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hill_climb.py
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hill_climb.py
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import numpy as np
from invpersim import InvPerSim
class HillClimber:
def __init__(self, par):
self.par = par
def __call__(self, t, av, p, v):
inp = np.array([t, av, p, v])
ap, an = inp @ self.par
if(ap > an):
return 20
else:
return -20
def train():
numenv = 25
noise = 0.01
randarr = lambda: 1 - 2 * np.random.rand(4, 2)
spar = randarr()
bestpt = 0
cnoise = 0.01
avgnoise = 0
updb = 0
for cb in range(1500):
dpar = spar + randarr() * cnoise
nowpt = 0
for _ in range(numenv):
ev = InvPerSim().randomize()
nowpt += 1/ev.ct_sim(HillClimber(dpar))
nowpt = numenv / nowpt
if(nowpt > bestpt):
print(cb, bestpt, nowpt, cnoise)
bestpt = nowpt
spar = dpar
avgnoise += cnoise
updb += 1
cnoise = noise
else:
cnoise *= 1.03
if cb % 100 == 0:
print("SHOW: ", cb)
yield HillClimber(spar)
cb += 1
avgnoise /= updb
print("FINISHED IN {} STEP WITH {} POINTS".format(cb, bestpt))
print("NEEDED {} IMPROVEMENT WITH {} NOISE".format(updb, avgnoise))
print("FINAL TENSOR:")
print(spar)
yield HillClimber(spar)
if __name__ == "__main__":
for ok in train():
cpt = 0
for _ in range(100):
tester = InvPerSim()
tester.randomize()
#tester.start_show(800)
res = tester.ct_sim(ok)
cpt += res
#tester.stop_show()
print("POINTS: ", cpt / 100)