from pklearn import Table

from pklearn.templates import simulate, BasicPlayer

from sklearn.ensemble import 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 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)

p.stopTraining()

players.append(p)

for p in players: t.addPlayer(p)

#train Player 1 for 1000 hands, training once

players[0].startTraining()

simulate(t, nHands=1000, nTrain=1000, nBuyIn=10)

players[0].stopTraining()

#train Player 2 for 10000 hands, training every 1000 hands

players[1].startTraining()

simulate(t, nHands=10000, nTrain=1000, nBuyIn=10)

players[1].stopTraining()

for p in players: p.setBankroll(10**6)

#simulate 20,000 hands and save bankroll history

bankrolls = simulate(t, nHands=20000, nTrain=0, nBuyIn=10)

#plot bankroll history of each player

for i in range(6):

bankroll = bankrolls[i]

plt.plot(range(len(bankroll)), bankroll, label=players[i].getName())

plt.title('Player bankroll vs Hands played')

plt.xlabel('Hands played')

plt.ylabel('Player bankroll/wealth')

plt.legend(loc='upper left')

plt.show()