API

ADAGRAD()

ADAM(β1 = .99, β2 = .999)

ADAMAX(η, β1 = .9, β2 = .999)

ADAMAX with momentum parameters β1, β2

AutoCov(b, T = Float64; weight=EqualWeight())

Calculate the auto-covariance/correlation for lags 0 to b for a data stream of type T.

Example

y = cumsum(randn(100))
o = AutoCov(5)
fit!(o, y)
autocov(o)
autocor(o)

BiasVec(x)

Lightweight wrapper of a vector which adds a "bias" term at the end.

Example

BiasVec(rand(5))

Bootstrap(o::OnlineStat, nreps = 100, d = [0, 2])

Calculate an nline statistical bootstrap of nrepsreplicates ofo. For each call tofit!, any given replicate will be updatedrand(d)` times (default is double or nothing).

Example

o = Bootstrap(Variance())
fit!(o, randn(1000))
confint(o)

CStat(stat)

Track a univariate OnlineStat for complex numbers. A copy of stat is made to separately track the real and imaginary parts.

Example

y = randn(100) + randn(100)im
fit!(CStat(Mean()), y)

CallFun(o::OnlineStat, f::Function)

Call f(o) every time the OnlineStat o gets updated.

Example

o = CallFun(Mean(), info)
fit!(o, [0,0,1,1])

CountMap(T::Type)
CountMap(dict::AbstractDict{T, Int})

Track a dictionary that maps unique values to its number of occurrences. Similar to StatsBase.countmap.

Example

o = fit!(CountMap(Int), rand(1:10, 1000))
value(o)

CovMatrix(p=0; weight=EqualWeight())

Calculate a covariance/correlation matrix of p variables. If the number of variables is unknown, leave the default p=0.

Example

o = fit!(CovMatrix(), randn(100, 4))
cor(o)

Diff(T::Type = Float64)

Track the difference and the last value.

Example

o = Diff()
fit!(o, [1.0, 2.0])
last(o)
diff(o)

Extrema(T::Type = Float64)

Maximum and minimum.

Example

fit!(Extrema(), rand(10^5))

FTSeries(stats...; filter=always, transform=identity)

Track multiple stats for one data stream that is filtered and transformed before being fitted.

FTSeries(T, stats...; filter, transform)

If the transformed value has a different type than the original, provide an argument to the constructor to specify the type of an input observation.

Example

o = FTSeries(Mean(), Variance(); transform=abs)
fit!(o, -rand(1000))

# Remove missing values represented as DataValues
using DataValues
y = DataValueArray(randn(100), rand(Bool, 100))
o = FTSeries(DataValue, Mean(); transform=get, filter=!isnull)
fit!(o, y)

FastForest(p, nkeys=2; stat=FitNormal(), kw...)

Calculate a random forest where each variable is summarized by stat.

Keyword Arguments

• nt=100): Number of trees in the forest

• b=floor(Int, sqrt(p)): Number of random features for each tree to receive

• maxsize=1000: Maximum size for any tree in the forest

• splitsize=5000: Number of observations in any given node before splitting

• λ = .05: Probability that each tree is updated on a new observation

FastTree(p::Int, nclasses=2; stat=FitNormal(), maxsize=5000, splitsize=1000)

Calculate a decision tree of p predictors variables and classes 1, 2, …, nclasses. Nodes split when they reach splitsize observations until maxsize nodes are in the tree. Each variable is summarized by stat, which can be FitNormal() or Hist(nbins).

FitBeta(; weight)

Online parameter estimate of a Beta distribution (Method of Moments).

Example

o = fit!(FitBeta(), rand(1000))

FitCauchy(; alg, rate)

Approximate parameter estimation of a Cauchy distribution. Estimates are based on quantiles, so that alg will be passed to Quantile.

Example

o = fit!(FitCauchy(), randn(1000))

FitGamma(; weight)

Online parameter estimate of a Gamma distribution (Method of Moments).

Example

o = fit!(FitGamma(), randexp(10^5))

FitLogNormal()

Online parameter estimate of a LogNormal distribution (MLE).

Example

o = fit!(FitLogNormal(), exp.(randn(10^5)))

FitMultinomial(p)

Online parameter estimate of a Multinomial distribution. The sum of counts does not need to be consistent across observations. Therefore, the n parameter of the Multinomial distribution is returned as 1.

Example

x = [1 2 3; 4 8 12]
fit!(FitMultinomial(3), x)

FitMvNormal(d)

Online parameter estimate of a d-dimensional MvNormal distribution (MLE).

Example

y = randn(100, 2)
o = fit!(FitMvNormal(2), y)

FitNormal()

Calculate the parameters of a normal distribution via maximum likelihood.

Example

o = fit!(FitNormal(), randn(1000))

Group(stats::OnlineStat...)
Group(tuple)

Create a vector-input stat from several scalar-input stats. For a new observation y, y[i] is sent to stats[i].

Examples

fit!(Group(Mean(), Mean()), randn(100, 2))
fit!(Group(Mean(), Variance()), randn(100, 2))

o = [Mean() CountMap(Int)]
fit!(o, zip(randn(100), rand(1:5, 100)))

GroupBy{T}(stat)

Update stat for each group (of type T).

Example

x = rand(1:10, 10^5)
y = x .+ randn(10^5)
fit!(GroupBy{Int}(Extrema()), zip(x,y))

Hist(nbins)
Hist(edges)

Calculate a histogram over fixed edges or adaptive nbins.

HyperLogLog(b, T::Type = Number)  # 4 ≤ b ≤ 16

Approximate count of distinct elements.

Example

fit!(HyperLogLog(12), rand(1:10,10^5))

IndexedPartition(T, stat, b=100)

Summarize data with stat over a partition of size b where the data is indexed by a variable of type T.

Example

o = IndexedPartition(Float64, Hist(10))
fit!(o, randn(10^4, 2))

using Plots 
plot(o)

KMeans(p, k; rate=LearningRate(.6))

Approximate K-Means clustering of k clusters and p variables.

Example

clusters = rand(Bool, 10^5)

x = [clusters[i] > .5 ? randn(): 5 + randn() for i in 1:10^5, j in 1:2]

o = fit!(KMeans(2, 2), x)

Lag{T}(b::Integer)

Store the last b values for a data stream of type T. Values are stored as

$v(t), v(t-1), v(t-2), …, v(t-b+1)$

Example

fit!(Lag{Int}(10), 1:12)

LinReg(p)

Linear regression of p variables, optionally with element-wise ridge regularization.

Example

x = randn(100, 5)
y = x * (1:5) + randn(100)
o = fit!(LinReg(10), (x,y))
coef(o)
coef(o, .1)
coef(o, [0,0,0,0,Inf])

LinRegBuilder(p)

Create an object from which any variable can be regressed on any other set of variables, optionally with element-wise ridge regularization. The main function to use with LinRegBuilder is coef:

coef(o::LinRegBuilder, λ = 0; y=1, x=[2,3,...], bias=true, verbose=false)

Return the coefficients of a regressing column y on columns x with ridge (L2Penalty) parameter λ. An intercept (bias) term is added by default.

Examples

x = randn(1000, 10)
o = fit!(LinRegBuilder(), x)

coef(o; y=3, verbose=true)

coef(o; y=7, x=[2,5,4])

Mean(; weight=EqualWeight())

Track a univariate mean.

Update

$μ = (1 - γ) * μ + γ * x$

Example

@time fit!(Mean(), randn(10^6))

Moments(; weight=EqualWeight())

First four non-central moments.

Example

o = fit!(Moments(), randn(1000))
mean(o)
var(o)
skewness(o)
kurtosis(o)

Mosaic(T::Type, S::Type)

Data structure for generating a mosaic plot, a comparison between two categorical variables.

Example

using OnlineStats, Plots 
x = [rand() > .8 for i in 1:10^5]
y = rand([1,2,2,3,3,3], 10^5)
o = fit!(Mosaic(Bool, Int), zip(x, y))
plot(o)

NBClassifier(p::Int, T::Type; stat = Hist(15))

Calculate a naive bayes classifier for classes of type T and p predictors.

OrderStats(b::Int, T::Type = Float64; weight=EqualWeight())

Average order statistics with batches of size b.

Example

fit!(OrderStats(100), randn(10^5))

P2Quantile(τ = 0.5)

Calculate the approximate quantile via the P^2 algorithm. It is more computationally expensive than the algorithms used by Quantile, but also more exact.

Ref: https://www.cse.wustl.edu/~jain/papers/ftp/psqr.pdf

Example

fit!(P2Quantile(.5), rand(10^5))

Partition(stat, nparts=100)

Split a data stream into nparts where each part is summarized by stat.

Example

o = Partition(Extrema())
fit!(o, cumsum(randn(10^5)))

using Plots
plot(o)

PlotNN(b=300)

Approximate scatterplot of b centers. This implementation is too slow to be useful.

Example

x = randn(10^4)
y = x + randn(10^4)
plot(fit!(PlotNN(), zip(x, y)))

ProbMap(T::Type; weight=EqualWeight())
ProbMap(A::AbstractDict{T, Float64}; weight=EqualWeight())

Track a dictionary that maps unique values to its probability. Similar to CountMap, but uses a weighting mechanism.

Example

o = ProbMap(Int)
fit!(o, rand(1:10, 1000))

Quantile(q = [.25, .5, .75]; alg=SGD(), rate=LearningRate(.6))

Calculate quantiles via a stochastic approximation algorithm OMAS, SGD, ADAGRAD, or MSPI.

Example

fit!(Quantile(), randn(10^5))

RMSPROP(α = .9)

ReservoirSample(k::Int, T::Type = Float64)

Create a sample without replacement of size k. After running through n observations, the probability of an observation being in the sample is 1 / n.

Example

fit!(ReservoirSample(100, Int), 1:1000)

SGD()

Series(stats...)

Track multiple stats for one data stream.

Example

s = Series(Mean(), Variance())
fit!(s, randn(1000))

StatHistory(stat, b)

Track a moving window (previous b copies) of stat.

Example

fit!(StatHistory(Mean(), 10), 1:20)

StatLearn(p, args...)

Fit a model that is linear in the parameters.

The (offline) objective function that StatLearn approximately minimizes is

$(1/n) ∑ᵢ f(yᵢ, xᵢ'β) + ∑ⱼ λⱼ g(βⱼ),$

where $fᵢ$ are loss functions of a single response and linear predictor, $λⱼ$s are nonnegative regularization parameters, and $g$ is a penalty function.

Arguments

• loss = .5 * L2DistLoss()

• penalty = NoPenalty()

• algorithm = SGD()

• rate = LearningRate(.6) (keyword arg)

Example

x = randn(1000, 5)
y = x * linspace(-1, 1, 5) + randn(1000)

o = fit!(StatLearn(5, MSPI()), (x, y))
coef(o)

Sum(T::Type = Float64)

Track the overall sum.

Example

fit!(Sum(Int), fill(1, 100))

Variance(; weight=EqualWeight())

Univariate variance.

Example

@time fit!(Variance(), randn(10^6))

eachcol(x::AbstractMatrix)

Create an iterator over the columns of a matrix as Vectors.

eachrow(x::AbstractMatrix)

Create an iterator over the rows of a matrix as Vectors.

eachrow(x::AbstractMatrix, y::Vector)

Create an iterator over the rows of a matrix/vector as Tuple{Vector, eltype(y)}s.

confint(b::Bootstrap, coverageprob = .95)

Return a confidence interval for a Bootstrap b.

Calculate a histogram adaptively.

Ref: http://www.jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf

Part(stat, a, b)

stat summarizes a Y variable over an X variable's range a to b.