private List <Rule> CreateRulesForNotQualitativeArguments(Data[] elements, int indexOfArgument)
{
List <Rule> rules = new List <Rule>();
double[] valuesOfArgument = SortValuesOfArgument(elements, indexOfArgument);
for (int j = 0; j < valuesOfArgument.Length; j++)
{
Rule temp = new Rule();
temp.IndexOfArgument = indexOfArgument;
temp.IsQualitative = false;
if (j + 1 < valuesOfArgument.Length)
{
temp.Rules.Add(CreateValueForRule(valuesOfArgument, j));
}
else
{
temp.Rules.Add(valuesOfArgument[j]);
}
Data[] left = null, right = null;
GeneralMethods.DivideSampleByNotQualitiveRule(out left, out right, elements, temp.Rules[0], indexOfArgument);
temp.Error = GeneralMethods.CountError(left);
temp.Error += GeneralMethods.CountError(right);
rules.Add(temp);
}
return(rules);
}
private void CountErrorInQualitativeRules(List <Rule> rules, Data[] elements)
{
for (int i = 0; i < rules.Count; i++)
{
Data[] left = null, right = null;
GeneralMethods.DivideSampleByQualitiveRule(out left, out right, elements,
rules[i].Rules, rules[i].IndexOfArgument);
rules[i].Error = GeneralMethods.CountError(left);
rules[i].Error += GeneralMethods.CountError(right);
}
}
private void Learn()
{
//build the tree using CART algorithm
Queue <DecisionTreeNode> qe = new Queue <DecisionTreeNode>();
qe.Enqueue(Head);
while (GeneralMethods.FindErrorSum(this) > 0)
{
DecisionTreeNode tempNode = new DecisionTreeNode();
tempNode = qe.Dequeue();
if (GeneralMethods.CountError(tempNode.Elements) != 0)
{
tempNode.Rule = new Rule(tempNode.Elements);
AddChildren(tempNode);
qe.Enqueue(tempNode.LeftChild);
qe.Enqueue(tempNode.RightChild);
}
}
TreeError = GeneralMethods.FindErrorSum(this);
}
public static double FindErrorInNode(Data[] elements, int allElements)
{
double error = GeneralMethods.CountError(elements) * elements.Length;
return(error / allElements);
}
