// Predict the label of an instance, given a model with probability
// information. This method returns the label of the predicted class,
// a map of class probabilities, and an error if the model was not
// trained without the required information to do probability estimates.
func (model *Model) PredictProbability(nodes []FeatureValue) (float64, map[int]float64, error) {
if C.svm_check_probability_model_wrap(model.model) == 0 {
return 0, nil, errors.New("Model was not trained to do probability estimates")
}
// Allocate sparse C feature vector.
cn := cNodes(nodes)
defer C.nodes_free(cn)
// Allocate C array for probabilities.
cProbs := C.probs_new(model.model)
defer C.free(unsafe.Pointer(cProbs))
r := C.svm_predict_probability_wrap(model.model, cn, cProbs)
// Store the probabilities in a slice
labels := model.labels()
probs := make(map[int]float64)
for idx, label := range labels {
probs[label] = float64(C.get_double_idx(cProbs, C.int(idx)))
}
return float64(r), probs, nil
}
func newProbs(model *C.model_t) *C.double {
probs := tryNew(func() unsafe.Pointer {
return unsafe.Pointer(C.probs_new(model))
})
return (*C.double)(probs)
}