import org.apache.flink.ml.feature.idf.IDF;
import org.apache.flink.ml.feature.idf.IDFModel;
import org.apache.flink.ml.linalg.DenseVector;
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 trains an IDF model and uses it for feature engineering. */
public class IDFExample {
	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(Vectors.dense(0, 1, 0, 2)),
				Row.of(Vectors.dense(0, 1, 2, 3)),
				Row.of(Vectors.dense(0, 1, 0, 0)));

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

		// Creates an IDF object and initializes its parameters.
		IDF idf = new IDF().setMinDocFreq(2);

		// Trains the IDF Model.
		IDFModel model = idf.fit(inputTable);

		// Uses the IDF Model for predictions.
		Table outputTable = model.transform(inputTable)[0];

		// Extracts and displays the results.
		for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
			Row row = it.next();
			DenseVector inputValue = (DenseVector) row.getField(idf.getInputCol());
			DenseVector outputValue = (DenseVector) row.getField(idf.getOutputCol());
			System.out.printf("Input Value: %s\tOutput Value: %s\n", inputValue, outputValue);
		}
	}
}

# Simple program that trains an IDF model and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.idf import IDF
from pyflink.table import StreamTableEnvironment

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

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

# Generates input for training and prediction.
input_table = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense(0, 1, 0, 2),),
        (Vectors.dense(0, 1, 2, 3),),
        (Vectors.dense(0, 1, 0, 0),),
    ],
        type_info=Types.ROW_NAMED(
            ['input', ],
            [DenseVectorTypeInfo(), ])))

# Creates an IDF object and initializes its parameters.
idf = IDF().set_min_doc_freq(2)

# Trains the IDF Model.
model = idf.fit(input_table)

# Uses the IDF Model for predictions.
output = model.transform(input_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_index = field_names.index(idf.get_input_col())
    output_index = field_names.index(idf.get_output_col())
    print('Input Value: ' + str(result[input_index]) +
          '\tOutput Value: ' + str(result[output_index]))