import org.apache.flink.ml.feature.dct.DCT;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.Vectors;
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 DCT instance and uses it for feature engineering. */
public class DCTExample {
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input data.
        List<Vector> inputData =
                Arrays.asList(
                        Vectors.dense(1.0, 1.0, 1.0, 1.0), Vectors.dense(1.0, 0.0, -1.0, 0.0));
        Table inputTable = tEnv.fromDataStream(env.fromCollection(inputData)).as("input");

        // Creates a DCT object and initializes its parameters.
        DCT dct = new DCT();

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

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

            Vector inputValue = row.getFieldAs(dct.getInputCol());
            Vector outputValue = row.getFieldAs(dct.getOutputCol());

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

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

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.feature.dct import DCT
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 = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense(1.0, 1.0, 1.0, 1.0),),
        (Vectors.dense(1.0, 0.0, -1.0, 0.0),),
    ],
        type_info=Types.ROW_NAMED(
            ['input'],
            [DenseVectorTypeInfo()])))

# create a DCT object and initialize its parameters
dct = DCT()

# use the dct for feature engineering
output = dct.transform(input_data)[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(dct.get_input_col())]
    output_value = result[field_names.index(dct.get_output_col())]
    print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value))