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Algorithm.py
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Algorithm.py
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from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.xmeans import xmeans
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
from sklearn.preprocessing import normalize
class XmeansAlgorithm(object):
def __init__(self, NxObj, amount_initial_centers=2, amount_max_centers=16):
self.dataset = dict(NxObj.nodes.data())
self.namelist = []
self.datalist = []
self.resultDict = {}
self.amount_initial_centers = amount_initial_centers
self.amount_max_centers = amount_max_centers
def importData(self):
for dp in self.dataset:
self.namelist.append(dp)
self.datalist.append([self.dataset[dp]['data']['SYNRequest'],
self.dataset[dp]['data']['SYNAccept'] ,
self.dataset[dp]['data']['flow']])
self.datalist = normalize(self.datalist) # Normalize Data
#del self.dataset
def xmeansRoutine(self):
self.initial_centers = kmeans_plusplus_initializer(self.datalist, self.amount_initial_centers).initialize()
self.xmeans_instance = xmeans(self.datalist, self.initial_centers, self.amount_max_centers)
self.xmeans_instance.process()
self.clusters = self.xmeans_instance.get_clusters()
self.centers = self.xmeans_instance.get_centers()
def exportData(self):
for clu, arr in enumerate(self.clusters):
for n in arr:
self.resultDict[self.namelist[n]] = clu
def process(self):
self.importData()
self.xmeansRoutine()
self.exportData()
def getResult(self):
return self.resultDict
def getCenters(self):
return self.centers
def getVisualization(self):
self.visualizer = cluster_visualizer()
self.visualizer.append_clusters(self.clusters, self.datalist)
self.visualizer.append_cluster(self.centers, None, marker='*', markersize=10)
self.visualizer.show()