本文整理汇总了Python中nltk.sem.logic.Expression.uncurry方法的典型用法代码示例。如果您正苦于以下问题：Python Expression.uncurry方法的具体用法？Python Expression.uncurry怎么用？Python Expression.uncurry使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nltk.sem.logic.Expression的用法示例。
在下文中一共展示了Expression.uncurry方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _get_transitions
# 需要导入模块: from nltk.sem.logic import Expression [as 别名]
# 或者: from nltk.sem.logic.Expression import uncurry [as 别名]
def _get_transitions(self,
expression: Expression,
current_transitions: List[str]) -> List[str]:
# The way we handle curried functions in here is a bit of a mess, but it works. For any
# function that takes more than one argument, the NLTK Expression object will be curried,
# and so the standard "visitor" pattern used by NLTK will result in action sequences that
# are also curried. We need to detect these curried functions and uncurry them in the
# action sequence. We do that by keeping around a dictionary mapping multi-argument
# functions to the number of arguments they take. When we see a multi-argument function,
# we check to see if we're at the top-level, first instance of that function by checking
# its number of arguments with NLTK's `uncurry()` function. If it is, we output an action
# using those arguments. Otherwise, we're at an intermediate node of a curried function,
# and we squelch the action that would normally be generated.
# TODO(mattg): There might be some way of removing the need for `curried_functions` here,
# using instead the `argument_types()` function I added to `ComplexType`, but my guess is
# that it would involve needing to modify nltk, and I don't want to bother with figuring
# that out right now.
curried_functions = self._get_curried_functions()
expression_type = expression.type
try:
# ``Expression.visit()`` takes two arguments: the first one is a function applied on
# each sub-expression and the second is a combinator that is applied to the list of
# values returned from the function applications. We just want the list of all
# sub-expressions here.
sub_expressions = expression.visit(lambda x: x, lambda x: x)
transformed_types = [sub_exp.type for sub_exp in sub_expressions]
if isinstance(expression, LambdaExpression):
# If the expression is a lambda expression, the list of sub expressions does not
# include the "lambda x" term. We're adding it here so that we will see transitions
# like
# <e,d> -> [\x, d] instead of
# <e,d> -> [d]
transformed_types = ["lambda x"] + transformed_types
elif isinstance(expression, ApplicationExpression):
function, arguments = expression.uncurry()
function_type = function.type
if function_type in curried_functions:
expected_num_arguments = curried_functions[function_type]
if len(arguments) == expected_num_arguments:
# This is the initial application of a curried function. We'll use this
# node in the expression to generate the action for this function, using
# all of its arguments.
transformed_types = [function.type] + [argument.type for argument in arguments]
else:
# We're at an intermediate node. We'll set `transformed_types` to `None`
# to indicate that we need to squelch this action.
transformed_types = None
if transformed_types:
transition = f"{expression_type} -> {transformed_types}"
current_transitions.append(transition)
for sub_expression in sub_expressions:
self._get_transitions(sub_expression, current_transitions)
except NotImplementedError:
# This means that the expression is a leaf. We simply make a transition from its type to itself.
original_name = str(expression)
if original_name in self.reverse_name_mapping:
original_name = self.reverse_name_mapping[original_name]
transition = f"{expression_type} -> {original_name}"
current_transitions.append(transition)
return current_transitions