nltk.probability.ProbDistI

class documentation

class ProbDistI: (source)

Known subclasses: nltk.probability.CrossValidationProbDist, nltk.probability.DictionaryProbDist, nltk.probability.HeldoutProbDist, nltk.probability.KneserNeyProbDist, nltk.probability.LidstoneProbDist, nltk.probability.MLEProbDist, nltk.probability.MutableProbDist, nltk.probability.RandomProbDist, nltk.probability.SimpleGoodTuringProbDist, nltk.probability.UniformProbDist, nltk.probability.WittenBellProbDist

Constructor: ProbDistI()

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A probability distribution for the outcomes of an experiment. A probability distribution specifies how likely it is that an experiment will have any given outcome. For example, a probability distribution could be used to predict the probability that a token in a document will have a given type. Formally, a probability distribution can be defined as a function mapping from samples to nonnegative real numbers, such that the sum of every number in the function's range is 1.0. A ProbDist is often used to model the probability distribution of the experiment used to generate a frequency distribution.

Method	`__init__`	Classes inheriting from ProbDistI should implement __init__.
Method	`discount`	Return the ratio by which counts are discounted on average: c*/c
Method	`generate`	Return a randomly selected sample from this probability distribution. The probability of returning each sample `samp` is equal to `self.prob(samp)`.
Method	`logprob`	Return the base 2 logarithm of the probability for a given sample.
Method	`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.
Method	`prob`	Return the probability for a given sample. Probabilities are always real numbers in the range [0, 1].
Method	`samples`	Return a list of all samples that have nonzero probabilities. Use `prob` to find the probability of each sample.
Constant	`SUM_TO_ONE`	True if the probabilities of the samples in this probability distribution will always sum to one.

@abstractmethod
def __init__(self): (source) ¶

overridden in nltk.probability.CrossValidationProbDist, nltk.probability.DictionaryProbDist, nltk.probability.HeldoutProbDist, nltk.probability.KneserNeyProbDist, nltk.probability.LidstoneProbDist, nltk.probability.MLEProbDist, nltk.probability.MutableProbDist, nltk.probability.RandomProbDist, nltk.probability.SimpleGoodTuringProbDist, nltk.probability.UniformProbDist, nltk.probability.WittenBellProbDist

Classes inheriting from ProbDistI should implement __init__.

def discount(self): (source) ¶

overridden in nltk.probability.CrossValidationProbDist, nltk.probability.HeldoutProbDist, nltk.probability.KneserNeyProbDist, nltk.probability.LidstoneProbDist, nltk.probability.SimpleGoodTuringProbDist, nltk.probability.WittenBellProbDist

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

Returns
float	Undocumented

def generate(self): (source) ¶

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

def logprob(self, sample): (source) ¶

overridden in nltk.probability.DictionaryProbDist, nltk.probability.MutableProbDist

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

Parameters
sample:any	The sample whose probability should be returned.
Returns
float	Undocumented

@abstractmethod
def max(self): (source) ¶

overridden in nltk.probability.DictionaryProbDist, nltk.probability.HeldoutProbDist, nltk.probability.KneserNeyProbDist, nltk.probability.LidstoneProbDist, nltk.probability.MLEProbDist, nltk.probability.MutableProbDist, nltk.probability.RandomProbDist, nltk.probability.SimpleGoodTuringProbDist, nltk.probability.UniformProbDist, nltk.probability.WittenBellProbDist

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.

Returns
any	Undocumented

@abstractmethod
def prob(self, sample): (source) ¶

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.
Returns
float	Undocumented

@abstractmethod
def samples(self): (source) ¶

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

Returns
list	Undocumented

SUM_TO_ONE: bool = (source) ¶

overridden in nltk.probability.CrossValidationProbDist, nltk.probability.HeldoutProbDist, nltk.probability.LidstoneProbDist, nltk.probability.SimpleGoodTuringProbDist

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

Value

True