spark-gpu-master/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
/*
 * 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
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package org.apache.spark.ml.feature;

import java.util.Arrays;

import org.junit.After;
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.api.java.JavaRDD;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;

public class JavaNormalizerSuite {
  private transient JavaSparkContext jsc;
  private transient SQLContext jsql;

  @Before
  public void setUp() {
    jsc = new JavaSparkContext("local", "JavaNormalizerSuite");
    jsql = new SQLContext(jsc);
  }

  @After
  public void tearDown() {
    jsc.stop();
    jsc = null;
  }

  @Test
  public void normalizer() {
    // The tests are to check Java compatibility.
    JavaRDD<VectorIndexerSuite.FeatureData> points = jsc.parallelize(Arrays.asList(
      new VectorIndexerSuite.FeatureData(Vectors.dense(0.0, -2.0)),
      new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 3.0)),
      new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 4.0))
    ));
    DataFrame dataFrame = jsql.createDataFrame(points, VectorIndexerSuite.FeatureData.class);
    Normalizer normalizer = new Normalizer()
      .setInputCol("features")
      .setOutputCol("normFeatures");

    // Normalize each Vector using $L^2$ norm.
    DataFrame l2NormData = normalizer.transform(dataFrame, normalizer.p().w(2));
    l2NormData.count();

    // Normalize each Vector using $L^\infty$ norm.
    DataFrame lInfNormData =
      normalizer.transform(dataFrame, normalizer.p().w(Double.POSITIVE_INFINITY));
    lInfNormData.count();
  }
}
