wehub-resource-sync
101 lines
2.4 KiB
Python
Executable File
101 lines
2.4 KiB
Python
Executable File
import numpy as np
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from prml.rv.rv import RandomVariable
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class MultivariateGaussian(RandomVariable):
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"""
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The multivariate Gaussian distribution
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p(x|mu, cov)
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= exp{-0.5 * (x - mu)^T @ cov^-1 @ (x - mu)}
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/ (2pi)^(D/2) / |cov|^0.5
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"""
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def __init__(self, mu=None, cov=None, tau=None):
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super().__init__()
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self.mu = mu
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if cov is not None:
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self.cov = cov
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elif tau is not None:
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self.tau = tau
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else:
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self.cov = None
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self.tau = None
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@property
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def mu(self):
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return self.parameter["mu"]
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@mu.setter
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def mu(self, mu):
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if isinstance(mu, np.ndarray):
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assert mu.ndim == 1
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self.parameter["mu"] = mu
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else:
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assert mu is None
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self.parameter["mu"] = None
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@property
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def cov(self):
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return self.parameter["cov"]
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@cov.setter
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def cov(self, cov):
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if isinstance(cov, np.ndarray):
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assert cov.ndim == 2
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self.tau_ = np.linalg.inv(cov)
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self.parameter["cov"] = cov
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else:
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assert cov is None
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self.tau_ = None
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self.parameter["cov"] = None
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@property
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def tau(self):
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return self.tau_
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@tau.setter
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def tau(self, tau):
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if isinstance(tau, np.ndarray):
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assert tau.ndim == 2
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self.parameter["cov"] = np.linalg.inv(tau)
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self.tau_ = tau
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else:
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assert tau is None
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self.tau_ = None
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self.parameter["cov"] = None
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@property
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def ndim(self):
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if hasattr(self.mu, "ndim"):
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return self.mu.ndim
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else:
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return None
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@property
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def size(self):
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if hasattr(self.mu, "size"):
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return self.mu.size
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else:
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return None
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@property
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def shape(self):
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if hasattr(self.mu, "shape"):
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return self.mu.shape
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else:
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return None
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def _fit(self, X):
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self.mu = np.mean(X, axis=0)
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self.cov = np.atleast_2d(np.cov(X.T, bias=True))
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def _pdf(self, X):
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d = X - self.mu
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return (
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np.exp(-0.5 * np.sum(d @ self.tau * d, axis=-1))
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* np.sqrt(np.linalg.det(self.tau))
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/ np.power(2 * np.pi, 0.5 * self.size))
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def _draw(self, sample_size=1):
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return np.random.multivariate_normal(self.mu, self.cov, sample_size)
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