wehub-resource-sync
97 lines
2.3 KiB
Python
Executable File
97 lines
2.3 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 Gamma(RandomVariable):
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"""
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Gamma distribution
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p(x|a, b)
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= b^a x^(a-1) exp(-bx) / gamma(a)
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"""
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def __init__(self, a, b):
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"""
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construct Gamma distribution
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Parameters
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----------
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a : int, float, or np.ndarray
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shape parameter
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b : int, float, or np.ndarray
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rate parameter
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"""
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super().__init__()
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a = np.asarray(a)
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b = np.asarray(b)
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assert a.shape == b.shape
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self.a = a
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self.b = b
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@property
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def a(self):
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return self.parameter["a"]
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@a.setter
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def a(self, a):
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if isinstance(a, (int, float, np.number)):
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if a <= 0:
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raise ValueError("a must be positive")
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self.parameter["a"] = np.asarray(a)
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elif isinstance(a, np.ndarray):
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if (a <= 0).any():
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raise ValueError("a must be positive")
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self.parameter["a"] = a
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else:
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if a is not None:
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raise TypeError(f"{type(a)} is not supported for a")
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self.parameter["a"] = None
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@property
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def b(self):
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return self.parameter["b"]
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@b.setter
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def b(self, b):
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if isinstance(b, (int, float, np.number)):
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if b <= 0:
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raise ValueError("b must be positive")
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self.parameter["b"] = np.asarray(b)
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elif isinstance(b, np.ndarray):
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if (b <= 0).any():
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raise ValueError("b must be positive")
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self.parameter["b"] = b
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else:
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if b is not None:
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raise TypeError(f"{type(b)} is not supported for b")
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self.parameter["b"] = None
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@property
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def ndim(self):
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return self.a.ndim
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@property
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def shape(self):
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return self.a.shape
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@property
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def size(self):
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return self.a.size
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def _pdf(self, X):
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return (
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self.b ** self.a
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* X ** (self.a - 1)
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* np.exp(-self.b * X)
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/ gamma(self.a))
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def _draw(self, sample_size=1):
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return np.random.gamma(
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shape=self.a,
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scale=1 / self.b,
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size=(sample_size,) + self.shape
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)
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