private static DecisionTreeNode <T, D> computeDepthFirst(DecisionTreeNode <T, D> node, List <T> data, IFeatureFactory <T, D> factory, int numFeatures, int numThresholds, int numLabels, float[] labelWeights, int depth)
{
GC.Collect();
if (data.Count == 0)
{
UpdateManager.WriteLine("No data at depth {0}", depth);
return(null);
}
if (data[0] is IComparable <T> )
{
data.Sort();
}
if (checkDelta(data))
{
UpdateManager.WriteLine("Delta function at depth {0}", depth);
int label = data[0].Label;
float[] dist = new float[numLabels];
dist[label] = 1;
return(new DecisionTreeNode <T, D>(dist));
}
int dataCount = data.Count;
Decider <T, D> bestDecider = null;
float bestScore = float.MinValue;
float[] bestLeftDistribution = null;
float[] bestRightDistribution = null;
using (ThreadLocal <DecisionResult> results = new ThreadLocal <DecisionResult>(() => new DecisionResult {
Score = bestScore
}, true))
{
Parallel.For(0, numFeatures, i =>
{
float[] leftDistribution;
float[] rightDistribution;
Decider <T, D> decider = new Decider <T, D>(factory);
decider.LoadData(data);
float score = decider.ChooseThreshold(numThresholds, numLabels, labelWeights, out leftDistribution, out rightDistribution);
if (score > results.Value.Score)
{
results.Value = new DecisionResult {
LeftDistribution = leftDistribution, RightDistribution = rightDistribution, Decider = decider, Score = score
};
}
});
foreach (var result in results.Values)
{
if (result.Score > bestScore)
{
bestLeftDistribution = result.LeftDistribution;
bestRightDistribution = result.RightDistribution;
bestDecider = result.Decider;
bestScore = result.Score;
}
}
}
float support = 0;
if (labelWeights != null)
{
foreach (T point in data)
{
support += labelWeights[point.Label];
}
}
else
{
support = dataCount;
}
if (bestScore == float.MinValue || dataCount < MinimumSupport)
{
UpdateManager.WriteLine("Stopping due to lack of data at depth {0}, {1} < {2}", depth, dataCount, MinimumSupport);
float[] distribution = new float[numLabels];
for (int i = 0; i < dataCount; i++)
{
distribution[data[i].Label]++;
}
if (labelWeights != null)
{
for (int i = 0; i < distribution.Length; i++)
{
distribution[i] *= labelWeights[i];
}
}
return(new DecisionTreeNode <T, D>(distribution));
}
if (depth == MaximumDepth - 2)
{
UpdateManager.WriteLine("Last branch node trained at depth {0}", depth);
node.Left = new DecisionTreeNode <T, D>(bestLeftDistribution);
node.Right = new DecisionTreeNode <T, D>(bestRightDistribution);
node.NodeType = NodeType.Branch;
node.Decider = bestDecider;
return(node);
}
Decision[] decisions = bestDecider.Decide(data);
List <T> leftData = new List <T>();
List <T> rightData = new List <T>();
for (int i = 0; i < decisions.Length; i++)
{
if (decisions[i] == Decision.Left)
{
leftData.Add(data[i]);
}
else
{
rightData.Add(data[i]);
}
}
if (leftData.Count == 0 || rightData.Count == 0)
{
throw new Exception("Error");
}
UpdateManager.WriteLine("Branch node at depth {0} trained.", depth);
node.Left = computeDepthFirst(new DecisionTreeNode <T, D>(), leftData, factory, numFeatures, numThresholds, numLabels, labelWeights, depth + 1);
node.Right = computeDepthFirst(new DecisionTreeNode <T, D>(), rightData, factory, numFeatures, numThresholds, numLabels, labelWeights, depth + 1);
node.Decider = bestDecider;
node.NodeType = NodeType.Branch;
return(node);
}
public DecisionTreeNode(Decider <T, D> decider, DecisionTreeNode <T, D> left, DecisionTreeNode <T, D> right)
{
_type = NodeType.Branch;
_decider = decider;
_left = left;
_right = right;
}
