# t2 = np.ones([1, 20]) * 2
#
# input = np.array([i1, i2, i1, i2]).reshape(20 * 4, 1)
# target = np.array([t1, t2, t1, t2]).reshape(20 * 4, 1)
# Create datasets
print 'Preparing dataset ...'
# ts = sampler.load_csv('data/series.csv')
ds = SequentialDataSet(inLayerCount, outLayerCount)
ds.newSequence()
# ds = SupervisedDataSet(inLayerCount, outLayerCount)
for row in df.itertuples(index=False):
ds.addSample(row[0:columns-2], row[columns-2])
ds.endOfData()
# Create bp trainer
trainer = BackpropTrainer(net, ds)
# Trains the datasets
print 'Training ...'
epoch = 1000
error = 1.0
while error > delta_error and epoch >= 0:
error = trainer.train()
epoch -= 1
print 'Epoch = %d, Error = %f' % (epoch, error)
# To store the epoch error
err_array = []
