source: prextra/kdnnc.m @ 5

Last change on this file since 5 was 5, checked in by bduin, 14 years ago
File size: 2.1 KB
Line 
1%KDNNC 1-Nearest Neighbor Classifier using KDTree
2%
3%   W = KNNC(A,TYPE)
4%
5% INPUT
6%   A      Dataset
7%   TYPE  'fast' (default) or 'good'
8%
9% OUTPUT
10%   W      NN classifier using KDTree for finding nearest neighbors
11%
12% DESCRIPTION 
13% This is a KD-Tree nearest neighbor implementation for PRTools using the
14% KD-Tree package by Pramod Vemulapalli, see
15% http://www.mathworks.com/matlabcentral/fileexchange/26649-kdtree-implementation-in-matlab
16% It should be in the Matlab search path.
17%
18% SEE ALSO
19% MAPPINGS, DATASETS, KNNC
20
21% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
22% Faculty EWI, Delft University of Technology
23% P.O. Box 5031, 2600 GA Delft, The Netherlands
24
25
26function out = kdnnc(a,par)
27
28        prtrace(mfilename);
29       
30        kdtreecheck;
31        name = 'KDTree NN Classifier';
32        % No input data, return an untrained classifier.
33        if nargin < 2, par = 'good'; end
34        if (nargin == 0) | (isempty(a))
35                out = mapping(mfilename,'untrained',par);
36                out = setname(out,name);
37        elseif isdataset(a) & isstr(par) % training
38                if isempty(strmatch(par,char('fast','good')))
39                        error('KDTree type should be either ''fast'' or ''good''')
40                end
41                islabtype(a,'crisp');
42                isvaldfile(a,1,2); % at least 1 object per class, 2 classes
43                a = testdatasize(a);
44                a = testdatasize(a,'objects');
45                a = seldat(a);    % get labeled objects only
46                [m,k,c] = getsize(a);
47                data.kdtree = kd_buildtree(+a,0);
48                data.nlab = getnlab(a);
49                data.type = par;
50                out = mapping(mfilename,'trained',data,getlablist(a),k,c);
51                out = setname(out,name);
52        elseif nargin == 2 & ismapping(par)  % execution
53                kdtree = getdata(par,'kdtree');
54                nlab = getdata(par,'nlab');
55                if strcmp(getdata(par,'type'),'fast')
56                        kdtype = @kd_closestpointfast;
57                else
58                        kdtype = @kd_closestpointgood;
59                end
60                a_data = +a;
61                % PRTools want posteriors. In this case these are just 0/1 's
62                out = zeros(size(a,1),size(getlab(par),1));
63                m = size(a,1);
64                s = sprintf('Testing %i objects: ',m);
65                prwaitbar(m,s);
66                for i=1:m;
67                        prwaitbar(m,i,[s int2str(i)]);
68                        j = kdtype(kdtree,a_data(i,:));
69                        out(i,nlab(j)) = 1;
70                end
71                prwaitbar(0);
72                out = setdat(a,out);
73        end
74               
75       
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