mmgp.backends.gpjax
===================

.. py:module:: mmgp.backends.gpjax


Classes
-------

.. autoapisummary::

   mmgp.backends.gpjax.Regressor


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

.. py:class:: Regressor(options: dict)

   Bases: :py:obj:`mmgp.regressor.RegressorBase`


   gpjax regressor

   :param algo: The regression algorithm to use.
   :type algo: str
   :param options: A dictionary of options specific to the chosen algorithm. Allowed fields are "kernel", "optim", "num_restarts", "max_iters" and "anisotropic".
   :type options: dict


   .. py:attribute:: algo
      :value: 'GPJax'



   .. py:attribute:: options


   .. py:method:: fit(X: numpy.ndarray, y: numpy.ndarray) -> Self

      Train the regression model on the provided data.

      :param X: The input features for training.
      :type X: np.ndarray
      :param y: The target values for training.
      :type y: np.ndarray



   .. py:method:: predict(X: numpy.ndarray) -> numpy.ndarray

      Make predictions using the trained regression model.

      :param X: The input features for making predictions.
      :type X: np.ndarray

      :returns: Predicted target values.
      :rtype: np.ndarray



   .. py:method:: predict_Monte_Carlo_draws(X: numpy.ndarray, size: int = 100) -> numpy.ndarray
      :abstractmethod:


      Generate Monte Carlo draws from the trained regression model.

      :param X: The input features for generating draws.
      :type X: np.ndarray
      :param size: The number of Monte Carlo draws to generate. Defaults to 100.
      :type size: int, optional

      :returns: Monte Carlo draws from the posterior of the regression model.
      :rtype: np.ndarray