def test(problemFile, problemName):
print("TEST")
problem = Problem.LoadProblem(problemFile)
t0 = time()
while (not problem.isSolved()):
vertexHeuristic, colorHeuristic = choice(HeuristicFunctionArray)
vertex = vertexHeuristic(problem)
color = colorHeuristic(problem, vertex)
problem.assignColor(vertex, color)
t1 = time()
print("Finished in {}".format(t1 - t0))
print("PROBLEM SOLVED USING {} colors".format(
np.count_nonzero(problem.colors)))
problem.printNodes()
problem.writeNodes("testTables", "RANDOMSOL-{}".format(problemName))
def main():
# problemFile = "./Instances/queen/queen8_8.col"
# problemFile = "./Instances/inithx/inithx.i.2.col"
problemFile = "./Instances/miles/miles1500.col"
problem = Problem.LoadProblem(problemFile)
problemName = problemFile.split("/")[-1]
hist = np.zeros((1, 3, 7))
currentStatus = problem.Status()
for i in range(0, len(hist[0])):
hist[0][i] = np.concatenate((problem.NumericalStatus(), [0]), axis=0)
with tf.Session() as sess:
model = loadModel()
t0 = time()
while (not problem.isSolved()):
heuristic = np.argmax(model.predict(hist)) #From one hot to index
vertexHeuristic, colorHeuristic = HeuristicFunctionArray[heuristic]
vertex = vertexHeuristic(problem)
color = colorHeuristic(problem, vertex)
problem.assignColor(vertex, color)
#Update hist and predict next step
hist[0][0] = hist[0][1]
hist[0][1] = hist[0][2]
hist[0][2] = np.concatenate(
(problem.NumericalStatus(), [heuristic]))
t1 = time()
print("Finished in {}".format(t1 - t0))
print("PROBLEM SOLVED USING {} colors".format(
np.count_nonzero(problem.colors)))
problem.printNodes()
problem.writeNodes("testTables", "LSTMSOL-{}".format(problemName))
print("RANDOM TEST")
test(problemFile, problemName)
bestMove = moveArray[maxFitnessIndex]
problem.assignColor(bestMove[0], bestMove[1])
return maxFitnessIndex, fitnessArray[maxFitnessIndex]
if __name__ == '__main__':
# problemFile = "./Instances/queen/queen16_16.col"
problemFile = "./Instances/mulsol/mulsol.i.1.col"
name = problemFile.split("/")[-1]
if (not os.path.isdir("data/{}".format(name))):
os.mkdir("data/{}".format(name))
problem = None
for i in range(10):
problem = Problem.LoadProblem(problemFile)
file = open("data/{}/{}.csv".format(name, i), "w")
t0 = time()
file.write(
"currentColorRatio, currentNodeRatio, averageNodesUsedPerColor, averageEdgesPerNode, minNeighbors, maxNeighbors, bestH\n"
)
with tqdm(total=len(problem.colors)) as bar:
while (not problem.isSolved()):
bestH, fit = EvalPosibilities(problem)
# file.write("Heuristic {} Fitness: {}\n".format(bestH, fit))
file.write("{},{}\n".format(problem.Status(), bestH))
# print(np.sum(problem.colors))
progress = np.sum(problem.colors)
bar.update(progress - bar.n)
# problem.printNodes()
t1 = time()
