This documentation is for an unreleased version of Apache Flink Machine Learning Library. We recommend you use the latest stable version.

Normalizer #

A Transformer that normalizes a vector to have unit norm using the given p-norm.

Input Columns #

Param name Type Default Description
inputCol Vector "input" Vectors to be normalized.

Output Columns #

Param name Type Default Description
outputCol Vector "output" Normalized vectors.

Parameters #

Key Default Type Required Description
inputCol "input" String no Input column name.
outputCol "output" String no Output column name.
p 2.0 Double no The p norm value.

Examples #

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.types.Row;
import org.apache.flink.util.CloseableIterator;

/** Simple program that creates a Normalizer instance and uses it for feature engineering. */
public class NormalizerExample {
	public static void main(String[] args) {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

		// Generates input data.
		DataStream<Row> inputStream =
				Row.of(Vectors.dense(2.1, 3.1, 1.2, 3.1, 4.6)),
				Row.of(Vectors.dense(1.2, 3.1, 4.6, 2.1, 3.1)));
		Table inputTable = tEnv.fromDataStream(inputStream).as("inputVec");

		// Creates a Normalizer object and initializes its parameters.
		Normalizer normalizer =
			new Normalizer().setInputCol("inputVec").setP(3.0).setOutputCol("outputVec");

		// Uses the Normalizer object for feature transformations.
		Table outputTable = normalizer.transform(inputTable)[0];

		// Extracts and displays the results.
		for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
			Row row =;

			Vector inputValue = (Vector) row.getField(normalizer.getInputCol());

			Vector outputValue = (Vector) row.getField(normalizer.getOutputCol());

			System.out.printf("Input Value: %s \tOutput Value: %s\n", inputValue, outputValue);

# Simple program that creates a Normalizer instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from import Vectors, DenseVectorTypeInfo
from import Normalizer
from pyflink.table import StreamTableEnvironment

# create a new StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()

# create a StreamTableEnvironment
t_env = StreamTableEnvironment.create(env)

# generate input data
input_data_table = t_env.from_data_stream(
        (1, Vectors.dense(2.1, 3.1, 1.2, 2.1)),
        (2, Vectors.dense(2.3, 2.1, 1.3, 1.2)),
            ['id', 'input_vec'],
            [Types.INT(), DenseVectorTypeInfo()])))

# create a normalizer object and initialize its parameters
normalizer = Normalizer() \
    .set_input_col('input_vec') \
    .set_p(1.5) \

# use the normalizer model for feature engineering
output = normalizer.transform(input_data_table)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    input_value = result[field_names.index(normalizer.get_input_col())]
    output_value = result[field_names.index(normalizer.get_output_col())]
    print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value))