Source code for pina.geometry.intersection_domain

"""Module for Intersection class. """

import torch
from ..label_tensor import LabelTensor
from .operation_interface import OperationInterface
import random


[docs] class Intersection(OperationInterface): def __init__(self, geometries): r""" PINA implementation of Intersection of Domains. Given two sets :math:`A` and :math:`B` then the domain difference is defined as: .. math:: A \cap B = \{x \mid x \in A \land x \in B\}, with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N` the dimension of the geometry space. :param list geometries: A list of geometries from ``pina.geometry`` such as ``EllipsoidDomain`` or ``CartesianDomain``. The intersection will be taken between all the geometries in the list. The resulting geometry will be the intersection of all the geometries in the list. :Example: >>> # Create two ellipsoid domains >>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]}) >>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]}) >>> # Create a Intersection of the ellipsoid domains >>> intersection = Intersection([ellipsoid1, ellipsoid2]) """ super().__init__(geometries)
[docs] def is_inside(self, point, check_border=False): """ Check if a point is inside the ``Intersection`` domain. :param point: Point to be checked. :type point: torch.Tensor :param bool check_border: If ``True``, the border is considered inside. :return: ``True`` if the point is inside the Intersection domain, ``False`` otherwise. :rtype: bool """ flag = 0 for geometry in self.geometries: if geometry.is_inside(point, check_border): flag += 1 return flag == len(self.geometries)
[docs] def sample(self, n, mode="random", variables="all"): """ Sample routine for ``Intersection`` domain. :param int n: Number of points to sample in the shape. :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. :param variables: Variables to be sampled, defaults to ``all``. :type variables: str | list[str] :return: Returns ``LabelTensor`` of n sampled points. :rtype: LabelTensor :Example: >>> # Create two Cartesian domains >>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) >>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]}) >>> # Create a Intersection of the ellipsoid domains >>> intersection = Intersection([cartesian1, cartesian2]) >>> # Sample >>> intersection.sample(n=5) LabelTensor([[1.7697, 1.8654], [1.2841, 1.1208], [1.7289, 1.9843], [1.3332, 1.2448], [1.9902, 1.4458]]) >>> len(intersection.sample(n=5) 5 """ if mode != "random": raise NotImplementedError( f"{mode} is not a valid mode for sampling." ) sampled = [] # calculate the number of points to sample for each geometry and the remainder. remainder = n % len(self.geometries) num_points = n // len(self.geometries) # sample the points # NB. geometries as shuffled since if we sample # multiple times just one point, we would end # up sampling only from the first geometry. iter_ = random.sample(self.geometries, len(self.geometries)) for i, geometry in enumerate(iter_): sampled_points = [] # int(i < remainder) is one only if we have a remainder # different than zero. Notice that len(geometries) is # always smaller than remaider. # makes sure point is uniquely inside 1 shape. while len(sampled_points) < (num_points + int(i < remainder)): sample = geometry.sample(1, mode, variables) if self.is_inside(sample): sampled_points.append(sample) sampled += sampled_points return LabelTensor(torch.cat(sampled), labels=self.variables)