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newML.py
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import random
from numpy import array
from sklearn.neural_network import MLPClassifier
from user import User
sube1user = []
sube2user = []
trainData = []
def append_to_file(file_name, text):
f = open(file_name, "a")
f.write(text + "\n")
f.close()
def print_to_new_file(file_name, text):
f = open(file_name, "w+")
f.write(text)
f.close()
def read_lines_from_file(file_name):
f = open(file_name, "r")
lines = f.readlines()
f.close()
return lines
def read_from_file(file_name):
f = open(file_name, "r")
text = f.read()
f.close()
return text
def getSubeEstimationTime(lst):
if not lst:
return 0
time = 0
for item in lst:
time = time + item[2]
return time
# yas gruplarina gore sure datasi olusturur. valuelari yas ve islem typei result ise sure olarak kullanilacak
def createTimeData():
timeTrainData = []
sampleSize = 400
for x in range(sampleSize):
age = random.randint(20, 35)
activityType = random.randint(1, 4)
if activityType == 1:
sure = random.randint(5, 10)
elif activityType == 2:
sure = random.randint(10, 15)
elif activityType == 3:
sure = random.randint(15, 20)
elif activityType == 4:
sure = random.randint(20, 25)
timeTrainData.append([age, activityType, sure])
for x in range(sampleSize):
age = random.randint(35, 60)
activityType = random.randint(1, 4)
if activityType == 1:
sure = random.randint(5, 15)
elif activityType == 2:
sure = random.randint(10, 20)
elif activityType == 3:
sure = random.randint(15, 25)
elif activityType == 4:
sure = random.randint(20, 30)
timeTrainData.append([age, activityType, sure])
for x in range(sampleSize):
age = random.randint(60, 85)
activityType = random.randint(1, 4)
if activityType == 1:
sure = random.randint(10, 15)
elif activityType == 2:
sure = random.randint(10, 25)
elif activityType == 3:
sure = random.randint(15, 30)
elif activityType == 4:
sure = random.randint(25, 35)
timeTrainData.append([age, activityType, sure])
values = []
result = []
for item in timeTrainData:
values.append([item[0], item[1]])
result.append(item[2])
trainValuesNpArray = array(values)
trainResultNpArray = array(result)
_solver = 'lbfgs'
_alpha = 1e-5
_hiddenLayerSize = (50, 100,)
clf = MLPClassifier(solver=_solver, alpha=_alpha, hidden_layer_sizes=_hiddenLayerSize)
clf.fit(trainValuesNpArray, trainResultNpArray)
print("Time data train ended")
return clf
def trainSubeData(timeClf):
for i in range(1200):
sube1arriveTime = random.randint(0, 15)
sube2arriveTime = random.randint(0, 15)
islemTipi = random.randint(1, 4)
age = random.randint(20, 85)
sube1totalTime = getSubeEstimationTime(sube1user)
sube2totalTime = getSubeEstimationTime(sube2user)
subeSecimi = 0
predictedTime = timeClf.predict(array([[age, islemTipi]]))
if sube1totalTime + predictedTime <= sube2totalTime + predictedTime:
sube1user.append([sube1arriveTime, sube2arriveTime, predictedTime[0], sube1totalTime])
subeSecimi = 1
else:
sube2user.append([sube1arriveTime, sube2arriveTime, predictedTime[0], sube2totalTime])
subeSecimi = 2
trainData.append(
[sube1arriveTime, sube2arriveTime, predictedTime[0], sube1totalTime, sube2totalTime, subeSecimi])
print("train data creation has ended")
"""print_to_new_file("subeData.txt","")
for item in trainData:
append_to_file("subeData.txt", str(item))
lines = read_lines_from_file("subeData.txt")
for line in lines:
line = line.replace("[","").replace("]","").replace(" ","").replace("\n","").split(",")
trainData.append([int(line[0]),int(line[1]),int(line[2]),int(line[3]),int(line[4]),int(line[5])])"""
values = []
result = []
for item in trainData:
values.append([item[0], item[1], item[2], item[3], item[4]])
result.append(item[5])
trainValuesNpArray = array(values)
trainResultNpArray = array(result)
_solver = 'lbfgs'
_alpha = 1e-5
_hiddenLayerSize = (50, 100,)
clf = MLPClassifier(solver=_solver, alpha=_alpha, hidden_layer_sizes=_hiddenLayerSize)
clf.fit(trainValuesNpArray, trainResultNpArray)
return clf
"""predicted = clf.predict(array([[10, 15, 15, 0, 0]]))
print(predicted)
predicted = clf.predict(array([[0, 2, 15, 15, 0]]))
print(predicted)
predicted = clf.predict(array([[14, 9, 25, 15, 15]]))
print(predicted)
predicted = clf.predict(array([[10, 9, 15, 40, 15]]))
print(predicted)
predicted = clf.predict(array([[13, 7, 16, 40, 30]]))
print(predicted)"""
def algoQueue(lst, sube):
for j in range(len(lst)):
i = len(lst) - 1
while i > 0:
eta = int(lst[i].get_eta())
if eta < 5:
break
eta = int(lst[i].get_eta())
time = int(lst[i].time)
previous_eta = int(lst[i - 1].get_eta())
if eta + time < previous_eta:
temp2 = lst[i].no
lst[i].no = lst[i - 1].no
lst[i - 1].no = temp2
temp = lst[i]
lst[i] = lst[i - 1]
lst[i - 1] = temp
i = i - 1
if __name__ == '__main__':
user1 = User('1', '6', '3', '4', '6')
user1.time = 1
user1.sube = '1'
user1.no = 4
user2 = User('2', '8', '3', '4', '6')
user2.time = 2
user2.no = 3
user2.sube = '1'
user3 = User('3', '10', '3', '4', '6')
user3.time = 3
user3.no = 2
user3.sube = '1'
user4 = User('4', '12', '3', '4', '6')
user4.time = 4
user4.no = 1
user4.sube = '1'
user5 = User('5', '14', '3', '4', '6')
user5.time = 5
user5.no = 0
user5.sube = '1'
lst = [user5, user4, user3, user2, user1]
algoQueue(lst, '1')
for user in lst:
print(user.uid, user.no)