online-0.2.0: readme.md
[online](https://github.com/tonyday567/online)
===
[](https://travis-ci.org/tonyday567/online) [](https://hackage.haskell.org/package/online) [](http://stackage.org/lts/package/online) [](http://stackage.org/nightly/package/online)
online turns a statistic (in haskell this can usually be thought of as a fold of a foldable) into an online algorithm.
motivation
===
Imagine a data stream, like an ordered indexed container or a
time-series of measurements. An exponential moving average can be
calculated as a repeated iteration over a stream of xs:
$$ ema_t = ema_{t-1} * 0.9 + x_t * 0.1 $$
The 0.1 is akin to the learning rate in machine learning, or 0.9 can be
thought of as a decaying or a rate of forgetting. An exponential moving
average learns about what the value of x has been lately, where lately
is, on average, about 1/0.1 = 10 x's ago. All very neat.
The first bit of neat is speed. There's 2 times and a plus. The next is
space: an ema is representing the recent xs in a size as big as a single
x. Compare that with a simple moving average where you have to keep the
history of the last n xs around to keep up (just try it).
It's so neat, it's probably a viable monoidal category all by itself.
online
======
Haskell allows us to abstract the compound ideas in an ema and create
polymorphic routines over a wide variety of statistics, so that they all
retain these properties of speed, space and rigour.
av xs = L.fold (online identity (.* 0.9)) xs
-- av [0..10] == 6.030559401413827
-- av [0..100] == 91.00241448887785
`online identity (.* 0.9)` is how you express an ema with a decay rate
of 0.9.
online works for any statistic. Here's the construction of standard
deviation using applicative style:
std :: Double -> L.Fold Double Double
std r = (\s ss -> sqrt (ss - s**2)) <$> ma r <*> sqma r
where
ma r = online identity (.*r)
sqma r = online (**2) (.*r)