def initClasses(param):
# The Network class
net = network.Network(param)
# The Genetic Algorithm class
ga = genetic.geneticAlgorithm(param)
# The class with comparison functions
com = network.compare()
return net, ga, com
def initClasses(param, MPI, groupSize, networkFitness):
# The Network class
net = network.Network(param)
# The Genetic Algorithm class
ga = genetic.geneticAlgorithm(param)
# The class with comparison functions
com = network.compare(networkFitness)
# The MPI class
pd = parallel.parallelDistributed(MPI, groupSize, param)
return net, ga, com, pd
def find_tour():
global last_update_ts
global traffic_analyzer
curr_ts = time()
if curr_ts - last_update_ts > 1000:
# update traffic data
traffic_analyzer = TrafficAnalyzer()
last_update_ts = curr_ts
print('Traffic data updated at', str(datetime.now()))
try:
data = request.get_json()
W = data['W']
R = data['R']
if 'custom_hour' not in data:
traffic_analyzer.scale_weights(W, R)
else:
get_analyzer(int(data['custom_hour'])).scale_weights(W, R)
tour = geneticAlgorithm(W, int(len(W)**2))
res = {'tour': tour}
return make_response(jsonify(res), 200)
except:
return make_response('{"code": 1, "message": "fail"}', 400)
# visualization
print("Best Found [(score, genrow)]:", best, ", Mutation number:", count+1)
time.sleep(0.5)
# continue with new gene row to mutate
bestMel = orderedTuple[-1][1]
lastGen = generation[-1][1]
count += 1
return ("Winning Generation[(score, genrow), (nextBestScore, nextBestGenRow), ...]:"), generation, ("Amount of mutations needed:"), count
# print(mutateGood(data.mel, 2, []))
print(geneticAlgorithm(1000, data.mel, data.mir))
# testArray1 = [2,3,1,4,5]
# testArray2 = [3,2,1,5,4]
# testArray3 = [1,2,3,4,5]
# print(scoreTest2(testArray1))
# print(scoreTest2(testArray2))
# print(scoreTest2(testArray3))
def experimentGraph(length):
# while length > 3:
for j in range(10):
title = "steepestAscend; N = " + str(length)
gen1 = [*range(1,length + 1)]
gen2 = [*range(1,length + 1)]