def sudoku_search_benchmarks(initial_state, runs=3):
puzzle = Sudoku(initial_state)
print("Start:")
puzzle.display(puzzle.infer_assignment())
options = [
backtracking_search, AC3, min_conflicts, depth_first_graph_search
]
exec_times = {
"backtracking_search": [],
'AC3': [],
'min_conflicts': [],
'depth_first_graph_search': []
}
for run in range(runs):
for i in range(3):
i = 0
start = time()
options[i](puzzle)
exec_time = time() - start
search_name = options[i].__name__
exec_times[search_name].append(exec_time)
# print_result(puzzle, options[i].__name__, exec_time, False)
for key in exec_times:
num_runs = len(exec_times[key])
if num_runs:
print(time_str(sum(exec_times[key]) / num_runs),
"average for",
key,
"(",
end='')
for ms_time in exec_times[key]:
print(time_str(ms_time), end=', ')
print(")")
from csp import Sudoku, backtracking_search
import time
easy = '003020600900305001001806400008102900700000008006708200002609500800203009005010300'
medium = '000012300000400000401005600705000010600020007030000806003600109000001000006590000'
hard = '417369805030000000000700000020000060000080400000010000000603070500200000104000000'
for grid in [easy, medium, hard]:
prob = Sudoku(grid)
start = time.time()
Sudoku.display(backtracking_search(prob))
print(f"Blank cell = {grid.count('0')}")
print(f"Time to execute: {(time.time()-start)*1000:.2f}ms")
print()
@version 14feb2013
"""
from csp import Sudoku, easy1, AC3, harder1, backtracking_search, mrv, \
forward_checking, min_conflicts
from search import depth_first_graph_search
# 1. Set up the puzzle.
#puzzle = Sudoku('483921657967345821251876493548132976729564138136798245372689514814253769695417382') # solved (Figure 6.4.b)
#puzzle = Sudoku('..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..') # easy (Figure 6.4.a)
#puzzle = Sudoku('4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......') # harder (csp.py)
puzzle = Sudoku(
'1....7.9..3..2...8..96..5....53..9...1..8...26....4...3......1..4......7..7...3..'
) # hardest (AI Escargot)
print('Start:')
puzzle.display(puzzle.infer_assignment())
# 2. Solve the puzzle.
#depth_first_graph_search(puzzle)
#AC3(puzzle)
backtracking_search(puzzle,
select_unassigned_variable=mrv,
inference=forward_checking)
#min_conflicts(puzzle)
# 3. Print the results.
if puzzle.goal_test(puzzle.infer_assignment()):
print('Solution:')
puzzle.display(puzzle.infer_assignment())
else:
print('Failed - domains: ' + str(puzzle.curr_domains))