:py:mod:`omnisolver.pt.bqm_tools`
=================================

.. py:module:: omnisolver.pt.bqm_tools

.. autoapi-nested-parse::

   Helper functions for converting BQM into a more PT friendly representation.



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


Functions
~~~~~~~~~

.. autoapisummary::

   omnisolver.pt.bqm_tools.vectorize_bqm
   omnisolver.pt.bqm_tools.adjacency_list_from_couplings



.. py:function:: vectorize_bqm(bqm: dimod.BQM) -> Tuple[numpy.ndarray, numpy.ndarray]

   Convert BQM into dense vector of biases and matrix of couplings.

   .. warning::
       This function assumes that variables are labelled 0,...,N-1,
       otherwise it will behave unexpectedly.

   :param bqm: Binary Quadratic Model to be vectorized
   :return: A tuple (h_vec, j_mat) comprising vector of biases and a matrix
    of quadratic coefficients. Missing biases and couplings are set to 0.


.. py:function:: adjacency_list_from_couplings(j_mat: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray]

   Produce adjacency-list representation of given Ising graph.

   :param j_mat: symmetric matrix of Ising model couplings.
   :return: a tuple (adjacency_list, neighbours_count) such that:
    - neighbours_count.shape == (N,) and neighbours_count[i] contains number of
      neighbours of i-th spin
    - adjacency_list.shape == (N, max(neighbours_count)) and adjacency_list[i, j]
      is i-th spin j-th neighbour (for j < neighbours_count[j]