nltk.probability.MutableProbDist

class documentation

class MutableProbDist(ProbDistI): (source)

Constructor: MutableProbDist(prob_dist, samples, store_logs)

An mutable probdist where the probabilities may be easily modified. This simply copies an existing probdist, storing the probability values in a mutable dictionary and providing an update method.

Method	`__init__`	Creates the mutable probdist based on the given prob_dist and using the list of samples given. These values are stored as log probabilities if the store_logs flag is set.
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.
Method	`update`	Update the probability for the given sample. This may cause the object to stop being the valid probability distribution - the user must ensure that they update the sample probabilities such that all samples have probabilities between 0 and 1 and that all probabilities sum to one.
Instance Variable	`_data`	Undocumented
Instance Variable	`_logs`	Undocumented
Instance Variable	`_sample_dict`	Undocumented
Instance Variable	`_samples`	Undocumented

Inherited from ProbDistI:

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

def __init__(self, prob_dist, samples, store_logs=True): (source) ¶

overrides nltk.probability.ProbDistI.__init__

Creates the mutable probdist based on the given prob_dist and using the list of samples given. These values are stored as log probabilities if the store_logs flag is set.

Parameters
prob_dist:ProbDist	the distribution from which to garner the probabilities
samples:sequence of any	the complete set of samples
store_logs:bool	whether to store the probabilities as logarithms

def logprob(self, sample): (source) ¶

overrides nltk.probability.ProbDistI.logprob

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

def max(self): (source) ¶

overrides nltk.probability.ProbDistI.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.

Returns
any	Undocumented

def prob(self, sample): (source) ¶

overrides nltk.probability.ProbDistI.prob

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

def samples(self): (source) ¶

overrides nltk.probability.ProbDistI.samples

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

Returns
list	Undocumented

def update(self, sample, prob, log=True): (source) ¶

Update the probability for the given sample. This may cause the object to stop being the valid probability distribution - the user must ensure that they update the sample probabilities such that all samples have probabilities between 0 and 1 and that all probabilities sum to one.

Parameters
sample:any	the sample for which to update the probability
prob:float	the new probability
log:bool	is the probability already logged

_data = (source) ¶

Undocumented

_logs = (source) ¶

Undocumented

_sample_dict = (source) ¶

Undocumented

_samples = (source) ¶

Undocumented