// double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates);
func PredictProba(model *Model, X *mat64.Dense) *mat64.Dense {
nRows, nCols := X.Dims()
nrClasses := int(C.get_nr_class(model.cModel))
cX := mapCDouble(X.RawMatrix().Data)
y := mat64.NewDense(nRows, nrClasses, nil)
result := doubleToFloats(C.call_predict_proba(
model.cModel, &cX[0], C.int(nRows), C.int(nCols), C.int(nrClasses)),
nRows*nrClasses)
for i := 0; i < nRows; i++ {
y.SetRow(i, result[i*nrClasses:(i+1)*nrClasses])
}
return y
}
// double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates);
func PredictProba(model *Model, X *mat64.Dense) *mat64.Dense {
nRows, _ := X.Dims()
nrClasses := int(C.get_nr_class(model.cModel))
cXs := toFeatureNodes(X)
y := mat64.NewDense(nRows, nrClasses, nil)
proba := make([]C.double, nrClasses, nrClasses)
for i, cX := range cXs {
C.predict_probability(model.cModel, cX, &proba[0])
for j := 0; j < nrClasses; j++ {
y.Set(i, j, float64(proba[j]))
}
}
return y
}