Types

Equations

Equations are specified as binary trees with the Node type. Operators defined in Base are re-defined for Node types, so that one can use, e.g., t=Node(1) * 3f0 to create a tree.

SymbolicRegression.CoreModule.EquationModule.Node — Method

Node(val::AbstractFloat)

Create a scalar constant node

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SymbolicRegression.CoreModule.EquationModule.Node — Method

Node(feature::Int)

Create a variable node using feature feature::Int

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SymbolicRegression.CoreModule.EquationModule.Node — Method

Node(var_string::String)

Create a variable node, using the format "x1" to mean feature 1

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SymbolicRegression.CoreModule.EquationModule.Node — Method

Node(var_string::String, varMap::Array{String, 1})

Create a variable node, using a user-passed format

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SymbolicRegression.CoreModule.EquationModule.Node — Method

Node(op::Int, l::Node)

Apply unary operator op (enumerating over the order given) to Node l

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Missing docstring.

Missing docstring for Node(op::Int, l::Union{AbstractFloat, Int}). Check Documenter's build log for details.

SymbolicRegression.CoreModule.EquationModule.Node — Method

Node(op::Int, l::Node, r::Node)

Apply binary operator op (enumerating over the order given) to Nodes l and r

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Missing docstring.

Missing docstring for Node(op::Int, l::Union{AbstractFloat, Int}, r::Node). Check Documenter's build log for details.

Missing docstring.

Missing docstring for Node(op::Int, l::Node, r::Union{AbstractFloat, Int}). Check Documenter's build log for details.

SymbolicRegression.CoreModule.EquationModule.Node — Method

Node(op::Int, l::Union{AbstractFloat, Int}, r::Union{AbstractFloat, Int})

Short-form for creating two scalar/variable node, and applying a binary operator

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Population

Groups of equations are given as a population, which is an array of trees tagged with score, loss, and birthdate–-these values are given in the PopMember.

SymbolicRegression.PopulationModule.Population — Method

Population(pop::Array{PopMember{T}, 1})

Create population from list of PopMembers.

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SymbolicRegression.PopulationModule.Population — Method

Population(dataset::Dataset{T}, baseline::T;
           npop::Int, nlength::Int=3, options::Options,
           nfeatures::Int)

Create random population and score them on the dataset.

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SymbolicRegression.PopulationModule.Population — Method

Population(X::AbstractMatrix{T}, y::AbstractVector{T},
           baseline::T; npop::Int, nlength::Int=3,
           options::Options, nfeatures::Int)

Create random population and score them on the dataset.

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Population members

Missing docstring.

Missing docstring for PopMember(t::Node, score::T, loss::T) where {T<:Real}. Check Documenter's build log for details.

Missing docstring.

Missing docstring for PopMember(dataset::Dataset{T}, baseline::T, t::Node, options::Options) where {T<:Real}. Check Documenter's build log for details.

Hall of Fame

Missing docstring.

Missing docstring for HallOfFame(options::Options). Check Documenter's build log for details.

Dataset

SymbolicRegression.CoreModule.DatasetModule.Dataset — Method

Dataset(X::AbstractMatrix{T}, y::AbstractVector{T};
        weights::Union{AbstractVector{T}, Nothing}=nothing,
        varMap::Union{Array{String, 1}, Nothing}=nothing)

Construct a dataset to pass between internal functions.

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