// GetSplitAttributeFromSelection returns the class Attribute which maximises
// the information gain amongst consideredAttributes
//
// IMPORTANT: passing a zero-length consideredAttributes parameter will panic()
func (r *InformationGainRuleGenerator) GetSplitAttributeFromSelection(consideredAttributes []int, f *base.Instances) base.Attribute {
// Next step is to compute the information gain at this node
// for each randomly chosen attribute, and pick the one
// which maximises it
maxGain := math.Inf(-1)
selectedAttribute := -1
// Compute the base entropy
classDist := f.GetClassDistribution()
baseEntropy := getBaseEntropy(classDist)
// Compute the information gain for each attribute
for _, s := range consideredAttributes {
proposedClassDist := f.GetClassDistributionAfterSplit(f.GetAttr(s))
localEntropy := getSplitEntropy(proposedClassDist)
informationGain := baseEntropy - localEntropy
if informationGain > maxGain {
maxGain = informationGain
selectedAttribute = s
}
}
// Pick the one which maximises IG
return f.GetAttr(selectedAttribute)
}