'time'
]
for set in range(6):
preFill = nol[set] * ll[set] * nof[set] // 10
st = VVR_Sim(num_color=noc[set],
num_model=nom[set],
num_lanes=nol[set],
lane_length=ll[set],
capacity=0,
cc_file='./csv_files/cost.csv')
s = Sim(num_color=noc[set],
num_model=nom[set],
num_lanes=nol[set],
lane_length=ll[set],
capacity=0,
VVR_temp=st,
cc_file='./csv_files/cost.csv',
color_dist_file='./csv_files/total_orders.csv',
repeat=0)
repeat_epoches = 10
# print(s.mc_tab)
# print(s.bank.get_view_state())
# print(s.bank.front_hist())
# print(s.bank.front_view())
cc_sum = 0
t_sum = 0
for epoch in range(repeat_epoches):
s.reset()
# for set in range(6):
set = 3
pres = [0.2, 0.4, 0.5, 0.6, 0.8]
for pref in pres:
preFill = nol[set] * ll[set] * nof[set] // 10
st = VVR_Sim(num_color=noc[set],
num_model=nom[set],
num_lanes=nol[set],
lane_length=ll[set],
capacity=preFill,
preference=pref)
s = Sim(num_color=noc[set],
num_model=nom[set],
num_lanes=nol[set],
lane_length=ll[set],
capacity=preFill,
VVR_temp=st,
repeat=100,
preference=pref)
repeat_epoches = 1
# print(s.mc_tab)
# print(s.bank.get_view_state())
# print(s.bank.front_hist())
# print(s.bank.front_view())
cc_sum = 0
td_sum = 0
t_sum = 0
for epoch in range(repeat_epoches):
s.reset()
paras = []
noc = [10, 20, 10, 20, 10, 20]
nom = [5, 10, 5, 10, 5, 10]
ll = [6, 6, 8, 8, 10, 10]
nol = [5, 5, 7, 7, 10, 10]
nof=[7,7,6,6,6,6]
ccs=[]
data=[noc,nom,nol,ll,nof]
times=[]
columns=['colors','models','lanes','lane length', 'filling','color change']
for set in range(6):
preFill = nol[set]*ll[set]*nof[set] // 10
st = VVR_Sim(num_color=noc[set], num_model=nom[set], num_lanes=nol[set], lane_length=ll[set], capacity=0)
s = Sim(num_color=noc[set], num_model=nom[set], num_lanes=nol[set], lane_length=ll[set], capacity=0, VVR_temp=st)
repeat_epoches=10
# print(s.mc_tab)
# print(s.bank.get_view_state())
# print(s.bank.front_hist())
# print(s.bank.front_view())
cc_sum = 0
for epoch in range(repeat_epoches):
start_time = time.time()
s.reset()
for i in range(1000):
# sp.bank_c.state=copy.deepcopy(s.bank_c.state)
if i%200==0:
print(i, ' out of 1000 finihsed! ', epoch, ' out of 10 epcoh')
from Simulator import Simulator as Sim
import GraphGenerator as graph
import os as os
from DataPool import DataPool as Data
from File_handler import File_handler as fileMaintainer
if __name__ == '__main__':
print("Welcome to COVID-19 simulator!")
people = input("How many people would you like in the simulation? ")
days = input("Over how many days would you like to simulate? ")
sim = Sim(people, days)
sim.simulate()
while True:
num = int(
input(
"Which graph would you like to check? \n Press ( 1 ) for asymptamatic \n Press ( 2 ) for symptamatic \n Press ( 3 ) for hospitalised \n Press ( 4 ) for ICU-ed \n Press ( 5 ) for ventilator cases \n Press ( 6 ) for death \n Press ( 7 ) for immune \n Press ( 8 ) for recovered \n Press ( 9 ) to exit"
))
if num is 9:
break
age = int(
input(
"Press ( 1 ) for checking the trend among kids \n Press ( 2 ) for checking the trend among the youth \n Press ( 3 ) for checking the trend among the adults \n Press ( 4 ) to check overall trend "
))
if num is 1:
if age is 1:
graph.generate(sim, sim.asympKidNum)
if age is 2:
graph.generate(sim, sim.asympYoungNum)
if age is 3:
from VVR_Bank import VVR_Bank as Bank
import numpy as np
from Simulator import Simulator as Sim
from VVR_Simulator import VVR_Simulator as vSim
import time
st = vSim(
num_color=14,
num_model=7,
num_lanes=7,
lane_length=8,
)
s = Sim(num_color=14,
num_model=7,
num_lanes=7,
lane_length=8,
capacity=0,
VVR_temp=st,
cc_file='./csv_files/cost.csv')
preFill = 30
ccsum = 0
start_time = time.time()
for rep in range(1):
s.reset()
for i in range(1000):
# sp.bank_c.state=copy.deepcopy(s.bank_c.state)
if i % 10 == 0:
print(i, ' out of 1000 finihsed')
if i < 2000:
s.BBA_rule_step_in()
if i > preFill:
if __name__ == "__main__":
"""
This is the main method to get data, setup an agent, and simulate trading.
"""
qlearning_values = []
buyholdvalue = 0
rounds = 10
for i in range(0, rounds):
print('Starting round: ' + str(i + 1) + '/' + str(rounds))
starting_cash = 100000
max_position = 100
symbol = 'TSLA'
df = get_data()
# Train agent, then get trades for target timeframe
agent = Agent(max_position=max_position, starting_cash=starting_cash)
agent.train(df, symbol)
orders = agent.test(df, symbol)
# simulate trades
simulator = Sim(max_position=max_position, starting_cash=starting_cash)
qvalue, buyholdvalue = simulator.simulate(df, symbol, orders,
i == 9) # graph last round
qlearning_values.append(qvalue)
print("Policy has earned and average of: $" +
str(round(np.average(qlearning_values) - starting_cash, 2)))
print("BuyHold has earned: $" +
str(round(buyholdvalue - starting_cash, 2)))