import org.apache.flink.ml.feature.vectorslicer.VectorSlicer;
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 VectorSlicer instance and uses it for feature engineering. */
public class VectorSlicerExample {
    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(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("vec");

        // Creates a VectorSlicer object and initializes its parameters.
        VectorSlicer vectorSlicer =
                new VectorSlicer().setInputCol("vec").setIndices(1, 2, 3).setOutputCol("slicedVec");

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

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

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

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

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

# Simple program that creates a VectorSlicer 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.vectorslicer import VectorSlicer
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(
    env.from_collection([
        (1, Vectors.dense(2.1, 3.1, 1.2, 2.1)),
        (2, Vectors.dense(2.3, 2.1, 1.3, 1.2)),
    ],
        type_info=Types.ROW_NAMED(
            ['id', 'vec'],
            [Types.INT(), DenseVectorTypeInfo()])))

# create a vector slicer object and initialize its parameters
vector_slicer = VectorSlicer() \
    .set_input_col('vec') \
    .set_indices(1, 2, 3) \
    .set_output_col('sub_vec')

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