:py:mod:`pycmtensor.functions`
==============================

.. py:module:: pycmtensor.functions

.. autoapi-nested-parse::

   PyCMTensor functions module



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

.. py:function:: logit(utility: list, avail: list = None)

   Computes the Logit function, with availability conditions.

   :param utility: List of M utility equations.
   :type utility: list
   :param avail: List of M availability conditions. If no availabilities are
   :type avail: list
   :param provided:
   :param defaults to 1 for all availabilities.:

   :returns: A NxM matrix of probabilities.
   :rtype: TensorVariable


.. py:function:: log_likelihood(prob, y)

   Symbolic representation of the log likelihood cost function.

   :param prob: Matrix describing the choice probabilites.
   :type prob: TensorVariable
   :param y: The ``TensorVariable`` referencing the choice column.
   :type y: TensorVariable

   :returns: a symbolic representation of the log likelihood with ndim=0.
   :rtype: TensorVariable


.. py:function:: errors(prob, y)

   Symbolic representation of the prediction as a percentage error.

   :param prob: Matrix describing the choice probabilites.
   :type prob: TensorVariable
   :param y: The ``TensorVariable`` referencing the choice column.
   :type y: TensorVariable

   :raises TypeError: ``y`` should have the same shape as ``pred``.
   :raises NotImplementedError: ``y`` should be an ``int`` Type.

   :returns: a symbolic representation of the prediction error with ndim=0.
   :rtype: TensorVariable


.. py:function:: hessians(ll, params)

   Symbolic representation of the Hessian matrix given the log likelihood.

   :param ll: the loglikelihood to compute the gradients over
   :type ll: TensorVariable
   :param params: list of params to compute the gradients over
   :type params: list

   :returns: the Hessian matrix with ndim=2
   :rtype: TensorVariable

   .. note:: Parameters with status=1 are ignored.


.. py:function:: bhhh(ll, params)

   Symbolic representation of the Berndt-Hall-Hall-Hausman (BHHH) algorithm given
   the log likelihood.

   :param ll: the loglikelihood to compute the gradients over
   :type ll: TensorVariable
   :param params: list of params to compute the gradients over
   :type params: list

   :returns: the outer product of the gradient with ndim=2
   :rtype: TensorVariable

   .. note:: Parameters with status=1 are ignored.


.. py:function:: gnorm(cost, params)

   Symbolic representation of the gradient norm given the cost.

   :param cost: the cost to compute the gradients over
   :type cost: TensorVariable
   :param params: list of params to compute the gradients over
   :type params: list

   :returns: the gradient norm value
   :rtype: TensorVariable

   .. note:: Parameters with status=1 are ignored.