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package org.apache.flink.ml.examples.feature;
import org.apache.flink.ml.feature.polynomialexpansion.PolynomialExpansion;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import org.apache.flink.util.CloseableIterator;
/**
* Simple program that creates a PolynomialExpansion instance and uses it for feature engineering.
*/
public class PolynomialExpansionExample {
public static void main(String[] args) {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
// Generates input data.
DataStream inputStream =
env.fromElements(
Row.of(Vectors.dense(2.1, 3.1, 1.2)), Row.of(Vectors.dense(1.2, 3.1, 4.6)));
Table inputTable = tEnv.fromDataStream(inputStream).as("inputVec");
// Creates a PolynomialExpansion object and initializes its parameters.
PolynomialExpansion polynomialExpansion =
new PolynomialExpansion()
.setInputCol("inputVec")
.setDegree(2)
.setOutputCol("outputVec");
// Uses the PolynomialExpansion object for feature transformations.
Table outputTable = polynomialExpansion.transform(inputTable)[0];
// Extracts and displays the results.
for (CloseableIterator it = outputTable.execute().collect(); it.hasNext(); ) {
Row row = it.next();
Vector inputValue = (Vector) row.getField(polynomialExpansion.getInputCol());
Vector outputValue = (Vector) row.getField(polynomialExpansion.getOutputCol());
System.out.printf("Input Value: %s \tOutput Value: %s\n", inputValue, outputValue);
}
}
}
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