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# Natural Language Toolkit: Text Trees
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Edward Loper <[email protected]>
# Steven Bird <[email protected]>
# Peter Ljunglöf <[email protected]>
# Tom Aarsen <>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
from nltk.internals import raise_unorderable_types
from nltk.probability import ProbabilisticMixIn
from nltk.tree.immutable import ImmutableProbabilisticTree
from nltk.tree.tree import Tree
######################################################################
## Probabilistic trees
######################################################################
class ProbabilisticTree(Tree, ProbabilisticMixIn):
def __init__(self, node, children=None, **prob_kwargs):
Tree.__init__(self, node, children)
ProbabilisticMixIn.__init__(self, **prob_kwargs)
# We have to patch up these methods to make them work right:
def _frozen_class(self):
return ImmutableProbabilisticTree
def __repr__(self):
return f"{Tree.__repr__(self)} (p={self.prob()!r})"
def __str__(self):
return f"{self.pformat(margin=60)} (p={self.prob():.6g})"
def copy(self, deep=False):
if not deep:
return type(self)(self._label, self, prob=self.prob())
else:
return type(self).convert(self)
@classmethod
def convert(cls, val):
if isinstance(val, Tree):
children = [cls.convert(child) for child in val]
if isinstance(val, ProbabilisticMixIn):
return cls(val._label, children, prob=val.prob())
else:
return cls(val._label, children, prob=1.0)
else:
return val
def __eq__(self, other):
return self.__class__ is other.__class__ and (
self._label,
list(self),
self.prob(),
) == (other._label, list(other), other.prob())
def __lt__(self, other):
if not isinstance(other, Tree):
raise_unorderable_types("<", self, other)
if self.__class__ is other.__class__:
return (self._label, list(self), self.prob()) < (
other._label,
list(other),
other.prob(),
)
else:
return self.__class__.__name__ < other.__class__.__name__
__all__ = ["ProbabilisticTree"]
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