source: distools/distools/knndc.m @ 10

%KNNDC K-Nearest Neighbor Classifier for dissimilarity matrices
%
%       [W,K,E] = KNNDC(D,K,PAR,EDIT,PAR1,PAR2,PAR3)
%       [W,K,E] = D*KNNDC([],K,PAR,EDIT,PAR1,PAR2,PAR3)
%
% INPUT
%   D     NxN dissimilarity matrix or dataset
%       K         Number of nearest neighbors; if [], then K is optimized       
%       PAR = 'LOO' - (default) compute leave-one-out optimization for K
%               it is assumed that the first objects in the training set
%               constitute the representation set.
%                 'ALL' - include all dissimilarities for optimization of K
%               (representation set should not be included in training set)
%   EDIT = 'ORG' editting and condensing is done by EDICON_ORG using 
%                       K = PAR1 and N = PAR2. K is set to 1. This only affects the
%                       representation set. During testing the reduced representation set 
%                       is used.
%        = 'DANDK', editting and condensing is done by EDICON using NSETS = PAR1, 
%                   NITERS = PAR2 and NTRIES = PAR3. K is set to 1. This only affects 
%                       the representation set. During testing the reduced representation
%                       set is used.
% OUTPUT
%   W     Classifier
%   K     Number of nearest neighbors
%       E         Error on D
%
% DESCRIPTION
% Compute K-Nearest Neigbor classifier for the dissimilarity set D by optimizing K 
% (if the routine is called with K = []), the error on D is returned in E.
% A test dissimilarity set DTE defined by the same representation set can now be mapped 
% by C = DTE*W:
%    C        - estimated class confidences
%    C*LABELD - assigned class labels
%    C*TESTC  - classification error
%
% NOTE
% NN errors for dissimilarity data can be directly estimated by TESTKD.
%
% SEE ALSO
% DATASETS, MAPPINGS, TESTKD, CROSSVALD, EDICON
%

% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% and Ela Pekalska, ela.pekalska@googlemail.com
% Faculty EWI, Delft University of Technology
% and University of Manchester, UK


function [W,knn,e] = knndc(d,knn,par,edit,par1,par2,par3)

if nargin < 7, par3 = []; end
if nargin < 6, par2 = []; end
if nargin < 5, par1 = []; end
if nargin < 4, edit = ''; end
if nargin < 3 | isempty(par), par = 'loo'; end
if nargin < 2,  knn = []; end


% empty call, to handle d*knnd, or d*knnd([],par)                       
if nargin < 1 | isempty(d)
        W = mapping(mfilename,'untrained',knn,par);
        W = setname(W,'KNND');
        return
end

nlab     = getnlab(d);
lablist  = getlablist(d);
featlist = getfeat(d);
[m,k,c]  = getsize(d);
p        = getprior(d);

%[nlab,lablist,m,k,c,p,featlist] = dataset(d);
[clab,classlist] = renumlab(featlist);
[cl,nc] = renumlab(classlist,lablist);

if size(nc,1) > c
        error('Object labels do not match representation set')
end
                   % correct for different classlist - lablist orders
J = matchlablist(classlist,lablist);
classlist = lablist;
clab = J(clab);

if ~ismapping(knn)  % training (find knn)
                        
        if strcmp(par,'loo')
                % get rid of leave-one-out problems
                km = min(k,m);
                dmax=max(max(+d))*2;
                d(1:km,1:km) = d(1:km,1:km) + dmax*eye(km);
        elseif ~strcmp(par,'all')
                error(['Unknown option ''' par ''''])
        end

        switch upper(edit)
                case 'ORG'
                        if isempty(par1) & isempty(par2)
                                JJ = edicon_org(d); 
                        elseif isempty(par2)
                                JJ = edicon_org(d,par1); 
                        else
                                JJ = edicon_org(d,par,par2); 
                        end
                        knn = 1;
                case 'DANDK'
                        JJ = edicon(d,par1,par2,par3);
                        knn = 1;
                otherwise
                        JJ = [1:k];
        end
        if isempty(knn) % optimize knn
                [Y,L] = sort(+d,2);
                L = clab(L);
                Ymax = zeros(m,k);
                Yc = zeros(m,k);
                for j = 1:c
                        Y = double(L == j);
                        for n = 2:k
                                Y(:,n) = Y(:,n-1) + Y(:,n);
                        end
                        J = Y > Ymax;
                        Ymax(J) = Y(J);
                        Yc(J) = j*ones(size(Yc(J)));
                end
                z = sum(Yc == nlab*ones(1,k),1);
                [e,knn]=max(z);
                e = 1 - e/m;
                z = 1 - z/m;
        elseif nargout == 3
                e = testkd(d,knn,par);
        end
        
        W = mapping(mfilename,'trained',{knn,JJ},lablist,k,c);
        W = setname(W,'KNNDC');
        
else        % testing for given mapping or knn

        w = knn; % mapping stored in knn
  wdata  = getdata(w); 
        knn = wdata{1};
        J = wdata{2};
  classlist = getlab(w);
  c = size(w,2);
  [nn,nf,fl] = renumlab(classlist,lablist);
        if max(nf) > c
                error('Representation set labels do not match with classifier')
        end
        [e,q] = testkd(d(:,J),knn);
        W = q;
        
end