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#base definitions for genetic algorithms
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import scipy.stats as rv
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def flip_coin(p): return (rv.random() < p)
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def flip_coin2(p): return (random.random() < p)
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class empty_class: pass
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def shallow_clone(item):
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new.__class__ = item.__class__
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new.__dict__.update(item.__dict__)
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#these are exacly correct, but htey prevent problems with -Inf and Inf
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if len(a) > 1: return stats.std(a)
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if len(a) > 1: return stats.mean(a)
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for i in range(10000): a = flip_coin(.5)
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for i in range(10000): a = flip_coin2(.5)
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for i in range(10000):
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from random import random
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for i in range(10000):
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from scipy import isnan, isinf, compress, logical_not
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return compress(logical_not( isnan(z)+isinf(z)),z)