# Home

OnlineStats is a Julia package for statistical analysis with algorithms that run both online and in parallel. Online algorithms are well suited for streaming data or when data is too large to hold in memory. Observations are processed one at a time and all algorithms use O(1) memory.

## Installation

import Pkg
Pkg.add("OnlineStats")

## Basics

### Every stat is <: OnlineStat{T}

(where T is the type of a single observation)

julia> using OnlineStats

julia> m = Mean()
Mean: n=0 | value=0.0

julia> supertype(Mean)
OnlineStat{Number}

### Stats can be updated

Note

fit! can be used to update the stat with a single observation or multiple observations: fit!(stat::OnlineStat{T}, y::S) will iterate through y and fit! each element if T != S.

julia> y = randn(100);

julia> fit!(m, y)
Mean: n=100 | value=0.0495537

### Stats can be merged

julia> y2 = randn(100);

julia> m2 = fit!(Mean(), y2)
Mean: n=100 | value=0.0510969

julia> merge!(m, m2)
Mean: n=200 | value=0.0503253

### Stats have a value

julia> value(m)
0.05032528455564986

## Collections of Stats

### Series

A Series tracks stats that should be applied to the same data stream.

y = rand(1000)
s = Series(Mean(), Variance())
fit!(s, y)
Series
├── Mean: n=1000 | value=0.493168
└── Variance: n=1000 | value=0.0828139

### FTSeries

An FTSeries tracks stats that should be applied to the same data stream, but filters and transforms (hence FT) the input data before it is sent to its stats.

s = FTSeries(Mean(), Variance(); filter = x->true, transform = abs)
fit!(s, -y)
FTSeries
├── Mean: n=1000 | value=0.493168
└── Variance: n=1000 | value=0.0828139

### Group

A Group tracks stats that should be applied to different data streams.

g = Group(Mean(), CountMap(Bool))
itr = zip(randn(100), rand(Bool, 100))
fit!(g, itr)
Group
├── Mean: n=100 | value=0.110933
└── CountMap: n=100 | value=OrderedCollections.OrderedDict(false=>53,true=>47)