def _distance_iter_random(self, other, ntimes):
self._logger.debug(
f"APTED: Heuristic search. {len(self.trees)} {len(other.trees)}")
config = AptedConfig(randomize=False)
yield Apted(self.first_tree, other.first_tree,
config).compute_edit_distance()
config = AptedConfig(randomize=True)
for _ in range(ntimes):
yield Apted(self.first_tree, other.first_tree,
config).compute_edit_distance()
def _distance_iter_random(self, other: "ReactionTreeWrapper",
ntimes: int) -> _FloatIterator:
self._logger.debug(
f"APTED: Heuristic search. {len(self.trees)} {len(other.trees)}")
config = AptedConfig(randomize=False)
yield Apted(self.first_tree, other.first_tree,
config).compute_edit_distance()
config = AptedConfig(randomize=True)
for _ in range(ntimes):
yield Apted(self.first_tree, other.first_tree,
config).compute_edit_distance()
def _distance_iter_exhaustive(
self, other: "ReactionTreeWrapper") -> _FloatIterator:
self._logger.debug(
f"APTED: Exhaustive search. {len(self.trees)} {len(other.trees)}")
config = AptedConfig(randomize=False)
for tree1, tree2 in itertools.product(self.trees, other.trees):
yield Apted(tree1, tree2, config).compute_edit_distance()
def distance_to_with_sorting(self, other: "ReactionTreeWrapper") -> float:
"""
Compute the distance to another tree, by simpling sorting the children
of both trees. This is not guaranteed to return the minimum distance.
:param other: another tree to calculate distance to
:return: the distance
"""
config = AptedConfig(sort_children=True, dist_func=self._dist_func)
return Apted(self.first_tree, other.first_tree, config).compute_edit_distance()
def _distance_iter_semi_exhaustive(self, other):
self._logger.debug(
f"APTED: Semi-exhaustive search. {len(self.trees)} {len(other.trees)}"
)
if len(self.trees) < len(other.trees):
first_wrapper = self
second_wrapper = other
else:
first_wrapper = other
second_wrapper = self
config = AptedConfig(randomize=False)
for tree1 in first_wrapper.trees:
yield Apted(tree1, second_wrapper.first_tree,
config).compute_edit_distance()