GoDoc: https://godoc.org/github.com/malaschitz/randomForest
This fork add Saving/Loading functions see the section Saving/Loading click on this link
Test:
go test ./... -cover -coverpkg=.
Random Forest implementation in golang.
xData := [][]float64{}
yData := []int{}
for i := 0; i < 1000; i++ {
x := []float64{rand.Float64(), rand.Float64(), rand.Float64(), rand.Float64()}
y := int(x[0] + x[1] + x[2] + x[3])
xData = append(xData, x)
yData = append(yData, y)
}
forest := randomForest.Forest{}
forest.Data = randomforest.ForestData{X: xData, Class: yData}
forest.Train(1000)
//test
fmt.Println("Vote", forest.Vote([]float64{0.1, 0.1, 0.1, 0.1}))
fmt.Println("Vote", forest.Vote([]float64{0.9, 0.9, 0.9, 0.9}))
forest.TrainX(1000)
Deep forest inspired by https://arxiv.org/abs/1705.07366
dForest := forest.BuildDeepForest()
dForest.Train(20, 100, 1000) //20 small forest with 100 trees help to build deep forest with 1000 trees
Continuos Random Forest for data where are still new and new data (forex, wheather, user logs, ...). New data create a new trees and oldest trees are removed.
forest := randomForest.Forest{}
data := []float64{rand.Float64(), rand.Float64()}
res := 1; //result
forest.AddDataRow(data, res, 1000, 10, 2000)
// AddDataRow : add new row, trim oldest row if there is more than 1000 rows, calculate a new 10 trees, but remove oldest trees if there is more than 2000 trees.
Boruta algorithm was developed as package for language R. It is one of most effective feature selection algorithm. There is paper in Journal of Statistical Software.
Boruta algorithm use random forest for selection important features.
xData := ... //data
yData := ... //labels
selectedFeatures := randomforest.BorutaDefault(xData, yData)
// or randomforest.BorutaDefault(xData, yData, 100, 20, 0.05, true, true)
In /examples is example with MNIST database. On picture are selected features (495 from 784) from images.
Will Save the forest structure into binary file
File name format:
forest-UUID[:8]+sha256.bin
if fileName, err := forest.Save("saved/"); err != nil {
t.Error(err)
return
}
Will load forest structure from binary file :
if forest, errForest = Load("saved/forestTest.bin"); errForest != nil {
return
}
fmt.Println("Vote", forest.Vote([]float64{0.9, 0.9, 0.9, 0.9}))