libsvm 0.10.0 · OCaml Package

SVM problem and model

val load_dataset : string -> (int * float) list array * Lacaml.D.vec

module Problem : sig ... end

module Model : sig ... end

SVM training

val 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.

val 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

val 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.

val 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.

val 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.

raises Invalid_argument
if the model does not support probability estimates.

val 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.

val predict : Model.t -> Lacaml.D.mat -> Lacaml.D.vec

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

val 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.

val 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.

raises Invalid_argument
if the model does not support probability estimates.

val 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.

package libsvm

SVM problem and model

SVM training

SVM prediction