nltk.featstruct.FeatStruct

class documentation

class FeatStruct(SubstituteBindingsI): (source)

Known subclasses: nltk.featstruct.FeatDict, nltk.featstruct.FeatList

Constructor: FeatStruct(features, **morefeatures)

A mapping from feature identifiers to feature values, where each feature value is either a basic value (such as a string or an integer), or a nested feature structure. There are two types of feature structure:

feature dictionaries, implemented by FeatDict, act like Python dictionaries. Feature identifiers may be strings or instances of the Feature class.

feature lists, implemented by FeatList, act like Python lists. Feature identifiers are integers.

Feature structures may be indexed using either simple feature identifiers or 'feature paths.' A feature path is a sequence of feature identifiers that stand for a corresponding sequence of indexing operations. In particular, fstruct[(f1,f2,...,fn)] is equivalent to fstruct[f1][f2]...[fn].

Feature structures may contain reentrant feature structures. A "reentrant feature structure" is a single feature structure object that can be accessed via multiple feature paths. Feature structures may also be cyclic. A feature structure is "cyclic" if there is any feature path from the feature structure to itself.

Two feature structures are considered equal if they assign the same values to all features, and have the same reentrancies.

By default, feature structures are mutable. They may be made immutable with the freeze() method. Once they have been frozen, they may be hashed, and thus used as dictionary keys.

Method	`__deepcopy__`	Undocumented
Method	`__eq__`	Return true if `self` and `other` are both feature structures, assign the same values to all features, and contain the same reentrances. I.e., return `self.equal_values(other, check_reentrance=True)`.
Method	`__hash__`	If this feature structure is frozen, return its hash value; otherwise, raise `TypeError`.
Method	`__lt__`	Undocumented
Method	`__ne__`	Undocumented
Method	`__new__`	Construct and return a new feature structure. If this constructor is called directly, then the returned feature structure will be an instance of either the `FeatDict` class or the `FeatList` class.
Method	`__repr__`	Display a single-line representation of this feature structure, suitable for embedding in other representations.
Method	`copy`	Return a new copy of `self`. The new copy will not be frozen.
Method	`cyclic`	Return True if this feature structure contains itself.
Method	`equal_values`	Return True if `self` and `other` assign the same value to to every feature. In particular, return true if `self[p]==other[p]` for every feature path p such that `self[p]` or `other[p]` is a base value (i.e., not a nested feature structure).
Method	`freeze`	Make this feature structure, and any feature structures it contains, immutable. Note: this method does not attempt to 'freeze' any feature value that is not a `FeatStruct`; it is recommended that you use only immutable feature values.
Method	`frozen`	Return True if this feature structure is immutable. Feature structures can be made immutable with the `freeze()` method. Immutable feature structures may not be made mutable again, but new mutable copies can be produced with the ...
Method	`remove_variables`	Return the feature structure that is obtained by deleting any feature whose value is a `Variable`.
Method	`rename_variables`	No summary
Method	`retract_bindings`	No summary
Method	`substitute_bindings`	No summary
Method	`subsumes`	Return True if `self` subsumes `other`. I.e., return true If unifying `self` with `other` would result in a feature structure equal to `other`.
Method	`unify`	Undocumented
Method	`variables`	No summary
Method	`walk`	Return an iterator that generates this feature structure, and each feature structure it contains. Each feature structure will be generated exactly once.
Method	`_calculate_hashvalue`	Return a hash value for this feature structure.
Method	`_equal`	Return True iff self and other have equal values.
Method	`_find_reentrances`	Return a dictionary that maps from the `id` of each feature structure contained in `self` (including `self`) to a boolean value, indicating whether it is reentrant or not.
Method	`_freeze`	Make this feature structure, and any feature structure it contains, immutable.
Method	`_items`	Return an iterable of (fid,fval) pairs, where fid is a feature identifier and fval is the corresponding feature value, for all features defined by this FeatStruct.
Method	`_keys`	Return an iterable of the feature identifiers used by this FeatStruct.
Method	`_repr`	Return a string representation of this feature structure.
Method	`_values`	Return an iterable of the feature values directly defined by this FeatStruct.
Method	`_walk`	Undocumented
Constant	`_FROZEN_ERROR`	Undocumented
Instance Variable	`_frozen`	frozen or not. Once this flag is set, it should never be un-set; and no further modification should be made to this feature structue.
Instance Variable	`_hash`	Undocumented

def __deepcopy__(self, memo): (source) ¶

overridden in nltk.featstruct.FeatDict, nltk.featstruct.FeatList

Undocumented

def __eq__(self, other): (source) ¶

Return true if self and other are both feature structures, assign the same values to all features, and contain the same reentrances. I.e., return self.equal_values(other, check_reentrance=True).

See Also
`equal_values()`

def __hash__(self): (source) ¶

overridden in nltk.grammar.FeatStructNonterminal

If this feature structure is frozen, return its hash value; otherwise, raise TypeError.

def __lt__(self, other): (source) ¶

Undocumented

def __ne__(self, other): (source) ¶

Undocumented

def __new__(cls, features=None, **morefeatures): (source) ¶

Construct and return a new feature structure. If this constructor is called directly, then the returned feature structure will be an instance of either the FeatDict class or the FeatList class.

Parameters
cls	Undocumented
features	The initial feature values for this feature structure: FeatStruct(string) -> FeatStructReader().read(string) FeatStruct(mapping) -> FeatDict(mapping) FeatStruct(sequence) -> FeatList(sequence) FeatStruct() -> FeatDict()
**morefeatures	If `features` is a mapping or None, then `morefeatures` provides additional features for the `FeatDict` constructor.

def __repr__(self): (source) ¶

Display a single-line representation of this feature structure, suitable for embedding in other representations.

def copy(self, deep=True): (source) ¶

Return a new copy of self. The new copy will not be frozen.

Parameters
deep	If true, create a deep copy; if false, create a shallow copy.

def cyclic(self): (source) ¶

Return True if this feature structure contains itself.

def equal_values(self, other, check_reentrance=False): (source) ¶

Return True if self and other assign the same value to to every feature. In particular, return true if self[p]==other[p] for every feature path p such that self[p] or other[p] is a base value (i.e., not a nested feature structure).

Parameters
other	Undocumented
check_reentrance	If True, then also return False if there is any difference between the reentrances of `self` and `other`.
Note
the `==` is equivalent to `equal_values()` with `check_reentrance=True`.

def freeze(self): (source) ¶

Make this feature structure, and any feature structures it contains, immutable. Note: this method does not attempt to 'freeze' any feature value that is not a FeatStruct; it is recommended that you use only immutable feature values.

def frozen(self): (source) ¶

Return True if this feature structure is immutable. Feature structures can be made immutable with the freeze() method. Immutable feature structures may not be made mutable again, but new mutable copies can be produced with the copy() method.

def remove_variables(self): (source) ¶

Return the feature structure that is obtained by deleting any feature whose value is a Variable.

Returns
FeatStruct	Undocumented

def rename_variables(self, vars=None, used_vars=(), new_vars=None): (source) ¶

See Also
`nltk.featstruct.rename_variables()`

def retract_bindings(self, bindings): (source) ¶

See Also
`nltk.featstruct.retract_bindings()`

def substitute_bindings(self, bindings): (source) ¶

overrides nltk.sem.logic.SubstituteBindingsI.substitute_bindings

See Also
`nltk.featstruct.substitute_bindings()`

def subsumes(self, other): (source) ¶

Return True if self subsumes other. I.e., return true If unifying self with other would result in a feature structure equal to other.

def unify(self, other, bindings=None, trace=False, fail=None, rename_vars=True): (source) ¶

Undocumented

def variables(self): (source) ¶

overrides nltk.sem.logic.SubstituteBindingsI.variables

See Also
`nltk.featstruct.find_variables()`

def walk(self): (source) ¶

Return an iterator that generates this feature structure, and each feature structure it contains. Each feature structure will be generated exactly once.

def _calculate_hashvalue(self, visited): (source) ¶

Return a hash value for this feature structure.

Parameters
visited	A set containing the ids of all feature structures we've already visited while hashing.
Unknown Field: require
`self` must be frozen.

def _equal(self, other, check_reentrance, visited_self, visited_other, visited_pairs): (source) ¶

Return True iff self and other have equal values.

Parameters
other	Undocumented
check_reentrance	Undocumented
visited_self	A set containing the ids of all `self` feature structures we've already visited.
visited_other	A set containing the ids of all `other` feature structures we've already visited.
visited_pairs	A set containing `(selfid, otherid)` pairs for all pairs of feature structures we've already visited.

def _find_reentrances(self, reentrances): (source) ¶

Return a dictionary that maps from the id of each feature structure contained in self (including self) to a boolean value, indicating whether it is reentrant or not.

def _freeze(self, visited): (source) ¶

Make this feature structure, and any feature structure it contains, immutable.

Parameters
visited	A set containing the ids of all feature structures we've already visited while freezing.

def _items(self): (source) ¶

overridden in nltk.featstruct.FeatDict, nltk.featstruct.FeatList

Return an iterable of (fid,fval) pairs, where fid is a feature identifier and fval is the corresponding feature value, for all features defined by this FeatStruct.

def _keys(self): (source) ¶

overridden in nltk.featstruct.FeatDict, nltk.featstruct.FeatList

Return an iterable of the feature identifiers used by this FeatStruct.

def _repr(self, reentrances, reentrance_ids): (source) ¶

overridden in nltk.featstruct.FeatDict, nltk.featstruct.FeatList

Return a string representation of this feature structure.

Parameters
reentrances	A dictionary that maps from the `id` of each feature value in self, indicating whether that value is reentrant or not.
reentrance_ids	A dictionary mapping from each `id` of a feature value to a unique identifier. This is modified by `repr`: the first time a reentrant feature value is displayed, an identifier is added to `reentrance_ids` for it.

def _values(self): (source) ¶

overridden in nltk.featstruct.FeatDict, nltk.featstruct.FeatList

Return an iterable of the feature values directly defined by this FeatStruct.

def _walk(self, visited): (source) ¶

Undocumented

_FROZEN_ERROR: str = (source) ¶

Undocumented

Value

'Frozen FeatStructs may not be modified.'

_frozen: bool = (source) ¶

frozen or not. Once this flag is set, it should never be un-set; and no further modification should be made to this feature structue.

_hash = (source) ¶

Undocumented