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Added spectral clustering function
author Mohamed Gomaa
date Tue, 25 Dec 2012 02:24:21 -0500
parents 8bafd054cda4
children 6c068047edbf
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308:8bafd054cda4 309:80cbafd69109
56 else: 56 else:
57 centroids[i].add(instance) 57 centroids[i].add(instance)
58 58
59 return centroids 59 return centroids
60 60
61
61 def spectralClustering(similarityMatrix,k): 62 def spectralClustering(similarityMatrix,k):
62 ''' Steps of Spectral Clustering''' 63 ''' Steps of Spectral Clustering'''
63 n= len(similarityMatrix) 64 n= len(similarityMatrix)
64 # create Laplacian matrix 65 # create Laplacian matrix
65 rowsum = np.sum(similarityMatrix,axis=0) 66 rowsum = np.sum(similarityMatrix,axis=0)
74 # k-means 75 # k-means
75 from scipy.cluster.vq import kmeans, whiten, vq 76 from scipy.cluster.vq import kmeans, whiten, vq
76 features = whiten(features) 77 features = whiten(features)
77 centroids,distortion = kmeans(features,k,iter=20) # default iter = 20 78 centroids,distortion = kmeans(features,k,iter=20) # default iter = 20
78 code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster) 79 code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster)
79 return code,sigma 80 return code,sigma
80