public void RecurseRead (string[] fileLines, ref int i)
{
string line = fileLines[i];
i++;
string[] lineParts = line.Split(',');
if (lineParts.Length == 1)
{
Classification = float.Parse(lineParts[0]);
}
else
{
BranchComponent = int.Parse(lineParts[0]);
BranchValue = float.Parse(lineParts[1]);
LeftBranch = new DecisionTree();
LeftBranch.RecurseRead(fileLines, ref i);
RightBranch = new DecisionTree();
RightBranch.RecurseRead(fileLines, ref i);
}
}
public void Unload ()
{
Root = null;
}
public void Load ()
{
Root = new DecisionTree(Filename);
}
public BigDecisionTree(Dataset Dataset, int MaxTreeDepth, int CurrentDepth = 0)
{
Root = new DecisionTree(Dataset, MaxTreeDepth);
Filename = "./trees/" + Now().ToString() + "." + RNG.Next(0, 65535);
Root.Write(Filename);
}
private void DoBranch(Dataset Dataset, int MaxTreeDepth, int CurrentDepth)
{
Dataset leftDataset;
Dataset rightDataset;
int timeout = 0;
do
{
timeout++;
if (timeout > 1000)
{
DetermineClassification(Dataset);
}
BranchComponent = RNG.Next(Dataset.Inputs[0].Count);
float componentLow;
float componentHigh;
float componentRange;
Dataset.GetInputComponentRange(BranchComponent, out componentLow, out componentHigh, out componentRange);
BranchValue = (float)((RNG.NextDouble() * componentRange) + componentLow);
leftDataset = new Dataset();
rightDataset = new Dataset();
Split(BranchComponent, BranchValue, Dataset, out leftDataset, out rightDataset);
} while (leftDataset.Inputs.Count == 0 || rightDataset.Inputs.Count == 0);
LeftBranch = new DecisionTree(leftDataset, MaxTreeDepth, CurrentDepth + 1);
RightBranch = new DecisionTree(rightDataset, MaxTreeDepth, CurrentDepth + 1);
}