Details of Updating (fit!)

Core Principles

  • Stats are subtypes of OnlineStat{T} where T is the type of a single observation.
    • E.g. Mean <: OnlineStat{Number}
  • fit!(o::OnlineStat{T}, data::T)
    • Update o with the single observation data.
  • fit!(o::OnlineStat{T}, data::S)
    • Iterate through data and fit! each item.

Why is Fitting Based on Iteration?

Reason 1: OnlineStats doesn't make assumptions on the shape of your data

Consider CovMatrix, for which a single observation is an AbstractVector, Tuple, or NamedTuple. If I try to fit! it with a Matrix, it's ambiguous whether I want rows or columns of the matrix to be treated as individual observations.


See eachrow and eachcol (after typing using LinearAlgebra).

x = randn(1000, 2)

fit!(CovMatrix(), eachrow(x))

fit!(CovMatrix(), eachcol(x'))
CovMatrix: n=1_000 | value=[0.989115 -0.0513987; -0.0513987 1.08399]

Reason 2: OnlineStats works out-of-the-box with many data structures

Tabular data structures such as those in JuliaDB iterate over named tuples of rows, so things like this just work:

using JuliaDB

t = table(randn(100), randn(100))

fit!(2Mean(), t)

A Common Error

Consider the following example:

julia> fit!(Mean(), "asdf")ERROR: The input for Mean is Number.  Found Char.

This causes an error because:

  1. "asdf" is not a Number, so OnlineStats attempts to iterate through it
  2. Iterating through "asdf" begins with the character 'a'