/**
* 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 org.apache.mahout.classifier.df.builder;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.classifier.df.data.Data;
import org.apache.mahout.classifier.df.data.DataLoader;
import org.apache.mahout.classifier.df.data.Dataset;
import org.apache.mahout.classifier.df.data.Utils;
import org.junit.Test;
import java.util.Random;
public final class InfiniteRecursionTest extends MahoutTestCase {
private static final double[][] dData = {
{ 0.25, 0.0, 0.0, 5.143998668220409E-4, 0.019847102289905324, 3.5216524641879855E-4, 0.0, 0.6225857142857143, 4 },
{ 0.25, 0.0, 0.0, 0.0010504411519893459, 0.005462138323171171, 0.0026130744829756746, 0.0, 0.4964857142857143, 3 },
{ 0.25, 0.0, 0.0, 0.0010504411519893459, 0.005462138323171171, 0.0026130744829756746, 0.0, 0.4964857142857143, 4 },
{ 0.25, 0.0, 0.0, 5.143998668220409E-4, 0.019847102289905324, 3.5216524641879855E-4, 0.0, 0.6225857142857143, 3 }
};
/**
* make sure DecisionTreeBuilder.build() does not throw a StackOverflowException
*/
@Test
public void testBuild() throws Exception {
Random rng = RandomUtils.getRandom();
String[] source = Utils.double2String(dData);
String descriptor = "N N N N N N N N L";
Dataset dataset = DataLoader.generateDataset(descriptor, false, source);
Data data = DataLoader.loadData(dataset, source);
TreeBuilder builder = new DecisionTreeBuilder();
builder.build(rng, data);
// regression
dataset = DataLoader.generateDataset(descriptor, true, source);
data = DataLoader.loadData(dataset, source);
builder = new DecisionTreeBuilder();
builder.build(rng, data);
}
}