nltk.probability.DictionaryConditionalProbDist

class nltk.probability.DictionaryConditionalProbDist[source]

Bases: ConditionalProbDistI

An alternative ConditionalProbDist that simply wraps a dictionary of ProbDists rather than creating these from FreqDists.

__init__(probdist_dict)[source]
Parameters

probdist_dict (dict any -> probdist) – a dictionary containing the probdists indexed by the conditions

__new__(**kwargs)
clear() None.  Remove all items from D.
conditions()

Return a list of the conditions that are represented by this ConditionalProbDist. Use the indexing operator to access the probability distribution for a given condition.

Return type

list

copy() a shallow copy of D
fromkeys(value=None, /)

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() a set-like object providing a view on D's items
keys() a set-like object providing a view on D's keys
pop(k[, d]) v, remove specified key and return the corresponding value.

If key is not found, default is returned if given, otherwise KeyError is raised

popitem()

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() an object providing a view on D's values