Add an option to make the success probability estimation nonlinear

Add an option to make the success probability estimation nonlinear

Our "what is the success probability of paying over a channel with
the given liquidity bounds" calculation currently assumes the
probability of where the liquidity lies in a channel is constant
across the entire capacity of a channel. This is obviously a
somewhat dubious assumption given most nodes don't materially
rebalance and flows within the network often push liquidity
"towards the edges".

Here we add an option to consider this when scoring channels during
routefinding. Specifically, if a new `linear_success_probability`
flag is unset on `ProbabilisticScoringFeeParameters`, rather than
assuming a PDF of `1` (across the channel's capacity scaled from 0
to 1), we use `(x - 0.5)^2`.

This assumes liquidity is likely to be near the edges, which
matches experimental results. Further, calculating the CDF (i.e.
integral) between arbitrary points on the PDF is trivial, which we
do as our main scoring function.

While this (finally) introduces floats in our scoring, its not
practical to exponentiate using fixed-precision, and benchmarks
show this is a performance regression, but not a huge one, more
than made up for by the increase in payment success rates.