/// <summary> Builds the tree structure.
///
/// </summary>
/// <param name="data">the data for which the tree structure is to be
/// generated.
/// </param>
/// <param name="keepData">is training data to be kept?
/// </param>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public virtual void buildTree(Instances data, bool keepData)
{
Instances[] localInstances;
if (keepData)
{
m_train = data;
}
m_test = null;
m_isLeaf = false;
m_isEmpty = false;
m_sons = null;
m_localModel = m_toSelectModel.selectModel(data);
if (m_localModel.numSubsets() > 1)
{
localInstances = m_localModel.split(data);
data = null;
m_sons = new ClassifierTree[m_localModel.numSubsets()];
for (int i = 0; i < m_sons.Length; i++)
{
m_sons[i] = getNewTree(localInstances[i]);
localInstances[i] = null;
}
}
else
{
m_isLeaf = true;
if (Utils.eq(data.sumOfWeights(), 0))
m_isEmpty = true;
data = null;
}
}
/// <summary> Builds the tree structure with hold out set
///
/// </summary>
/// <param name="train">the data for which the tree structure is to be
/// generated.
/// </param>
/// <param name="test">the test data for potential pruning
/// </param>
/// <param name="keepData">is training Data to be kept?
/// </param>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public virtual void buildTree(Instances train, Instances test, bool keepData)
{
Instances[] localTrain, localTest;
int i;
if (keepData)
{
m_train = train;
}
m_isLeaf = false;
m_isEmpty = false;
m_sons = null;
m_localModel = m_toSelectModel.selectModel(train, test);
m_test = new Distribution(test, m_localModel);
if (m_localModel.numSubsets() > 1)
{
localTrain = m_localModel.split(train);
localTest = m_localModel.split(test);
train = test = null;
m_sons = new ClassifierTree[m_localModel.numSubsets()];
for (i = 0; i < m_sons.Length; i++)
{
m_sons[i] = getNewTree(localTrain[i], localTest[i]);
localTrain[i] = null;
localTest[i] = null;
}
}
else
{
m_isLeaf = true;
if (Utils.eq(train.sumOfWeights(), 0))
m_isEmpty = true;
train = test = null;
}
}
/// <summary> Creates a distribution according to given instances and
/// split model.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public Distribution(Instances source, ClassifierSplitModel modelToUse)
{
int index;
Instance instance;
double[] weights;
m_perClassPerBag = new double[modelToUse.numSubsets()][];
for (int i = 0; i < modelToUse.numSubsets(); i++)
{
m_perClassPerBag[i] = new double[0];
}
m_perBag = new double[modelToUse.numSubsets()];
totaL = 0;
m_perClass = new double[source.numClasses()];
for (int i = 0; i < modelToUse.numSubsets(); i++)
m_perClassPerBag[i] = new double[source.numClasses()];
System.Collections.IEnumerator enu = source.enumerateInstances();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
instance = (Instance) enu.Current;
index = modelToUse.whichSubset(instance);
if (index != - 1)
add(index, instance);
else
{
weights = modelToUse.GetWeights(instance);
addWeights(instance, weights);
}
}
}
