import ci.helper as helper a = helper.crossvalidation([[1,2,3,4,5],[2,2,3,4,5],[3,2,3,4,5],[4,2,3,4,5],[5,2,3,4,5]], 0.4) print a.getAllSet() print "" print a.getTrain(0) print a.getTrain(1) print "" print a.getTest(0) print a.getTest(1)
argmax = lambda array: max(izip(array, xrange(len(array))))[1]
pttNet = [mlp.randNet([2, 5, 2], type=mlp.SIGMOID), mlp.randNet([2, 10, 2], type=mlp.SIGMOID), mlp.randNet([2, 15, 2], type=mlp.SIGMOID), mlp.randNet([2, 5, 5, 2], type=mlp.SIGMOID)]
learningRate = [0.01, 0.05, 0.1, 0.2]
epoch = 100
f = open("cross.pat", "r")
fw = open("report/cross/report.txt", "w")
lines = f.readlines()
datas = []
for line in lines:
words = line.split(" ")
datas.append([float(word) for word in words])
floods = helper.crossvalidation(datas, 0.1, shuffer=True)
plt.ion()
plt.show()
m_cor = 0.0
for pn in pttNet:
for lr in learningRate:
s_cor = 0.0
b_cor = 0.0
for i in range(0, floods.state):
train = floods.getTrain(i)
test = floods.getTest(i)
print "Net Structure " + str(pn.getLayer()) + " Learning Rate and Momentum " + str(lr) + " Flood " + str(i+1)
