import pickle
from biasedPerceptron import BiasedPerceptron, diff
import anotherStatus as fw
import calling_station
import betting_station
import time
import numpy as np
name = 'frenzy_perceptron_.0005_vs_frenzy.p'
start = time.time()
ALPHA = 0.0005
LAMBS = [0.8, 0.85, 0.9, 0.95, 1]
n_train = 500000
csBot = calling_station.Calling_station()
bsBot = betting_station.Betting_station()
for LAMB in LAMBS:
net = BiasedPerceptron(fw.n_in,
fw.n_hidden,
fw.n_out,
alpha=ALPHA,
lamb=LAMB,
randomInit=True)
net2 = BiasedPerceptron(fw.n_in,
fw.n_hidden,
fw.n_out,
alpha=ALPHA,
lamb=LAMB,
randomInit=True)
auto = fw.AnotherAutoPlayer(net, name="superbot")
ai = fw.AnotherAutoPlayer(net2, name='cpu', frenzy=1)
import pickle
import UnbiasedNet
import anotherStatus as fw
import calling_station
import betting_station
import time
import numpy as np
name = 'frenzy_vs_betting.p'
start = time.time()
ALPHA = 0.001
LAMBS = [0.75]
n_train = []
callBot = calling_station.Calling_station()
bsbot = betting_station.Betting_station()
for LAMB in LAMBS:
net = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
alpha=ALPHA,
lamb=LAMB,
randomInit=True)
auto = fw.AnotherAutoPlayer(net, name="superbot")
distance = 10
i = 0
while distance > 0.0002:
oldnet = auto.net.deepcopy()
auto.net.alpha /= 1.01
auto.train(1000, bsbot, debug=0, frenzy=1)