for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {
      assertWithMessage("Mismatch between %s and %s", algorithm, REFERENCE_ALGORITHM)
          .that(algorithm.singleQuantile(1, 2, dataset.clone()))
          .isWithin(ALLOWED_ERROR)
          .of(referenceValue);
    }
  }

  public void testSingleQuantile_percentile99() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(99, 100, dataset.clone());

näihin
näillä
näiltä
näille
näinä
näiksi
nuo
noiden
noita
noissa
noista
noihin
noilla
noilta
noille
noina
noiksi
ne
niiden
niitä
niissä
niistä
niihin
niillä
niiltä
niille
niinä
niiksi
kuka
kenen
kenet
ketä
kenessä
kenestä
keneen
kenellä
keneltä
kenelle
kenenä
keneksi
ketkä
keiden
ketkä

    assertThat(verticalValuesAccumulatorByAddAllPartitionedPairedStats.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(0.0);
    assertThat(constantValuesAccumulator.sampleCovariance()).isWithin(ALLOWED_ERROR).of(0.0);
    assertThat(constantValuesAccumulatorByAddAllPartitionedPairedStats.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(0.0);
  }

 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *

            .that(mean)
            .isWithin(ALLOWED_ERROR)
            .of(MANY_VALUES_MEAN);
      }
    }
    assertThat(MANY_VALUES_STATS_ITERABLE.mean()).isWithin(ALLOWED_ERROR).of(MANY_VALUES_MEAN);
    assertThat(MANY_VALUES_STATS_ITERATOR.mean()).isWithin(ALLOWED_ERROR).of(MANY_VALUES_MEAN);
    assertThat(MANY_VALUES_STATS_SNAPSHOT.mean()).isWithin(ALLOWED_ERROR).of(MANY_VALUES_MEAN);

    assertThat(oneValueAccumulator.mean()).isWithin(ALLOWED_ERROR).of(ONE_VALUE);
    assertThat(oneValueAccumulatorByAddAllEmptyStats.mean()).isWithin(ALLOWED_ERROR).of(ONE_VALUE);
    assertThat(twoValuesAccumulator.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    assertThat(twoValuesAccumulatorByAddAllStats.mean())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_MEAN);

    assertThat(oneValueAccumulator.mean()).isWithin(ALLOWED_ERROR).of(ONE_VALUE);
    assertThat(oneValueAccumulatorByAddAllEmptyStats.mean()).isWithin(ALLOWED_ERROR).of(ONE_VALUE);
    assertThat(twoValuesAccumulator.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    assertThat(twoValuesAccumulatorByAddAllStats.mean())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_MEAN);

    for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {
      assertWithMessage("Mismatch between %s and %s", algorithm, REFERENCE_ALGORITHM)
          .that(algorithm.singleQuantile(1, 2, dataset.clone()))
          .isWithin(ALLOWED_ERROR)
          .of(referenceValue);
    }
  }

  public void testSingleQuantile_percentile99() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(99, 100, dataset.clone());

    assertThat(transformation.isVertical()).isFalse();
    assertThat(transformation.isHorizontal()).isFalse();
    assertThat(transformation.slope()).isWithin(ALLOWED_ERROR).of(slope);
    // Note that we cannot test the actual mapping of points, as the results will be unreliable due
    // to loss of precision with this value of the slope.
  }

  public void testMappingWithSlope_maximalSlope() {
    double x1 = 1.2;
    double y1 = 3.4;

    assertThat(transformation.isVertical()).isFalse();
    assertThat(transformation.isHorizontal()).isFalse();
    assertThat(transformation.slope()).isWithin(ALLOWED_ERROR).of(slope);
    // Note that we cannot test the actual mapping of points, as the results will be unreliable due
    // to loss of precision with this value of the slope.
  }

  public void testMappingWithSlope_maximalSlope() {
    double x1 = 1.2;
    double y1 = 3.4;