4 examples of 'np random binomial' in Python

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144def binomial(trials, p, shape=[]):
145 """binomial(trials, p) or binomial(trials, p, [n, m, ...]) returns array of binomially distributed random integers.
146
147 trials is the number of trials in the binomial distribution.
148 p is the probability of an event in each trial of the binomial distribution."""
149 if shape == []:
150 shape = None
151 return mt.binomial(trials, p, shape)
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76def test_binomial(self):
77 assert self.rs.binomial(10, .5) >= 0
78 assert self.rs.binomial(1000, .5) >= 0
123def binomial(big, small):
124 '''
125 Get the binomial coefficient (big small).
126
127 This is used in combinatorical calculations. More information:
128 http://en.wikipedia.org/wiki/Binomial_coefficient
129 '''
130 if big == small:
131 return 1
132 if big < small:
133 return 0
134 else:
135 return (math.factorial(big) // math.factorial(big - small)
136 // math.factorial(small))
658def binomial_like(self, x, n, p, name='binomial', prior=False):
659 """Binomial log-likelihood"""
660
661 if not shape(n) == shape(p): raise ParameterError, 'Parameters must have same dimensions'
662
663 if ndim(n) > 1:
664
665 return sum([self.binomial_like(y, _n, _p, name, prior) for y, _n, _p in zip(x, n, p)])
666
667 else:
668
669 # Ensure valid values of parameters
670 self.constrain(p, 0, 1)
671 self.constrain(n, lower=x)
672 self.constrain(x, 0)
673
674 # Enforce array type
675 x = atleast_1d(x)
676 p = resize(p, shape(x))
677 n = resize(n, shape(x))
678
679 # Goodness-of-fit
680 if self._gof and not prior:
681
682 try:
683 self._like_names.append(name)
684 except AttributeError:
685 pass
686
687 expval = p * n
688
689 # Simulated values
690 y = array([rbinomial(_n, _p) for _n, _p in zip(n, p)])
691
692 # Generate GOF points
693 gof_points = sum(transpose([self.loss(x, expval), self.loss(y, expval)]))
694
695 self._gof_loss.append(gof_points)
696
697 return sum([fbinomial(xx, nn, pp) for xx, nn, pp in zip(x, n, p)])

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