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PODistance calculation on matrix using numpy
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copied!<p>I am trying to implement a K-means algorithm in Python (I know there is libraries for that, but I want to learn how to implement it myself.) Here is the function I am havin problem with:</p> <pre><code>def AssignPoints(points, centroids): """ Takes two arguments: points is a numpy array such that points.shape = m , n where m is number of examples, and n is number of dimensions. centroids is numpy array such that centroids.shape = k , n where k is number of centroids. k < m should hold. Returns: numpy array A such that A.shape = (m,) and A[i] is index of the centroid which points[i] is assigned to. """ m ,n = points.shape temp = [] for i in xrange(n): temp.append(np.subtract.outer(points[:,i],centroids[:,i])) distances = np.hypot(*temp) return distances.argmin(axis=1) </code></pre> <p>Purpose of this function, given m points in n dimensional space, and k centroids in n dimensional space, produce a numpy array of (x1 x2 x3 x4 ... xm) where x1 is the index of centroid which is closest to first point. This was working fine, until I tried it with 4 dimensional examples. When I try to put 4 dimensional examples, I get this error:</p> <pre><code> File "/path/to/the/kmeans.py", line 28, in AssignPoints distances = np.hypot(*temp) ValueError: invalid number of arguments </code></pre> <p>How can I fix this, or if I can't, how do you suggest I calculate what I am trying to calculate here?</p> <h2>My Answer</h2> <pre><code>def AssignPoints(points, centroids): m ,n = points.shape temp = [] for i in xrange(n): temp.append(np.subtract.outer(points[:,i],centroids[:,i])) for i in xrange(len(temp)): temp[i] = temp[i] ** 2 distances = np.add.reduce(temp) ** 0.5 return distances.argmin(axis=1) </code></pre>

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