8b7b61b3bd
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
109 lines
4.3 KiB
Python
109 lines
4.3 KiB
Python
"""Module for Intersection class. """
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import torch
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from ..label_tensor import LabelTensor
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from .operation_interface import OperationInterface
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import random
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class Intersection(OperationInterface):
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def __init__(self, geometries):
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r"""
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PINA implementation of Intersection of Domains.
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Given two sets :math:`A` and :math:`B` then the
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domain difference is defined as:
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.. math::
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A \cap B = \{x \mid x \in A \land x \in B\},
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with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N`
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the dimension of the geometry space.
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:param list geometries: A list of geometries from ``pina.geometry``
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such as ``EllipsoidDomain`` or ``CartesianDomain``. The intersection
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will be taken between all the geometries in the list. The resulting
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geometry will be the intersection of all the geometries in the list.
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:Example:
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>>> # Create two ellipsoid domains
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>>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]})
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>>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]})
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>>> # Create a Intersection of the ellipsoid domains
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>>> intersection = Intersection([ellipsoid1, ellipsoid2])
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"""
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super().__init__(geometries)
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def is_inside(self, point, check_border=False):
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"""
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Check if a point is inside the ``Intersection`` domain.
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:param point: Point to be checked.
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:type point: torch.Tensor
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:param bool check_border: If ``True``, the border is considered inside.
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:return: ``True`` if the point is inside the Intersection domain, ``False`` otherwise.
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:rtype: bool
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"""
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flag = 0
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for geometry in self.geometries:
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if geometry.is_inside(point, check_border):
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flag += 1
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return flag == len(self.geometries)
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def sample(self, n, mode='random', variables='all'):
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"""
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Sample routine for ``Intersection`` domain.
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:param int n: Number of points to sample in the shape.
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:param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``.
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:param variables: Variables to be sampled, defaults to ``all``.
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:type variables: str | list[str]
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:return: Returns ``LabelTensor`` of n sampled points.
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:rtype: LabelTensor
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:Example:
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>>> # Create two Cartesian domains
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>>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]})
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>>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]})
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>>> # Create a Intersection of the ellipsoid domains
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>>> intersection = Intersection([cartesian1, cartesian2])
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>>> # Sample
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>>> intersection.sample(n=5)
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LabelTensor([[1.7697, 1.8654],
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[1.2841, 1.1208],
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[1.7289, 1.9843],
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[1.3332, 1.2448],
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[1.9902, 1.4458]])
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>>> len(intersection.sample(n=5)
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5
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"""
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if mode != 'random':
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raise NotImplementedError(
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f'{mode} is not a valid mode for sampling.')
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sampled = []
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# calculate the number of points to sample for each geometry and the remainder.
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remainder = n % len(self.geometries)
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num_points = n // len(self.geometries)
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# sample the points
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# NB. geometries as shuffled since if we sample
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# multiple times just one point, we would end
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# up sampling only from the first geometry.
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iter_ = random.sample(self.geometries, len(self.geometries))
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for i, geometry in enumerate(iter_):
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sampled_points = []
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# int(i < remainder) is one only if we have a remainder
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# different than zero. Notice that len(geometries) is
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# always smaller than remaider.
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# makes sure point is uniquely inside 1 shape.
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while len(sampled_points) < (num_points + int(i < remainder)):
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sample = geometry.sample(1, mode, variables)
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if self.is_inside(sample):
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sampled_points.append(sample)
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sampled += sampled_points
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return LabelTensor(torch.cat(sampled), labels=self.variables)
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