from abc import ABC, abstractmethod
import numpy as np
[docs]
class Prior(ABC):
"""Abstract base class for PyVBMC prior distributions."""
[docs]
def log_pdf(self, x, keepdims=True):
"""Compute the log-pdf of the distribution.
Parameters
----------
x : np.ndarray
The array of input point(s), of dimension `(D,)` or `(n, D)`, where
`d` is the distribution dimension.
keepdims : bool
Whether to keep the input dimensions and return an array of shape
`(1, D)`, or discard them and return an array of shape `(D,)`.
Returns
-------
log_pdf : np.ndarray
The log-density of the prior at the input point(s), of dimension
`(n, 1)` or `(n,)` (depending on ``keepdims``).
"""
x_orig_shape = x.shape
x = np.atleast_2d(x)
n, D = x.shape
if self.D == 1 and n == 1:
x = x.T
elif D != self.D:
raise ValueError(
f"x should have shape ({self.D},) or (n, {self.D}) but has shape {x_orig_shape}."
)
log_pdf = self._log_pdf(x)
if keepdims:
return log_pdf
else:
return log_pdf.ravel()
[docs]
def pdf(self, x, keepdims=True):
"""Compute the pdf of the distribution.
Parameters
----------
x : np.ndarray
The array of input point(s), of dimension `(D,)` or `(n, D)`, where
`D` is the distribution dimension.
keepdims : bool
Whether to keep the input dimensions and return an array of shape
`(1, D)`, or discard them and return an array of shape `(D,)`.
returns
-------
pdf : np.ndarray
The density of the prior at the input point(s), of dimension `(n,
1)` or `(n,)` (depending on ``keepdims``).
"""
return np.exp(self.log_pdf(x, keepdims=keepdims))
[docs]
def support(self):
r"""Returns the support of the distribution.
Used to test that the distribution integrates to one, so it is also
acceptable to return a box which bounds the support of the
distribution.
Returns
-------
lb, ub : tuple(np.ndarray, np.ndarray)
A tuple of lower and upper bounds of the support, such that
[``lb[i]``, ``ub[i]``] bounds the support of the ``i``\ th marginal.
"""
if hasattr(self, "_support"):
return self._support()
else:
return np.full(self.D, -np.inf), np.full(self.D, np.inf)
@abstractmethod
def __init__(self):
"""Initialize the distribution."""
self.D = 1
@abstractmethod
def _log_pdf(self, x):
"""Compute the log-pdf of the distribution.
This private method is wrapped by ``self.log_pdf()``, which handles
input validation and output shape.
Parameters
----------
x : np.ndarray
The array of input point(s), of dimension `(D,)` or `(n, D)`, where
`d` is the distribution dimension.
Returns
-------
log_pdf : np.ndarray
The log-density of the prior at the input point(s), of dimension
`(n, 1)`.
"""
pass
[docs]
@abstractmethod
def sample(self, n):
"""Sample random variables from the distribution.
Parameters
----------
n : int
The number of points to sample.
Returns
-------
rvs : np.ndarray
The samples points, of shape `(n, D)`, where `D` is the dimension.
"""
pass
@classmethod
@abstractmethod
def _generic(cls, D=1):
"""Return a generic instance of the class (used for tests)."""
return cls(D=D)
