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
}
Example #2
0
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
}
Example #4
0
func (c *KNN) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
	x := sample.GetFeatureVector()
	predictions := []*eval.LabelPrediction{}
	for i, s := range c.sv {
		predictions = append(predictions, &(eval.LabelPrediction{Label: c.labels[i], Prediction: c.Kernel(s, x)}))
	}

	compare := func(p1, p2 *eval.LabelPrediction) bool {
		return p1.Prediction > p2.Prediction
	}

	eval.By(compare).Sort(predictions)

	ret := core.NewArrayVector()
	for i, pred := range predictions {
		if i > c.k {
			break
		}
		ret.AddValue(pred.Label, 1.0)
	}
	return ret
}
Example #5
0
func (dt *CART) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
	msample := sample.ToMapBasedSample()
	node, _ := PredictBySingleTree(&dt.tree, msample)
	return node.prediction
}
Example #6
0
func (dt *CART) Predict(sample *core.Sample) float64 {
	msample := sample.ToMapBasedSample()
	node, _ := PredictBySingleTree(&dt.tree, msample)
	return node.prediction.GetValue(1)
}
Example #7
0
func (c *SVM) Predict(sample *core.Sample) float64 {
	x := sample.GetFeatureVector()
	return c.PredictVector(x)
}
Example #8
0
func (dt *RegressionTree) Predict(sample * core.Sample) float64 {
	msample := sample.ToMapBasedSample()
	node,_ := dt.PredictBySingleTree(&dt.tree, msample)
	return node.prediction.GetValue(0)
}