Design windowed process

MZ is about to add a feature to RapidLib to allow users to run "continuous" processing on a window of input frames. In other words, this function gets input data frame-by-frame and buffers it into the right size series and calls dtw.run() every n number of frames.

Since this is going to co-exist with similar functions in Rapid-Mix API, we should come up with some general design for this kind of use.

@francisco This is basically what is happening when GVF is in following mode. AFAIK, GVF only does following -- there is no discreet mode for processing a pre-recorded input series.

@Joseph I think that hhmm.filter() is the same type of function as GVF.update() for continuous recognition?

This suggests to me that RapidLib/DTW should handle its own windowing.

I did consider a design that was some kind of windowedML object. That might take a pointer to a specific model and some window parameters. So something like:

temporalClassification myModel;
windowedMLObject myWindowThing(*myModel, <window size>, <hopsize>);
myWindowThing.continuousProcessMethod(<new data frame>);

That would need better names.

Is there any value in having this kind of object in the RAPID-MIX API?

I'll tag @rebeccaf and @FredB here, too,