HSvm-0.1.0.3.22: Haskell Bindings for libsvm

Safe Haskell	None
Language	Haskell98

Data.SVM

Synopsis

type Vector = IntMap Double
type Problem = [(Double, Vector)]
data KernelType
- = Linear
- | RBF {
  - gamma :: Double
  }
- | Sigmoid {
  - gamma :: Double
  - coef0 :: Double
  }
- | Poly {
  - gamma :: Double
  - coef0 :: Double
  - degree :: Int
  }
data Algorithm
- = CSvc {
  - c :: Double
  }
- | NuSvc {
  - nu :: Double
  }
- | NuSvr {
  - nu :: Double
  - c :: Double
  }
- | EpsilonSvr {
  - epsilon :: Double
  - c :: Double
  }
- | OneClassSvm {
  - nu :: Double
  }
data ExtraParam = ExtraParam {
- cacheSize :: Double
- shrinking :: Int
- probability :: Int
}
data Model
train :: Algorithm -> KernelType -> Problem -> IO Model
train' :: ExtraParam -> Algorithm -> KernelType -> Problem -> IO Model
crossValidate :: Algorithm -> KernelType -> Problem -> Int -> IO [Double]
crossValidate' :: ExtraParam -> Algorithm -> KernelType -> Problem -> Int -> IO [Double]
loadModel :: FilePath -> IO Model
saveModel :: Model -> FilePath -> IO ()
predict :: Model -> Vector -> IO Double

Documentation

type Vector = IntMap Double #

type Problem = [(Double, Vector)] #

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

data 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

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

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

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

loadModel :: FilePath -> IO Model #

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

predict :: Model -> Vector -> IO Double #