source: distools/pe_distm.m @ 103

Last change on this file since 103 was 79, checked in by bduin, 11 years ago
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[10]1%PE_DISTM Square Pseudo-Euclidean (PE) Distance Between Two Datasets
2%
3%   D = PE_DISTM(A)
4%     OR
5%   D = PE_DISTM(A,B)
6%
7% INPUT
8%   A    PE dataset of size NxK
9%   B    PE dataset of size MxK (default A = B)
10%
11% OUTPUT
12%   D   NxM dissimilarity matrix or dataset
13%
14% DESCRIPTION
15% Computation of the square pseudo-Euclidean distance matrix D between two sets
16% of vectors, A and B. The pseudo-Euclidean distance with the signature SIG
17% (e.g. SIG = [10 5]) between vectors X and Y is computed as an indefinite
18% 'Euclidean' distance:
19%     D(X,Y) = (X-Y)'*J*(X-Y),
20% where J is a diagonal matrix with 1's, followed by -1's.
21% J = diag ([ONES(SIG(1),1);  -ONES(SIG(2),1)]);
22%
23% In a PE dataset the signature is stored in the user field, see
24% SETSIG. This signature is derived from A. It is not stored in D as D
25% does not contain vectors in a PE space.
26%
27% D is a dataset with the labels defined by the labels of A and feature labels
28% defined by the labels of B.
29%
30% REMARKS
31% Note that square pseudo-Euclidean distances can be negative.
32%
33% SEE ALSO
[79]34% PRDATASET, SETSIG, DISTM
[10]35
36% Copyright: Elzbieta Pekalska, ela.pekalska@googlemail.com
37% Faculty EWI, Delft University of Technology and
38% School of Computer Science, University of Manchester
39
40
41
42function D = pe_distm(A,B)
43
44  isdataset(A);
45  bisa = 0;
46  if nargin < 2, B = A; bisa = 1; end
47  sig = getsig(A);
48
[79]49  if ~isdataset(B), B = prdataset(B,1); end
[10]50
51  a    = +A;
52  b    = +B;
53  [ra,ca] = size(a);
54  [rb,cb] = size(b);
55
56  if ca ~= cb,
57    error ('The datasets should have the same number of features.');
58  end
59
60  if any(sig) < 0 | sum(sig) ~= ca,
61    error('Signature vector SIG is invalid: its sum should equal feature size');
62  end
63
64  J = [ones(1,sig(1))  -ones(1,sig(2))];
65  D = - 2 .* a * diag(J) * b';
66  D = D + ones(ra,1) * (J*(b'.*b'));
67  D = D + (J * (a'.*a'))' * ones(1,rb);
68
69  if bisa
70    D = 0.5*(D+D');         % Make sure that distances are symmetric for D(A,A)
71  end
72
73  % Set object labels and feature labels
74  D = setdata(A,D,getlab(B));
75  D.name = 'Square Pseudo-Euclidean distance matrix';
76  if ~isempty(A.name)
77    D.name = [D.name ' for ' A.name];
78  end
79
80return
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