wehub-resource-sync
75 lines
2.0 KiB
Python
Executable File
75 lines
2.0 KiB
Python
Executable File
import numpy as np
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from scipy.special import gamma
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from prml.rv.rv import RandomVariable
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np.seterr(all="ignore")
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class Beta(RandomVariable):
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"""
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Beta distribution
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p(mu|n_ones, n_zeros)
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= gamma(n_ones + n_zeros)
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* mu^(n_ones - 1) * (1 - mu)^(n_zeros - 1)
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/ gamma(n_ones) / gamma(n_zeros)
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"""
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def __init__(self, n_zeros, n_ones):
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"""
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construct beta distribution
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Parameters
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----------
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n_zeros : int, float, or np.ndarray
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pseudo count of zeros
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n_ones : int, float, or np.ndarray
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pseudo count of ones
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"""
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super().__init__()
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if not isinstance(n_ones, (int, float, np.number, np.ndarray)):
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raise ValueError(
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"{} is not supported for n_ones"
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.format(type(n_ones))
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)
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if not isinstance(n_zeros, (int, float, np.number, np.ndarray)):
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raise ValueError(
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"{} is not supported for n_zeros"
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.format(type(n_zeros))
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)
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n_ones = np.asarray(n_ones)
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n_zeros = np.asarray(n_zeros)
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if n_ones.shape != n_zeros.shape:
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raise ValueError(
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"the sizes of the arrays don't match: {}, {}"
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.format(n_ones.shape, n_zeros.shape)
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)
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self.n_ones = n_ones
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self.n_zeros = n_zeros
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@property
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def ndim(self):
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return self.n_ones.ndim
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@property
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def size(self):
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return self.n_ones.size
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@property
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def shape(self):
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return self.n_ones.shape
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def _pdf(self, mu):
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return (
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gamma(self.n_ones + self.n_zeros)
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* np.power(mu, self.n_ones - 1)
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* np.power(1 - mu, self.n_zeros - 1)
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/ gamma(self.n_ones)
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/ gamma(self.n_zeros)
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)
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def _draw(self, sample_size=1):
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return np.random.beta(
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self.n_ones, self.n_zeros, size=(sample_size,) + self.shape
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)
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