def run(self, start = 0, end = 1011):
rules, _ = load_dimacs('rules/sudoku_rules_9x9.txt')
sudokus = load_sudokus('sudokus/1000_sudokus_9x9.txt')[start:end]
data = []
heuristicDict = {
"Random": RandomBranchDecisionFactory,
"DLIS(True)": DynamicLargestIndividualSumTrueFactory,
"DLIS(False)": DynamicLargestIndividualSumFalseFactory,
"JW-OS": JeroslowWangOneSidedFactory,
"DLCS": DynamicLargestCombinedSumFactory,
"Dummy": DummyBranchDecisionFactory,
"RandomFalse": RandomFalseBranchDecisionFactory,
}
inputValues = []
for i in range(len(sudokus)):
sudoku = sudokus[i]
sudokuID = start + i + 1
inputValues.append((sudokuID, sudoku, rules, heuristicDict))
print("cpu_count={}".format(mp.cpu_count()))
pool = mp.Pool(mp.cpu_count())
results = pool.starmap(processOneSudokuWithHeuristic, inputValues)
pool.close()
for result in results:
data.extend(result)
# Save output to csv
filename="SolverComparison_{}_{}.csv".format(start, end)
save_csv(filename, heuristicDataHeader, data)
print("")
def run(self, numOfConstraints, timeout, start=0, end=1011):
sudokus = load_sudokus('sudokus/1000_sudokus_9x9.txt')[start:end]
data = []
rulesDict = self.generateRules(numOfConstraints)
inputValues = []
for i in range(len(sudokus)):
sudoku = sudokus[i]
sudokuID = i + 1
inputValues.append(
(sudokuID, sudoku, rulesDict, numOfConstraints, timeout))
print("cpu_count={}".format(mp.cpu_count()))
pool = mp.Pool(mp.cpu_count())
results = pool.starmap(processOneSudoku, inputValues)
pool.close()
for result in results:
data.extend(result)
# Save output to csv
header = [
"sudokuID", "name", "numOfConstraints", "result", "totalTime",
"loop", "backtrack", "flip", "unit"
]
filename = "constraints_{}_{}_{}.csv".format(numOfConstraints, start,
end)
save_csv(filename, header, data)
print("")
def main():
rules, numOfVars = load_dimacs('rules/sudoku_rules_9x9.txt')
sudokus = load_sudokus('sudokus/1000_sudokus_9x9.txt')
solverSpecs = [{
"SolverClass": BasicDPLL,
}]
# cnf = [[1,2,3], [1,-2],[1,-3],[-1,3]]
# numOfVars = 3
for solverSpec in solverSpecs:
SolverClass = solverSpec["SolverClass"]
overallMetrics = InMemoryMetrics()
for sudoku in sudokus:
cnf = rules + sudoku
instanceMetrics = InMemoryMetrics()
before = time.time()
solver = SolverClass(cnf,
numOfVars,
# DynamicLargestIndividualSum(True),
# JeroslowWangOneSided(),
# DynamicLargestCombinedSum(),
DummyBranchDecision(),
-1,
0,
instanceMetrics
)
result, model = solver.solve()
totalTime = time.time()-before
overallMetrics.observeMany(instanceMetrics.getCounters())
overallMetrics.observe("totalTime", totalTime)
overallMetrics.observe("result", result)
before = time.time()
validCnf, someDontCare = validation.validateCnfModel(cnf, model)
validCnfTime = time.time()-before
overallMetrics.observe("someDontCare", someDontCare)
overallMetrics.observe("validCnf", validCnf)
overallMetrics.observe("validCnfTime", validCnfTime)
before = time.time()
validSudoku = validation.validateSudoku(model, 9)
validSudokuTime = time.time()-before
overallMetrics.observe("validSudoku", validSudoku)
overallMetrics.observe("validSudokuTime", validSudokuTime)
overallMetrics.printObservations()
def run(self, numOfConstraints, timeout, start=0, end=1011):
rules, numOfVars = load_dimacs('rules/sudoku_rules_9x9.txt')
sudokus = load_sudokus('sudokus/1000_sudokus_9x9.txt')[start:end]
data = []
rulesDict = self.generateRules(numOfConstraints)
for i in range(len(sudokus)):
if i % 10 == 9:
print(".", end='', flush=True)
sudoku = sudokus[i]
sudokuID = i + 1
for name in rulesDict:
rules = rulesDict[name]
cnf = rules + sudoku
instanceMetrics = InMemoryMetrics()
before = time.time()
solver = BasicDPLL(cnf, numOfVars,
self.decisionHeuristicFactory, timeout, 5,
instanceMetrics)
result, _ = solver.solve()
totalTime = time.time() - before
counters = instanceMetrics.getCounters()
if result == "TIMEOUT":
print("T", end='', flush=True)
data.append((
sudokuID,
name,
numOfConstraints,
result,
totalTime,
counters.get("loop", 0),
counters.get("backtrack", 0),
counters.get("flip", 0),
counters.get("unit", 0),
))
# Save output to csv
header = [
"sudokuID", "name", "numOfConstraints", "result", "totalTime",
"loop", "backtrack", "flip", "unit"
]
filename = "constraints_{}_{}_{}.csv".format(numOfConstraints, start,
end)
save_csv(filename, header, data)
print("")
def run(self, experimentName, rulesDict, timeout, start = 0, end = 1011):
sudokus = load_sudokus('sudokus/1000_sudokus_9x9.txt')[start:end]
data = []
inputValues = []
for i in range(len(sudokus)):
sudoku = sudokus[i]
sudokuID = start + i + 1
inputValues.append((sudokuID, sudoku, rulesDict, timeout))
print("cpu_count={}".format(mp.cpu_count()))
pool = mp.Pool(mp.cpu_count())
results = pool.starmap(processOneSudoku, inputValues)
pool.close()
for result in results:
data.extend(result)
# Save output to csv
filename="{}_{}_{}_{}.csv".format(experimentName, start, end, time.time())
save_csv(filename, dataHeader, data)
print("")