private NormalDistributionDecisionTree(NormalDistributionDecisionTree tree1, NormalDistributionDecisionTree tree2)
{
decisions = new List <PercentageDecision>();
foreach (PercentageDecision decision in tree1.decisions)
{
decisions.Add(decision);
}
foreach (PercentageDecision decision in tree2.decisions)
{
PercentageDecision foundDecision = decisions.FirstOrDefault(x => (int)x.DecisionObject == (int)decision.DecisionObject);
if (foundDecision != null)
{
foundDecision.Percentage += decision.Percentage;
}
else
{
decisions.Add(decision);
}
}
decisions.ForEach(x => x.Percentage /= 2);
//Creates the decision tree
decisionTree = new PercentageDecisionTree();
decisionTree.AddDecisions(decisions);
}
private void CreateBalancedNormalDecisionTree(int[] decisionArray)
{
double currentChance = 50.0;
for (int i = 0; i < decisionArray.Length / 2; i++)
{
int index1 = 0 + (decisionArray.Length / 2) - i - 1;
int index2 = decisionArray.Length - (decisionArray.Length / 2) + i;
currentChance /= 2;
decisions.Add(new PercentageDecision(decisionArray[index1], currentChance));
decisions.Add(new PercentageDecision(decisionArray[index2], currentChance));
}
//Check whats left and adds to main decisions
double leftoverValue = 100.0 - decisions.Sum(x => x.Percentage);
decisions.Where(x => x.Percentage == 25.0).ToList().ForEach(x => x.Percentage += leftoverValue / 2);
//Creates the decision tree
decisionTree = new PercentageDecisionTree();
decisionTree.AddDecisions(decisions);
}
