Data preparation for NeurEco Discrete Dynamic with the Python API
Data preparation for NeurEco Discrete Dynamic with the Python API#
The python API expects the data for model construction or evaluation in form of a list of NumPy arrays containing the data.
allowed types of arrays: int, float, double
each time array contains one column corresponding to a time variable, it is a finite arithmetic sequence with spacing equal to time-step
each input (excitation) array contains a table:
number of columns is the number of input features (excitations)
number of line is the same as in corresponding time array
each line contains: the values of input features (excitations) at point of time found at the same line number of the corresponding time array
each output array contains a table:
number of lines is the same as in the corresponding time and input (excitation) arrays
number of columns is equal to the number of output features
each line contains: the values of output features at point of time found at the same line number of the corresponding time array
The time-step must be the same for all tables
When data represent multiple experiences, they are passed as multiple time, input (excitation) and output arrays. In this case pay attention to preserving the correspondence between time, input (excitation) and output arrays.
The time array in different set of time/input (excitation)/output arrays are not required to be the same, they can have different length and/or initial time-point, but the time-step must stay the same for all experiences.
There is no need to normalize the data, as the normalization is handled by NeurEco, Data normalization for Discrete Dynamic.