store_grabbag.m

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function D = Store_Grabbag(train_features, train_targets, Knn, region)

% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% 	features	- Train features
%	targets	- Train targets
%	Knn		- Number of nearest neighbors
%	region	- Decision region vector: [-x x -y y number_of_points]
%
% Outputs
%	D			- Decision sufrace

L		= length(train_features);
N		= region(5);
D		= zeros(N);

%Placing first sample in STORE

Store_features(:,1) = train_features(:,1);
Store_targets       = train_targets(1);
Grabbag_targets     = [];
Grabbag_features    = [];

for i = 2:L,
   target = Knn_Rule(train_features(:,i), Store_features, Store_targets, Knn);
   if target == train_targets(i)
      Grabbag_features = [Grabbag_features , train_features(:,i)];  
      Grabbag_targets = [Grabbag_targets train_targets(i)];
   else
      Store_features = [Store_features, train_features(:,i)];
      Store_targets  = [Store_targets train_targets(i)];
   end 
end      

New_Grabbag_features = Grabbag_features;

while (Grabbag_features ~= New_Grabbag_features)
   Grabbag_features = New_Grabbag_features;
   New_Grabbag_targets = [];
   for i = 1:length(Grabbag_features),
      target = Knn_Rule(Grabbag_features(:,i), Store_features, Store_targets);
      if target == train_targets(i)
	New_Grabbag_features = [New_Grabbag_features, train_features(:,i)];  
	New_Grabbag_targets  = [New_Grabbag_targets train_targets(i)];
      else
	Store_features = [Store_features, train_features(:,i)];
	Store_targets  = [Store_targets , train_targets(i)];
      end
   end
end
    
      
disp(['Calling Nearest Neighbor algorithm']);
D = Nearest_Neighbor(Store_features, Store_targets, Knn, region);