knn_map
KNN_MAP
Map a dataset on a KNN classifier
F = KNN_MAP(A,W)
Input  A  Dataset  W  KNN classifier trained by KNNC 
Output  F  Posterior probabilities 
Description Maps the dataset A by the KNN classifier W on the [0,1] interval for each of the classes that W is trained on. The posterior probabilities, stored in F, are computed in the following ways soft labeled training set: the normalised average of the soft labels of the K neighbors. crisp labeled training set, K = 1: normalisation of sigm(log(F)) with F(1:C) = sum(NN_Dist(1:C))./NN_Dist(1:C)  1 in which C is the number of classes and NN_Dist stores the distance to the nearest neighbor of each class. crisp labeled training set, K > 1: normalisation of (N(1:C) + 1)/(K+C), in which N stores the number of objects per class within the K first neighbors.
This routine is called automatically to determine A*W if W is trained by KNNC.
Warning: Class prior probabilities in the dataset A are neglected. See also
mappings, datasets, knnc, testk, This file has been automatically generated. If badly readable, use the helpcommand in Matlab. 
