/
cart_tree.go
89 lines (71 loc) · 1.95 KB
/
cart_tree.go
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package main
import (
"fmt"
"github.com/sjwhitworth/golearn/base"
//"github.com/sjwhitworth/golearn/ensemble"
//"github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/filters"
//"github.com/sjwhitworth/golearn/trees"
"math"
"math/rand"
"reflect"
)
func findBestSplit(partition base.FixedDataGrid) {
var delta float64
delta = math.MinInt64
attrs := partition.AllAttributes()
classAttrs := partition.AllClassAttributes()
candidates := base.AttributeDifferenceReferences(attrs, classAttrs)
fmt.Println(delta)
fmt.Println(classAttrs)
fmt.Println(reflect.TypeOf(partition))
fmt.Println(reflect.TypeOf(candidates))
for i, n := range attrs {
fmt.Println(i)
//fmt.Println(partition)
fmt.Println(reflect.TypeOf(n))
attributeSpec, _ := partition.GetAttribute(n)
fmt.Println(partition.GetAttribute(n))
_, rows := partition.Size()
for j := 0; j < rows; j++ {
data := partition.Get(attributeSpec, j)
fmt.Println(base.UnpackBytesToFloat(data))
}
}
}
func findBestSplitsJOnXj(partion base.FixedDataGrid, attribute base.Attribute) {
delta := 0.0
k := 0.0
vK := -999999.99 //TODO
for i := 1; i < 99; {
//TODO second while with i++
i++
}
fmt.Println(delta)
fmt.Println(k)
fmt.Println(vK)
}
func main() {
var tree base.Classifier
rand.Seed(44111342)
// Load in the iris dataset
iris, err := base.ParseCSVToInstances("/home/kralli/go/src/github.com/sjwhitworth/golearn/examples/datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
// Discretise the iris dataset with Chi-Merge
filt := filters.NewChiMergeFilter(iris, 0.999)
for _, a := range base.NonClassFloatAttributes(iris) {
filt.AddAttribute(a)
}
filt.Train()
irisf := base.NewLazilyFilteredInstances(iris, filt)
// Create a 60-40 training-test split
//testData
trainData, _ := base.InstancesTrainTestSplit(iris, 0.60)
findBestSplit(trainData)
//fmt.Println(trainData)
//fmt.Println(testData)
fmt.Println(tree)
fmt.Println(irisf)
}