wehub-resource-sync
73 lines
1.9 KiB
Python
Executable File
73 lines
1.9 KiB
Python
Executable File
import numpy as np
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from prml.nn.math.gamma import gamma
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from prml.nn.math.log import log
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from prml.nn.math.product import prod
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from prml.nn.math.sum import sum
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from prml.nn.random.random import RandomVariable
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from prml.nn.tensor.tensor import Tensor
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class Dirichlet(RandomVariable):
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"""
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Dirichlet distribution
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Parameters
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----------
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alpha : (..., K) tensor_like
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pseudo-count of each outcome
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axis : int
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axis along which represents each outcome
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data : tensor_like
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realization
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p : RandomVariable
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original distribution of a model
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Attributes
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----------
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n_category : int
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number of categories
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"""
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def __init__(self, alpha, axis=-1, data=None, p=None):
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super().__init__(data, p)
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assert axis == -1
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self.axis = axis
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self.alpha = self._convert2tensor(alpha)
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@property
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def alpha(self):
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return self.parameter["alpha"]
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@alpha.setter
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def alpha(self, alpha):
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self._atleast_ndim(alpha, 1)
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if (alpha.value <= 0).any():
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raise ValueError("alpha must all be positive")
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self.parameter["alpha"] = alpha
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def forward(self):
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if self.alpha.ndim == 1:
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return Tensor(np.random.dirichlet(self.alpha.value), function=self)
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else:
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raise NotImplementedError
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def backward(self):
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raise NotImplementedError
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def _pdf(self, x):
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return (
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gamma(self.alpha.sum(axis=self.axis))
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* prod(
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x ** (self.alpha - 1)
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/ gamma(self.alpha),
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axis=self.axis
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)
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)
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def _log_pdf(self, x):
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return (
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log(gamma(self.alpha.sum(axis=self.axis)))
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+ sum(
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(self.alpha - 1) * log(x) - log(gamma(self.alpha)),
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axis=self.axis
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)
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)
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