jpt.distributions.univariate.distribution
=========================================

.. py:module:: jpt.distributions.univariate.distribution

.. autoapi-nested-parse::

   © Copyright 2021, Mareike Picklum, Daniel Nyga.



Attributes
----------

.. autoapisummary::

   jpt.distributions.univariate.distribution.SYMBOLIC
   jpt.distributions.univariate.distribution.NUMERIC
   jpt.distributions.univariate.distribution.CONTINUOUS
   jpt.distributions.univariate.distribution.DISCRETE


Classes
-------

.. autoapisummary::

   jpt.distributions.univariate.distribution.ValueMap
   jpt.distributions.univariate.distribution.Identity
   jpt.distributions.univariate.distribution.Distribution


Module Contents
---------------

.. py:data:: SYMBOLIC
   :value: 'symbolic'


.. py:data:: NUMERIC
   :value: 'numeric'


.. py:data:: CONTINUOUS
   :value: 'continuous'


.. py:data:: DISCRETE
   :value: 'discrete'


.. py:class:: ValueMap

   Bases: :py:obj:`collections.abc.Hashable`


   .. py:method:: __iter__()
      :abstractmethod:



   .. py:method:: __len__()
      :abstractmethod:



   .. py:method:: __getitem__(label: Any)
      :abstractmethod:



   .. py:method:: __hash__()
      :abstractmethod:



   .. py:property:: map


   .. py:method:: __eq__(other)
      :abstractmethod:



   .. py:method:: __contains__(item)
      :abstractmethod:



.. py:class:: Identity

   Bases: :py:obj:`ValueMap`


   Simple identity mapping that mimics the __getitem__ protocol of dicts.


   .. py:method:: __getitem__(item)


   .. py:property:: map


   .. py:method:: __eq__(o)


   .. py:method:: __hash__()


   .. py:method:: __contains__(item)


.. py:class:: Distribution(**settings)

   Abstract supertype of all domains and distributions


   .. py:attribute:: values
      :type:  ValueMap
      :value: None



   .. py:attribute:: labels
      :type:  ValueMap
      :value: None



   .. py:attribute:: SETTINGS


   .. py:attribute:: _cl
      :value: 'jpt.distributions.univariate.distribution.Distribution'



   .. py:attribute:: settings


   .. py:method:: __getattr__(name)


   .. py:method:: hash()
      :classmethod:

      :abstractmethod:



   .. py:method:: __hash__()


   .. py:method:: __getitem__(value)


   .. py:method:: value2label(value)
      :classmethod:

      :abstractmethod:



   .. py:method:: label2value(label)
      :classmethod:

      :abstractmethod:



   .. py:method:: _sample(n: int) -> Iterable
      :abstractmethod:



   .. py:method:: _sample_one()
      :abstractmethod:



   .. py:method:: sample(n: int) -> Iterable


   .. py:method:: sample_one() -> Any


   .. py:method:: p(value) -> float
      :abstractmethod:



   .. py:method:: _p(value) -> float
      :abstractmethod:



   .. py:method:: mpe()
      :abstractmethod:



   .. py:method:: crop(restriction: Set) -> Distribution
      :abstractmethod:



   .. py:method:: _crop(restriction: Set) -> Distribution
      :abstractmethod:



   .. py:method:: entropy() -> float
      :abstractmethod:



   .. py:method:: merge(distributions: Iterable[Distribution], weights: Iterable[numbers.Real]) -> Distribution
      :staticmethod:

      :abstractmethod:



   .. py:method:: update(dist: Distribution, weight: float) -> Distribution
      :abstractmethod:



   .. py:method:: fit(data: numpy.ndarray, rows: numpy.ndarray = None, col: numbers.Integral = None) -> Distribution
      :abstractmethod:



   .. py:method:: _fit(data: numpy.ndarray, rows: numpy.ndarray = None, col: numbers.Integral = None) -> Distribution
      :abstractmethod:



   .. py:method:: set(params: Any) -> Distribution
      :abstractmethod:



   .. py:method:: kl_divergence(other: Distribution)
      :abstractmethod:



   .. py:method:: number_of_parameters() -> int
      :abstractmethod:



   .. py:method:: jaccard_similarity(d1: Distribution, d2: Distribution) -> float
      :staticmethod:

      :abstractmethod:



   .. py:method:: plot(engine: str, title: str = None, fname: str = None, directory: str = '/tmp', view: bool = False, **kwargs) -> Any
      :abstractmethod:


      Generates a plot of the distribution.

      :param title:       the name of the variable this distribution represents
      :param fname:       the name of the file to be stored. Available file formats: png, svg, jpeg, webp, html
      :param directory:   the directory to store the generated plot files
      :param view:        whether to display generated plots, default False (only stores files)
      :return:            the figure object of the plotting engine



   .. py:method:: to_json()
      :abstractmethod:



   .. py:method:: __reduce__()


   .. py:method:: type_from_json(data: Dict[str, Any]) -> Type[Distribution]
      :staticmethod:



   .. py:method:: from_json(dtype: Dict[str, Any], dinst: Dict[str, Any] = None) -> Union[Distribution, Type[Distribution]]
      :staticmethod: