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0018 package org.apache.spark.examples.ml;
0019
0020 import org.apache.spark.sql.SparkSession;
0021
0022
0023 import java.util.Arrays;
0024 import java.util.List;
0025
0026 import org.apache.spark.ml.feature.PolynomialExpansion;
0027 import org.apache.spark.ml.linalg.VectorUDT;
0028 import org.apache.spark.ml.linalg.Vectors;
0029 import org.apache.spark.sql.Dataset;
0030 import org.apache.spark.sql.Row;
0031 import org.apache.spark.sql.RowFactory;
0032 import org.apache.spark.sql.types.Metadata;
0033 import org.apache.spark.sql.types.StructField;
0034 import org.apache.spark.sql.types.StructType;
0035
0036
0037 public class JavaPolynomialExpansionExample {
0038 public static void main(String[] args) {
0039 SparkSession spark = SparkSession
0040 .builder()
0041 .appName("JavaPolynomialExpansionExample")
0042 .getOrCreate();
0043
0044
0045 PolynomialExpansion polyExpansion = new PolynomialExpansion()
0046 .setInputCol("features")
0047 .setOutputCol("polyFeatures")
0048 .setDegree(3);
0049
0050 List<Row> data = Arrays.asList(
0051 RowFactory.create(Vectors.dense(2.0, 1.0)),
0052 RowFactory.create(Vectors.dense(0.0, 0.0)),
0053 RowFactory.create(Vectors.dense(3.0, -1.0))
0054 );
0055 StructType schema = new StructType(new StructField[]{
0056 new StructField("features", new VectorUDT(), false, Metadata.empty()),
0057 });
0058 Dataset<Row> df = spark.createDataFrame(data, schema);
0059
0060 Dataset<Row> polyDF = polyExpansion.transform(df);
0061 polyDF.show(false);
0062
0063
0064 spark.stop();
0065 }
0066 }