Closed joshday closed 8 years ago
It'll take me a little while to go through this in detail, but just based on your summary: :+1:
These changes are now in master. New docs coming soon.
Changes are now in METADATA. Docs mostly moved to README to make them easier to maintain.
I started toying around with changes to OnlineStats in a separate package, https://github.com/joshday/OnlineStatistics.jl (It only exists as a separate package to run comparisons between the old and the new. It is not, nor will it be, a replacement). What was meant to be just a few performance tests turned into a major rewrite (details on changes below). I've managed some performance improvements (some marginal, some orders of magnitude):
Changes:
state(o)
andstatenames(o)
, replace withvalue(o)
value(o)
returns only the statistic, nonessential information (ex: Vector of quantiles) should show up inBase.show
methodsWeighting
toWeight
,EqualWeighting
toEqualWeight
, etc.nup
(number of updates) to each OnlineStat, which is useful forLearningRate
or similar stochastic weighting mechanisms to be added in the futureweight!(o, n2)
updates then
andnup
fields and returns the weight. Take a look at https://github.com/joshday/OnlineStatistics.jl/blob/master/src/summary.jl to see how this changesupdate!
methodsupdate!
toStatsBase.fit!
sweep!
operator with additional method with a placeholder vector to avoid gc.sweep!
has also been changed to store the upper triangular matrix, rather than lower.FitDistribution
andFitMvDistribution
typesSparseReg
, it's functionality (coefficients from penalized likelihood) is now handled byLinReg
summary/
is now in one file,summary.jl
.StochasticModel
toStatLearn
. I think hinting at statistical learning is better than "algorithms based on a stochastic subgradient". If anyone has a better name for something that incorporates SVMs and linear, logistic, poisson, huber, l1 loss, and quantile regression, I'm all ears.I wanted to get this out in the open before I start moving things over to OnlineStats. The biggest impact change is how weightings are handled.