from xevo import erun, crossevo
from bitflip import *
from boolflip import *
obj = bitflip()
obj = boolflip()
genetics = crossevo() #1120+-34
goalstrength = 10000.0
maxsteps = 1500
def statinf(q):
return {
"shape": np.array(q).shape,
"mean": np.mean(q),
"std": np.std(q),
"max": np.max(q),
"min": np.min(q),
"stdm": np.std(q) / np.sqrt(len(q))
}
def runone(show=True):
counter.reset()
global c
c = erun(genetics.copy(), obj, show=show)
c.run(goalstrength=goalstrength, maxsteps=maxsteps)
return counter.i
def quickmut(obj, population=20, goal=0.0, maxsteps=1000, show=True):
c = erun(crossevo(), obj, population=population, show=show)
c.run(goalstrength=goal, maxsteps=maxsteps)
return c.getwinner()
sigma = 0.03
x = [i / 10 for i in range(100)]
y = [math.exp(3 * xx) + np.random.normal(0, sigma, 1)[0] for xx in x]
# y=[3*xx+np.random.normal(0,sigma,1)[0] for xx in x]
# obj=evofit(x=x,y=y,sigma=sigma,f=fmath.exp(fmath.variable("a")*fmath.variable("x")))
# obj=evofit(x=x,y=y,sigma=sigma,f=fmath.sin(fmath.tanh(fmath.variable("x"))))
# print(obj)
# print(obj.strength())
# exit()
genetics = xevo.crossevo()
obj = evofit(x=x, y=y, sigma=sigma)
goalstrength = 100.0
maxsteps = 10000000
def runone(show=True):
global c
c = xevo.erun(genetics.copy(), obj, show=show, population=100, delay=0.001)
c.run(goalstrength=goalstrength, maxsteps=maxsteps)
runone()
print(c.getwinner())