def demoClassification():
# Teach network XOR function
#pat = [
# [[0,0], [0]],
# [[0,1], [1]],
# [[1,0], [1]],
# [[1,1], [0]]
#]
print "loading training.txt"
pat = dewload.dew_load_train_file("./txt/training.txt")
print "pat = "
print pat
pat2 = dewnormal.dew_normal_big_set_for_ol(pat)
print "paterrn from Training.txt"
print pat2
# create a network with two input, two hidden, and one output nodes
#load and retrain
loa = True
if not loa:
ggg = []
ggg = loadweight("./txt/weight_1.txt")
n = NN(ggg[0][0], ggg[0][1], ggg[0][2], ggg[0][3], regression=False)
n.wi = ggg[1][0]
n.wo = ggg[1][1]
n.wo2 = ggg[1][2]
else:
n = NN(67, 10, 11, 8, regression=False)
# train it with some patterns then test it.
#print pat2
n.train(pat2, 1000, 0.1, 0.2)
#n.test(pat2, verbose = True)
n.weights()
saveweight("./txt/weight_1.txt", n)
#save
#txtx = []
#txtx = n.wi , n.wo
#dewweight.dew_write_weight_file("./txt/weight_1.txt",txtx)
#predict = [
# [[1,0],[1]]
# ]
# n.test(predict, verbose = True)
try:
plot(inputs, outputs, actual)
#plot(pat, pat, pat)
print "Press a key to quit."
value = raw_input()
except:
print "Must have matplotlib to plot."
def demotesting():
# Teach network XOR function
#pat = [
# [[0,0], [0]],
# [[0,1], [1]],
# [[1,0], [1]],
# [[1,1], [0]]
#]
pat = dewload.dew_load_train_file("./txt/training.txt")
print pat
pat2 = dewnormal.dew_normal_big_set_for_ol(pat)
print "paterrn from Training.txt"
print pat2
# create a network with two input, two hidden, and one output nodes
n = NN(67, 4, 1, regression = False)
#load
n.wi, n.wo = loadweight("./txt/weight_1.txt",n)
# train it with some patterns then test it.
#n.train(pat2, 500, 0.1, 0.2)
#n.test(pat2, verbose = True)
n.weights()
print "offline test pat2 after training"
print n.test(pat2)
#predict = [
# [[1,0],[1]]
# ]
n.test(pat2, verbose = True)
try:
plot(inputs, outputs, actual)
#plot(pat, pat, pat)
print "Press a key to quit."
value = raw_input()
except:
print "Must have matplotlib to plot."