'''
Kmean on position
'''
import loadData as ld
import clustering as clus
import sklearn.cluster as cluster
import numpy as np
from statistics import mean,median
data = ld.getID1Data()
data = ld.dataSelectTime(data,10,16,[0,1,2,3,4],1)
data = clus.temporalAround24removed(data)
data=clus.removeTime(data)
kk = cluster.KMeans(n_clusters=10,random_state=0)
out = kk.fit(data)
clus.plotCluster(data,out.labels_,10)
print("Cluster center " + str(out.cluster_centers_))
ld.fromDataClusterToCsv(data,out.labels_,"Work")
Kmean on position
"""
import loadData as ld
import clustering as clus
import sklearn.cluster as cluster
import numpy as np
from statistics import mean, median
data = ld.getID2Data()
######If you want to select particular moments in the day
timeShift = -8 # San Francisco
data = ld.dataSelectTime(data, 0, 24, [0, 1, 2, 5, 6], timeShift)
####### Modulo 24 hours
# data = clus.temporalAround24removed(data)
data = clus.temporalAround7Daysremoved(data)
#######Remove time
# data=clus.removeTime(data)
kk = cluster.KMeans(n_clusters=5, random_state=0)
out = kk.fit(data)
clus.plotCluster(data, out.labels_, 5)
print("Cluster center " + str(out.cluster_centers_))
ld.fromDataClusterToCsv(data, out.labels_, "WorkingDays")
###### Select a center and get the 80% closest values to this center removed to far points
Kmean on position
"""
import loadData as ld
import clustering as clus
import sklearn.cluster as cluster
import numpy as np
from statistics import mean, median
data = ld.getID3Data()
######If you want to select particular moments in the day
timeShift = -8 # San Francisco
data = ld.dataSelectTime(data, 0, 24, range(0, 6), timeShift)
####### Modulo 24 hours
# data = clus.temporalAround24removed(data)
data = clus.temporalAround7Daysremoved(data)
#######Remove time
# data=clus.removeTime(data)
kk = cluster.KMeans(n_clusters=7, random_state=0)
out = kk.fit(data)
clus.plotCluster(data, out.labels_, 7)
print("Cluster center " + str(out.cluster_centers_))
ld.fromDataClusterToCsv(data, out.labels_, "WorkingDays")
###### Select a center and get the 80% closest values to this center removed to far points
