def experiment1():
#digraph, threshold_function, t_func_params = None, dep_coef=1, past_coef=0, normalize_initial_values = True
myFCM = FCM.FCM(nx.DiGraph(), FCM.trivalent)
node_list = [1, 2, 3, 4, 5, 6]
initial_state = [1, 1, 1, 1, 1, 1]
density_percent = .4
valid_input = False
seed = 0
while not valid_input:
seed += time.time()
print "seed is: "
print seed
input_FCM = FCM.generate_random_FCM_matrix(node_list,
density_percent,
seed,
FCM.pos_neg_int,
allow_self_edge=True)
input_data = myFCM.calculate_next_states_matrix(input_FCM,
initial_state,
n=30)
print input_data
if not myFCM.simple_pattern_recognition(input_data):
valid_input = True
print "\n\nINPUT FCM IS:"
print input_FCM
print "INPUT DATA IS:"
for row in input_data:
print row
print "RUNNING GA TO ATTEMPT TO LEARN FCM"
genome = G2DList.G2DList(len(node_list), len(node_list))
genome.setParams(rangemin=-1, rangemax=1)
myGA = FCM_GA.LearnFCM(input_data, node_list, myFCM, genome)
myGA.setPopulationSize(1000)
myGA.setGenerations(100)
myGA.setMutationRate(0.3)
myGA.setMinimax(Consts.minimaxType["minimize"])
myGA.evolve(10)
print myGA.bestIndividual()
print "THE ERROR FOR THE BEST INDIVIDUAL IS:"
print myGA.eval_func_matrix(myGA.bestIndividual())
print "ACTUAL FCM"
print input_FCM
def experiment1():
#digraph, threshold_function, t_func_params = None, dep_coef=1, past_coef=0, normalize_initial_values = True
myFCM = FCM.FCM(nx.DiGraph(), FCM.trivalent)
node_list = [1, 2, 3, 4, 5, 6]
initial_state = [1, 1, 1, 1, 1, 1]
density_percent = .4
valid_input = False
seed = 0
while not valid_input:
seed += time.time()
print "seed is: "
print seed
input_FCM = FCM.generate_random_FCM_matrix(node_list, density_percent, seed, FCM.pos_neg_int, allow_self_edge = True)
input_data = myFCM.calculate_next_states_matrix(input_FCM, initial_state, n = 30)
print input_data
if not myFCM.simple_pattern_recognition(input_data):
valid_input = True
print "\n\nINPUT FCM IS:"
print input_FCM
print "INPUT DATA IS:"
for row in input_data:
print row
print "RUNNING GA TO ATTEMPT TO LEARN FCM"
genome = G2DList.G2DList(len(node_list), len(node_list))
genome.setParams(rangemin=-1, rangemax=1)
myGA = FCM_GA.LearnFCM(input_data, node_list, myFCM, genome)
myGA.setPopulationSize(1000)
myGA.setGenerations(100)
myGA.setMutationRate(0.3)
myGA.setMinimax(Consts.minimaxType["minimize"])
myGA.evolve(10)
print myGA.bestIndividual()
print "THE ERROR FOR THE BEST INDIVIDUAL IS:"
print myGA.eval_func_matrix(myGA.bestIndividual())
print "ACTUAL FCM"
print input_FCM
