used but I don't think |
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52 ## it's worth the price. |
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53 |
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54 ## actual number of entries in S |
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55 k = min (length (idx), k); |
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56 j = floor ((idx(1:k)-1)/m); |
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57 i = idx(1:k) - j*m; |
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58 if (isempty (i)) |
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59 S = sparse (m, n); |
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60 else |
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61 S = sparse (i, j+1, randn (k, 1), m, n); |
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62 endif |
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63 else |
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64 print_usage (); |
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65 endif |
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66 endfunction |
