Package nltk :: Module probability :: Class ProbDistI

Class ProbDistI

object --+
         |
        ProbDistI

Known Subclasses:

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. ProbDists are often used to model the probability distribution of the experiment used to generate a frequency distribution.

Instance Methods

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__init__(self)
x.__init__(...) initializes x; see x.__class__.__doc__ for signature

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float

prob(self, sample)
Returns: the probability for a given sample.

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float

logprob(self, sample)
Returns: the base 2 logarithm of the probability for a given sample.

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any

max(self)
Returns: the sample with the greatest probability.

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list

samples(self)
Returns: A list of all samples that have nonzero probabilities.

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float

discount(self)
Returns: The ratio by which counts are discounted on average: c*/c

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generate(self)
Returns: A randomly selected sample from this probabilitiy distribution.

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Inherited from object: __delattr__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __str__

Class Variables

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

Properties

[hide private]

Inherited from object: __class__

Method Details

[hide private]

init(self)
(Constructor)

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x.__init__(...) initializes x; see x.__class__.__doc__ for signature

Overrides: object.__init__: (inherited documentation)

prob(self, sample)

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Parameters:

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

Returns: float

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

logprob(self, sample)

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Parameters:

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

Returns: float

the base 2 logarithm of the probability for a given sample. Log probabilities range from negitive infinity to zero.

max(self)

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Returns: any: 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.

samples(self)

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Returns: list: A list of all samples that have nonzero probabilities. Use prob to find the probability of each sample.

discount(self)

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Returns: float: The ratio by which counts are discounted on average: c*/c

generate(self)

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Returns:: A randomly selected sample from this probabilitiy distribution. The probability of returning each sample samp is equal to self.prob(samp).

Class ProbDistI

__init__(self) (Constructor)

prob(self, sample)

logprob(self, sample)

max(self)

samples(self)

discount(self)

generate(self)

init(self)
(Constructor)