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,
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=2000, nTrain=100, nBuyIn=10)
players[0].stopTraining()
#for p in players: p.setBankroll(10**6)
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
simulate(t, nHands=1000, nBuyIn=10, nTrain=0, vocal=False)
features = []
labels = []
for p in players:
features.extend(p.getFeatures())
