Python priority queue with tuples code#

the cost so far so get to this state ( g(x)).

a list of actions to get from the start state to this state.

Where node is a namedtupled that should contains 4 items: Your function ( count is imported from itertools): unique = count()Īn example of adding a new item to the priority queue is: pq.put(f(x), next(unique), node)) In your A* method, you can create a counter at the beginning of That follow are never evaluated for key order. It breaks all ties when the f(x) values are the same, and the elements Where the first element is f(x), the second element is some counter value (unique byĬonstruction), and then any remaining elements. Therefore, just store an extra element within the tuple stored on the priority queue, Given two elements on the priority queue with the same f(x), the A* algorithm does not care which one isĮxtracted. The one that worked well for me is a simple and common approach in Python. While several solutions exist for this situation, including adding comparative methodsĪnd or wrapping the item in a class as suggested in the PriorityQueue documentation. Since node is a namedtuple, it does not have a comparison method and thus, Python throws an error. Than 1 node in the priority queue will have the same f(x) value, which would require the priority queue to compare the second element in the tupe to break the tie, which isĪ node object. You might try storing a tuple with ( f(x), node). It would never need to access the other elements (which it would only use to break a tie). That it will use for extracting the minimum value.įor example, if all the first elements were distinct values, (with any number of elements) and uses all the element in the tuple for creating the “key” Priority Queues in Python and Frontier ManagementĪ* and UCS both required a priority queue. An example of creating a plot with two lines is provided in the You will have 3 lines (one for each search: UCS, A* w/tiles, A* w/manhatten). This single plot needs to show the 6 puzzles on the X axis (the ones built by and usedīy informed_search_test.py), the Y axis for each of these 6 points is the number of expand Showcase the performance (expanded state count) between the different searches byĬreating a plot in python. Methods inside of eightpuzzle.py so that they conform to their Python docstrings. HINT: It is a good idea to use a namedtuple that stores f(x), g(x), and h(x) separately.Ĭomplete the tilesEightPuzzleHeuristic and manhattenEightPuzzleHeuristic You couldĬhange whateverFirstSearch to handle all 4 searches (although this is not a requirement). It is required that UCS be two lines and just calls aStarSearch. Hint: Complete the aStarSearch methodįirst, you can just use the nullHeuristic function for uniformCostSearchStats. So that they conform to their Python docstrings. Informed_search_test.py A unittest file to test your code.Ĭomplete the aStarSearch, aStarSearchStats, uniformCostSearch, uniformCostSearchStats methods within search.py

Python priority queue with tuples code#

Plot_search_performance.py (UNFINISHED) Python code to plot the results of your searchs on the 6 benchmark puzzles The UCS method by adapting whateverFirstSearch to work with this. Search.py (UNFINISHED) A set of search functions that operate on SearchProblem instances. You will need your search methods and the modifiedĮightpuzzle.py file with the hash functions added for this lab.Įightpuzzle.py (UNFINISHED) a representation of an eight piece sliding puzzle.

Resourcesįiles over to a new directory for this lab. That we can compare how it performs against A*. Not use knowledge about the goal location, but it is part of this lab so NOTE Technically UCS is an uninformed search method since it does This lab introduces priority queue objects in Python. You will construct heuristics for A* specifcally for the eightpiece puzzle problem (the In this lab, you will write uniform cost search (UCS) and A* search methods in Python.