/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package edu.uci.jforests.learning.trees.decision;
import java.util.Arrays;
import edu.uci.jforests.learning.trees.CandidateSplitsForLeaf;
import edu.uci.jforests.learning.trees.LeafInstances;
import edu.uci.jforests.learning.trees.TreeLeafInstances;
import edu.uci.jforests.sample.Sample;
/**
* @author Yasser Ganjisaffar
*/
public class DecisionCandidateSplitsForLeaf extends CandidateSplitsForLeaf {
protected double[] targetDist;
public DecisionCandidateSplitsForLeaf(int numFeatures, int numInstances, int numClasses) {
super(numFeatures, numInstances);
targetDist = new double[numClasses];
bestSplitPerFeature = new DecisionTreeSplit[numFeatures];
for (int f = 0; f < numFeatures; f++) {
bestSplitPerFeature[f] = new DecisionTreeSplit(targetDist.length);
}
}
public double[] getTargetDist() {
return targetDist;
}
@Override
public void init(int curLeafIndex, TreeLeafInstances treeLeafInstances, Sample trainSet) {
this.init(curLeafIndex);
totalWeightedCount = 0;
LeafInstances leafInstances = treeLeafInstances.getLeafInstances(curLeafIndex);
numInstancesInLeaf = leafInstances.end - leafInstances.begin;
Arrays.fill(targetDist, 0);
for (int i = 0; i < numInstancesInLeaf; i++) {
indices[i] = leafInstances.indices[leafInstances.begin + i];
double target = trainSet.targets[indices[i]];
double weight = trainSet.weights[indices[i]];
this.targets[i] = target;
this.weights[i] = weight;
targetDist[(int) target] += weight;
totalWeightedCount += weight;
}
}
}
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