model = ESN(input_size=input_size,
output_size=output_size,
reservoir_size=reservoir_size,
adjacency_density=0.0006,
spectral_radius=0.1,
input_scale=0.5)
W_out, reservoir = model.train(train_data,
target_data=target_data,
washout=washout,
ridge_param=ridge_param)
predict = model.predict(reservoir,
test_observed,
ptb_func=None,
ptb_scale=1.0,
nexttime=nexttime,
extended_interval=1000)
predict = Lorenz96(predict.T.flatten(),
nsteps=test_length,
npoints=output_size)
test_target = Lorenz96(test_target.T.flatten(),
nsteps=test_length,
npoints=output_size)
predict.saveData(savepath="./predict.csv")
test_target.saveData(savepath="./nature.csv")
diff = predict - test_target
if args.t == 'dnf':
data = np.log(data)
elif args.t == 'ig':
data = data[0::2] / 10000 - 1.1
esn = ESN(n_in=1, n_fb=1, n_units=args.u, spectral_radius=args.s)
trainlen = args.trainlen
predlen = args.predlen
print('using: %d, predicting: %d' % (trainlen, predlen))
print('fitting...')
fit = esn.fit(np.ones(trainlen), data[:trainlen])
print('predicting...')
pred = esn.predict(np.ones(predlen), cont=True)
if args.t == 'dnf':
data = np.exp(data)
pred = np.exp(pred)
elif args.t == 'ig':
data = data + 1.1 * 10000
pred = pred + 1.1 * 10000
plt.figure(figsize=(200, 30))
end = trainlen + predlen + 1
datalen = min(end, len(data))
plt.plot(range(0, datalen),
data[0:datalen],
'--bo',
linestyle="solid",
