吴恩达的机器学习编程作业16:findClosestCentroids KMeans算法第一步,划分样本归类

function idx = findClosestCentroids(X, centroids)
%FINDCLOSESTCENTROIDS computes the centroid memberships for every example
%   idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids
%   in idx for a dataset X where each row is a single example. idx = m x 1 
%   vector of centroid assignments (i.e. each entry in range [1..K])
%

% Set K
K = size(centroids, 1);

% You need to return the following variables correctly.
idx = zeros(size(X,1), 1);

% ====================== YOUR CODE HERE ======================
% Instructions: Go over every example, find its closest centroid, and store
%               the index inside idx at the appropriate location.
%               Concretely, idx(i) should contain the index of the centroid
%               closest to example i. Hence, it should be a value in the 
%               range 1..K
%
% Note: You can use a for-loop over the examples to compute this.
%
for i=1:size(X,1)
	minC = Inf;
	for j = 1:K
		if(minC > sum((centroids(j,:)-	X(i,:)).^2))
			minC = sum((centroids(j,:)-X(i,:)).^2);
			idx(i,1) = j;
		end 
	end
end






% =============================================================

end

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转载自blog.csdn.net/melon__/article/details/82150381