I have a system where I am trying to predict a 3-tuple. The input is previous values of the 3-tuple, plus some other scalar inputs, plus a timestamp.
With particular reference to the encoders, what's the best way to go about doing this?
Specifically, within model_params,
is there any difference between specifying the sensorParams as individual encoders (e.g. using ScalarEncoder), or in using the MultiEncoder (with the individual encoders as an input dict to it)? In other words, if specified as individual encoders, are the encoders just put into a MultiEncoder (under the hood) anyway? Do both approaches achieve the same result?
How should the '_classifierInput' field be specified? I can only find examples where the predicted field is a scalar not a tuple. (I haven't dug into the CoordinateEncoder, which seems to be the most likely candidate. Is that right?) It's not even clear to me why it is required (many examples don't seem to have it), however an "TypeError: list indices must be integers, not NoneType" error occurs if it isn't specified.
Are there any examples where the predicted field is a tuple?