class nltk.ConditionalProbDistI[source]

A collection of probability distributions for a single experiment run under different conditions. Conditional probability distributions are used to estimate the likelihood of each sample, given the condition under which the experiment was run. For example, a conditional probability distribution could be used to estimate the probability of each word type in a document, given the length of the word type. Formally, a conditional probability distribution can be defined as a function that maps from each condition to the ProbDist for the experiment under that condition.


clear(() -> None.  Remove all items from D.)
conditions() Return a list of the conditions that are represented by this ConditionalProbDist.
copy(() -> a shallow copy of D)
fromkeys(...) v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
keys(() -> list of D’s keys)
pop((k[,d]) -> v, ...) If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...) 2-tuple; but raise KeyError if D is empty.
setdefault((k[,d]) -> D.get(k,d), ...)
unicode_repr() Return a string representation of this ConditionalProbDist.
update(([E, ...) If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)