reservoirpy.datasets.mso#

reservoirpy.datasets.mso(n_timesteps, freqs, normalize=True)[source]#

Multiple superimposed oscillator task [22]

This task is usually performed to evaluate a model resistance to perturbations and its asymptotic stability. See, for example: [23].

\[MSO(t) = \sum_{i} sin(f_i t)\]
Parameters:

  • n_timesteps (int) – Number of timesteps to generate

  • freqs (list) – Frequencies of the sin waves

  • normalize (bool, optional) – If True, scales the range of values in [-1, 1]

Returns:

Multiple superimposed oscillator timeseries.

Return type:

array of shape (n_timesteps, 1)

Examples

>>> from reservoirpy.datasets import mso
>>> timeseries = mso(200, freqs=[0.2, 0.44])
>>> print(timeseries.shape)
(200, 1)

References