Java Code Examples for org.apache.flink.api.java.tuple.Tuple4#of()

@Override
public Tuple4<Integer, Long, Integer, String> map(Tuple3<Integer, Long, String> value) throws IOException {
	// update counter
	Integer counterValue = counter.value();
	if (counterValue == null) {
		counterValue = 0;
	}
	counter.update(counterValue + 1);

	// save last value
	Long lastValue = last.value();
	if (lastValue == null) {
		lastValue = initialValue;
	}
	last.update(value.f1);

	return Tuple4.of(value.f0, value.f1 - lastValue, counterValue, value.f2);
}
 

@Override
public Tuple4<Integer, Long, Integer, String> map(Tuple3<Integer, Long, String> value) throws IOException {
	// update counter
	Integer counterValue = counter.value();
	if (counterValue == null) {
		counterValue = 0;
	}
	counter.update(counterValue + 1);

	// save last value
	Long lastValue = last.value();
	if (lastValue == null) {
		lastValue = initialValue;
	}
	last.update(value.f1);

	return Tuple4.of(value.f0, value.f1 - lastValue, counterValue, value.f2);
}
 

@Override
public Tuple4<Integer, Long, Integer, String> map(Tuple3<Integer, Long, String> value) throws IOException {
	// update counter
	Integer counterValue = counter.value();
	if (counterValue == null) {
		counterValue = 0;
	}
	counter.update(counterValue + 1);

	// save last value
	Long lastValue = last.value();
	if (lastValue == null) {
		lastValue = initialValue;
	}
	last.update(value.f1);

	return Tuple4.of(value.f0, value.f1 - lastValue, counterValue, value.f2);
}
 

/**
 * Deserialize the model data.
 *
 * @param meta         The model meta data.
 * @param data         The model concrete data.
 * @param additionData The additional data.
 * @return The model data used by mapper.
 */
@Override
public Tuple4<Boolean, Boolean, double[], double[]> deserializeModel(Params meta, Iterable<String> data, Iterable<Row> additionData) {
    double[] means = JsonConverter.fromJson(data.iterator().next(), double[].class);
    double[] stdDevs = JsonConverter.fromJson(data.iterator().next(), double[].class);

    Boolean withMean = meta.get(VectorStandardTrainParams.WITH_MEAN);
    Boolean withStd = meta.get(VectorStandardTrainParams.WITH_STD);

    return Tuple4.of(withMean, withStd, means, stdDevs);
}
 

/**
 * Deserialize the model data.
 *
 * @param meta         The model meta data.
 * @param data         The model concrete data.
 * @param additionData The additional data.
 * @return The model data used by mapper.
 */
@Override
public Tuple4<Double, Double, double[], double[]> deserializeModel(Params meta, Iterable<String> data, Iterable<Row> additionData) {
    double min = meta.get(VectorMinMaxScalerTrainParams.MIN);
    double max = meta.get(VectorMinMaxScalerTrainParams.MAX);

    double[] eMins = JsonConverter.fromJson(data.iterator().next(), double[].class);
    double[] eMaxs = JsonConverter.fromJson(data.iterator().next(), double[].class);

    return Tuple4.of(min, max, eMins, eMaxs);
}
 

/**
 * @param crossTabWithId: f0 is id, f1 is cross table
 * @return tuple4: f0 is id which is id of cross table, f1 is pValue, f2 is chi-square Value, f3 is df
 */
protected static Tuple4<Integer, Double, Double, Double> test(Tuple2<Integer, Crosstab> crossTabWithId) {
    int colIdx = crossTabWithId.f0;
    Crosstab crosstab = crossTabWithId.f1;

    int rowLen = crosstab.rowTags.size();
    int colLen = crosstab.colTags.size();

    //compute row sum and col sum
    double[] rowSum = crosstab.rowSum();
    double[] colSum = crosstab.colSum();
    double n = crosstab.sum();


    //compute statistic value
    double chiSq = 0;
    for (int i = 0; i < rowLen; i++) {
        for (int j = 0; j < colLen; j++) {
            double nij = rowSum[i] * colSum[j] / n;
            double temp = crosstab.data[i][j] - nij;
            chiSq += temp * temp / nij;
        }
    }

    //set result
    double p;
    if (rowLen <= 1 || colLen <= 1) {
        p = 1;
    } else {
        ChiSquaredDistribution distribution =
            new ChiSquaredDistribution(null, (rowLen - 1) * (colLen - 1));
        p = 1.0 - distribution.cumulativeProbability(Math.abs(chiSq));
    }

    return Tuple4.of(colIdx, p, chiSq, (double)(rowLen - 1) * (colLen - 1));
}
 

public Tuple4<Float, Double, Double, Float> getData() {
    int currentIndex = this.iteratorArray.getPoint() * 24;
    return Tuple4.of(buffer.getFloat(currentIndex),
            buffer.getDouble(currentIndex + 4),
            buffer.getDouble(currentIndex + 12),
            buffer.getFloat(currentIndex + 20));
}
 

@Test
public void testTupleType() {
    Tuple4 row = Tuple4.of(1, "test", 56.7, CURRENT);
    StreamSchema<Tuple4> schema = new StreamSchema<>(new TupleTypeInfo<>(
            TypeExtractor.createTypeInfo(Integer.class),
            TypeExtractor.createTypeInfo(String.class),
            TypeExtractor.createTypeInfo(Double.class),
            TypeExtractor.createTypeInfo(Long.class))
            , "id", "name", "price", "timestamp");
    StreamSerializer<Tuple4> reader = new StreamSerializer<>(schema);
    Assert.assertArrayEquals(new Object[]{1, "test", 56.7, CURRENT}, reader.getRow(row));
}
 

@Override
public Tuple4 <Long, Row, Row, Double> join(Tuple3 <Vector, Long, Row> left,
												  Tuple3 <Vector, Row, Long> right) throws Exception {
	return Tuple4.of(left.f1, left.f2, right.f1, lsh.keyDistance(left.f0, right.f0));
}