Safe Haskell	None
Language	Haskell2010

DataFrame.SymbolicRegression

Description

Symbolic regression by genetic programming (modelled on the symbolic-regression library, ported dependency-light: no e-graphs, no NLOPT). predict is the best discovered Expr Double; the search also returns the accuracy-vs-complexity Pareto front. Deterministic given the seed.

Synopsis

module DataFrame.Model
data UnOp
- = SNeg
- | SSin
- | SCos
- | SExp
- | SLog
- | SSqrt
data SRConfig = SRConfig {
- srSeed :: !Int
- srPopSize :: !Int
- srGenerations :: !Int
- srMaxSize :: !Int
- srTournament :: !Int
- srCrossoverP :: !Double
- srMutationP :: !Double
- srOptimizeP :: !Double
- srParsimony :: !Double
- srUnaryOps :: ![UnOp]
}
defaultSRConfig :: SRConfig
data SRModel = SRModel {
- srBest :: !(Expr Double)
- srBestMSE :: !Double
- srPareto :: ![(Int, Double, Expr Double)]
- srGenerationsRun :: !Int
}

Documentation

module DataFrame.Model

data UnOp #

Constructors

SNeg
SSin
SCos
SExp
SLog
SSqrt

Instances

Instances details

Bounded UnOp
Instance details Defined in DataFrame.SymbolicRegression.Expr Methods minBound :: UnOp # maxBound :: UnOp #
Enum UnOp
Instance details Defined in DataFrame.SymbolicRegression.Expr Methods succ :: UnOp -> UnOp # pred :: UnOp -> UnOp # toEnum :: Int -> UnOp # fromEnum :: UnOp -> Int # enumFrom :: UnOp -> [UnOp] # enumFromThen :: UnOp -> UnOp -> [UnOp] # enumFromTo :: UnOp -> UnOp -> [UnOp] # enumFromThenTo :: UnOp -> UnOp -> UnOp -> [UnOp] #
Show UnOp
Instance details Defined in DataFrame.SymbolicRegression.Expr Methods showsPrec :: Int -> UnOp -> ShowS # show :: UnOp -> String # showList :: [UnOp] -> ShowS #
Eq UnOp
Instance details Defined in DataFrame.SymbolicRegression.Expr Methods (==) :: UnOp -> UnOp -> Bool # (/=) :: UnOp -> UnOp -> Bool #
Ord UnOp
Instance details Defined in DataFrame.SymbolicRegression.Expr Methods compare :: UnOp -> UnOp -> Ordering # (<) :: UnOp -> UnOp -> Bool # (<=) :: UnOp -> UnOp -> Bool # (>) :: UnOp -> UnOp -> Bool # (>=) :: UnOp -> UnOp -> Bool # max :: UnOp -> UnOp -> UnOp # min :: UnOp -> UnOp -> UnOp #

data SRConfig Source #

Constructors

SRConfig
Fields srSeed :: !Int srPopSize :: !Int srGenerations :: !Int srMaxSize :: !Int srTournament :: !Int srCrossoverP :: !Double srMutationP :: !Double srOptimizeP :: !Double srParsimony :: !Double srUnaryOps :: ![UnOp]

Instances

Instances details

Show SRConfig Source #

Instance details

Defined in DataFrame.SymbolicRegression

Methods

showsPrec :: Int -> SRConfig -> ShowS #

show :: SRConfig -> String #

showList :: [SRConfig] -> ShowS #

Eq SRConfig Source #

Instance details

Defined in DataFrame.SymbolicRegression

Methods

(==) :: SRConfig -> SRConfig -> Bool #

(/=) :: SRConfig -> SRConfig -> Bool #

SegmentFit SRConfig Double Source #

Symbolic-regression segments support independent fitting (lambda = 0) only.

Instance details

Defined in DataFrame.Segmented

Methods

fitSegments :: SRConfig -> Double -> Expr Double -> [DataFrame] -> [ModelOf SRConfig (Expr Double)] Source #

Fit SRConfig (Expr Double) Source #

Instance details

Defined in DataFrame.SymbolicRegression

Associated Types

type ModelOf SRConfig (Expr Double)
Instance details Defined in DataFrame.SymbolicRegression type ModelOf SRConfig (Expr Double) = SRModel
type FrameReq SRConfig (Expr Double)
Instance details Defined in DataFrame.SymbolicRegression type FrameReq SRConfig (Expr Double) = 'AnyFrame