How does the prediction and training work?

[tornyit]

Hi,

I'm a little confused about how the ESN actually trains and predicts. In the Mackey-Glass example training is done in the following way:
pred_training = esn.fit(np.ones(trainlen),data[:trainlen])

This seems like I only give the inputs?
I guess the np.ones gives the bias term, but shouldn't I give the inputs and the desired outputs? I mean something like esn.fit(data[:trainlen], data[1:trainlen+1]) for 1 step prediction. ( I think something like this is done in freqgen.ipynb, but correct me if I'm wrong.)

In the Mackey-Glass example for prediction you only give np.ones(future) as input. Does this mean the network starts with the last training sample and then starts to generate the timeseries by feeding the output back as input?