pyuncertainnumber.calibration.pdfs
==================================

.. py:module:: pyuncertainnumber.calibration.pdfs

.. autoapi-nested-parse::

   @author: Mukesh K. Ramancha

   A collection of common probability distributions



Classes
-------

.. autoapisummary::

   pyuncertainnumber.calibration.pdfs.ProbabilityDensityFun
   pyuncertainnumber.calibration.pdfs.Uniform
   pyuncertainnumber.calibration.pdfs.HalfNormal
   pyuncertainnumber.calibration.pdfs.Normal
   pyuncertainnumber.calibration.pdfs.TruncatedNormal
   pyuncertainnumber.calibration.pdfs.MultivariateNormal


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

.. py:class:: ProbabilityDensityFun

   Bases: :py:obj:`abc.ABC`


   Blueprint for other classes.
   Base class.
   Abstract class is not a concrete class, it cannot be instantiated


   .. py:method:: generate_rns(N)
      :abstractmethod:


      Method to generate 'N' random numbers

      :param N: number of random numbers needed.
      :type N: int

      :rtype: numpy array of size N



   .. py:method:: log_pdf_eval(x)
      :abstractmethod:


      Method to compute log of the pdf at x

      :param x: value where to evalute the pdf.
      :type x: float

      :rtype: float - log of pdf evaluated at x.



.. py:class:: Uniform(lower=0, upper=1)

   Bases: :py:obj:`ProbabilityDensityFun`


   Uniform continuous distribution


   .. py:attribute:: lower
      :value: 0



   .. py:attribute:: upper
      :value: 1



   .. py:method:: generate_rns(N)

      Method to generate 'N' random numbers

      :param N: number of random numbers needed.
      :type N: int

      :rtype: numpy array of size N



   .. py:method:: log_pdf_eval(x)

      Method to compute log of the pdf at x

      :param x: value where to evalute the pdf.
      :type x: float

      :rtype: float - log of pdf evaluated at x.



.. py:class:: HalfNormal(sig=1)

   Bases: :py:obj:`ProbabilityDensityFun`


   Half Normal distribution with zero mean


   .. py:attribute:: sig
      :value: 1



   .. py:method:: generate_rns(N)

      Method to generate 'N' random numbers

      :param N: number of random numbers needed.
      :type N: int

      :rtype: numpy array of size N



   .. py:method:: log_pdf_eval(x)

      Method to compute log of the pdf at x

      :param x: value where to evalute the pdf.
      :type x: float

      :rtype: float - log of pdf evaluated at x.



.. py:class:: Normal(mu=0, sig=1)

   Bases: :py:obj:`ProbabilityDensityFun`


   Normal distribution


   .. py:attribute:: mu
      :value: 0



   .. py:attribute:: sig
      :value: 1



   .. py:method:: generate_rns(N)

      Method to generate 'N' random numbers

      :param N: number of random numbers needed.
      :type N: int

      :rtype: numpy array of size N



   .. py:method:: log_pdf_eval(x)

      Method to compute log of the pdf at x

      :param x: value where to evalute the pdf.
      :type x: float

      :rtype: float - log of pdf evaluated at x.



.. py:class:: TruncatedNormal(mu=0, sig=1, low=-np.Inf, up=np.Inf)

   Bases: :py:obj:`ProbabilityDensityFun`


   Truncated Normal distribution


   .. py:attribute:: mu
      :value: 0



   .. py:attribute:: sig
      :value: 1



   .. py:attribute:: low


   .. py:attribute:: up


   .. py:method:: generate_rns(N)

      Method to generate 'N' random numbers

      :param N: number of random numbers needed.
      :type N: int

      :rtype: numpy array of size N



   .. py:method:: log_pdf_eval(x)

      Method to compute log of the pdf at x

      :param x: value where to evalute the pdf.
      :type x: float

      :rtype: float - log of pdf evaluated at x.



.. py:class:: MultivariateNormal(mu=np.zeros(2), E=np.identity(2))

   Bases: :py:obj:`ProbabilityDensityFun`


   Multivariate Normal distribution


   .. py:attribute:: mu


   .. py:attribute:: E


   .. py:attribute:: d


   .. py:attribute:: logdetE


   .. py:attribute:: Einv


   .. py:method:: generate_rns(N)

      Method to generate 'N' random numbers

      :param N: number of random numbers needed.
      :type N: int

      :rtype: numpy array of size N



   .. py:method:: log_pdf_eval(x)

      Method to compute log of the pdf at x

      :param x: value where to evalute the pdf.
      :type x: float

      :rtype: float - log of pdf evaluated at x.