metasyn.distribution.categorical.MultinoulliDistribution

class metasyn.distribution.categorical.MultinoulliDistribution(labels, probs)

Categorical distribution using labels and probabilities.

This class represents a multinoulli (categorical) distribution. It is used in cases where there are multiple potential outcomes, each with a specified probability. The class stores the labels for each category and their corresponding probabilities.

Parameters:

• labels (list of str) – The labels for each category in the distribution, representing the possible outcomes.

• probs (list of float) – The probabilities or frequencies of each category. These will be normalized internally.

Examples

>>> MultinoulliDistribution(labels=["a", "b", "b"], probs=[0.1, 0.3, 0.6])
>>> MultinoulliDistribution(labels=[1, 3, 6], probs=[0.3, 0.4, 0.3])

name

core.multinoulli

unique

False

version

1.0

var_type

[‘categorical’, ‘discrete’, ‘string’]

__init__(labels, probs)

Parameters:

• labels (ndarray[tuple[Any, ...], dtype[str_ | int64]] | list[str | int])

• probs (ndarray[tuple[Any, ...], dtype[float64]] | list[float])

Methods

__init__(labels, probs)
default_distribution([var_type])	Get a distribution with default parameters.
draw()	Draw a random element from the fitted distribution.
draw_list(n)	Draw a list of values from the distribution.
draw_reset()	Reset the drawing of elements to start again.
from_dict(dist_dict)	Create a distribution from a dictionary.
information_criterion(values)	Get the BIC value for a particular set of values.
matches_name(name)	Check whether the name matches the distribution.
provides_var_type(var_type)
schema()	Create sub-schema to validate GMF file.
to_dict()	Convert the distribution to a dictionary.

Attributes

name	The identifier for the implemented distribution
unique	Whether the distribution creates only unique values
var_type	The variable type of the distribution
version	Version of the implemented distribution