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

** [Class,P]=knn_old(Data, Proto, proto_class, K)**

KNN_OLD A K-nearest neighbor classifier using Euclidean distance [Class,P]=knn_old(Data, Proto, proto_class, K) [sM_class,P]=knn_old(sM, sData, [], 3); [sD_class,P]=knn_old(sD, sM, class); [class,P]=knn_old(data, proto, class); [class,P]=knn_old(sData, sM, class,5); Input and output arguments ([]'s are optional): Data (matrix) size Nxd, vectors to be classified (=classifiees) (struct) map or data struct: map codebook vectors or data vectors are considered as classifiees. Proto (matrix) size Mxd, prototype vector matrix (=prototypes) (struct) map or data struct: map codebook vectors or data vectors are considered as prototypes. [proto_class] (vector) size Nx1, integers 1,2,...,k indicating the classes of corresponding protoptypes, default: see the explanation below. [K] (scalar) the K in KNN classifier, default is 1 Class (matrix) size Nx1, vector of 1,2, ..., k indicating the class desicion according to the KNN rule P (matrix) size Nxk, the relative amount of prototypes of each class among the K closest prototypes for each classifiee. If 'proto_class' is _not_ given, 'Proto' _must_ be a labeled SOM Toolbox struct. The label of the data vector or the first label of the map model vector is considered as class label for th prototype vector. In this case the output 'Class' is a copy of 'Data' (map or data struct) relabeled according to the classification. If input argument 'proto_class' _is_ given, the output argument 'Class' is _always_ a vector of integers 1,2,...,k indiacating the class. If there is a tie between representatives of two or more classes among the K closest neighbors to the classifiee, the class is selected randomly among these candidates. IMPORTANT ** Even if prototype vectors are given in a map struct the mask _is not taken into account_ when calculating Euclidean distance ** The function calculates the total distance matrix between all classifiees and prototype vectors. This results to an MxN matrix; if N is high it is recommended to divide the matrix 'Data' (the classifiees) into smaller sets in order to avoid memory overflow or swapping. Also, if K>1 this function uses 'sort' which is considerably slower than 'max' which is used for K==1. See also KNN, SOM_LABEL, SOM_AUTOLABEL