# brewer2mpl.get_map args: set name set type number of colors
#bmap = brewer2mpl.get_map('Paired', 'qualitative', datasetSize)
#colors = bmap.mpl_colors
#samp = 0
#for data in dataset:
# plt.subplot(5,2,samp)
# plt.plot(data, color=colors[samp])
# samp += 1
#nn.displayNetwork()
errorEvolution=[]
# First example :
inputdata=dataset[np.random.randint(0,len(dataset)-1)]
nn.inputData(inputdata)
nn.computeOutput()
nn.learn()
iterNb = 1
errorEvolution.append(abs(nn.endError))
meanError = abs(nn.endError)
alpha = 0.1
while meanError > 0.05:
inputdata=dataset[np.random.randint(0,len(dataset)-1)]
# # Make network learn from input
nn.inputData(inputdata)
nn.computeOutput()
nn.learn()
iterNb = iterNb + 1
errorEvolution.append(abs(nn.endError))
meanError = (1 - alpha) * meanError + alpha * abs(nn.endError)
# # Generate answer (which is way more tricky)
