source: hypertools/hypertools/dkl2.m @ 3

Last change on this file since 3 was 3, checked in by dtax, 15 years ago

Hypertools, first

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1%DKL Kullback-Leibler divergence
2%
3%     D=DKL(DATA,PROTO)
4%
5% INPUT
6%   DATA   a dataset
7%   PROTO  the dataset with prototypes (representation set)
8% OUTPUT
9%   D      dataset with a distance matrix
10%
11% DESCRIPTION
12% The Kullback-Leibler divergence (pp. 151, Pattern Recognition,
13% Theodoridis, p. 176 in 2nd edition)
14%
15
16% $Id: dkl2.m,v 1.2 2005/03/02 13:39:45 serguei Exp $
17
18function d=dkl2(data,proto)
19
20fprintf(' dkl2:');
21
22if size(data,2)~=size(proto,2)
23  error('both datasets must have the same feature sizes');
24end
25sc=size(data,1); % training sample size
26pc=size(proto,1); % test sample size
27d=zeros(sc,pc);
28
29labdata=getlab(data);
30labproto=getlab(proto);
31data2=data; data=+data;
32proto2=proto; proto=+proto;
33
34
35% normalization: we consider a histogram being a set of probability
36% estimates
37data=data./repmat(sum(data,2),[1,size(data,2)]);
38proto=proto./repmat(sum(proto,2),[1,size(proto,2)]);
39
40step=round(pc/10);
41for i=1:pc
42  t=repmat(proto(i,:),[sc,1]);
43
44  w = warning;
45  warning('off');
46  l = log(t./data);
47  warning(w); 
48
49  l(~isfinite(l)) = 0;
50  d(:,i)=sum((t-data).*l ,2);
51
52  if mod(i,step)==0, fprintf('.'); end
53end
54
55d=setdat(data2,d);
56d=setfeatlab(d,labproto);
57
58return
59 
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