Data.SVM

Documentation

type Vector = IntMap Double

type Problem = [(Double, Vector)]

newtype Model

Constructors

Model (ForeignPtr CSvmModel)

data KernelType

Constructors

Linear

RBF

Fields

gamma :: Double

Sigmoid

Fields

gamma :: Double

coef0 :: Double

Poly

Fields

gamma :: Double

coef0 :: Double

degree :: Int

data Algorithm

Constructors

CSvc

Fields

c :: Double

NuSvc

Fields

nu :: Double

NuSvr

Fields

nu :: Double

c :: Double

EpsilonSvr

Fields

epsilon :: Double

c :: Double

OneClassSvm

Fields

nu :: Double

data ExtraParam

Constructors

ExtraParam

Fields

cacheSize :: Double

shrinking :: Int

probability :: Int

mergeKernel :: KernelType -> CSvmParameter -> CSvmParameter

mergeAlgo :: Algorithm -> CSvmParameter -> CSvmParameter

newCSvmNodeArray :: Vector -> IO (Ptr CSvmNode)

newCSvmProblem :: Problem -> IO (Ptr CSvmProblem)

freeCSVmProblem :: Ptr CSvmProblem -> IO ()

withProblem :: Problem -> (Ptr CSvmProblem -> IO a) -> IO a

withParam :: ExtraParam -> Algorithm -> KernelType -> (Ptr CSvmParameter -> IO a) -> IO a

checkParam :: Ptr CSvmProblem -> Ptr CSvmParameter -> IO ()

train' :: ExtraParam -> Algorithm -> KernelType -> Problem -> IO Model

train :: Algorithm -> KernelType -> Problem -> IO Model

The train function allows training a Model starting from a Problem by specifying an Algorithm and a KernelType

crossValidate' :: ExtraParam -> Algorithm -> KernelType -> Problem -> Int -> IO [Double]

saveModel :: Model -> FilePath -> IO ()

loadModel :: FilePath -> IO Model

predict :: Model -> Vector -> Double