Edit on GitHub

Symbolic Regression

DataDrivenDiffEq includes the following symbolic regression algorithms.

EQSearch

Warning

This feature requires the explicit loading of SymbolicRegression.jl in addition to DataDrivenDiffEq. It will only be useable if loaded like:

using DataDrivenDiffEq
using SymbolicRegression

Currently DataDrivenDiffEq supports version >= 0.9.

This algorithm wraps SymbolicRegression.jl.

Symbolic Regression

See the tutorial.

DataDrivenDiffEq.EQSearch — Type
struct EQSearch <: DataDrivenDiffEq.AbstractSymbolicRegression

Options for using SymbolicRegression.jl within the solve function. Automatically creates Options with the given specification. Sorts the operators stored in functions into unary and binary operators on conversion.

Fields

  • functions

    Operators used for symbolic regression

  • kwargs

    Additionally keyworded arguments passed to SymbolicRegression.Options

source

OccamNet

Warning

This feature requires the explicit loading of Flux.jl in addition to DataDrivenDiffEq. It will only be useable if loaded like:

using DataDrivenDiffEq
using Flux
DataDrivenDiffEq.OccamNet — Type
mutable struct OccamNet{F, C, P} <: DataDrivenDiffEq.AbstractOccam

Defines an OccamNet which learns symbolic expressions from data using a probabilistic approach. See Interpretable Neuroevolutionary Models for Learning Non-Differentiable Functions and Programs for more details.

It get constructed via:

net = OccamNet(inp::Int, outp::Int, layers::Int, f::Vector{Function}, t::Real = 1.0; constants = typeof(t)[], parameters::Int = 0, skip::Bool = false, init_w = ones, init_p = Flux.glorot_uniform)

inp describes the size of the input domain, outp the size of the output domain, layers the number of layers (including the input layer and excluding the linear output layer) and f the functions to be used. Optional is the temperature t which is set to 1.0 at the beginning.

Keyworded arguments are constants, a vector of constants like π, ℯ which can concatenated to the input, the number of trainable parameters and if skip connections should be used. The constructors to the weights and parameters can be passed in via init_w and init_p.

OccamNet is callable with and without a specific route, which can be sampled from the networks weights via rand(net).

Fields

  • c

    The Chain representing the network

  • constants

    Additional constants added to the input which are not trainable.

  • parameters

    Additional parameters added to the input which are trainable.

source
DataDrivenDiffEq.OccamSR — Type
struct OccamSR{F, C, T} <: DataDrivenDiffEq.AbstractSymbolicRegression

Options for using OccamNet within the solve function. Automatically creates a network with the given specification.

Fields

  • functions

    Functions used within the network

  • constants

    Constants added to the input

  • layers

    Number of layers

  • parameters

    Number of parameters

  • skip

    Activate skip connections

source
DataDrivenDiffEq.ProbabilityLayer — Type
mutable struct ProbabilityLayer{F, W, T, A} <: DataDrivenDiffEq.AbstractProbabilityLayer

Defines a basic ProbabilityLayer in which the parameters act as probabilities via the softmax function for an array of functions.

The layer is callable either via layer(x), using all weights to form the output or by layer(x, route) where route is the result of rand(layer) which samples the function arguments from the underlying distribution.

Fields

  • op

    Nonlinear functions forming the basis of the layer

  • weight

    Weights

  • t

    Temperature controlling the shape of the distribution

  • arieties

    Arities of the functions

  • skip

    Skip connection

source