:py:mod:`MomentGauge.Sampler.Base`
==================================

.. py:module:: MomentGauge.Sampler.Base


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

Classes
~~~~~~~

.. autoapisummary::

   MomentGauge.Sampler.Base.BaseSampler




.. py:class:: BaseSampler(constant)

   
   The base class for sampler.
   A sampler is a probability distribution :math:`f(\mathbf{u};\boldsymbol{\beta})` parametrized by :math:`\boldsymbol{\beta}` from which we could draw samples and compute likelihoods.

   :param constant: a dictionary with necessary constants provided as key-value pairs.
   :type constant: dictionary

   .. attribute:: constant

      a dictionary with necessary constants provided as key-value pairs.

      :type: dict

   .. py:method:: sample(betas)
      :abstractmethod:

      Generate N samples :math:`\mathbf{u}_i` from the distribution :math:`f(\mathbf{u})` with proper weights :math:`w_i` such that

      .. math::
          :nowrap:

          \begin{equation}
          \int \phi(\mathbf{u}) f(\mathbf{u}) d \mathbf{u} \approx \sum_{i=1}^N w_i \phi(\mathbf{u}_i),
          \end{equation}

      in whic N depends on the particular implementation of the sampler.

      :param betas: the n-dim parameter :math:`\boldsymbol{\beta}` specifying the distributions
      :type betas: array of shape (n)

      :returns:

                A tuple containing

                    **samples**: *array of shape (N,d)* - N samples of d-dim vectors :math:`\mathbf{u}_i` draw from the distribution.

                    **weights**: *array of shape (N)* - non-negative weights :math:`w_i` for each samples. The summation of weights equals to 1.

                    **log_likelihoods**: *array of shape (N)* - the log-likelihoods :math:`\log f(\mathbf{u}_i)` for each samples
      :rtype: Tuple

      :raises NotImplementedError: This method is not implemented