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KNN.m
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KNN.m
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classdef KNN < handle
%k nearest neighbor
properties
K;
dist;
X; %will matlab really copy that or just keep the pointer?
Y;
end
methods
function self = KNN(K, dist)
self.K = K;
%not useful now
if ~exist('dist','var')
self.dist = @(X1,X2)(sum((X1-X2).^2)); %use euclidean distance by default
else
self.dist = dist;
end
end
function [] = train(self, X,Y)
self.X = X;
self.Y = Y;
end
function [pred accu] = classify(self, X, Y)
pred = zeros(size(X,2),1);
for i = 1 : size(X,2)
% if (mod(i,100) == 0)
% fprintf('[%d/%d]',i,size(X,2));
% end
%too slow.. give up this custom distance function idea
% dist = zeros(size(self.X,2),1);
% for j = 1 : size(self.X,2)
% dist(j) = self.dist(self.X(:,j),X(:,i));
% end
dist = sum(bsxfun(@minus,self.X,X(:,i)).^2,1);
[~, idx] = sort(dist, 'ascend');
pred(i) = mode(self.Y(idx(1:self.K)));
end
accu = [];
if exist('Y','var')
accu = mean(pred == Y);
end
end
end
end