Adaptive DBSCAN achievement

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Adaptive DBSCAN achievement

I am doing the DBSCAN clustering in python. I want to achieve an adaptive way to return the number of clusters by self calculating its eps and Minpts parameters. Below is my code.

import math
import copy
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN


def loadDataSet(fileName, splitChar='\t'):

dataSet = []
with open(fileName) as fr:
for line in fr.readlines():
curline = line.strip().split(splitChar)
fltline = list(map(float, curline))
dataSet.append(fltline)
return dataSet


def dist(a,b):

return math.sqrt(math.pow(a[0]-b[0],2) + math.pow(a[1]-b[1],2))


def returnDk(matrix,k):

Dk = []
for i in range(len(matrix)):
Dk.append(matrix[i][k])
return Dk


def returnDkAverage(Dk):

sum = 0
for i in range(len(Dk)):
sum = sum + Dk[i]
return sum/len(Dk)


def CalculateDistMatrix(dataset):

DistMatrix = [[0 for j in range(len(dataset))] for i in range(len(dataset))]
for i in range(len(dataset)):
for j in range(len(dataset)):
DistMatrix[i][j] = dist(dataset[i], dataset[j])
return DistMatrix


def returnEpsCandidate(dataSet):

DistMatrix = CalculateDistMatrix(dataSet)
tmp_matrix = copy.deepcopy(DistMatrix)
for i in range(len(tmp_matrix)):
tmp_matrix[i].sort()
EpsCandidate = []
for k in range(1,len(dataSet)):
Dk = returnDk(tmp_matrix,k)
DkAverage = returnDkAverage(Dk)
EpsCandidate.append(DkAverage)
return EpsCandidate


def returnMinptsCandidate(DistMatrix,EpsCandidate):

MinptsCandidate = []
for k in range(len(EpsCandidate)):
tmp_eps = EpsCandidate[k]
tmp_count = 0
for i in range(len(DistMatrix)):
for j in range(len(DistMatrix[i])):
if DistMatrix[i][j] <= tmp_eps:
tmp_count = tmp_count + 1
MinptsCandidate.append(tmp_count/len(dataSet))
return MinptsCandidate


def returnClusterNumberList(dataset,EpsCandidate,MinptsCandidate):

np_dataset = np.array(dataset)
ClusterNumberList = []
for i in range(len(EpsCandidate)):
clustering = DBSCAN(eps= EpsCandidate[i],min_samples= MinptsCandidate[i]).fit(np_dataset)
num_clustering = max(clustering.labels_)
ClusterNumberList.append(num_clustering)
return ClusterNumberList

if __name__ == '__main__':
data = pd.read_csv('/Users/Desktop/Mic/recorder_test1/New folder/MFCCresultsforclustering/MFCCresultsforclustering.csv')
dataSet = data.iloc[:,0:13].values
EpsCandidate = returnEpsCandidate(dataSet)
DistMatrix = CalculateDistMatrix(dataSet)
MinptsCandidate = returnMinptsCandidate(DistMatrix,EpsCandidate)
ClusterNumberList = returnClusterNumberList(dataSet,EpsCandidate,MinptsCandidate)
print(EpsCandidate)
print(MinptsCandidate)
print('cluster number list is')
print(ClusterNumberList)
However, the output with the loading data set is all [-1]s. I am wondering where is the mistake. Am I right for this general direction? If not, how can I achieve the adaptive DBSCAN clustering?