def saveweight(str, n):
#save
tntn = []
tntn = n.ni - 1, n.nh - 1, n.nh2 - 1, n.no
txtx = []
txtx = n.wi, n.wo, n.wo2
tgtg = []
tgtg.append(tntn)
tgtg.append(txtx)
print 'save...'
print tgtg
print '...save'
dewweight.dew_write_weight_file(str, tgtg)
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
n = NN(67, 38, 8, regression = False)
#load and retrain
#n.wi, n.wo = loadweight("./txt/weight_1.txt",n)
# train it with some patterns then test it.
#print pat2
n.train(pat2, 100, 0.1, 0.2)
#n.test(pat2, verbose = True)
n.weights()
#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 saveweight(str,n):
#save
txtx = []
txtx = n.wi , n.wo
dewweight.dew_write_weight_file(str,txtx)