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  1. PO
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    <p>You need to define a distance function where you are using hypot. Usually in K-means it is Distance=sum((point-centroid)^2) Here is some matlab code that does it ... I can port it if you can't, but give it a go. Like you said, only way to learn.</p> <pre><code>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); [numberOfExamples numberOfDimensions] = size(X); % You need to return the following variables correctly. idx = zeros(size(X,1), 1); % 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 % for loop=1:numberOfExamples Distance = sum(bsxfun(@minus,X(loop,:),centroids).^2,2); [value index] = min(Distance); idx(loop) = index; end; end </code></pre> <p>UPDATE</p> <p>This should return the distance, notice that the above matlab code just returns the distance(and index) of the closest centroid...your function returns all distances, as does the one below.</p> <pre><code>def FindDistance(X,centroids): K=shape(centroids)[0] examples, dimensions = shape(X) distance = zeros((examples,K)) for ex in xrange(examples): distance[ex,:] = np.sum((X[ex,:]-centroids)**2,1) return distance </code></pre>
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    1. PODistance calculation on matrix using numpy
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    1. USMatt
    1. USMatt
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    1. COSo I have to calculate the distances for each points seperately in a loop?
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      1. USyasar
    2. COYou should be able to do it in the for loop you already have, just define Distance[i] in the for loop instead of temp...hold on and I'll see if I haven't done this
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      1. USMatt
    3. CO@yasar11732 actually... you should be able to do it in one go... but that depends on the size of the sample (or, if you prefer it, on the amount of RAM you can devote to this). Let's see if I can come up with it...
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      1. USRicardo Cárdenes
 

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