def run_test(algorithm, dataset):
if algorithm == "BruteForce":
result = BruteForce.BruteForce(dataset[1])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3], result[1], 1).get_result()
elif algorithm == "BranchNBound":
result = BranchNBound.BranchNBound(dataset[1])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3], result[1], 1).get_result()
elif algorithm == "Greedy":
#bb_result = BranchNBound.BranchNBound(dataset[1])
result = Greedy.better_greedy(dataset[1])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3],
result[1], result[1]).get_result()
elif algorithm == "Randomized":
#bb_result = BranchNBound.BranchNBound(dataset[1])
result = Randomized.better_result_of_randomized(dataset[1])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3],
result[1], result[1]).get_result()
elif algorithm == "MinSpanTree":
#bb_result = BranchNBound.BranchNBound(dataset[1])
result = MinSpanTree.MinSpanTree(dataset[1])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3],
result[1], result[1]).get_result()
elif algorithm == "EA_hillclimbing":
#bb_result = BranchNBound.BranchNBound(dataset[1])
result = EA_hillclimbing.hillclimbing_algorithm(dataset[1], dataset[0][0])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3],
result[1], result[1]).get_result()
elif algorithm == "Genetic":
#bb_result = BranchNBound.BranchNBound(dataset[1])
result = GA.genetic_algorithm(dataset[1], dataset[0][0])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3],
result[1], result[1]).get_result()
elif algorithm == "TwoOpt":
#bb_result = BranchNBound.BranchNBound(dataset[1])
result = TwoOpt.two_opt(dataset[1])
test_result = TestResult(result[0], dataset[0][0], dataset[0][1],
dataset[0][2], dataset[0][3], dataset[0][4],
result[3],
result[1], result[1]).get_result()
else:
raise Exception("Wrong algorithm chosen!")
return test_result
