nltk.probability.DictionaryProbDist

class nltk.probability.DictionaryProbDist

Bases: ProbDistI

A probability distribution whose probabilities are directly specified by a given dictionary. The given dictionary maps samples to probabilities.

__init__(prob_dict=None, log=False, normalize=False)

Construct a new probability distribution from the given dictionary, which maps values to probabilities (or to log probabilities, if log is true). If normalize is true, then the probability values are scaled by a constant factor such that they sum to 1.

If called without arguments, the resulting probability distribution assigns zero probability to all values.

prob(sample)

Return the probability for a given sample. Probabilities are always real numbers in the range [0, 1].

Parameters

sample (any) – The sample whose probability should be returned.

Return type

float

logprob(sample)

Return the base 2 logarithm of the probability for a given sample.

Parameters

sample (any) – The sample whose probability should be returned.

Return type

float

max()

Return the sample with the greatest probability. If two or more samples have the same probability, return one of them; which sample is returned is undefined.

Return type

any

samples()

Return a list of all samples that have nonzero probabilities. Use prob to find the probability of each sample.

Return type

list

SUM_TO_ONE = True

True if the probabilities of the samples in this probability distribution will always sum to one.

discount()

Return the ratio by which counts are discounted on average: c*/c

Return type

float

generate()

Return a randomly selected sample from this probability distribution. The probability of returning each sample samp is equal to self.prob(samp).