comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/knn.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [C,P]=knn(d, Cp, K)
+
+%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
+%
+%  [C,P]=knn(d, Cp, [K])
+%
+%  Input and output arguments ([]'s are optional): 
+%   d     (matrix) of size NxP: This is a precalculated dissimilarity (distance matrix).
+%           P is the number of prototype vectors and N is the number of data vectors
+%           That is, d(i,j) is the distance between data item i and prototype j.
+%   Cp    (vector) of size Px1 that contains integer class labels. Cp(j) is the class of 
+%            jth prototype.
+%   [K]   (scalar) the maximum K in K-NN classifier, default is 1
+%   C     (matrix) of size NxK: integers indicating the class 
+%           decision for data items according to the K-NN rule for each K.
+%           C(i,K) is the classification for data item i using the K-NN rule
+%   P     (matrix) of size NxkxK: the relative amount of prototypes of 
+%           each class among the K closest prototypes for each classifiee. 
+%           That is, P(i,j,K) is the relative amount of prototypes of class j 
+%           among K nearest prototypes for data item i.
+%
+% If there is a tie between representatives of two or more classes
+% among the K closest neighbors to the classifiee, the class i selected randomly 
+% among these candidates.
+%
+% IMPORTANT  If K>1 this function uses 'sort' which is considerably slower than 
+%            'max' which is used for K=1. If K>1 the knn always calculates 
+%            results for all K-NN models from 1-NN up to K-NN.   
+%
+% EXAMPLE 1 
+%
+% sP;                           % a SOM Toolbox data struct containing labeled prototype vectors
+% [Cp,label]=som_label2num(sP); % get integer class labels for prototype vectors                 
+% sD;                           % a SOM Toolbox data struct containing vectors to be classified
+% d=som_eucdist2(sD,sP);        % calculate euclidean distance matrix
+% class=knn(d,Cp,10);           % classify using 1,2,...,10-rules
+% class(:,5);                   % includes results for 5NN 
+% label(class(:,5))             % original class labels for 5NN
+%
+% EXAMPLE 2 (leave-one-out-crossvalidate KNN for selection of proper K)
+%
+% P;                          % a data matrix of prototype vectors (rows)
+% Cp;                         % column vector of integer class labels for vectors in P 
+% d=som_eucdist2(P,P);        % calculate euclidean distance matrix PxP
+% d(eye(size(d))==1)=NaN;     % set self-dissimilarity to NaN:
+%                             % this drops the prototype itself away from its neighborhood 
+%                             % leave-one-out-crossvalidation (LOOCV)
+% class=knn(d,Cp,size(P,1));  % classify using all possible K
+%                             % calculate and plot LOOC-validated errors for all K
+% failratep = ...
+%  100*sum((class~=repmat(Cp,1,size(P,1))))./size(P,1); plot(1:size(P,1),failratep) 
+
+% See also SOM_LABEL2NUM, SOM_EUCDIST2, PDIST. 
+%
+% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg
+% Copyright (c) by Johan Himberg
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 2.0beta Johan 291000
+
+%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+% Check K 
+if nargin<3 | isempty(K),
+  K=1;
+end
+
+if ~vis_valuetype(K,{'1x1'})
+  error('Value for K must be a scalar');
+end
+
+% Check that dist is a matrix
+if ~vis_valuetype(d,{'nxm'}),
+  error('Distance matrix not valid.')
+end
+
+[N_data N_proto]=size(d);
+
+% Check class label vector: must be numerical and of integers
+if ~vis_valuetype(Cp,{[N_proto 1]});
+  error(['Class vector is invalid: has to be a N-of-data_rows x 1' ...
+	 ' vector of integers']);
+elseif sum(fix(Cp)-Cp)~=0
+  error('Class labels in vector ''Cp'' must be integers.');
+end
+
+if size(d,2) ~= length(Cp),
+  error('Distance matrix and prototype class vector dimensions do not match.');
+end
+
+% Check if the classes are given as labels (no class input arg.)
+% if they are take them from prototype struct
+
+% Find all class labels
+ClassIndex=unique(Cp);
+N_class=length(ClassIndex); % number of different classes  
+
+
+%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if K==1,   % sort distances only if K>1
+  
+  % 1NN
+  % Select the closest prototype
+  [tmp,proto_index]=min(d,[],2); 
+  C=Cp(proto_index);
+
+else 
+  
+  % Sort the prototypes for each classifiee according to distance
+  [tmp, proto_index]=sort(d');
+  
+  %% Select up to K closest prototypes
+  proto_index=proto_index(1:K,:);
+  knn_class=Cp(proto_index);
+  for i=1:N_class,
+    classcounter(:,:,i)=cumsum(knn_class==ClassIndex(i));
+  end
+  
+  %% Vote between classes of K neighbors 
+  [winner,vote_index]=max(classcounter,[],3);
+  
+  %%% Handle ties
+  
+  % Set index to classes that got as much votes as winner
+  
+  equal_to_winner=(repmat(winner,[1 1 N_class])==classcounter);
+ 
+  % set index to ties
+  [tie_indexi,tie_indexj]=find(sum(equal_to_winner,3)>1); % drop the winner from counter 
+  
+  % Go through tie cases and reset vote_index randomly to one
+  % of them 
+  
+  for i=1:length(tie_indexi),
+    tie_class_index=find(squeeze(equal_to_winner(tie_indexi(i),tie_indexj(i),:)));
+    fortuna=randperm(length(tie_class_index));
+    vote_index(tie_indexi(i),tie_indexj(i))=tie_class_index(fortuna(1));
+  end
+  
+  C=ClassIndex(vote_index)';
+end
+
+%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+% Relative amount of classes in K neighbors for each classifiee
+
+if K==1,
+  P=zeros(N_data,N_class);
+  if nargout>1,
+    for i=1:N_data,
+      P(i,ClassIndex==C(i))=1;
+    end
+  end
+else
+  P=shiftdim(classcounter,1)./repmat(shiftdim(1:K,-1), [N_data N_class 1]);
+end
+