if __name__ == '__main__':
my_id = "pk-man"
# my_id = "730908575451990f4f3dc625baef4697"
my_md5 = hashlib.md5(my_id).hexdigest()
# my_md5 = "730908575451990f4f3dc625baef4697"
print my_md5
try:
import matplotlib.pyplot as plt
except:
print 'Must install matplotlib to run this demo.\n'
t = Table(smallBlind=1, bigBlind=2, maxBuyIn=200)
players = []
for i in range(6):
#create BasicPlayer that uses GradientBoostingRegressor as machine learning model
#with wealth of 1 million and 10 discrete choices for raising,
#with each raise choice .7 times the next largest raise choice
#Player forgets training samples older than 100,000
r = GradientBoostingRegressor()
name = 'Player ' + str(i + 1)
p = BasicPlayer(name=name,
reg=r,
bankroll=10000,
nRaises=4,
rFactor=.7,
from pklearn import Table
from pklearn.templates import simulate, BasicPlayer
from sklearn.ensemble import GradientBoostingRegressor
if __name__ == '__main__':
t = Table(smallBlind=1, bigBlind=2, maxBuyIn=200)
players = []
for i in range(6):
#create BasicPlayer that uses GradientBoostingRegressor as machine learning model
#with wealth of 1 million and 10 discrete choices for raising,
#with each raise choice .7 times the next largest raise choice
#Player forgets training samples older than 100,000
r = GradientBoostingRegressor()
name = 'Player ' + str(i+1)
p = BasicPlayer(name=name, reg=r, bankroll=10**6, nRaises=10, rFactor=.7, memory=10**5)
players.append(p)
for p in players: t.addPlayer(p)
#simulate 'nHands' hands
#begin training after 'firstTrain' hands
#before which players take random actions and explore state space
#players train every 'nTrain' hands after 'firstTrain'
#players cash out/ buy in every 'nBuyIn' hands
#table narrates each hands if 'vocal' is True
simulate(t, nHands=10000, firstTrain=2000, nTrain=1000, nBuyIn=10)
simulate(t, nHands=20, nBuyIn=10, vocal=True)
from pklearn import Table
from pklearn.templates import simulate, BasicPlayer
import numpy as np
from sklearn.cross_validation import cross_val_score
from sklearn.linear_model import LinearRegression, Lasso
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
if __name__ == '__main__':
try:
import matplotlib.pyplot as plt
except:
print 'Must install matplotlib to run this demo.\n'
t = Table(smallBlind=1, bigBlind=2, maxBuyIn=200)
players = []
for i in range(6):
# create BasicPlayer without a machine learning model
# with wealth of 1 million and 10 discrete choices for raising,
# with each raise choice .7 times the next largest raise choice
# Player forgets training samples older than 100,000
name = 'Player ' + str(i + 1)
p = BasicPlayer(name=name, bankroll=10 ** 6, nRaises=10, rFactor=.7,
memory=10 ** 5)
players.append(p)
for p in players: t.addPlayer(p)
# simulate 1,000 hands, cashing out/buying in every 10 hands, without training or narrating