109 lines
4.0 KiB
Python
109 lines
4.0 KiB
Python
"""Module for the Difference Operation."""
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import torch
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from .operation_interface import OperationInterface
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from ..label_tensor import LabelTensor
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class Difference(OperationInterface):
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r"""
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Implementation of the difference operation between of a list of domains.
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Given two sets :math:`A` and :math:`B`, define the difference of the two
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sets as:
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.. math::
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A - B = \{x \mid x \in A \land x \not\in B\},
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where :math:`x` is a point in :math:`\mathbb{R}^N`.
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"""
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def __init__(self, geometries):
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"""
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Initialization of the :class:`Difference` class.
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:param list[DomainInterface] geometries: A list of instances of the
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:class:`~pina.domain.DomainInterface` class on which the difference
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operation is performed. The first domain in the list serves as the
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base from which points are sampled, while the remaining domains
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define the regions to be excluded from the base domain to compute
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the difference.
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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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>>> # Define the difference between the domains
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>>> difference = Difference([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 resulting domain.
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:param LabelTensor point: Point to be checked.
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:param bool check_border: If ``True``, the border is considered inside
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the domain. Default is ``False``.
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:return: ``True`` if the point is inside the domain, ``False`` otherwise.
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:rtype: bool
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"""
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for geometry in self.geometries[1:]:
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if geometry.is_inside(point):
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return False
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return self.geometries[0].is_inside(point, check_border)
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def sample(self, n, mode="random", variables="all"):
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"""
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Sampling routine.
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:param int n: Number of points to sample.
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:param str mode: Sampling method. Default is ``random``.
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Available modes: random sampling, ``random``;
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:param list[str] variables: variables to be sampled. Default is ``all``.
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:raises NotImplementedError: If the sampling method is not implemented.
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:return: 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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>>> # Define the difference between the domains
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>>> difference = Difference([cartesian1, cartesian2])
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>>> # Sampling
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>>> difference.sample(n=5)
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LabelTensor([[0.8400, 0.9179],
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[0.9154, 0.5769],
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[1.7403, 0.4835],
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[0.9545, 1.2851],
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[1.3726, 0.9831]])
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>>> len(difference.sample(n=5)
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5
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"""
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if mode not in self.sample_modes:
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raise NotImplementedError(
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f"{mode} is not a valid mode for sampling."
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)
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sampled = []
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# sample the points
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while len(sampled) < n:
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# get sample point from first geometry
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point = self.geometries[0].sample(1, mode, variables)
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is_inside = False
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# check if point is inside any other geometry
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for geometry in self.geometries[1:]:
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# if point is inside any other geometry, break
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if geometry.is_inside(point):
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is_inside = True
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break
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# if point is not inside any other geometry, add to sampled
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if not is_inside:
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sampled.append(point)
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return LabelTensor(torch.cat(sampled), labels=self.variables)
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