source: distools/discc.m @ 70

%DISCC Dissimilarity based combining classifier
%
%       W = discc(D,par)
%
% If D is a square dissimilarity matrix then the distances to each
% representation object are used for building a classifier such that
% sigmoid(a*d+b), can be used to estimate the posterior probability of a
% new object at distance d. The scalar parameters a and b are optimized
% over the training set using a maximum likelihood estimator.
% W is a set of parallel classifiers such that D*W*classc yields the posterior
% probabilities. Note that D*W*maxc yields the same classification results as
% the NN classifier.
%
% par = 'loo' - (default) compute leave-one-out optimization for a and b.
%               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)
%
% See also mappings, datasets, classc, classd, testd, knnd
%
% $Id: discc.m,v 1.2 2001/07/10 16:35:58 pavel Exp $

% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands

function W = discc(d,par)
                        % set default leave-one-out
if nargin < 2
        par = 'loo';
end
                        % empty call, to handle d*knnd, or d*knnd([],par)
if nargin < 1 | isempty(d)
        if isstr(par)   % untrained classifier, par = 'loo' or 'all'
                W = mapping('discc',par);
        else                    % fixed classifier, par = knn
                W = mapping('discc','fixed',par);
        end
        return
end

[nlab,lablist,m,k,c,p,featlist] = dataset(d);
[clab,classlist] = renumlab(featlist);
c = size(classlist,1);
[cl,nc] = renumlab(classlist,lablist);
if size(nc,1) > c
        error('Object labels do not match representation set')
end

if isstr(par)
        
        % training (find parameters a and b)
                        
        if strcmp(par,'loo')
                n = k*(k-1) + (m-k)*k;
                s = zeros(n,1);
                t = zeros(n,1);
                r = zeros(n,1);
                K = 0;
                for j=1:k
                        if j == 1
                                J = 2:k;
                        elseif j < k
                                J = [1:(j-1) j+1:k];
                        else
                                J = [1:k-1];
                        end
                        s(K+1:K+k-1) = d(j,J)';
                        t(K+1:K+k-1) = repmat(nlab(j),k-1,1);
                        r(K+1:K+k-1) = nlab(J);
                        K = K+k-1;
                end
                for j=k+1:m
                        s(K+1:K+k) = d(j,:)';
                        t(K+1:K+k) = repmat(nlab(j),k,1);
                        r(K+1:K+k) = nlab(1:k);
                        K = K+k;
                end
                w = +(dataset(s,2-(t==r))*loglc);
                W = mapping('discc',w,lablist,k,c,1);
        elseif strcmp(par,'all')
                n = m*k;
                s = zeros(n,1);
                t = zeros(n,1);
                r = zeros(n,1);
                K = 0;  
                for j=1:m
                        s(K+1:K+k) = d(j,:)';
                        t(K+1:K+k) = repmat(nlab(j),k,1);
                        r(K+1:K+k) = nlab(1:k);
                        K = K+k;
                end
                w = +(dataset(s,2-(t==r))*loglc);
                W = mapping('discc',w,lablist,k,c,1);
        else
                error(['Unknown option ''' par ''''])
        end
        
else

        % testing for given mapping
                
        if isa(par,'mapping')
                w = +par;
                W = d*w(1) + w(2);
        else
                error('Second parameter should be string or mapping')
        end
end