pyuncertainnumber.propagation.cauchy_deviate
============================================

.. py:module:: pyuncertainnumber.propagation.cauchy_deviate


Functions
---------

.. autoapisummary::

   pyuncertainnumber.propagation.cauchy_deviate.cauchy_deviate_method
   pyuncertainnumber.propagation.cauchy_deviate.cauchy_deviate_raw


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

.. py:function:: cauchy_deviate_method(func, input_vector_interval: list[pyuncertainnumber.Interval], n_sam: int = 200) -> pyuncertainnumber.Interval

   Cauchy Deviate Method for interval propagation

   :param func: The function, vectorised style, to be evaluated.
   :param input_vector_interval: The input vector of intervals.
   :param n_sam: The number of samples to draw from each Cauchy distribution.

   .. note::

      The function must be vectorised, i.e. it must be able to take in a
      2D array of shape (n, d) and return a 1D array of shape (n,).


.. py:function:: cauchy_deviate_raw(func, nominal_measurement: numpy.ndarray, scalar_param: numpy.ndarray, n_sam: int = 200) -> tuple

   Raw implementation of the Cauchy Deviate Method

   :param func: The function, vectorised style, to be evaluated.
   :param nominal_measurement: The nominal measurement values, i.e. x tilda.
   :param scalar_param: The scalar parameters for each component Cauchy distribution, i.e. Delta.
   :param n_sam: The number of samples to draw from each Cauchy distribution.

   :returns: mean_y and Delta_y