import org.apache.flink.ml.feature.standardscaler.StandardScaler;
import org.apache.flink.ml.feature.standardscaler.StandardScalerModel;
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 StandardScaler model and uses it for feature engineering. */
public class StandardScalerExample {
    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(-2.5, 9, 1)),
                        Row.of(Vectors.dense(1.4, -5, 1)),
                        Row.of(Vectors.dense(2, -1, -2)));
        Table inputTable = tEnv.fromDataStream(inputStream).as("input");

        // Creates a StandardScaler object and initializes its parameters.
        StandardScaler standardScaler = new StandardScaler();

        // Trains the StandardScaler Model.
        StandardScalerModel model = standardScaler.fit(inputTable);

        // Uses the StandardScaler 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(standardScaler.getInputCol());
            DenseVector outputValue = (DenseVector) row.getField(standardScaler.getOutputCol());
            System.out.printf("Input Value: %s\tOutput Value: %s\n", inputValue, outputValue);
        }
    }
}

# Simple program that trains a StandardScaler model and uses it for feature
# engineering.
#
# Before executing this program, please make sure you have followed Flink ML's
# quick start guideline to set up Flink ML and Flink environment. The guideline
# can be found at
#
# https://nightlies.apache.org/flink/flink-ml-docs-master/docs/try-flink-ml/quick-start/

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.lib.feature.standardscaler import StandardScaler
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(-2.5, 9, 1),),
        (Vectors.dense(1.4, -5, 1),),
        (Vectors.dense(2, -1, -2),),
    ],
        type_info=Types.ROW_NAMED(
            ['input'],
            [DenseVectorTypeInfo()])
    ))

# create a standard-scaler object and initialize its parameters
standard_scaler = StandardScaler()

# train the standard-scaler model
model = standard_scaler.fit(input_data)

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