searches.hill_climbing
======================

.. py:module:: searches.hill_climbing


Classes
-------

.. autoapisummary::

   searches.hill_climbing.SearchProblem


Functions
---------

.. autoapisummary::

   searches.hill_climbing.hill_climbing
   searches.hill_climbing.test_f1


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

.. py:class:: SearchProblem(x: int, y: int, step_size: int, function_to_optimize)

   An interface to define search problems.
   The interface will be illustrated using the example of mathematical function.


   .. py:method:: __eq__(obj)

      Check if the 2 objects are equal.



   .. py:method:: __hash__()

      hash the string representation of the current search state.



   .. py:method:: __str__()

      string representation of the current search state.
      >>> str(SearchProblem(0, 0, 1, None))
      'x: 0 y: 0'
      >>> str(SearchProblem(2, 5, 1, None))
      'x: 2 y: 5'



   .. py:method:: get_neighbors()

      Returns a list of coordinates of neighbors adjacent to the current coordinates.

      Neighbors:
      | 0 | 1 | 2 |
      | 3 | _ | 4 |
      | 5 | 6 | 7 |



   .. py:method:: score() -> int

      Returns the output of the function called with current x and y coordinates.
      >>> def test_function(x, y):
      ...     return x + y
      >>> SearchProblem(0, 0, 1, test_function).score()  # 0 + 0 = 0
      0
      >>> SearchProblem(5, 7, 1, test_function).score()  # 5 + 7 = 12
      12



   .. py:attribute:: function


   .. py:attribute:: step_size


   .. py:attribute:: x


   .. py:attribute:: y


.. py:function:: hill_climbing(search_prob, find_max: bool = True, max_x: float = math.inf, min_x: float = -math.inf, max_y: float = math.inf, min_y: float = -math.inf, visualization: bool = False, max_iter: int = 10000) -> SearchProblem

   Implementation of the hill climbling algorithm.
   We start with a given state, find all its neighbors,
   move towards the neighbor which provides the maximum (or minimum) change.
   We keep doing this until we are at a state where we do not have any
   neighbors which can improve the solution.
       Args:
           search_prob: The search state at the start.
           find_max: If True, the algorithm should find the maximum else the minimum.
           max_x, min_x, max_y, min_y: the maximum and minimum bounds of x and y.
           visualization: If True, a matplotlib graph is displayed.
           max_iter: number of times to run the iteration.
       Returns a search state having the maximum (or minimum) score.


.. py:function:: test_f1(x, y)