print('You must set the no. of neural in each hidden layer !')
# array of transfer function that related with no. of hidden layer
# dim=len(nNeuronArr)-2 (set transfer funciton only neuron in each hidden layer)
transfNetwork = ['hardlim']
# print('\ninput=')
# print(p)
#
# print('\ntarget=')
# print(target)
# singleNN=nn()
# singleNN.inAndOut(p,1,target,0,[2,1,1],['hardlim'])
# Build the network
nnSP1 = skNN.MLP()
nnSP1.build(p, indexSampleP, target, indexSampleTarget, nNodeInEachHid,
transfNetwork)
nnSP1.setWeightAndBias(1, 30)
percError = 10
# nnSP1.train.unified(loopInInput=10,tol=tol,visualize='text',visualizeStep='none')
# nnSP1.train.unified(loopInInput=10,tol=tol,visualize='graph',visualizeStep='click')
nnSP1.train.unified(loopInInput=10,
tol=percError,
visualize='graph',
visualizeStep=0.05)
# --------------------- Try another input -------------------------
# # input of network
# plt.plot(p,target,'or')
# plt.show()
# network structure
# nHidLayer=1
nNodeInEachHid = np.array([[15, 1]])
nHidLayer = len(nNodeInEachHid)
# if len(nNodeInEachHid) != nHidLayer:
# print('You must set the no. of neural in each hidden layer !')
# array of transfer function that related with no. of hidden layer
# dim=len(nNeuronArr)-2 (set transfer function only neuron in each hidden layer)
transfNetwork = ['sigm', 'purelin']
# Build the network
nnfit = wbxnn.MLP()
nnfit.build(p, indexSampleP, target, indexSampleTarget, nNodeInEachHid,
transfNetwork)
# training
# visualize='text','graph'
# epoch is no. of repeat the entire input set to train
# nIte is np. of repeat batch input
nnfit.train.BP(lr=0.0015,
batchSize=3,
epoch=1000,
nIte=5,
errorType='SE',
TolBatch=0.1,
TolAll=0.1,
visualize='graph',
