Data.SVM

Synopsis

• type Vector = IntMap Double
• type Problem = [(Double, Vector)]
• newtype Model = Model (ForeignPtr CSvmModel)
• 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
  }
• defaultExtra :: ExtraParam
• mergeKernel :: KernelType -> CSvmParameter -> CSvmParameter
• mergeAlgo :: Algorithm -> CSvmParameter -> CSvmParameter
• mergeExtra :: ExtraParam -> 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
• crossValidate' :: ExtraParam -> Algorithm -> KernelType -> Problem -> Int -> IO [Double]
• crossValidate :: Algorithm -> KernelType -> Problem -> Int -> IO [Double]
• saveModel :: Model -> FilePath -> IO ()
• loadModel :: FilePath -> IO Model
• predict :: Model -> Vector -> Double

Documentation