spark JavaDCTExample 源码

2022-10-20
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spark JavaDCTExample 代码

文件路径：/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.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
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.SparkSession;

// $example on$
import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.DCT;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
// $example off$

public class JavaDCTExample {
  public static void main(String[] args) {
    SparkSession spark = SparkSession
      .builder()
      .appName("JavaDCTExample")
      .getOrCreate();

    // $example on$
    List<Row> data = Arrays.asList(
      RowFactory.create(Vectors.dense(0.0, 1.0, -2.0, 3.0)),
      RowFactory.create(Vectors.dense(-1.0, 2.0, 4.0, -7.0)),
      RowFactory.create(Vectors.dense(14.0, -2.0, -5.0, 1.0))
    );
    StructType schema = new StructType(new StructField[]{
      new StructField("features", new VectorUDT(), false, Metadata.empty()),
    });
    Dataset<Row> df = spark.createDataFrame(data, schema);

    DCT dct = new DCT()
      .setInputCol("features")
      .setOutputCol("featuresDCT")
      .setInverse(false);

    Dataset<Row> dctDf = dct.transform(df);

    dctDf.select("featuresDCT").show(false);
    // $example off$

    spark.stop();
  }
}

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