weightedknnrule.m

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% Learns classifier and classifies test set
% using weighted k-NN rule
% Usage
%      [trainError, testError, estTrainLabels, estTestLabels] = ...
%           weightedKNNRule_VC(trainFeatures, trainLabels,params ,testFeatures, testLabels)
% where
%
% Inputs:
% 	trainFeatures  - the training set vectors, one vector per column
%	trainLabels    - the labels of the above
%       params         - k, the number of nearest neighbors
%                        weightFile, a file with the weights, a
%                           a vector having k (non-increasing) entries. 
%                           Entry 1 is the weight of the nearest neighbor,
%                           while entry k is the weight of the farthest neighbor
%                         IF weightFile IS NOT PROVIDED, DEFAULT weights are
%                           provided using the MacLeod, Luk and Titterington
%                           weights with parameters s = 2k, and alpha = .5
%                           (see lecture notes)
%       testFeatures   - test set, one column per vector
%       testLabels     - labels  for test set%
% Outputs
%	trainError     - the error rate on the training set (one entry per
%	                   class + total error)
%	testError      - the error rate on the test set (one entry per class
%	                  + total error)
%       estTrainLabels - the labels produced by the algorithm for the
%                          training samples
%       estTestLabels - the labels produced by the algorithm for the
%                          test samples



function [features, targets] = weightedKNNRule(train_features, train_targets, params, region, plot_on)

fprintf('The method has not been implemented\n');