legendre_decomp.naive
=====================

.. py:module:: legendre_decomp.naive

.. autoapi-nested-parse::

   Implementations found in the original Jupyter Notebook



Functions
---------

.. autoapisummary::

   legendre_decomp.naive.kl
   legendre_decomp.naive.get_eta
   legendre_decomp.naive.get_h
   legendre_decomp.naive.LD


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

.. py:function:: kl(P, Q)

   Kullback-Leibler divergence.

   :param P: P tensor
   :param Q: Q tensor

   :returns: KL divergence.


.. py:function:: get_eta(Q, D)

   Eta tensor.

   :param Q: Q tensor
   :param D: Dimensionality

   :returns: Eta tensor.


.. py:function:: get_h(theta, D)

   H tensor.

   :param theta: Theta tensor
   :param D: Dimensionality

   :returns: Updated theta.


.. py:function:: LD(X, B = None, order = 2, n_iter = 10, lr = 1.0, eps = 1e-05, error_tol = 1e-05, ngd = True, verbose = True)

   Compute many-body tensor approximation.

   :param X: Input tensor.
   :param B: B tensor.
   :param order: Order of default tensor B, if not provided.
   :param n_iter: Maximum number of iteration.
   :param lr: Learning rate.
   :param eps: (see paper).
   :param error_tol: KL divergence tolerance for the iteration.
   :param ngd: Use natural gradient.
   :param verbose: Print debug messages.

   :returns: KL divergence history.
             scaleX: Scaled X tensor.
             Q: Q tensor.
             theta: Theta.
   :rtype: all_history_kl