-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/


-- | Haskell Bindings for libsvm
--   
--   The library embeds libsvm and provides a type-safe interface into SVM
--   models.
@package HSvm
@version 1.0.3.35


-- | This is a module with raw bindings to libsvm
module Data.SVM.Raw
data CSvmNode
CSvmNode :: CInt -> CDouble -> CSvmNode
[index] :: CSvmNode -> CInt
[value] :: CSvmNode -> CDouble
data CSvmProblem
CSvmProblem :: CInt -> Ptr CDouble -> Ptr (Ptr CSvmNode) -> CSvmProblem
[l] :: CSvmProblem -> CInt
[y] :: CSvmProblem -> Ptr CDouble
[x] :: CSvmProblem -> Ptr (Ptr CSvmNode)
newtype CSvmType
CSvmType :: CInt -> CSvmType
[unCSvmType] :: CSvmType -> CInt
cSvc :: CSvmType
nuSvc :: CSvmType
newtype CKernelType
CKernelType :: CInt -> CKernelType
[unCKernelType] :: CKernelType -> CInt
oneClass :: CSvmType
linear :: CKernelType
poly :: CKernelType
epsilonSvr :: CSvmType
data CSvmParameter
CSvmParameter :: CSvmType -> CKernelType -> CInt -> CDouble -> CDouble -> CDouble -> CDouble -> CDouble -> CInt -> Ptr CInt -> Ptr CDouble -> CDouble -> CDouble -> CInt -> CInt -> CSvmParameter
[svm_type] :: CSvmParameter -> CSvmType
[kernel_type] :: CSvmParameter -> CKernelType
[degree] :: CSvmParameter -> CInt
[gamma] :: CSvmParameter -> CDouble
[coef0] :: CSvmParameter -> CDouble
[cache_size] :: CSvmParameter -> CDouble
[eps] :: CSvmParameter -> CDouble
[c] :: CSvmParameter -> CDouble
[nr_weight] :: CSvmParameter -> CInt
[weight_label] :: CSvmParameter -> Ptr CInt
[weight] :: CSvmParameter -> Ptr CDouble
[nu] :: CSvmParameter -> CDouble
[p] :: CSvmParameter -> CDouble
[shrinking] :: CSvmParameter -> CInt
[probability] :: CSvmParameter -> CInt
nuSvr :: CSvmType
rbf :: CKernelType
sigmoid :: CKernelType
precomputed :: CKernelType
defaultCParam :: CSvmParameter

-- | Managed type for struct svm_model.
data CSvmModel
c_svm_train :: Ptr CSvmProblem -> Ptr CSvmParameter -> IO (Ptr CSvmModel)
c_svm_cross_validation :: Ptr CSvmProblem -> Ptr CSvmParameter -> CInt -> Ptr CDouble -> IO ()
c_svm_predict :: Ptr CSvmModel -> Ptr CSvmNode -> IO CDouble
c_svm_save_model :: CString -> Ptr CSvmModel -> IO CInt
c_svm_load_model :: CString -> IO (Ptr CSvmModel)
c_svm_check_parameter :: Ptr CSvmProblem -> Ptr CSvmParameter -> CString
c_svm_destroy_model :: FinalizerPtr CSvmModel
c_clone_model_support_vectors :: Ptr CSvmModel -> IO CInt
type CSvmPrintFn = CString -> IO ()
c_svm_set_print_string_function :: FunPtr CSvmPrintFn -> IO ()
createSvmPrintFnPtr :: CSvmPrintFn -> IO (FunPtr CSvmPrintFn)
instance GHC.Show.Show Data.SVM.Raw.CSvmType
instance Foreign.Storable.Storable Data.SVM.Raw.CSvmType
instance GHC.Show.Show Data.SVM.Raw.CKernelType
instance Foreign.Storable.Storable Data.SVM.Raw.CKernelType
instance GHC.Show.Show Data.SVM.Raw.CSvmParameter
instance Foreign.Storable.Storable Data.SVM.Raw.CSvmParameter
instance Foreign.Storable.Storable Data.SVM.Raw.CSvmProblem
instance Foreign.Storable.Storable Data.SVM.Raw.CSvmNode


-- | This module provides a safe bindings to libsvm functions and
--   structures with implicit memory handling.
module Data.SVM

-- | Vector type provides a sparse implementation of vector. It uses IntMap
--   as underlying implementation.
type Vector = IntMap Double

-- | SVM problem is a list of maps from training vectors to 1.0 or -1.0
type Problem = [(Double, Vector)]

-- | Kernel function for SVM algorithm.
data KernelType

-- | Linear kernel function, i.e. dot product
Linear :: KernelType

-- | Gaussian radial basis function with parameter <a>gamma</a>
RBF :: Double -> KernelType
[gamma] :: KernelType -> Double

-- | Sigmoid kernel function
Sigmoid :: Double -> Double -> KernelType
[gamma] :: KernelType -> Double
[coef0] :: KernelType -> Double

-- | Inhomogeneous polynomial function
Poly :: Double -> Double -> Int -> KernelType
[gamma] :: KernelType -> Double
[coef0] :: KernelType -> Double
[degree] :: KernelType -> Int

-- | SVM Algorithm with parameters
data Algorithm

-- | c-SVC algorithm
CSvc :: Double -> Algorithm
[c] :: Algorithm -> Double

-- | nu-SVC algorithm
NuSvc :: Double -> Algorithm
[nu] :: Algorithm -> Double

-- | nu-SVR algorithm
NuSvr :: Double -> Double -> Algorithm
[nu] :: Algorithm -> Double
[c] :: Algorithm -> Double

-- | eps-SVR algorithm
EpsilonSvr :: Double -> Double -> Algorithm
[epsilon] :: Algorithm -> Double
[c] :: Algorithm -> Double

-- | One class SVM
OneClassSvm :: Double -> Algorithm
[nu] :: Algorithm -> Double

-- | Extra parameters of SVM implementation
data ExtraParam
ExtraParam :: Double -> Int -> Int -> ExtraParam
[cacheSize] :: ExtraParam -> Double
[shrinking] :: ExtraParam -> Int
[probability] :: ExtraParam -> Int

-- | <a>Model</a> is a wrapper over foreign pointer to <a>CSvmModel</a>
data Model

-- | The <a>train</a> function allows training a <a>Model</a> starting from
--   a <a>Problem</a> by specifying an <a>Algorithm</a> and a
--   <a>KernelType</a>
train :: Algorithm -> KernelType -> Problem -> IO Model

-- | Like <a>train</a> but with extra parameters
train' :: ExtraParam -> Algorithm -> KernelType -> Problem -> IO Model

-- | Stratified cross validation
crossValidate :: Algorithm -> KernelType -> Problem -> Int -> IO [Double]

-- | Like <tt>crossvalidate</tt> but with extra parameters
crossValidate' :: ExtraParam -> Algorithm -> KernelType -> Problem -> Int -> IO [Double]

-- | Load model from the file
loadModel :: FilePath -> IO Model

-- | Save model to the file
saveModel :: Model -> FilePath -> IO ()

-- | Predict a value for <a>Vector</a> by using <a>Model</a>
predict :: Model -> Vector -> IO Double

-- | Wrapper to change the libsvm output reporting function.
--   
--   libsvm by default writes some statistics to stdout. If you don't want
--   any output from libsvm, you can do e.g.:
--   
--   <pre>
--   &gt;&gt;&gt; withPrintFn (\_ -&gt; return ()) $ train (NuSvc 0.25) (RBF 1) feats
--   </pre>
withPrintFn :: CSvmPrintFn -> IO a -> IO a
type CSvmPrintFn = CString -> IO ()