def __init__(self,dataList,k,r,Kernel="Gaussian",autoselected_r=True):
self.dataList = dataList
self.ClusterSet = [0]*k
self.k = k
self.r = r
self.kernel = Kernel
if autoselected_r:
self.r = Cluster.DataCluster(dataList).ComputeDiameter() / k**(1/len(dataList[0]))
self.weight = None
def UpdateClusterSet(self):
"""
:param k: int, number of clusters to be constructed
:return: array of type Cluster, the set of clusters constructed
"""
CenterSet = []
for i in self.ClusterSet:
CenterSet.append(i.GetCenter())
CenterSet = np.asarray(CenterSet)
for i in range(0,self.k,1):
cluster_points = []
for a in self.dataList:
if self.GetPointDistance(self.FindNearestNeighbor(CenterSet, a), CenterSet[i]) == 0.0:
cluster_points.append(a)
cluster_points = np.asarray(cluster_points)
self.ClusterSet[i] = Cluster.DataCluster(cluster_points)
return self.ClusterSet
def GreedyPartition(self):
"""
:param dataList: matrix, the data set to be partitioned
:param M: int, number of centers required
:return: array of arrays, the set of M centers found in this method
"""
CenterSet = np.zeros((self.k,len(self.dataList[0])))
CenterSet[0] = np.asarray(self.dataList[0])
for i in range(1,self.k,1):
CenterSet[i] = np.asarray(self.FindNextCenter(self.dataList,CenterSet))
for i in range(0,self.k,1):
cluster_points = []
for a in self.dataList:
if self.GetPointDistance(self.FindNearestNeighbor(CenterSet,a), CenterSet[i])== 0.0:
cluster_points.append(a)
cluster_points = np.asarray(cluster_points)
self.ClusterSet[i] = Cluster.DataCluster(cluster_points)
return self.ClusterSet
