/**
* Computes class distribution of an instance using the FastRandomTree.
*
* <p>In Weka's RandomTree, the distributions were normalized so that all probabilities sum to 1;
* this would abolish the effect of instance weights on voting. In FastRandomForest 0.97 onwards,
* the distributions are normalized by dividing with the number of instances going into a leaf.
*
* <p>
*
* @param instance the instance to compute the distribution for
* @return the computed class distribution
* @throws Exception if computation fails
*/
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
double[] returnedDist = null;
if (m_Attribute > -1) { // ============================ node is not a leaf
if (instance.isMissing(m_Attribute)) { // ---------------- missing value
returnedDist = new double[m_MotherForest.getM_Info().numClasses()];
// split instance up
for (int i = 0; i < m_Successors.length; i++) {
double[] help = m_Successors[i].distributionForInstance(instance);
if (help != null) {
for (int j = 0; j < help.length; j++) {
returnedDist[j] += m_Prop[i] * help[j];
}
}
}
} else if (m_MotherForest.getM_Info().attribute(m_Attribute).isNominal()) { // ------ nominal
// returnedDist = m_Successors[(int) instance.value(m_Attribute)]
// .distributionForInstance(instance);
// 0.99: new - binary splits (also) for nominal attributes
if (instance.value(m_Attribute) == m_SplitPoint) {
returnedDist = m_Successors[0].distributionForInstance(instance);
} else {
returnedDist = m_Successors[1].distributionForInstance(instance);
}
} else { // ------------------------------------------ numeric attributes
if (instance.value(m_Attribute) < m_SplitPoint) {
returnedDist = m_Successors[0].distributionForInstance(instance);
} else {
returnedDist = m_Successors[1].distributionForInstance(instance);
}
}
return returnedDist;
} else { // =============================================== node is a leaf
return m_ClassProbs;
}
}
/**
* Computes class distribution of an instance using the FastRandomTree.
*
* <p>Works correctly only if the DataCache has the same attributes as the one used to train the
* FastRandomTree - but this function does not check for that!
*
* <p>Main use of this is to compute out-of-bag error (also when finding feature importances).
*
* @param instance the instance to compute the distribution for
* @return the computed class distribution
* @throws Exception if computation fails
*/
public double[] distributionForInstanceInDataCache(DataCache data, int instIdx) {
double[] returnedDist = null;
if (m_Attribute > -1) { // ============================ node is not a leaf
if (data.isValueMissing(m_Attribute, instIdx)) { // ---------------- missing value
returnedDist = new double[m_MotherForest.getM_Info().numClasses()];
// split instance up
for (int i = 0; i < m_Successors.length; i++) {
double[] help = m_Successors[i].distributionForInstanceInDataCache(data, instIdx);
if (help != null) {
for (int j = 0; j < help.length; j++) {
returnedDist[j] += m_Prop[i] * help[j];
}
}
}
} else if (data.isAttrNominal(m_Attribute)) { // ------ nominal
// returnedDist = m_Successors[(int) instance.value(m_Attribute)]
// .distributionForInstance(instance);
// 0.99: new - binary splits (also) for nominal attributes
if (data.vals[m_Attribute][instIdx] == m_SplitPoint) {
returnedDist = m_Successors[0].distributionForInstanceInDataCache(data, instIdx);
} else {
returnedDist = m_Successors[1].distributionForInstanceInDataCache(data, instIdx);
}
} else { // ------------------------------------------ numeric attributes
if (data.vals[m_Attribute][instIdx] < m_SplitPoint) {
returnedDist = m_Successors[0].distributionForInstanceInDataCache(data, instIdx);
} else {
returnedDist = m_Successors[1].distributionForInstanceInDataCache(data, instIdx);
}
}
return returnedDist;
} else { // =============================================== node is a leaf
return m_ClassProbs;
}
}
