func (rdt *RandomDecisionTree) Predict(sample * core.Sample) float64 {
ret := 0.0
total := 0.0
msample := sample.ToMapBasedSample()
for _,tree := range rdt.trees{
node, _ := PredictBySingleTree(tree, msample)
ret += node.prediction.GetValue(1)
total += 1.0
}
return ret / total
}
func (dt *RandomForest) Predict(sample *core.Sample) float64 {
msample := sample.ToMapBasedSample()
predictions := 0.0
total := 0.0
for _, tree := range dt.trees {
node, _ := PredictBySingleTree(tree, msample)
predictions += node.prediction.GetValue(1)
total += 1.0
}
return predictions / total
}
func (rdt *RandomDecisionTree) PredictMultiClass(sample * core.Sample) *core.ArrayVector {
msample := sample.ToMapBasedSample()
predictions := core.NewArrayVector()
total := 0.0
for _, tree := range rdt.trees{
node, _ := PredictBySingleTree(tree, msample)
predictions.AddVector(node.prediction, 1.0)
total += 1.0
}
predictions.Scale(1.0 / total)
return predictions
}
func (dt *CART) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
msample := sample.ToMapBasedSample()
node, _ := PredictBySingleTree(&dt.tree, msample)
return node.prediction
}
func (dt *CART) Predict(sample *core.Sample) float64 {
msample := sample.ToMapBasedSample()
node, _ := PredictBySingleTree(&dt.tree, msample)
return node.prediction.GetValue(1)
}
func (dt *RegressionTree) Predict(sample * core.Sample) float64 {
msample := sample.ToMapBasedSample()
node,_ := dt.PredictBySingleTree(&dt.tree, msample)
return node.prediction.GetValue(0)
}
