content: /*
   * 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.util.Arrays;
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.feature.LabeledPoint;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  
  public class JavaMultilayerPerceptronClassifierSuite extends SharedSparkSession {
  
    @Test
    public void testMLPC() {
      List<LabeledPoint> data = 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))
      );
      Dataset<Row> dataFrame = spark.createDataFrame(data, LabeledPoint.class);
  
      MultilayerPerceptronClassifier mlpc = new MultilayerPerceptronClassifier()
        .setLayers(new int[]{2, 5, 2})
        .setBlockSize(1)
        .setSeed(123L)
        .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));
      }
    }
  }
  