2018-03-30 18:19:23 -07:00
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import numpy as np
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2018-03-31 15:11:35 -07:00
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import operator as op
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2018-03-30 18:19:23 -07:00
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def sigmoid(x):
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return 1.0/(1 + np.exp(-x))
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def choose(n, r):
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if n < r: return 0
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if r == 0: return 1
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denom = reduce(op.mul, xrange(1, r+1), 1)
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numer = reduce(op.mul, xrange(n, n-r, -1), 1)
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return numer//denom
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# Just to have a less heavyweight name for this extremely common operation
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#
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# We may wish to have more fine-grained control over division by zero behavior
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# in the future (separate specifiable values for 0/0 and x/0 with x != 0),
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# but for now, we just allow the option to handle indeterminate 0/0.
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def fdiv(a, b, zero_over_zero_value = None):
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if zero_over_zero_value != None:
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out = np.full_like(a, zero_over_zero_value)
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where = np.logical_or (a != 0, b != 0)
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else:
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out = None
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where = True
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return np.true_divide(a, b, out = out, where = where)
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