def computeNeighborhood(p, setPoints, typeDistance, minPoints, eps):
pointsOfCluster = []
for q in setPoints:
if typeDistance == 0:
if p.is_in_neighborhoodByEUSimple(q, eps):
pointsOfCluster.append(q)
elif typeDistance == 1:
if p.is_in_neighborhoodEURelativeSpeed(q, eps):
pointsOfCluster.append(q)
if len(pointsOfCluster) < minPoints:
return None
else:
return Cluster(None, pointsOfCluster)
def dbscan(setPoints, eps, minPoints):
clusters = []
noises = []
IDs = []
classifications = {}
for pt in setPoints:
IDs.append(pt.id)
classifications[pt.id] = 0
for pt in setPoints:
if classifications[pt.id] == 0:
points = _expand_cluster(setPoints, pt, classifications, noises,
eps, minPoints)
if points:
clusters.append(Cluster(None, points))
# s=""
# for p in noises:
# s+=p.toString()+' '
# print(s)
# s=""
# for c in clusters:
# s += c.toString()+'\n'
# print(s)
return [clusters, noises, IDs]
import sys
sys.path.append("../src")
from simulator import Simulator
from model import Cluster
#import stacktracer
#stacktracer.trace_start("trace.html",interval=5,auto=True) # Set auto flag to always update file!
model = Cluster(2)
sim = Simulator(model)
sim.setVerbose(None)
#sim.setTerminationTime(10.0)
sim.setStateSaving("custom")
sim.simulate()
