nltk.featstruct.FeatDict

class documentation

class FeatDict(FeatStruct, dict): (source)

Known subclasses: nltk.grammar.FeatStructNonterminal

Constructor: FeatDict(features, **morefeatures)

A feature structure that acts like a Python dictionary. I.e., a mapping from feature identifiers to feature values, where a feature identifier can be a string or a Feature; and where a feature value can be either a basic value (such as a string or an integer), or a nested feature structure. A feature identifiers for a FeatDict is sometimes called a "feature name".

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

See Also
`FeatStruct` for information about feature paths, reentrance, cyclic feature structures, mutability, freezing, and hashing.

Method	`__contains__`	Return true if a feature with the given name or path exists.
Method	`__deepcopy__`	Undocumented
Method	`__delitem__`	If the feature with the given name or path exists, delete its value; otherwise, raise `KeyError`.
Method	`__getitem__`	If the feature with the given name or path exists, return its value; otherwise, raise `KeyError`.
Method	`__init__`	Create a new feature dictionary, with the specified features.
Method	`__setitem__`	Set the value for the feature with the given name or path to `value`. If `name_or_path` is an invalid path, raise `KeyError`.
Method	`__str__`	Display a multi-line representation of this feature dictionary as an FVM (feature value matrix).
Method	`get`	If the feature with the given name or path exists, return its value; otherwise, return `default`.
Method	`has_key`	Return true if a feature with the given name or path exists.
Method	`update`	Undocumented
Class Variable	`clear`	Undocumented
Class Variable	`pop`	Undocumented
Class Variable	`popitem`	Undocumented
Class Variable	`setdefault`	Undocumented
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	`_str`	No summary
Method	`_values`	Return an iterable of the feature values directly defined by this FeatStruct.
Constant	`_INDEX_ERROR`	Undocumented

Inherited from FeatStruct:

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	`_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 __contains__(self, name_or_path): (source) ¶

Return true if a feature with the given name or path exists.

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

overrides nltk.featstruct.FeatStruct.__deepcopy__

Undocumented

def __delitem__(self, name_or_path): (source) ¶

If the feature with the given name or path exists, delete its value; otherwise, raise KeyError.

def __getitem__(self, name_or_path): (source) ¶

If the feature with the given name or path exists, return its value; otherwise, raise KeyError.

def __init__(self, features=None, **morefeatures): (source) ¶

Create a new feature dictionary, with the specified features.

Parameters
features	The initial value for this feature dictionary. If `features` is a `FeatStruct`, then its features are copied (shallow copy). If `features` is a dict, then a feature is created for each item, mapping its key to its value. If `features` is a string, then it is processed using `FeatStructReader`. If `features` is a list of tuples `(name, val)`, then a feature is created for each tuple.
**morefeatures	Additional features for the new feature dictionary. If a feature is listed under both `features` and `morefeatures`, then the value from `morefeatures` will be used.

def __setitem__(self, name_or_path, value): (source) ¶

Set the value for the feature with the given name or path to value. If name_or_path is an invalid path, raise KeyError.

def __str__(self): (source) ¶

Display a multi-line representation of this feature dictionary as an FVM (feature value matrix).

def get(self, name_or_path, default=None): (source) ¶

If the feature with the given name or path exists, return its value; otherwise, return default.

def has_key(self, name_or_path): (source) ¶

Return true if a feature with the given name or path exists.

def update(self, features=None, **morefeatures): (source) ¶

Undocumented

clear = (source) ¶

Undocumented

pop = (source) ¶

Undocumented

popitem = (source) ¶

Undocumented

setdefault = (source) ¶

Undocumented

def _items(self): (source) ¶

overrides nltk.featstruct.FeatStruct._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.

def _keys(self): (source) ¶

overrides nltk.featstruct.FeatStruct._keys

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

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

overrides nltk.featstruct.FeatStruct._repr

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 _str(self, reentrances, reentrance_ids): (source) ¶

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.
Returns
A list of lines composing a string representation of this feature dictionary.

def _values(self): (source) ¶

overrides nltk.featstruct.FeatStruct._values

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

_INDEX_ERROR = (source) ¶

Undocumented

Value

str('Expected feature name or path.  Got %r.')