// InferID3Tree builds a decision tree using a RuleGenerator
// from a set of Instances (implements the ID3 algorithm)
func InferID3Tree(from *base.Instances, with RuleGenerator) *DecisionTreeNode {
// Count the number of classes at this node
classes := from.CountClassValues()
// If there's only one class, return a DecisionTreeLeaf with
// the only class available
if len(classes) == 1 {
maxClass := ""
for i := range classes {
maxClass = i
}
ret := &DecisionTreeNode{
LeafNode,
nil,
nil,
classes,
maxClass,
from.GetClassAttrPtr(),
}
return ret
}
// Only have the class attribute
maxVal := 0
maxClass := ""
for i := range classes {
if classes[i] > maxVal {
maxClass = i
maxVal = classes[i]
}
}
// If there are no more Attributes left to split on,
// return a DecisionTreeLeaf with the majority class
if from.GetAttributeCount() == 2 {
ret := &DecisionTreeNode{
LeafNode,
nil,
nil,
classes,
maxClass,
from.GetClassAttrPtr(),
}
return ret
}
ret := &DecisionTreeNode{
RuleNode,
nil,
nil,
classes,
maxClass,
from.GetClassAttrPtr(),
}
// Generate a return structure
// Generate the splitting attribute
splitOnAttribute := with.GenerateSplitAttribute(from)
if splitOnAttribute == nil {
// Can't determine, just return what we have
return ret
}
// Split the attributes based on this attribute's value
splitInstances := from.DecomposeOnAttributeValues(splitOnAttribute)
// Create new children from these attributes
ret.Children = make(map[string]*DecisionTreeNode)
for k := range splitInstances {
newInstances := splitInstances[k]
ret.Children[k] = InferID3Tree(newInstances, with)
}
ret.SplitAttr = splitOnAttribute
return ret
}
