FeatureSet(methods, [names, keywords, descriptions])
FeatureSet(features::Vector{T}) where {T <: AbstractFeature}

Construct a FeatureSet from methods (a vector of functions) and optionally provide names as a vector of symbols, keywords as a vector of vectors of strings, and descriptions as a vector of strings. A FeatureSet can be called on a time-series vector or matrix X (with time series occupying columns) to return a FeatureArray of feature values. Subsets of a FeatureSet 𝒇 can be obtained by indexing with feature names (as symbols) or the regular linear and logical indices. FeatureSets also support simple set operations defined for arrays, such as unions and intersections, as well as convenient syntax for concatenation (+) and set differencing (\). Note that two features are considered the same if and only if their names are equal.

Examples

𝒇 = FeatureSet([sum, length], [:sum, :length], [["distribution"], ["sampling"]], ["∑x¹", "∑x⁰"])
X = randn(100, 2) # 2 time series, 100 samples long
F = 𝒇(X)

# Joining feature sets
𝒇₁ = FeatureSet([x->min(x...), x->max(x...)], [:min, :max], [["distribution"], ["distribution"]], ["minimum", "maximum"])
𝒈₁ = 𝒇 + 𝒇₁
G = 𝒈₁(X)

# Intersecting feature sets, where features are identified exclusively by their names
𝒇₂ = FeatureSet(x->prod, :sum, ["distributions"], "∏x")
𝒈₂ = 𝒇 ∩ 𝒇₂ # The intersection of two feature sets, both with their own :sum
G = 𝒈₂(X) # The intersection contains the :sum of the first argument to ∩; 𝒇