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SnappyData distributed data store and execution engine
/*
* 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,
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* See the License for the specific language governing permissions and
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package org.apache.spark.examples.ml;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.SQLContext;
// $example on$
import java.util.Arrays;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.feature.Bucketizer;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.DataTypes;
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 JavaBucketizerExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("JavaBucketizerExample");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext jsql = new SQLContext(jsc);
// $example on$
double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY};
JavaRDD data = jsc.parallelize(Arrays.asList(
RowFactory.create(-0.5),
RowFactory.create(-0.3),
RowFactory.create(0.0),
RowFactory.create(0.2)
));
StructType schema = new StructType(new StructField[]{
new StructField("features", DataTypes.DoubleType, false, Metadata.empty())
});
DataFrame dataFrame = jsql.createDataFrame(data, schema);
Bucketizer bucketizer = new Bucketizer()
.setInputCol("features")
.setOutputCol("bucketedFeatures")
.setSplits(splits);
// Transform original data into its bucket index.
DataFrame bucketedData = bucketizer.transform(dataFrame);
bucketedData.show();
// $example off$
jsc.stop();
}
}
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