Not done with followers just yet


Viewing from different "vantage points" yielded no particular benefit.

Creating a recent list for weighting was not statistically significant because the data does NOT exhibit seasonality.

Enter recursion... I never tried to reprocess the resultant list for a follower among followers...

Recursively reducing this follower list by running it through the same function might just be what I was missing... a better indicator of what is next than just the most frequent number in the follower list.

It is sometimes difficult to get a proper base case (when recursion should stop) without running experiments, so that is the focus for now, with parallel development on the time series classification angle.

Pick 3 and pick 5 are still the targets. And this time I am writing the functions to auto detect the number of columns so I do not need a different function for each game type... a one size fits all approach!

I walked away from the laptop for a few days to try and re-imagine the next steps and this was my " a ha" moment...

Ready to get back in the ring!

Entry #239


Avatar hypersoniq -
Coding is relatively easy so far. Since the output includes the follower list and a distribution of follower numbers, a simple loop with an input that asks if you want to process the current follower list lets you make the decision to reprocess. No base case needed.
One pass shows you ALL of the followers based on the history...
Two runs shows you the followers among the followers and so on. Then you pick from the distribution list on the last pass... no machine learning needed!
Avatar hypersoniq -
And yes, one modular function to do it all, no more editing 18 modules when a change is implemented... now to see if it works...

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