package libsvm

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package libsvm
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Module Libsvm.SvmSource

SVM problem and model

Sourceval load_dataset : string -> (int * float) list array * Lacaml.D.vec
Sourcemodule Problem : sig ... end
Sourcemodule Model : sig ... end

SVM training

Sourceval train : ?svm_type:[ `C_SVC | `NU_SVC | `ONE_CLASS | `EPSILON_SVR | `NU_SVR ] -> ?kernel:[ `LINEAR | `POLY | `RBF | `SIGMOID | `PRECOMPUTED ] -> ?degree:int -> ?gamma:float -> ?coef0:float -> ?c:float -> ?nu:float -> ?eps:float -> ?cachesize:float -> ?tol:float -> ?shrinking:[ `on | `off ] -> ?probability:bool -> ?weights:(int * float) list -> ?verbose:bool -> Problem.t -> Model.t

train params problem trains a SVM model on the given problem and parameters params:

  • svm_type - type of SVM classification/regression (default C_SVC)
  • kernel - type of the SVM kernel (default RBF)
  • degree - the exponent in the POLY kernel (default 3)
  • gamma - parameter for POLY, RBF and SIGMOID kernel (default 0)
  • coef0 - parameter for POLY and SIGMOID kernel (default 0)
  • c - the cost of constraints violation in C_SVC, EPSILON_SVR, and NU_SVR (default 1)
  • nu - the parameter in NU_SVM, NU_SVR and ONE_CLASS (default 0.5)
  • eps - the epsilon in the epsilon-sensitive loss function of EPSILON_SVR (default 0.1)
  • cachesize - the size of the kernel cache in megabytes (default 100)
  • tol - the stopping criterion (default 1e-3)
  • shrinking - use on to conduct shrinking, otherwise off (default on)
  • probability - if probability = true, then a model with probability information will be obtained (default false)
  • weights - weights to penalize classes (default = )
  • verbose - if verbose = true, then train the SVM in verbose mode (default false)
  • returns

    the trained model.

  • raises Failure

    if parameters are not feasible.

Sourceval cross_validation : ?svm_type:[ `C_SVC | `NU_SVC | `ONE_CLASS | `EPSILON_SVR | `NU_SVR ] -> ?kernel:[ `LINEAR | `POLY | `RBF | `SIGMOID | `PRECOMPUTED ] -> ?degree:int -> ?gamma:float -> ?coef0:float -> ?c:float -> ?nu:float -> ?eps:float -> ?cachesize:float -> ?tol:float -> ?shrinking:[ `on | `off ] -> ?probability:bool -> ?weights:(int * float) list -> ?verbose:bool -> n_folds:int -> Problem.t -> Lacaml.D.vec

cross_validation params problem n_folds conducts n-fold cross-validation on the given problem and parameters params. The parameters params are the same as in train above.

  • returns

    vector with all predicted values (of all problem instances) in the validation process.

  • raises Failure

    if parameters are not feasible.

SVM prediction

Sourceval predict_sparse : Model.t -> (int * float) list -> float

predict_sparse model ~x does classification or regression on a test vector x given a model. For a classification model, the predicted class for x is returned. For a regression model, the function value of x is returned. For a one-class model, +1 or -1 is returned.

Sourceval predict_values_sparse : Model.t -> (int * float) list -> float array array

predict_values_sparse model x

  • returns

    a matrix with decision values on a test vector x.

Sourceval predict_probability_sparse : Model.t -> (int * float) list -> float * float array

predict_probability m x does classification or regression on a test vector x based on a model with probability information.

Sourceval predict_one : Model.t -> Lacaml.D.vec -> float

predict_one model x does classification or regression on a test vector x given a model. For a classification model, the predicted class for x is returned. For a regression model, the function value of x is returned. For a one-class model, +1 or -1 is returned.

predict model x applies predict_one to each row of the matrix x.

Sourceval predict_values : Model.t -> Lacaml.D.vec -> float array array

predict_values model x

  • returns

    a matrix with decision values on a test vector x.

Sourceval predict_probability : Model.t -> Lacaml.D.vec -> float * float array

predict_probability m x does classification or regression on a test vector x based on a model with probability information.

Sourceval predict_from_file : Model.t -> string -> [ `Expected of Lacaml.D.vec ] * [ `Predicted of Lacaml.D.vec ]

predict_from_file model filename does classification or regression on the testing data given in filename.

  • returns

    a pair vectors containing the expected (true) values form the test file and the predicted ones computed from the given model.

  • raises Failure

    if an error occured during parsing of filename.