populationVariance - Code Search

guava-tests/test/com/google/common/math/StatsAccumulatorTest.java

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationVariance());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationVariance());
    assertThrows(
        IllegalStateException.class, () -> emptyAccumulatorByAddAllEmptyStats.populationVariance());
    assertThat(oneValueAccumulator.populationVariance()).isWithin(0.0).of(0.0);

Java

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github.com/google/guava

android/guava-tests/test/com/google/common/math/StatsAccumulatorTest.java

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationVariance());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationVariance());
    assertThrows(
        IllegalStateException.class, () -> emptyAccumulatorByAddAllEmptyStats.populationVariance());
    assertThat(oneValueAccumulator.populationVariance()).isWithin(0.0).of(0.0);

Java

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github.com/google/guava

android/guava/src/com/google/common/math/PairedStatsAccumulator.java

   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result is not
   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

Java

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github.com/google/guava

guava-tests/test/com/google/common/math/StatsTest.java

    assertThrows(IllegalStateException.class, () -> EMPTY_STATS_VARARGS.populationVariance());
    assertThrows(IllegalStateException.class, () -> EMPTY_STATS_ITERABLE.populationVariance());
    assertThat(ONE_VALUE_STATS.populationVariance()).isWithin(0.0).of(0.0);
    assertThat(Stats.of(POSITIVE_INFINITY).populationVariance()).isNaN();
    assertThat(Stats.of(NEGATIVE_INFINITY).populationVariance()).isNaN();

Java

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github.com/google/guava

android/guava-tests/test/com/google/common/math/StatsTest.java

    assertThrows(IllegalStateException.class, () -> EMPTY_STATS_VARARGS.populationVariance());
    assertThrows(IllegalStateException.class, () -> EMPTY_STATS_ITERABLE.populationVariance());
    assertThat(ONE_VALUE_STATS.populationVariance()).isWithin(0.0).of(0.0);
    assertThat(Stats.of(POSITIVE_INFINITY).populationVariance()).isNaN();
    assertThat(Stats.of(NEGATIVE_INFINITY).populationVariance()).isNaN();

Java

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github.com/google/guava

guava-tests/test/com/google/common/math/PairedStatsAccumulatorTest.java

        twoValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance());
    assertDiagonalLinearTransformation(
        manyValuesAccumulator.leastSquaresFit(),
        manyValuesAccumulator.xStats().mean(),
        manyValuesAccumulator.yStats().mean(),
        manyValuesAccumulator.xStats().populationVariance(),

Java

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github.com/google/guava

android/guava-tests/test/com/google/common/math/PairedStatsAccumulatorTest.java

        twoValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance());
    assertDiagonalLinearTransformation(
        manyValuesAccumulator.leastSquaresFit(),
        manyValuesAccumulator.xStats().mean(),
        manyValuesAccumulator.yStats().mean(),
        manyValuesAccumulator.xStats().populationVariance(),

Java

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github.com/google/guava

android/guava-tests/test/com/google/common/math/StatsTesting.java

      assertThat(actualStats.populationVariance()).isWithin(0.0).of(0.0);
      assertThat(actualStats.min()).isWithin(ALLOWED_ERROR).of(expectedStats.min());
      assertThat(actualStats.max()).isWithin(ALLOWED_ERROR).of(expectedStats.max());
    } else {
      assertThat(actualStats.mean()).isWithin(ALLOWED_ERROR).of(expectedStats.mean());
      assertThat(actualStats.populationVariance())
          .isWithin(ALLOWED_ERROR)

Java

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github.com/google/guava

android/guava/src/com/google/common/math/PairedStats.java

   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result is not
   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

Java

- Registered: Fri Apr 26 12:43:10 GMT 2024

- Last Modified: Fri May 12 17:02:53 GMT 2023

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github.com/google/guava

guava-tests/test/com/google/common/math/StatsTesting.java

      assertThat(actualStats.populationVariance()).isWithin(0.0).of(0.0);
      assertThat(actualStats.min()).isWithin(ALLOWED_ERROR).of(expectedStats.min());
      assertThat(actualStats.max()).isWithin(ALLOWED_ERROR).of(expectedStats.max());
    } else {
      assertThat(actualStats.mean()).isWithin(ALLOWED_ERROR).of(expectedStats.mean());
      assertThat(actualStats.populationVariance())
          .isWithin(ALLOWED_ERROR)

Java

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- Last Modified: Thu Nov 09 22:49:56 GMT 2023

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