Source code for

from __future__ import division

import numpy as np

from .euclidean import EuclideanDomain

[docs]class RandomDomain(EuclideanDomain): r"""Abstract base class for domains with an associated sampling measure """
[docs] def pdf(self, X): r""" Probability density function associated with the domain This evaluates a probability density function :math:`p:\mathcal{D}\to \mathbb{R}_*` at the requested points. By definition, this density function is normalized to have measure over the domain to be one: .. math:: \int_{\mathbf{x} \in \mathcal{D}} p(\mathbf{x}) \mathrm{d} \mathbf{x}. Parameters ---------- X: array-like, either (m,) or (N,m) points to evaluate the density function at Returns ------- array-like (N,) evaluation of the density function """ X = np.atleast_2d(np.array(X)) return self._pdf(X)
def _pdf(self, x): raise NotImplementedError