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1 ## Copyright (C) 2004 Paul Kienzle |
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2 ## |
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3 ## This program is free software and is in the public domain |
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4 |
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5 ## -*- texinfo -*- |
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6 ## @deftypefn {Function File} {} sprandn (@var{m}, @var{n}, @var{d}) |
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7 ## @deftypefnx {Function File} {} sprandn (@var{s}) |
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8 ## Generate a random sparse matrix. The size of the matrix will be |
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9 ## @var{m} by @var{n}, with a density of values given by @var{d}. |
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10 ## @var{d} should be between 0 and 1. Values will be normally |
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11 ## distributed with mean of zero and variance 1. |
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12 ## |
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13 ## Note: sometimes the actual density may be a bit smaller than @var{d}. |
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14 ## This is unlikely to happen for large really sparse matrices. |
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15 ## |
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16 ## If called with a single matrix argument, a random sparse matrix is |
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17 ## generated wherever the matrix @var{S} is non-zero. |
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18 ## @seealso{sprand} |
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19 ## @end deftypefn |
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20 |
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21 ## This program is public domain |
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22 ## Author: Paul Kienzle <pkienzle@users.sf.net> |
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23 |
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24 function S = sprandn(m,n,d) |
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25 if nargin == 1 |
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26 [i,j,v,nr,nc] = spfind(m); |
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27 S = sparse(i,j,randn(size(v)),nr,nc); |
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28 elseif nargin == 3 |
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29 mn = m*n; |
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30 k = round(d*mn); |
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31 idx=unique(fix(rand(min(k*1.01,k+10),1)*mn))+1; |
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32 # idx contains random numbers in [1,mn] |
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33 # generate 1% or 10 more random values than necessary |
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34 # in order to reduce the probability that there are less than k |
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35 # distinct values; |
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36 # maybe a better strategy could be used |
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37 # but I don't think it's worth the price |
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38 k = min(length(idx),k); # actual number of entries in S |
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39 j = floor((idx(1:k)-1)/m); |
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40 i = idx(1:k) - j*m; |
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41 if isempty(i) |
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42 S = sparse(m,n); |
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43 else |
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44 S = sparse(i,j+1,randn(k,1),m,n); |
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45 endif |
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46 else |
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47 usage("sprandn(m,n,density) OR sprandn(S)"); |
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48 endif |
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49 endfunction |
