/// <summary>
/// Generate decision tree via ID3 algorithm
/// </summary>
public void Train(IEnumerable <Predicate <TObj> > patterns, IInformativityIndex <TObj> informativity)
{
if (patterns == null || !patterns.Any())
{
throw new MLException("Patterns are empty or null");
}
if (informativity == null)
{
throw new MLException("Informativity is null");
}
var root = trainID3Core(patterns, TrainingSample, informativity);
m_Result = new DecisionTree <TObj>(root);
}
private DecisionNode <TObj> trainID3Core(IEnumerable <Predicate <TObj> > patterns, ClassifiedSample <TObj> sample, IInformativityIndex <TObj> informativity)
{
if (!sample.Any())
{
throw new MLException("Empty sample");
}
var cls = sample.First().Value;
if (sample.All(kvp => kvp.Value.Equals(cls)))
{
return(new LeafNode <TObj>(cls));
}
var pattern = informativity.Max(patterns, sample);
var negSample = new ClassifiedSample <TObj>();
var posSample = new ClassifiedSample <TObj>();
foreach (var pData in sample)
{
if (pattern(pData.Key))
{
posSample.Add(pData.Key, pData.Value);
}
else
{
negSample.Add(pData.Key, pData.Value);
}
}
if (!negSample.Any() || !posSample.Any())
{
var majorClass = sample.GroupBy(pd => pd.Value)
.Select(g => new KeyValuePair <Class, int>(g.Key, g.Count()))
.OrderByDescending(c => c.Value)
.First();
return(new LeafNode <TObj>(majorClass.Key));
}
var node = new InnerNode <TObj>(pattern);
var negNode = trainID3Core(patterns, negSample, informativity);
var posNode = trainID3Core(patterns, posSample, informativity);
node.SetNegativeNode(negNode);
node.SetPositiveNode(posNode);
return(node);
}