import org.apache.flink.ml.feature.minmaxscaler.MinMaxScaler;
import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel;
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 a MinMaxScaler model and uses it for feature engineering. */
public class MinMaxScalerExample {
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input training and prediction data.
        DataStream<Row> trainStream =
                env.fromElements(
                        Row.of(Vectors.dense(0.0, 3.0)),
                        Row.of(Vectors.dense(2.1, 0.0)),
                        Row.of(Vectors.dense(4.1, 5.1)),
                        Row.of(Vectors.dense(6.1, 8.1)),
                        Row.of(Vectors.dense(200, 400)));
        Table trainTable = tEnv.fromDataStream(trainStream).as("input");

        DataStream<Row> predictStream =
                env.fromElements(
                        Row.of(Vectors.dense(150.0, 90.0)),
                        Row.of(Vectors.dense(50.0, 40.0)),
                        Row.of(Vectors.dense(100.0, 50.0)));
        Table predictTable = tEnv.fromDataStream(predictStream).as("input");

        // Creates a MinMaxScaler object and initializes its parameters.
        MinMaxScaler minMaxScaler = new MinMaxScaler();

        // Trains the MinMaxScaler Model.
        MinMaxScalerModel minMaxScalerModel = minMaxScaler.fit(trainTable);

        // Uses the MinMaxScaler Model for predictions.
        Table outputTable = minMaxScalerModel.transform(predictTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();
            DenseVector inputValue = (DenseVector) row.getField(minMaxScaler.getInputCol());
            DenseVector outputValue = (DenseVector) row.getField(minMaxScaler.getOutputCol());
            System.out.printf("Input Value: %-15s\tOutput Value: %s\n", inputValue, outputValue);
        }
    }
}

# Simple program that trains a MinMaxScaler model 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.minmaxscaler import MinMaxScaler
from pyflink.table import StreamTableEnvironment

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

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

# generate input training and prediction data
train_data = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense(0.0, 3.0),),
        (Vectors.dense(2.1, 0.0),),
        (Vectors.dense(4.1, 5.1),),
        (Vectors.dense(6.1, 8.1),),
        (Vectors.dense(200, 400),),
    ],
        type_info=Types.ROW_NAMED(
            ['input'],
            [DenseVectorTypeInfo()])
    ))

predict_data = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense(150.0, 90.0),),
        (Vectors.dense(50.0, 40.0),),
        (Vectors.dense(100.0, 50.0),),
    ],
        type_info=Types.ROW_NAMED(
            ['input'],
            [DenseVectorTypeInfo()])
    ))

# create a min-max-scaler object and initialize its parameters
min_max_scaler = MinMaxScaler()

# train the min-max-scaler model
model = min_max_scaler.fit(train_data)

# use the min-max-scaler model for predictions
output = model.transform(predict_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(min_max_scaler.get_input_col())]
    output_value = result[field_names.index(min_max_scaler.get_output_col())]
    print('Input Value: ' + str(input_value) + ' \tOutput Value: ' + str(output_value))