I was wondering whether it would be possible to use logfloat with the ad library. In machine learning we commonly need to compute a gradient of the log-likelihood. I don't imagine this is currently feasible since I can't see any way to differentiate through logarithms in logFloat etc.
I can think of two potential solutions - either to abstract all the logfloat functions using suitable typeclasses and then write instances of those for ad types or to abstract the LogFloat over the underlying numeric type. I don't know how feasible is the former, but I suspect the latter could be done in principle although in practice may be problematic since logfloat performs some low-level operations specific to Double.
In any case, do you think there's some way to make the two libraries work together? And if so how much effort would it take?
I was wondering whether it would be possible to use logfloat with the ad library. In machine learning we commonly need to compute a gradient of the log-likelihood. I don't imagine this is currently feasible since I can't see any way to differentiate through logarithms in
logFloatetc.I can think of two potential solutions - either to abstract all the logfloat functions using suitable typeclasses and then write instances of those for ad types or to abstract the
LogFloatover the underlying numeric type. I don't know how feasible is the former, but I suspect the latter could be done in principle although in practice may be problematic since logfloat performs some low-level operations specific toDouble.In any case, do you think there's some way to make the two libraries work together? And if so how much effort would it take?