graphs.markov_chain
===================

.. py:module:: graphs.markov_chain


Classes
-------

.. autoapisummary::

   graphs.markov_chain.MarkovChainGraphUndirectedUnweighted


Functions
---------

.. autoapisummary::

   graphs.markov_chain.get_transitions


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

.. py:class:: MarkovChainGraphUndirectedUnweighted

   Undirected Unweighted Graph for running Markov Chain Algorithm


   .. py:method:: add_node(node: str) -> None


   .. py:method:: add_transition_probability(node1: str, node2: str, probability: float) -> None


   .. py:method:: get_nodes() -> list[str]


   .. py:method:: transition(node: str) -> str


   .. py:attribute:: connections


.. py:function:: get_transitions(start: str, transitions: list[tuple[str, str, float]], steps: int) -> dict[str, int]

   Running Markov Chain algorithm and calculating the number of times each node is
   visited

   >>> transitions = [
   ... ('a', 'a', 0.9),
   ... ('a', 'b', 0.075),
   ... ('a', 'c', 0.025),
   ... ('b', 'a', 0.15),
   ... ('b', 'b', 0.8),
   ... ('b', 'c', 0.05),
   ... ('c', 'a', 0.25),
   ... ('c', 'b', 0.25),
   ... ('c', 'c', 0.5)
   ... ]

   >>> result = get_transitions('a', transitions, 5000)

   >>> result['a'] > result['b'] > result['c']
   True