public void Algorithm(double[][] dataInput, Node node)
{
/*
* Choose class for node if is the case.
*/
node.classType = sameClass(dataInput);
if (node.classType != -1)
{
node.leaf = dataInput.Length;
return;
}
node.classType = sameAtributtes(dataInput);
if (node.classType != -1)
{
node.leaf = dataInput.Length;
return;
}
/*
* Choose best axis-parallel split node for dataInput.
*/
bestAxisParallel(dataInput, node);
/*
* Apply OC1
*/
Node auxNode = (Node)node.Clone();
for (int i = 0; i < R; i++)
{
if (i > 0)
auxNode.Random();
step1:
double initialGain = gain(dataInput, node); // Gain for better node.
for (int d = 0; d < dataInput[0].Length; d++)
{
pertube(d, auxNode, dataInput);
if (gain(dataInput, auxNode) > initialGain)
{
node.SetNode(auxNode);
break;
}
}
// step 2
initialGain = gain(dataInput, node);
for (int j = 0; j < J; j++)
{
Random rand = new Random();
int direction = rand.Next(dataInput[0].Length - 1);
pertube(direction, auxNode, dataInput);
if (gain(dataInput, auxNode) > initialGain)
{
node.SetNode(auxNode);
goto step1;
}
}
if (gain(dataInput, auxNode) > gain(dataInput, node))
node.SetNode(auxNode);
}
/*
* Recursive Algorithm for each branch for node.
*/
Node left = new Node(new double[dataInput[0].Length], 0);
Node right = new Node(new double[dataInput[0].Length], 0);
node.SetLeft(left);
node.SetRight(right);
List<double[]>[] data = divideData(dataInput, node);
Algorithm(data[0].ToArray(), node.GetLeft());
Algorithm(data[1].ToArray(), node.GetRight());
}
private void pertube(int m, Node node, double[][] dataInput)
{
if (!node.ActiveAtributtes[m])
return;
Node auxNode = (Node)node.Clone();
double[] U = new double[dataInput.Length];
for (int i = 0; i < dataInput.Length; i++)
U[i] = Math.Min((node.weights[m] * dataInput[i][m] - node.Evaluate(dataInput[i])) / dataInput[i][m], 0);
Array.Sort(U);
int bestA = 0;
int dif = Int16.MaxValue;
for (int i = 0; i < U.Length; i++)
{
int positive = 0;
int negative = 0;
for (int j = 0; j < U.Length; j++)
{
if (j <= i && U[j] <= 0)
negative++;
else if (j > i && U[j] > 0)
positive++;
}
if (Math.Abs(positive - negative) < dif)
{
dif = Math.Abs(positive - negative);
bestA = i;
}
}
auxNode.weights[m] = U[bestA];
if (gain(dataInput, auxNode) > gain(dataInput, node))
{
node.weights[m] = U.Max();
node.ResetPmove();
}
else if (gain(dataInput, auxNode) == gain(dataInput, node))
{
if(node.EvaluatePmove())
node.weights[m] = U.Max();
node.DecreasePmove();
}
}
