def test_delete(self):
repo=repository()
repo.add_bicicleta(1,'tip',5)
repo.add_bicicleta(5,'tip',5)
repo.add_bicicleta(3,'tip',5)
repo.delete(5)
lungime=len(repo.get_all())
self.assertEquals(lungime,2)
def main():
m = Map(mapLengh, mapLengh)
m.randomMap()
# init = setUpParamsManually(m)
init = setUpParamsRandom(m)
if init is None:
print(
"Incorrect data introduced. Sensors/Drone may overlap forbidden positions."
)
return
drone = init[0]
droneEnergy = init[1]
sensors = init[2]
repo = repository(m, drone, droneEnergy, sensors)
c = controller(repo)
c.initializeDistanceMatrix()
dm = c.getDistanceMatrix()
antColonyDistanceMatrix = np.zeros((SENSORS + 1, SENSORS + 1))
for i in range(SENSORS):
antColonyDistanceMatrix[i][SENSORS] = drone.distances[i]
antColonyDistanceMatrix[SENSORS][i] = drone.distances[i]
for i in range(SENSORS):
for j in range(SENSORS):
antColonyDistanceMatrix[i][j] = dm[i][j]
for i in range(len(antColonyDistanceMatrix)):
for j in range(len(antColonyDistanceMatrix)):
if antColonyDistanceMatrix[i][j] == 0.0 and i != j:
print("One or more sensors is/are unreachable")
return
"""
for i in range(SENSORS + 1):
for j in range(SENSORS + 1):
print(antColonyDistanceMatrix[i][j], end=" ")
print()
"""
aco = AntColonyOptimization(antColonyDistanceMatrix)
pathLength, path = aco.run()
finalPath = c.makePath(pathLength, path)
path, result = c.adjustBatteryToPath(finalPath)
for e in result:
print("Sensor (" + str(e[0]) + ", " + str(e[1]) + ") : " + str(e[2]) +
" seen squares using " + str(e[3]) + " battery")
movingDrone(m, path, sensors, drone, 0.1)
def test_add(self):
repo=repository()
repo.add_bicicleta(1,'tip',5)
lungime=len(repo.get_all())
self.assertEquals(lungime,1)
def __init__(self):
self.__currentIteration = 0
self.__repository = repository()
plt.ylabel('Average fitness per generation')
plt.xlabel('Generation number')
plt.show()
return results[0]
if __name__ == '__main__':
args = []
args.append(populationSize)
args.append(inidividualSize)
args.append(initialCoordinates)
args.append(map)
args.append(crossOverProb)
args.append(mutateProb)
repository = repository()
controller = controller(args, repository)
# f = open("Stats/test.txt", "a")
# f.write("-------------------------------------------------------\n")
# f.write("| Seed | Average Deviation | Standard Deviation |\n")
# f.close()
for i in range(1):
repository.createPopulation(args)
path = oneRun()
seed = seeds[i]
random.seed(seed)
fitAvg = repository.getFitnessAvg()
data = np.array(fitAvg)
# averageDev = np.average(data)
# standardDev = data.std()
