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DeepESN for multivariate time-series classification problem

hello,

I would like to use this implementation for a multivariable time series classification.

My dataset is composed of several examples of 9 time-series segments with 128 steps.
Each segment belongs to one of 5 classes.

Input shape (2128,128,9) -> (number of examples, time-steps, variables)
output shape (2128,1) - > (number of examples, class)
(the class is one-hot-encoded so I use categorical-cross-entropy with 5 neurons output layer)

I have it working with a LSTMs based model.

I would like to test with deepESN to see if there is any improvement.

If you can help to provide some lights/base code to do this adaptation it would be nice.

Thanks in advance,
Rui

Accepting PR or updates ?

I recently found your repo after reading about DPS and models, and was curious if this repo is still active or if it's been deprecated

ValueError when running main_MultivariatePolyphonic.py 

D:\ProgramFilesNoSpace\Miniconda3\envs\py36\python.exe C:/Users/MONKEY/Desktop/DeepESN-master/main_MultivariatePolyphonic.py
Traceback (most recent call last):
  File "C:/Users/MONKEY/Desktop/DeepESN-master/main_MultivariatePolyphonic.py", line 80, in <module>
    main()
  File "C:/Users/MONKEY/Desktop/DeepESN-master/main_MultivariatePolyphonic.py", line 61, in main
    states = deepESN.computeState(dataset.inputs, deepESN.IPconf.DeepIP)
  File "C:\Users\MONKEY\Desktop\DeepESN-master\DeepESN\DeepESN.py", line 206, in computeState
    states = self.computeDeepIntrinsicPlasticity(inputs)
  File "C:\Users\MONKEY\Desktop\DeepESN-master\DeepESN\DeepESN.py", line 174, in computeDeepIntrinsicPlasticity
    self.computeLayerState(inputs[i], layer, 1)   
  File "C:\Users\MONKEY\Desktop\DeepESN-master\DeepESN\DeepESN.py", line 130, in computeLayerState
    state[:,0:1] = (1-self.lis[layer]) * initialStatesLayer + self.lis[layer] * np.tanh( np.multiply(self.Gain[layer], self.W[layer].dot(initialStatesLayer) + input[:,0:1]) + self.Bias[layer])        
ValueError: could not broadcast input array from shape (100,100) into shape (100,1)

Process finished with exit code 1

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