// PredictProbabilitySlice predicts the label of an instance, given a
// model with probability information. This method returns the label
// of the predicted class and a slice of class probabilities. Probability
// estimates are currently given for logistic regression only. If another
// solver is used, the probability of each class is zero.
//
// The PredictProbability function is more user-friendly, but has the
// overhead of constructing a map. If you are only interested in the
// classes with the highest probabilities, it may be better to use
// this function in conjunction with Labels().
func (model *Model) PredictProbabilitySlice(nodes []FeatureValue) (float64, []float64, error) {
// Allocate sparse C feature vector.
cn := cNodes(nodes)
defer C.nodes_free(cn)
probs := make([]float64, len(model.Labels()))
r := C.predict_probability_wrap(model.model, cn, (*C.double)(unsafe.Pointer(&probs[0])))
return float64(r), probs, nil
}
// 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. Probability estimates are currently
// given for logistic regression only. If another solver is used,
// the probability of each class is zero.
func (model *Model) PredictProbability(nodes []FeatureValue) (float64, map[int]float64, error) {
// Allocate sparse C feature vector.
cn := cNodes(nodes)
defer C.nodes_free(cn)
// Allocate C array for probabilities.
cProbs := newProbs(model.model)
defer C.free(unsafe.Pointer(cProbs))
r := C.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
}