コード例 #1
0
ファイル: execute5.py プロジェクト: JasonArce/UMN
def test_iddfs():

    print("--- Solving NQueens with IDDFS Tree")
    nqueens = NQueens(5, pruning=True)
    iddfs_tree = ID_DFS(verbose=False, tree=True)
    solution = iddfs_tree.solve(nqueens)
    iddfs_tree.print_solution()

    print("")
    print("--- Solving Towers with IDDFS Graph")
    towers = TowersOfHanoi(tower_size=4)
    iddfs_graph = ID_DFS(verbose=False, tree=False)
    solution = iddfs_graph.solve(towers)
    iddfs_graph.print_solution()
コード例 #2
0
ファイル: execute.py プロジェクト: JasonArce/UMN
import sys
sys.path.append('problems')
sys.path.append('algorithms')

from towers import TowersOfHanoi
from nqueens import NQueens
from search import BFS_Tree, BFS_Graph
from search import DFS_Tree, DFS_Graph

# You can try different algorithms with different problems
# path=True indicates that the path is part of the solution
# FYI: tree searches for Towers take a loooooong time.

if __name__ == "__main__":
    towers = TowersOfHanoi(tower_size=4)
    nqueens = NQueens(5, pruning=False)

    #search = BFS_Tree(verbose=False)
    #search = DFS_Tree(verbose=False)

    search = DFS_Graph()
    #search = BFS_Graph()

    #search.solve(towers, path=True)
    search.solve(towers, path=True, all_solutions=False)

    search.print_solution()
    search.print_stats()
コード例 #3
0
def test_proj():

    # EXPERIMENT 1: THE GOAL EXPERIMENT 1 IS MEANT TO TEST EACH OF THE 3 ALGORITHMS, DFS, BFS, AND DLDFS. EACH ALGORITHM WILL SOLVE TOWERS WITH A DISC SIZE OF 3,4 AND 5. THE DEPTH
    # LIMIT FOR DLDFS WILL ALWAYS BE SET TO THE OPTIMAL SOLUTION DEPTH HERE, WHICH IS 2^N - 1 MOVES. THE IDEA IS THAT THIS TEST WILL DETERMINE WHICH ALGORITHM CAN SOLVE THE TOWERS
    # OF HANOI PROBLEM THE FASTEST.

    # ---- BEGIN EXPERIMENT 1 TESTS ----

    # -- NUMBER OF DISCS = 3 --

    # towers = 3

    # # TEST 1 - BFS_GRAPH
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = BFS_Graph(verbose=True)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # # TEST 2 - DFS_GRAPH (PARENT)
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = DFS_Graph(verbose=True)
    # Utils.solveProblem(title, problem, searchAlgorithm)
    # towers = towers + 1

    # # TEST 3 - DL_DFS (SET TO THE OPTIMAL SOLUTION)
    # depthLimit = 7
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = DL_DFS(verbose=True, depth_limit=depthLimit)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # # -- NUMBER OF DISCS = 4 --

    # towers = 4

    # # TEST 1 - BFS_GRAPH
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = BFS_Graph(verbose=True)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # # TEST 2 - DFS_GRAPH (PARENT)
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = DFS_Graph(verbose=True)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # # TEST 3 - DL_DFS (SET TO THE OPTIMAL SOLUTION)
    # depthLimit = 15
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = DL_DFS(verbose=True, depth_limit=depthLimit)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # # -- NUMBER OF DISCS = 5 --

    # towers = 5

    # # TEST 1 - BFS_GRAPH
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = BFS_Graph(verbose=True)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # # TEST 2 - DFS_GRAPH (PARENT)
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = DFS_Graph(verbose=True)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # # TEST 3 - DL_DFS (SET TO THE OPTIMAL SOLUTION)
    # depthLimit = 31
    # title = "Towers: %d " % (towers)
    # problem = TowersOfHanoi(towers)
    # searchAlgorithm = DL_DFS(verbose=True, depth_limit=depthLimit)
    # Utils.solveProblem(title, problem, searchAlgorithm)

    # ---- END OF EXPERIMENT 1 TESTS ----

    # EXPERIMENT 2: THE GOAL OF EXPERIMENT 2 IS TO DETERMINE WHAT THE "LIMITS" OF EACH ALGORITHM IS. THIS WILL HELP IN THE DISCOVERY OF WHICH ALGORITHM IS CONSISTENLY
    # PERFORMING FASTER THAN THE OTHERS. IF THE ALGORITHM TAKES MORE THAN ONE HOUR TO FIND THE SOLUTION, THE TEST WILL BE TERMINATED AND THE "LIMIT" OF THE ALGORITHM
    # WILL BE THE LAST TEST THAT TOOK UNDER AN HOUR.

    # ---- BEGIN EXPERIMENT 2 TESTS ----

    # RUN A LOOP THAT GOES UP TO 20 TO TEST THE TIME IT TAKES FOR EACH ALGORITHM TO SOLVE A CERTAIN AMOUNT OF DISCS AT A GIVEN TIME. IF THE ALGORITHM POSTS RESULTS
    # THAT EXCEED AN HOUR IN LENGTH, THE TESTS WILL BE TERMINATED AND THAT WILL BE THE THEORETICAL LIMIT FOR THAT ALGORITHM.
    # towers = 3
    # while towers < 15:
    #     # TEST 1 - BFS_GRAPH
    #     title = "Towers: %d " % (towers)
    #     problem = TowersOfHanoi(towers)
    #     searchAlgorithm = BFS_Graph(verbose=True)
    #     Utils.solveProblem(title, problem, searchAlgorithm)
    #     towers = towers + 1

    # towers = 3
    # while towers < 15:
    #      # TEST 2 - DFS_GRAPH (PARENT)
    #     depthLimit = 50
    #     title = "Towers: %d " % (towers)
    #     problem = TowersOfHanoi(towers)
    #     searchAlgorithm = DL_DFS(verbose=True)
    #     Utils.solveProblem(title, problem, searchAlgorithm)
    #     towers = towers + 1

    # towers = 3
    # while towers < 15:
    #      # TEST 3 - DL_DFS (SET TO THE OPTIMAL SOLUTION DEPTH)
    #     depthLimit = (2**towers) -1
    #     title = "Towers: %d " % (towers)
    #     problem = TowersOfHanoi(towers)
    #     searchAlgorithm = DL_DFS(verbose=True,depth_limit=depthLimit)
    #     Utils.solveProblem(title, problem, searchAlgorithm)
    #     towers = towers + 1

    # towers = 3
    # while towers < 15:
    #      # TEST 3 - DL_DFS (SET TO THE 5000)
    #     depthLimit = 500
    #     title = "Towers: %d " % (towers)
    #     problem = TowersOfHanoi(towers)
    #     searchAlgorithm = DL_DFS(verbose=True,depth_limit=depthLimit)
    #     Utils.solveProblem(title, problem, searchAlgorithm)
    #     towers = towers + 1

    # ---- END OF EXPERIMENT 2 TESTS ----

    # EXPERIMENT 3: THE GOAL OF EXPERIMENT 3 IS TO TEST HOW DIFFERENT DEPTH LIMITS FOR DLDFS WILL EFFECT THE TIME IT TAKES TO FIND A SOLUTION. THE IDEA IS TO LEARN WHETHER
    # OR NOT RANDOM CHANCE CAN INCREASE THE LIKELIHOOD OF FINDING A SOLUTION TO TOWERS FASTER THAN BFS OR DFS. THE LIMITS ARE SET TO THE CURRENT NUMBER OF DISCS X100, X1000,
    # AND X10000. WE WILL START THE TESTS AT A DISC SIZE OF 3 AND INCREMENT UP TO A MAX OF 15, SINCE EXPERIMENT 2 SHOWED US THAT THE LIMITS OF THESE ALGORITHMS
    # WITH RESPECT TO TIME WON'T EXCEED MORE THAN 15 DISCS.

    # ---- BEGIN EXPERIMENT 3 TESTS ----

    towers = 3
    while towers < 9:
        # TEST 3 - DLDFS (x100, x1000, x10000)
        depthLimit = 50
        title = "Towers: %d " % (towers)
        problem = TowersOfHanoi(towers)
        searchAlgorithm = DL_DFS(verbose=True, depth_limit=towers * 100)
        Utils.solveProblem(title, problem, searchAlgorithm)

        title = "Towers: %d " % (towers)
        problem = TowersOfHanoi(towers)
        searchAlgorithm = DL_DFS(verbose=True, depth_limit=towers * 1000)
        Utils.solveProblem(title, problem, searchAlgorithm)

        title = "Towers: %d " % (towers)
        problem = TowersOfHanoi(towers)
        searchAlgorithm = DL_DFS(verbose=True, depth_limit=towers * 10000)
        Utils.solveProblem(title, problem, searchAlgorithm)
        towers = towers + 1