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())
labels.extend(p.getLabels())
features = np.array(features)
labels = np.array(labels)
#shuffle features/labels
index = np.arange(len(labels))
np.random.shuffle(index)
features = features[index]
labels = labels[index]
#initialize regressors with default parameters
regressors = {
LinearRegression(): 'LinearRegression',
Lasso(): 'Lasso',
#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())
labels.extend(p.getLabels())
features = np.array(features)
labels = np.array(labels)
#shuffle features/labels
index = np.arange(len(labels))
np.random.shuffle(index)
features = features[index]
labels = labels[index]
#initialize regressors with default parameters
regressors = {LinearRegression(): 'LinearRegression',
Lasso(): 'Lasso',
RandomForestRegressor(): 'RandomForestRegressor',
