Chris Stratton
New Member
Can anyone propose an algorithm for computationally optimizing a social playlist?
What I'd like to do is input a set of frequency coefficients, ie something like
W5 T4 V3 F5 Q4
And then have it randomly generate a list. That would be easy - just total, calculate an random number over that interval, assign anything in the first 5 to waltz, anything in the next 4 to tango, etc.
Except that it should also give some weight to how frequently that style was last played.
And it probably should not put Vw and Quickstep back to back.
I could do some kind of history decreases probability thing, but then it could still happen.
Or maybe I should just build in a "roll again" rule - roll again all the time if the dance was just played, and some fraction of the time if it was played recently - and half of that for Qs vs. Vw rather than themselves....
Or maybe some kind of genetic algorithm?
I wonder if a journal paper could be written by the end of the month...
What I'd like to do is input a set of frequency coefficients, ie something like
W5 T4 V3 F5 Q4
And then have it randomly generate a list. That would be easy - just total, calculate an random number over that interval, assign anything in the first 5 to waltz, anything in the next 4 to tango, etc.
Except that it should also give some weight to how frequently that style was last played.
And it probably should not put Vw and Quickstep back to back.
I could do some kind of history decreases probability thing, but then it could still happen.
Or maybe I should just build in a "roll again" rule - roll again all the time if the dance was just played, and some fraction of the time if it was played recently - and half of that for Qs vs. Vw rather than themselves....
Or maybe some kind of genetic algorithm?
I wonder if a journal paper could be written by the end of the month...