When a minicolumn activates: the TM makes sure that at least one of the cells in the minicolumn is a winner cell. If there were no predictive cells it picks an arbitrary winner. If there are predictive cells, then all of the predicted cells in the mini-column are winners.
So if I understand correctly, you want to tell the user “how many things are predicted”.
If this number is zero, then you’ve hit an anomaly.
If this number is one, then TM is predicting exactly one thing will happen.
If this number is two, then TM is predicting that one of two different things could happen.
The minimum number of cells that the TM will activate to represent a thing is the number of active minicolumns.
The equation to try is: n_predicted_things = n_pred_cells / n_active_minicolumns
Yes you make perfect sense.
I hadn’t considered your point about winnerCells including predicted cells – enabling >1 per column.
I’ve since updated the code to use equivalent of n_active_minicolumns as denominator, like you show.
I name it n_cols_per_pred, in case the person reading the code is unfamiliar w/HTM terms. I get it by multiplying sp params columnCount & localAreaDensity - since their product is ~72.
I think I have it right now – but all ears for any corrections/improvements!