import org.apache.flink.ml.classification.naivebayes.NaiveBayes;
import org.apache.flink.ml.classification.naivebayes.NaiveBayesModel;
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 NaiveBayes model and uses it for classification. */
public class NaiveBayesExample {
    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.), 11),
                        Row.of(Vectors.dense(1, 0), 10),
                        Row.of(Vectors.dense(1, 1.), 10));
        Table trainTable = tEnv.fromDataStream(trainStream).as("features", "label");

        DataStream<Row> predictStream =
                env.fromElements(
                        Row.of(Vectors.dense(0, 1.)),
                        Row.of(Vectors.dense(0, 0.)),
                        Row.of(Vectors.dense(1, 0)),
                        Row.of(Vectors.dense(1, 1.)));
        Table predictTable = tEnv.fromDataStream(predictStream).as("features");

        // Creates a NaiveBayes object and initializes its parameters.
        NaiveBayes naiveBayes =
                new NaiveBayes()
                        .setSmoothing(1.0)
                        .setFeaturesCol("features")
                        .setLabelCol("label")
                        .setPredictionCol("prediction")
                        .setModelType("multinomial");

        // Trains the NaiveBayes Model.
        NaiveBayesModel naiveBayesModel = naiveBayes.fit(trainTable);

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

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();
            DenseVector features = (DenseVector) row.getField(naiveBayes.getFeaturesCol());
            double predictionResult = (Double) row.getField(naiveBayes.getPredictionCol());
            System.out.printf("Features: %s \tPrediction Result: %s\n", features, predictionResult);
        }
    }
}

# Simple program that trains a NaiveBayes model and uses it for classification.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.classification.naivebayes import NaiveBayes
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_table = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense([0, 0.]), 11.),
        (Vectors.dense([1, 0]), 10.),
        (Vectors.dense([1, 1.]), 10.),
    ],
        type_info=Types.ROW_NAMED(
            ['features', 'label'],
            [DenseVectorTypeInfo(), Types.DOUBLE()])))

predict_table = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense([0, 1.]),),
        (Vectors.dense([0, 0.]),),
        (Vectors.dense([1, 0]),),
        (Vectors.dense([1, 1.]),),
    ],
        type_info=Types.ROW_NAMED(
            ['features'],
            [DenseVectorTypeInfo()])))

# create a naive bayes object and initialize its parameters
naive_bayes = NaiveBayes() \
    .set_smoothing(1.0) \
    .set_features_col('features') \
    .set_label_col('label') \
    .set_prediction_col('prediction') \
    .set_model_type('multinomial')

# train the naive bayes model
model = naive_bayes.fit(train_table)

# use the naive bayes model for predictions
output = model.transform(predict_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():
    features = result[field_names.index(naive_bayes.get_features_col())]
    prediction_result = result[field_names.index(naive_bayes.get_prediction_col())]
    print('Features: ' + str(features) + ' \tPrediction Result: ' + str(prediction_result))