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function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Train targets
% test_patterns - Test patterns
% Knn - Number of nearest neighbors
%
% Outputs
% test_targets - Predicted targets
L = length(train_targets);
Uc = unique(train_targets);
if (L < Knn),
error('You specified more neighbors than there are points.')
end
N = size(test_patterns, 2);
test_targets = zeros(1,N);
for i = 1:N,
dist = sum((train_patterns - test_patterns(:,i)*ones(1,L)).^2);
[m, indices] = sort(dist);
n = hist(train_targets(indices(1:Knn)), Uc);
[m, best] = max(n);
test_targets(i) = Uc(best);
end
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