public static string SelectBestAxis(DecisionTreeSet set)
{
var baseEntropy = Entropy(set);
//Console.WriteLine("Base entropy is {0}", baseEntropy);
var bestInfoGain = 0.0;
var uniqueFeaturesByAxis = set.UniqueFeatures().GroupBy(i => i.Axis).ToList();
string bestAxisSplit = uniqueFeaturesByAxis.First().Key;
foreach (var axis in uniqueFeaturesByAxis)
{
// calculate the total entropy based on splitting by this axis. The total entropy
// is the sum of the entropy of each branch that would be created by this split
var newEntropy = EntropyForSplitBranches(set, axis.ToList());
var infoGain = baseEntropy - newEntropy;
if (infoGain > bestInfoGain)
{
bestInfoGain = infoGain;
bestAxisSplit = axis.Key;
}
}
return(bestAxisSplit);
}
private static double EntropyForSplitBranches(DecisionTreeSet set, IEnumerable <Feature> allPossibleAxisValues)
{
return((from possibleValue in allPossibleAxisValues
select set.Split(possibleValue) into subset
let prob = (float)subset.NumberOfInstances / set.NumberOfInstances
select prob * Entropy(subset)).Sum());
}
public static double Entropy(DecisionTreeSet set)
{
var total = set.Instances.Count();
var outputs = set.Instances.Select(i => i.Output).GroupBy(f => f.Value).ToList();
var entropy = 0.0;
foreach (var target in outputs)
{
var probability = (float)target.Count() / total;
entropy -= probability * Math.Log(probability, 2);
}
return(entropy);
}
public DecisionTreeSet Parse(string file)
{
var set = new DecisionTreeSet();
set.Instances = new List <Instance>();
using (var stream = new StreamReader(file))
{
while (!stream.EndOfStream)
{
var line = stream.ReadLine();
set.Instances.Add(ParseLine(line));
}
}
return(set);
}