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#base definitions for genetic algorithms
import scipy.stats as rv
stats = rv
GAError = 'GA Error'
def nop(x): return x
def flip_coin(p): return (rv.random() < p)
import random
def flip_coin2(p): return (random.random() < p)
class empty_class: pass
def shallow_clone(item):
new = empty_class()
new.__class__ = item.__class__
new.__dict__.update(item.__dict__)
return new
#these are exacly correct, but htey prevent problems with -Inf and Inf
def my_std(s):
# try:
a = remove_NaN(s)
if len(a) > 1: return stats.std(a)
else: return 0.
# except:
# import pdb
# pdb.set_trace()
def my_mean(s):
a = remove_NaN(s)
if len(a) > 1: return stats.mean(a)
else: return 0.
def testflip():
import time
b = time.clock()
for i in range(10000): a = flip_coin(.5)
e = time.clock()
print 'rv_flip',e-b
b = time.clock()
for i in range(10000): a = flip_coin2(.5)
e = time.clock()
print 'wh_flip',e-b
from rv import random
b = time.clock()
for i in range(10000):
a = random() < .5
e = time.clock()
print 'rv',e-b
from random import random
b = time.clock()
for i in range(10000):
a = random() < .5
e = time.clock()
print 'wh',e-b
def remove_NaN(z):
from numpy import isnan, isinf, compress, logical_not
return compress(logical_not( isnan(z)+isinf(z)),z)
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