SOM Toolbox | Online documentation | http://www.cis.hut.fi/projects/somtoolbox/ |

** [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)