Class IntVariance
- java.lang.Object
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- org.apache.commons.statistics.descriptive.IntVariance
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- All Implemented Interfaces:
DoubleSupplier,IntConsumer,IntSupplier,LongSupplier,IntStatistic,StatisticAccumulator<IntVariance>,StatisticResult
public final class IntVariance extends Object implements IntStatistic, StatisticAccumulator<IntVariance>
Computes the variance of the available values. The default implementation uses the following definition of the sample variance:\[ \tfrac{1}{n-1} \sum_{i=1}^n (x_i-\overline{x})^2 \]
where \( \overline{x} \) is the sample mean, and \( n \) is the number of samples.
- The result is
NaNif no values are added. - The result is zero if there is one value in the data set.
The use of the term \( n − 1 \) is called Bessel's correction. This is an unbiased estimator of the variance of a hypothetical infinite population. If the
biasedoption is enabled the normalisation factor is changed to \( \frac{1}{n} \) for a biased estimator of the sample variance.The implementation uses an exact integer sum to compute the scaled (by \( n \)) sum of squared deviations from the mean; this is normalised by the scaled correction factor.
\[ \frac {n \times \sum_{i=1}^n x_i^2 - (\sum_{i=1}^n x_i)^2}{n \times (n - 1)} \]
Supports up to 263 (exclusive) observations. This implementation does not check for overflow of the count.
This class is designed to work with (though does not require) streams.
This implementation is not thread safe. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the
acceptorcombinemethod, it must be synchronized externally.However, it is safe to use
acceptandcombineasaccumulatorandcombinerfunctions ofCollectoron a parallel stream, because the parallel implementation ofStream.collect()provides the necessary partitioning, isolation, and merging of results for safe and efficient parallel execution.- Since:
- 1.1
- See Also:
- variance (Wikipedia), Algorithms for computing the variance (Wikipedia), Bessel's correction
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description voidaccept(int value)Updates the state of the statistic to reflect the addition ofvalue.IntVariancecombine(IntVariance other)Combines the state of theotherstatistic into this one.static IntVariancecreate()Creates an instance.doublegetAsDouble()Gets the variance of all input values.static IntVarianceof(int... values)Returns an instance populated using the inputvalues.IntVariancesetBiased(boolean v)Sets the value of the biased flag.-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface java.util.function.IntConsumer
andThen
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Methods inherited from interface org.apache.commons.statistics.descriptive.StatisticResult
getAsBigInteger, getAsInt, getAsLong
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Method Detail
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create
public static IntVariance create()
Creates an instance.The initial result is
NaN.- Returns:
IntVarianceinstance.
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of
public static IntVariance of(int... values)
Returns an instance populated using the inputvalues.- Parameters:
values- Values.- Returns:
IntVarianceinstance.
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accept
public void accept(int value)
Updates the state of the statistic to reflect the addition ofvalue.- Specified by:
acceptin interfaceIntConsumer- Parameters:
value- Value.
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getAsDouble
public double getAsDouble()
Gets the variance of all input values.When no values have been added, the result is
NaN.- Specified by:
getAsDoublein interfaceDoubleSupplier- Returns:
- variance of all values.
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combine
public IntVariance combine(IntVariance other)
Description copied from interface:StatisticAccumulatorCombines the state of theotherstatistic into this one.- Specified by:
combinein interfaceStatisticAccumulator<IntVariance>- Parameters:
other- Another statistic to be combined.- Returns:
thisinstance after combiningother.
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setBiased
public IntVariance setBiased(boolean v)
Sets the value of the biased flag. The default value isfalse.If
falsethe sum of squared deviations from the sample mean is normalised byn - 1wherenis the number of samples. This is Bessel's correction for an unbiased estimator of the variance of a hypothetical infinite population.If
truethe sum of squared deviations is normalised by the number of samplesn.Note: This option only applies when
n > 1. The variance ofn = 1is always 0.This flag only controls the final computation of the statistic. The value of this flag will not affect compatibility between instances during a
combineoperation.- Parameters:
v- Value.- Returns:
thisinstance
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