// Perform cross validation. The instances in the problem are separated
// in the given number of folds. Each fold is sequentially evaluated
// using the model trained with the remaining folds. The slice that is
// returned contains the predicted instance classes.
func CrossValidation(problem *Problem, param Parameters, nFolds uint) ([]float64, error) {
cParam := toCParameter(param)
defer func() {
C.destroy_param_wrap(cParam)
C.free(unsafe.Pointer(cParam))
}()
r := C.check_parameter_wrap(problem.problem, cParam)
if r != nil {
msg := C.GoString(r)
return nil, errors.New(msg)
}
nInstances := uint(problem.problem.l)
target := newDouble(C.size_t(nInstances))
defer C.free(unsafe.Pointer(target))
C.cross_validation_wrap(problem.problem, cParam, C.int(nFolds), target)
classifications := make([]float64, nInstances)
for idx, _ := range classifications {
classifications[idx] = float64(C.get_double_idx(target, C.int(idx)))
}
return classifications, nil
}
// TrainModel trains an SVM using the given parameters and problem.
func TrainModel(param Parameters, problem *Problem) (*Model, error) {
cParam := toCParameter(param)
defer func() {
C.parameter_free(cParam)
C.destroy_param_wrap(cParam)
C.free(unsafe.Pointer(cParam))
}()
// Check validity of the parameters.
r := C.check_parameter_wrap(problem.problem, cParam)
if r != nil {
msg := C.GoString(r)
return nil, errors.New(msg)
}
cmodel := C.train_wrap(problem.problem, cParam)
model := &Model{cmodel, problem, nil}
runtime.SetFinalizer(model, finalizeModel)
return model, nil
}