import org.apache.flink.ml.feature.hashingtf.HashingTF;
import org.apache.flink.ml.linalg.SparseVector;
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;

import java.util.Arrays;
import java.util.List;

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

		// Generates input data.
		DataStream<Row> inputStream =
			env.fromElements(
				Row.of(
					Arrays.asList(
						"HashingTFTest", "Hashing", "Term", "Frequency", "Test")),
				Row.of(
					Arrays.asList(
						"HashingTFTest", "Hashing", "Hashing", "Test", "Test")));

		Table inputTable = tEnv.fromDataStream(inputStream).as("input");

		// Creates a HashingTF object and initializes its parameters.
		HashingTF hashingTF =
			new HashingTF().setInputCol("input").setOutputCol("output").setNumFeatures(128);

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

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

			List<Object> inputValue = (List<Object>) row.getField(hashingTF.getInputCol());
			SparseVector outputValue = (SparseVector) row.getField(hashingTF.getOutputCol());

			System.out.printf(
				"Input Value: %s \tOutput Value: %s\n",
				Arrays.toString(inputValue.stream().toArray()), outputValue);
		}
	}
}

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

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.hashingtf import HashingTF
from pyflink.table import StreamTableEnvironment

env = StreamExecutionEnvironment.get_execution_environment()

t_env = StreamTableEnvironment.create(env)

# Generates input data.
input_data_table = t_env.from_data_stream(
    env.from_collection([
        (['HashingTFTest', 'Hashing', 'Term', 'Frequency', 'Test'],),
        (['HashingTFTest', 'Hashing', 'Hashing', 'Test', 'Test'],),
    ],
        type_info=Types.ROW_NAMED(
            ["input", ],
            [Types.OBJECT_ARRAY(Types.STRING())])))

# Creates a HashingTF object and initializes its parameters.
hashing_tf = HashingTF() \
    .set_input_col('input') \
    .set_num_features(128) \
    .set_output_col('output')

# Uses the HashingTF object for feature transformations.
output = hashing_tf.transform(input_data_table)[0]

# Extracts and displays 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(hashing_tf.get_input_col())]
    output_value = result[field_names.index(hashing_tf.get_output_col())]
    print('Input Value: ' + ' '.join(input_value) + '\tOutput Value: ' + str(output_value))