edu.uci.jforestsx.learning.trees.decision.DecisionPredictions Maven / Gradle / Ivy
/**
* 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.jforestsx.learning.trees.decision;
import java.util.Arrays;
import edu.uci.jforestsx.eval.EvaluationMetric;
import edu.uci.jforestsx.learning.LearningUtils;
import edu.uci.jforestsx.learning.trees.Tree;
import edu.uci.jforestsx.sample.Predictions;
import edu.uci.jforestsx.util.ArraysUtil;
/**
* @author Yasser Ganjisaffar
*/
public class DecisionPredictions extends Predictions {
protected double[][] perInstanceDistribution;
protected double[] perInstancePredictions;
protected int numClasses;
public DecisionPredictions(int numClasses) {
this.numClasses = numClasses;
}
@Override
public void allocate(int maxNumInstances) {
perInstanceDistribution = new double[maxNumInstances][numClasses];
perInstancePredictions = new double[maxNumInstances];
for (int i = 0; i < maxNumInstances; i++) {
perInstanceDistribution[i] = new double[numClasses];
}
}
@Override
public void update(Tree tree, double weight) {
LearningUtils.updateDistributions(sample, perInstanceDistribution, (DecisionTree) tree, weight);
}
@Override
public double evaluate(EvaluationMetric evalMetric) throws Exception {
if (numClasses == 2) {
for (int i = 0; i < sample.size; i++) {
perInstancePredictions[i] = perInstanceDistribution[i][1] / (perInstanceDistribution[i][0] + perInstanceDistribution[i][1]);
}
} else {
for (int i = 0; i < sample.size; i++) {
perInstancePredictions[i] = ArraysUtil.findMaxIndex(perInstanceDistribution[i]);
}
}
return sample.evaluate(perInstancePredictions, evalMetric);
}
@Override
public void reset() {
Arrays.fill(perInstancePredictions, 0);
}
}