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first simple clustering algorithm implementation
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 24 Nov 2011 19:20:07 -0500
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children d70e9b36889c
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183
ed944ff45e8c first simple clustering algorithm implementation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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1 #! /usr/bin/env python
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2 '''Libraries for machine learning algorithms'''
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3
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4 __metaclass__ = type
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5
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6 def kMeansFixedDistance(data, sameCluster, centroid):
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7 '''k-means algorithm with similarity function
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8 Two instances should be in the same cluster if the sameCluster function returns true for two instances. It is supposed that the centroid of a set of instances can be computed, using the function.
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9 The number of clusters will be determined accordingly
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10
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11 data: list of instances
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12 centroid: '''
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13
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14 # todo randomize input
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15 centroids = [data[0]]
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16 for instance in data:
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17 i = 0
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18 while i<len(centroids) and not sameCluster(instance, centroids[i]):
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19 i += 1
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20 if i == len(centroids):
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21 centroids.append(instance)
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22 else:
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23 centroids[i] = centroid(centroids[i], instance)
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24
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25 return centroids