def getEpsilon(self, dataSet):
distances = []
sumOfDist = 0.0
for i in range(len(dataSet)):
point = dataSet[i]
for j in range(len(dataSet)):
if i == j:
continue
pt = dataSet[j]
dist = getEuclideanDist(point.x, point.y, pt.x, pt.y)
distances.append(dist)
distances.sort()
sumOfDist += distances[7]
distances = []
return sumOfDist/len(dataSet)
def dbscan(self, dataSet):
clusters = []
visited = set()
noise = set()
# Iterate over data points
for i in range(len(dataSet)):
point = dataSet[i]
if point in visited:
continue
visited.add(point)
N = []
minPtsNeighbours = 0
# check which point satisfies minPts condition
for j in range(len(dataSet)):
if i==j:
continue
pt = dataSet[j]
dist = getEuclideanDist(point.x, point.y, pt.x, pt.y)
if dist <= self.e:
minPtsNeighbours += 1
N.append(pt)
if minPtsNeighbours >= self.minPts:
cluster = set()
cluster.add(point)
point.isAssignedToCluster = True
j = 0
while j < len(N):
point1 = N[j]
minPtsNeighbours1 = 0
N1 = []
if not point1 in visited:
visited.add(point1)
for l in range(len(dataSet)):
pt = dataSet[l]
dist = getEuclideanDist(point1.x, point1.y, pt.x, pt.y)
if dist <= self.e:
minPtsNeighbours1 += 1
N1.append(pt)
if minPtsNeighbours1 >= self.minPts:
self.removeDuplicates(N, N1)
# Add point1 is not yet member of any other cluster then add it to cluster
# Hint: use self.isAssignedToCluster function to check if a point is assigned to any clusters
# ========================#
# STRART YOUR CODE HERE #
# ========================#
def isAssignedToCluster(point, clusters):
for cluster in clusters:
for pt in cluster:
if pt.x == point.x and pt.y == point.y:
return True
return False
if not isAssignedToCluster(point1, clusters):
cluster.add(point1)
# ========================#
# END YOUR CODE HERE #
# ========================#
j += 1
# add cluster to the list of clusters
clusters.append(cluster)
else:
noise.add(point)
# List clusters
print("Number of clusters formed :" + str(len(clusters)))
print("Noise points :" + str(len(noise)))
# Calculate purity
compute_purity(clusters,len(self.dataSet))
compute_NMI(clusters,self.noOfLabels)
DataPoints.writeToFile(noise, clusters, "DBSCAN_"+ self.dataname + ".csv")