Copyright	(c) Fabricio Olivetti de Franca 2020
License	GPL-3
Maintainer	fabricio.olivetti@gmail.com
Stability	experimental
Portability	POSIX
Safe Haskell	None

IT.Regression

Contents

IT specific stuff

Description

Definitions of IT data structure and support functions.

Synopsis

data Task
- = Regression
- | Classification
- | ClassMult
type FitFun = Vector -> Vector -> Double
data Penalty
- = NoPenalty
- | Len Double
- | Shape Double
predict :: Matrix Double -> Vector -> Vector
solveOLS :: Matrix Double -> Vector -> Vector
isInvalidMatrix :: Matrix Double -> Bool
regress :: Matrix Double -> Vector -> [Vector]
classify :: Matrix Double -> Vector -> [Vector]
classifyMult :: Matrix Double -> Vector -> [Vector]
fitTask :: Task -> Matrix Double -> Vector -> [Vector]
predictTask :: Task -> Matrix Double -> [Vector] -> Vector
evalPenalty :: Penalty -> Int -> Double -> Double
applyMeasures :: NonEmpty Measure -> Vector -> Vector -> [Double]
evalTrain :: Task -> NonEmpty Measure -> Constraint -> Penalty -> Dataset Double -> Vector -> Dataset Double -> Vector -> Expr -> Maybe Solution
evalTest :: Task -> NonEmpty Measure -> Dataset Double -> Vector -> Solution -> Maybe [Double]

IT specific stuff

data Task Source #

Constructors

Regression
Classification
ClassMult

Instances

Instances details

Eq Task Source #
Instance details Defined in IT.Regression Methods (==) :: Task -> Task -> Bool (/=) :: Task -> Task -> Bool
Read Task Source #
Instance details Defined in IT.Regression Methods readsPrec :: Int -> ReadS Task readList :: ReadS [Task] readPrec :: ReadPrec Task readListPrec :: ReadPrec [Task]

type FitFun = Vector -> Vector -> Double Source #

data Penalty Source #

Constructors

NoPenalty
Len Double
Shape Double

Instances

Instances details

Read Penalty Source #
Instance details Defined in IT.Regression Methods readsPrec :: Int -> ReadS Penalty readList :: ReadS [Penalty] readPrec :: ReadPrec Penalty readListPrec :: ReadPrec [Penalty]
Show Penalty Source #
Instance details Defined in IT.Regression Methods showsPrec :: Int -> Penalty -> ShowS show :: Penalty -> String showList :: [Penalty] -> ShowS

predict :: Matrix Double -> Vector -> Vector Source #

Predict a linear model

solveOLS :: Matrix Double -> Vector -> Vector Source #

Solve the OLS *zss*w = ys*

isInvalidMatrix :: Matrix Double -> Bool Source #

regress :: Matrix Double -> Vector -> [Vector] Source #

Applies OLS and returns a Solution if the expression is invalid, it returns Infinity as a fitness

classify :: Matrix Double -> Vector -> [Vector] Source #

classifyMult :: Matrix Double -> Vector -> [Vector] Source #

fitTask :: Task -> Matrix Double -> Vector -> [Vector] Source #

Fitness function for regression

Split the dataset into twice the available cores evaluate the expressions in parallel run a Linear regression on the evaluated expressions Remove from the population any expression that leads to NaNs or Infs it was fitnessReg

predictTask :: Task -> Matrix Double -> [Vector] -> Vector Source #

evalPenalty :: Penalty -> Int -> Double -> Double Source #

applyMeasures :: NonEmpty Measure -> Vector -> Vector -> [Double] Source #

evalTrain :: Task -> NonEmpty Measure -> Constraint -> Penalty -> Dataset Double -> Vector -> Dataset Double -> Vector -> Expr -> Maybe Solution Source #

evalTest :: Task -> NonEmpty Measure -> Dataset Double -> Vector -> Solution -> Maybe [Double] Source #

Evaluates an expression into the test set. This is different from fitnessReg since it doesn't apply OLS. It was: fitnessTest