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/*
* 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.spark.ml.classification;
import java.io.Serializable;
import java.util.Arrays;
import java.util.List;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
public class JavaMultilayerPerceptronClassifierSuite implements Serializable {
private transient JavaSparkContext jsc;
private transient SQLContext sqlContext;
@Before
public void setUp() {
jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
sqlContext = new SQLContext(jsc);
}
@After
public void tearDown() {
jsc.stop();
jsc = null;
sqlContext = null;
}
@Test
public void testMLPC() {
Dataset<Row> dataFrame = sqlContext.createDataFrame(
jsc.parallelize(Arrays.asList(
new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)),
new LabeledPoint(1.0, Vectors.dense(0.0, 1.0)),
new LabeledPoint(1.0, Vectors.dense(1.0, 0.0)),
new LabeledPoint(0.0, Vectors.dense(1.0, 1.0)))),
LabeledPoint.class);
MultilayerPerceptronClassifier mlpc = new MultilayerPerceptronClassifier()
.setLayers(new int[] {2, 5, 2})
.setBlockSize(1)
.setSeed(11L)
.setMaxIter(100);
MultilayerPerceptronClassificationModel model = mlpc.fit(dataFrame);
Dataset<Row> result = model.transform(dataFrame);
List<Row> predictionAndLabels = result.select("prediction", "label").collectAsList();
for (Row r: predictionAndLabels) {
Assert.assertEquals((int) r.getDouble(0), (int) r.getDouble(1));
}
}
}
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