self.points[i].clusterId = self.classfication[i]
for i in set(self.classfication):
cluster = {}
cluster['cluster'] = set()
for p in self.points:
if p.clusterId == i:
cluster['cluster'].add(p)
self.clusters.append(cluster)
if __name__ == '__main__':
es, ps, fs = [], [], []
for i in xrange(1, 10):
points = readPoints('dataset2.dat', 'dataset2-label.dat')
dbscan = DBscanClustering(points, i, 100)
dbscan.clustering()
es.append(i)
ps.append(purity(dbscan.clusters, dbscan.points))
fs.append(fscore(dbscan.clusters, dbscan.points))
print '\t'.join([str(i) for i in es])
print '\t'.join([str(i) for i in ps])
print '\t'.join([str(i) for i in fs])
print "\n\n"
ms, ps, fs = [], [], []
d = distance(point, cluster["mean"])
ds.append(d)
minId = ds.index(min(ds))
if point.clusterId != minId:
self.clusters[point.clusterId]["cluster"].remove(point)
self.clusters[minId]["cluster"].add(point)
point.clusterId = minId
stop = False
self._reCalculateMean()
if __name__ == "__main__":
ks, ps, fs = [], [], []
for k in xrange(5, 26):
points = readPoints("dataset1.dat", "dataset1-label.dat")
kmeans = KmeansClustering(points, k)
kmeans.clustering()
ks.append(k)
ps.append(purity(kmeans.clusters, kmeans.points))
fs.append(fscore(kmeans.clusters, kmeans.points))
print "\t".join([str(i) for i in ks])
print "\t".join([str(i) for i in ps])
print "\t".join([str(i) for i in fs])
