'..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..'
)
# ------ harder
# puzzle = Sudoku('4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......')
# ------ hardest
# puzzle = Sudoku('1....7.9..3..2...8..96..5....53..9...1..8...26....4...3......1..4......7..7...3..')
puzzle.display(puzzle.infer_assignment())
start = time()
# 2. Solve the puzzle; select ONLY one of the following algorithms
# depth_first_graph_search(puzzle)
backtracking_search(puzzle)
# AC3(puzzle)
# min_conflicts(puzzle)
# 3. Print the results
print("")
if puzzle.goal_test(puzzle.infer_assignment()):
print("Solution:")
puzzle.display(puzzle.infer_assignment())
else:
print("Failed - domains: ")
print(str(puzzle.curr_domains))
# 4. Print elapsed time
end = time()
elapsed = end - start
): # Ensures that there is no block on proximity and that they are assigned the same "Block"
return False
elif (int(a[1]) == int(b[1]) - 1) and a[1:2] != b[
1:
2]: # Checks for block continuity and avoids multiple duos.
return True
if (
int(a[2]) <= challengeColoursColumn[col][index]
and int(a[2]) <= challengeColoursRow[row][index]
): # Ensures that values can be put if they are still some spots left
return True
if (a[0] == "N"):
if (a == b):
return False
if (int(a[1]) <= challengeColoursColumn[col][3]
and int(a[1]) <= challengeColoursRow[row][3]
): # Ensures only the maximum available blank spaces
return False
challengeColoursRow = [[2, 0, 0, 3], [1, 1, 1, 2], [4, 0, 0, 1], [3, 1, 0, 1],
[2, 0, 0, 3]]
challengeColoursColumn = [[2, 0, 0, 3], [1, 1, 1, 2], [4, 0, 0, 1],
[3, 1, 0, 1], [2, 0, 0, 3]]
maxLengthRow = [[2, 0, 0], [1, 1, 1], [2, 0, 0], [1, 1, 0], [2, 0, 0]]
maxLengthColumn = [[2, 0, 0], [1, 1, 1], [2, 0, 0], [1, 1, 0], [2, 0, 0]]
game = PicPix()
print(csp.backtracking_search(game))
print(game.nassigns)
def now():
return secondsToStr(time())
# 1. Set up the problem and starting time
n = 5
print("\nStarting at at " + now()[12:20])
print("problem with n =", n)
start = time()
problem = NQueensCSP(n)
# 2. Solve the problem
solution = backtracking_search(problem)
# solution = AC3(problem);
# solution = min_conflicts(problem)
# 3. Print the results
# Handle AC3 solutions (boolean values) first, they behave differently.
if type(solution) is bool:
if solution and problem.goal_test(problem.infer_assignment()):
print("AC3 Solution:")
print(problem.curr_domains)
else:
print("AC3 Failure")
print(problem.curr_domains)
# Handle other solutions next
elif problem.goal_test(solution):
@version 14feb2013
"""
import time
from aima.csp import Zebra, min_conflicts, backtracking_search, AC3
from aima.search import depth_first_graph_search
def print_solution(result):
"""A CSP solution printer copied from csp.py."""
for h in range(1, 6):
print('House', h)
for (var, val) in result.items():
if val == h: print('\t', var)
puzzle = Zebra()
# result = depth_first_graph_search(puzzle)
# result = AC3(puzzle)
result = backtracking_search(puzzle)
# result = min_conflicts(puzzle, max_steps=1000)
if puzzle.goal_test(puzzle.infer_assignment()):
print("Solution:\n")
print_solution(result)
else:
print("failed...")
print(puzzle.curr_domains)
puzzle.display(puzzle.infer_assignment())