// generateTrainingAttrs selects RandomFeatures number of base.Attributes from
// the provided base.Instances.
func (b *BaggedModel) generateTrainingAttrs(model int, from *base.Instances) []base.Attribute {
ret := make([]base.Attribute, 0)
if b.RandomFeatures == 0 {
for j := 0; j < from.Cols; j++ {
attr := from.GetAttr(j)
ret = append(ret, attr)
}
} else {
for {
if len(ret) >= b.RandomFeatures {
break
}
attrIndex := rand.Intn(from.Cols)
if attrIndex == from.ClassIndex {
continue
}
attr := from.GetAttr(attrIndex)
matched := false
for _, a := range ret {
if a.Equals(attr) {
matched = true
break
}
}
if !matched {
ret = append(ret, attr)
}
}
}
ret = append(ret, from.GetClassAttr())
b.lock.Lock()
b.selectedAttributes[model] = ret
b.lock.Unlock()
return ret
}
// Predict outputs a base.Instances containing predictions from this tree
func (d *DecisionTreeNode) Predict(what *base.Instances) *base.Instances {
outputAttrs := make([]base.Attribute, 1)
outputAttrs[0] = what.GetClassAttr()
predictions := base.NewInstances(outputAttrs, what.Rows)
for i := 0; i < what.Rows; i++ {
cur := d
for {
if cur.Children == nil {
predictions.SetAttrStr(i, 0, cur.Class)
break
} else {
at := cur.SplitAttr
j := what.GetAttrIndex(at)
if j == -1 {
predictions.SetAttrStr(i, 0, cur.Class)
break
}
classVar := at.GetStringFromSysVal(what.Get(i, j))
if next, ok := cur.Children[classVar]; ok {
cur = next
} else {
var bestChild string
for c := range cur.Children {
bestChild = c
if c > classVar {
break
}
}
cur = cur.Children[bestChild]
}
}
}
}
return predictions
}
