mikoto10032--deeplearning
wehub-resource-sync
60 行
1.6 KiB
Python
可执行文件
60 行
1.6 KiB
Python
可执行文件
import numpy as np
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from prml.nn.math.exp import exp
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from prml.nn.math.log import log
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from prml.nn.random.random import RandomVariable
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from prml.nn.tensor.constant import Constant
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from prml.nn.tensor.tensor import Tensor
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class Exponential(RandomVariable):
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"""
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Exponential distribution aka negative exponential distribution
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p(x|rate) = rate * exp(-rate * x)
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rate > 0
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Parameters
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----------
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rate : tensor_like
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rate parameter
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data : tensor_like
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realization of this distribution
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p : RandomVariable
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original distribution of a model
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"""
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def __init__(self, rate, data=None, p=None):
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super().__init__(data, p)
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rate = self._convert2tensor(rate)
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self.rate = rate
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@property
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def rate(self):
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return self.parameter["rate"]
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@rate.setter
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def rate(self, rate):
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try:
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ispositive = (rate.value > 0).all()
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except AttributeError:
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ispositive = (rate.value > 0)
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if not ispositive:
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raise ValueError("value of rate must be positive")
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self.parameter["rate"] = rate
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def forward(self):
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eps = np.random.standard_exponential(size=self.rate.shape)
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self.output = eps / self.rate.value
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if isinstance(self.rate, Constant):
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return Constant(self.output)
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return Tensor(self.output, self)
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def backward(self, delta):
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drate = -delta * self.output / self.rate.value
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self.rate.backward(drate)
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def _pdf(self, x):
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return self.rate * exp(-self.rate * x)
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def _log_pdf(self, x):
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return -self.rate * x + log(self.rate)
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