:py:mod:`cotengra.experimental.path_compressed_mcts`
====================================================

.. py:module:: cotengra.experimental.path_compressed_mcts

.. autoapi-nested-parse::

   Compressed contraction tree search using monte carlo tree search.



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

Classes
~~~~~~~

.. autoapisummary::

   cotengra.experimental.path_compressed_mcts.Node
   cotengra.experimental.path_compressed_mcts.MCTS




.. py:class:: Node(hg, nid_path, size, local_score, forward_score)


   .. py:attribute:: __slots__
      :value: ('hg', 'n', 'graph_key', 'nid_path', 'size', 'local_score', 'forward_score', 'mean', 'count',...

      

   .. py:method:: update(x)

      Report the score ``x``, presumably from a child node, updating this
      nodes score.


   .. py:method:: __hash__()

      Return hash(self).


   .. py:method:: __lt__(other)

      Return self<value.


   .. py:method:: __repr__()

      Return repr(self).



.. py:class:: MCTS(chi, T=0.1, prune=True, optimize=None, optimize_factory=False, seed=None)


   .. py:property:: ssa_path


   .. py:property:: path


   .. py:method:: __repr__()

      Return repr(self).


   .. py:method:: setup(inputs, output, size_dict)


   .. py:method:: get_ssa_path()

      Convert unique node identifiers to ssa.


   .. py:method:: check_node(node)


   .. py:method:: delete_node(node)


   .. py:method:: backprop(node)


   .. py:method:: simulate_node(node)


   .. py:method:: simulate_optimized(node)


   .. py:method:: is_deadend(node)


   .. py:method:: descend()


   .. py:method:: run(inputs, output, size_dict)


   .. py:method:: search(inputs, output, size_dict)


   .. py:method:: __call__(inputs, output, size_dict)