Package org.apache.commons.math3.stat.descriptive

Class DescriptiveStatistics

java.lang.Object
org.apache.commons.math3.stat.descriptive.DescriptiveStatistics
All Implemented Interfaces:
Serializable, StatisticalSummary
Direct Known Subclasses:
SynchronizedDescriptiveStatistics
public class DescriptiveStatistics extends Object implements StatisticalSummary, Serializable
Maintains a dataset of values of a single variable and computes descriptive statistics based on stored data. The windowSize property sets a limit on the number of values that can be stored in the dataset. The default value, INFINITE_WINDOW, puts no limit on the size of the dataset. This value should be used with caution, as the backing store will grow without bound in this case. For very large datasets, SummaryStatistics, which does not store the dataset, should be used instead of this class. If windowSize is not INFINITE_WINDOW and more values are added than can be stored in the dataset, new values are added in a "rolling" manner, with new values replacing the "oldest" values in the dataset.

Note: this class is not threadsafe. Use SynchronizedDescriptiveStatistics if concurrent access from multiple threads is required.

See Also:

  • Field Details

    • INFINITE_WINDOW

      public static final int INFINITE_WINDOW
      Represents an infinite window size. When the getWindowSize() returns this value, there is no limit to the number of data values that can be stored in the dataset.
      See Also:

    • windowSize

      protected int windowSize
      hold the window size

  • Constructor Details

    • DescriptiveStatistics

      public DescriptiveStatistics()
      Construct a DescriptiveStatistics instance with an infinite window

    • DescriptiveStatistics

      public DescriptiveStatistics(int window) throws MathIllegalArgumentException
      Construct a DescriptiveStatistics instance with the specified window
      Parameters:
      window - the window size.
      Throws:
      MathIllegalArgumentException - if window size is less than 1 but not equal to INFINITE_WINDOW

    • DescriptiveStatistics

      public DescriptiveStatistics(double[] initialDoubleArray)
      Construct a DescriptiveStatistics instance with an infinite window and the initial data values in double[] initialDoubleArray. If initialDoubleArray is null, then this constructor corresponds to DescriptiveStatistics()
      Parameters:
      initialDoubleArray - the initial double[].

    • DescriptiveStatistics

      public DescriptiveStatistics(DescriptiveStatistics original) throws NullArgumentException
      Copy constructor. Construct a new DescriptiveStatistics instance that is a copy of original.
      Parameters:
      original - DescriptiveStatistics instance to copy
      Throws:
      NullArgumentException - if original is null

  • Method Details

    • addValue

      public void addValue(double v)
      Adds the value to the dataset. If the dataset is at the maximum size (i.e., the number of stored elements equals the currently configured windowSize), the first (oldest) element in the dataset is discarded to make room for the new value.
      Parameters:
      v - the value to be added

    • removeMostRecentValue

      public void removeMostRecentValue() throws MathIllegalStateException
      Removes the most recent value from the dataset.
      Throws:
      MathIllegalStateException - if there are no elements stored

    • replaceMostRecentValue

      public double replaceMostRecentValue(double v) throws MathIllegalStateException
      Replaces the most recently stored value with the given value. There must be at least one element stored to call this method.
      Parameters:
      v - the value to replace the most recent stored value
      Returns:
      replaced value
      Throws:
      MathIllegalStateException - if there are no elements stored

    • getMean

      public double getMean()
      Returns the arithmetic mean of the available values
      Specified by:
      getMean in interface StatisticalSummary
      Returns:
      The mean or Double.NaN if no values have been added.

    • getGeometricMean

      public double getGeometricMean()
      Returns the geometric mean of the available values.

      See GeometricMean for details on the computing algorithm.

      Returns:
      The geometricMean, Double.NaN if no values have been added, or if any negative values have been added.

    • getVariance

      public double getVariance()
      Returns the (sample) variance of the available values.

      This method returns the bias-corrected sample variance (using n - 1 in the denominator). Use getPopulationVariance() for the non-bias-corrected population variance.

      Specified by:
      getVariance in interface StatisticalSummary
      Returns:
      The variance, Double.NaN if no values have been added or 0.0 for a single value set.

    • getPopulationVariance

      public double getPopulationVariance()
      Returns the population variance of the available values.
      Returns:
      The population variance, Double.NaN if no values have been added, or 0.0 for a single value set.

    • getStandardDeviation

      public double getStandardDeviation()
      Returns the standard deviation of the available values.
      Specified by:
      getStandardDeviation in interface StatisticalSummary
      Returns:
      The standard deviation, Double.NaN if no values have been added or 0.0 for a single value set.

    • getQuadraticMean

      public double getQuadraticMean()
      Returns the quadratic mean, a.k.a. root-mean-square of the available values
      Returns:
      The quadratic mean or Double.NaN if no values have been added.

    • getSkewness

      public double getSkewness()
      Returns the skewness of the available values. Skewness is a measure of the asymmetry of a given distribution.
      Returns:
      The skewness, Double.NaN if less than 3 values have been added.

    • getKurtosis

      public double getKurtosis()
      Returns the Kurtosis of the available values. Kurtosis is a measure of the "peakedness" of a distribution.
      Returns:
      The kurtosis, Double.NaN if less than 4 values have been added.

    • getMax

      public double getMax()
      Returns the maximum of the available values
      Specified by:
      getMax in interface StatisticalSummary
      Returns:
      The max or Double.NaN if no values have been added.

    • getMin

      public double getMin()
      Returns the minimum of the available values
      Specified by:
      getMin in interface StatisticalSummary
      Returns:
      The min or Double.NaN if no values have been added.

    • getN

      public long getN()
      Returns the number of available values
      Specified by:
      getN in interface StatisticalSummary
      Returns:
      The number of available values

    • getSum

      public double getSum()
      Returns the sum of the values that have been added to Univariate.
      Specified by:
      getSum in interface StatisticalSummary
      Returns:
      The sum or Double.NaN if no values have been added

    • getSumsq

      public double getSumsq()
      Returns the sum of the squares of the available values.
      Returns:
      The sum of the squares or Double.NaN if no values have been added.

    • clear

      public void clear()
      Resets all statistics and storage

    • getWindowSize

      public int getWindowSize()
      Returns the maximum number of values that can be stored in the dataset, or INFINITE_WINDOW (-1) if there is no limit.
      Returns:
      The current window size or -1 if its Infinite.

    • setWindowSize

      public void setWindowSize(int windowSize) throws MathIllegalArgumentException
      WindowSize controls the number of values that contribute to the reported statistics. For example, if windowSize is set to 3 and the values {1,2,3,4,5} have been added in that order then the available values are {3,4,5} and all reported statistics will be based on these values. If windowSize is decreased as a result of this call and there are more than the new value of elements in the current dataset, values from the front of the array are discarded to reduce the dataset to windowSize elements.
      Parameters:
      windowSize - sets the size of the window.
      Throws:
      MathIllegalArgumentException - if window size is less than 1 but not equal to INFINITE_WINDOW

    • getValues

      public double[] getValues()
      Returns the current set of values in an array of double primitives. The order of addition is preserved. The returned array is a fresh copy of the underlying data -- i.e., it is not a reference to the stored data.
      Returns:
      returns the current set of numbers in the order in which they were added to this set

    • getSortedValues

      public double[] getSortedValues()
      Returns the current set of values in an array of double primitives, sorted in ascending order. The returned array is a fresh copy of the underlying data -- i.e., it is not a reference to the stored data.
      Returns:
      returns the current set of numbers sorted in ascending order

    • getElement

      public double getElement(int index)
      Returns the element at the specified index
      Parameters:
      index - The Index of the element
      Returns:
      return the element at the specified index

    • getPercentile

      public double getPercentile(double p) throws MathIllegalStateException, MathIllegalArgumentException
      Returns an estimate for the pth percentile of the stored values.

      The implementation provided here follows the first estimation procedure presented here.

      Preconditions:

      • 0 < p ≤ 100 (otherwise an MathIllegalArgumentException is thrown)
      • at least one value must be stored (returns Double.NaN otherwise)

      Parameters:
      p - the requested percentile (scaled from 0 - 100)
      Returns:
      An estimate for the pth percentile of the stored data
      Throws:
      MathIllegalStateException - if percentile implementation has been overridden and the supplied implementation does not support setQuantile
      MathIllegalArgumentException - if p is not a valid quantile

    • toString

      public String toString()
      Generates a text report displaying univariate statistics from values that have been added. Each statistic is displayed on a separate line.
      Overrides:
      toString in class Object
      Returns:
      String with line feeds displaying statistics

    • apply

      public double apply(UnivariateStatistic stat)
      Apply the given statistic to the data associated with this set of statistics.
      Parameters:
      stat - the statistic to apply
      Returns:
      the computed value of the statistic.

    • getMeanImpl

      public UnivariateStatistic getMeanImpl()
      Returns the currently configured mean implementation.
      Returns:
      the UnivariateStatistic implementing the mean
      Since:
      1.2

    • setMeanImpl

      public void setMeanImpl(UnivariateStatistic meanImpl)

      Sets the implementation for the mean.

      Parameters:
      meanImpl - the UnivariateStatistic instance to use for computing the mean
      Since:
      1.2

    • getGeometricMeanImpl

      public UnivariateStatistic getGeometricMeanImpl()
      Returns the currently configured geometric mean implementation.
      Returns:
      the UnivariateStatistic implementing the geometric mean
      Since:
      1.2

    • setGeometricMeanImpl

      public void setGeometricMeanImpl(UnivariateStatistic geometricMeanImpl)

      Sets the implementation for the gemoetric mean.

      Parameters:
      geometricMeanImpl - the UnivariateStatistic instance to use for computing the geometric mean
      Since:
      1.2

    • getKurtosisImpl

      public UnivariateStatistic getKurtosisImpl()
      Returns the currently configured kurtosis implementation.
      Returns:
      the UnivariateStatistic implementing the kurtosis
      Since:
      1.2

    • setKurtosisImpl

      public void setKurtosisImpl(UnivariateStatistic kurtosisImpl)

      Sets the implementation for the kurtosis.

      Parameters:
      kurtosisImpl - the UnivariateStatistic instance to use for computing the kurtosis
      Since:
      1.2

    • getMaxImpl

      public UnivariateStatistic getMaxImpl()
      Returns the currently configured maximum implementation.
      Returns:
      the UnivariateStatistic implementing the maximum
      Since:
      1.2

    • setMaxImpl

      public void setMaxImpl(UnivariateStatistic maxImpl)

      Sets the implementation for the maximum.

      Parameters:
      maxImpl - the UnivariateStatistic instance to use for computing the maximum
      Since:
      1.2

    • getMinImpl

      public UnivariateStatistic getMinImpl()
      Returns the currently configured minimum implementation.
      Returns:
      the UnivariateStatistic implementing the minimum
      Since:
      1.2

    • setMinImpl

      public void setMinImpl(UnivariateStatistic minImpl)

      Sets the implementation for the minimum.

      Parameters:
      minImpl - the UnivariateStatistic instance to use for computing the minimum
      Since:
      1.2

    • getPercentileImpl

      public UnivariateStatistic getPercentileImpl()
      Returns the currently configured percentile implementation.
      Returns:
      the UnivariateStatistic implementing the percentile
      Since:
      1.2

    • setPercentileImpl

      public void setPercentileImpl(UnivariateStatistic percentileImpl) throws MathIllegalArgumentException
      Sets the implementation to be used by getPercentile(double). The supplied UnivariateStatistic must provide a setQuantile(double) method; otherwise IllegalArgumentException is thrown.
      Parameters:
      percentileImpl - the percentileImpl to set
      Throws:
      MathIllegalArgumentException - if the supplied implementation does not provide a setQuantile method
      Since:
      1.2

    • getSkewnessImpl

      public UnivariateStatistic getSkewnessImpl()
      Returns the currently configured skewness implementation.
      Returns:
      the UnivariateStatistic implementing the skewness
      Since:
      1.2

    • setSkewnessImpl

      public void setSkewnessImpl(UnivariateStatistic skewnessImpl)

      Sets the implementation for the skewness.

      Parameters:
      skewnessImpl - the UnivariateStatistic instance to use for computing the skewness
      Since:
      1.2

    • getVarianceImpl

      public UnivariateStatistic getVarianceImpl()
      Returns the currently configured variance implementation.
      Returns:
      the UnivariateStatistic implementing the variance
      Since:
      1.2

    • setVarianceImpl

      public void setVarianceImpl(UnivariateStatistic varianceImpl)

      Sets the implementation for the variance.

      Parameters:
      varianceImpl - the UnivariateStatistic instance to use for computing the variance
      Since:
      1.2

    • getSumsqImpl

      public UnivariateStatistic getSumsqImpl()
      Returns the currently configured sum of squares implementation.
      Returns:
      the UnivariateStatistic implementing the sum of squares
      Since:
      1.2

    • setSumsqImpl

      public void setSumsqImpl(UnivariateStatistic sumsqImpl)

      Sets the implementation for the sum of squares.

      Parameters:
      sumsqImpl - the UnivariateStatistic instance to use for computing the sum of squares
      Since:
      1.2

    • getSumImpl

      public UnivariateStatistic getSumImpl()
      Returns the currently configured sum implementation.
      Returns:
      the UnivariateStatistic implementing the sum
      Since:
      1.2

    • setSumImpl

      public void setSumImpl(UnivariateStatistic sumImpl)

      Sets the implementation for the sum.

      Parameters:
      sumImpl - the UnivariateStatistic instance to use for computing the sum
      Since:
      1.2

    • copy

      public DescriptiveStatistics copy()
      Returns a copy of this DescriptiveStatistics instance with the same internal state.
      Returns:
      a copy of this

    • copy

      public static void copy(DescriptiveStatistics source, DescriptiveStatistics dest) throws NullArgumentException
      Copies source to dest.

      Neither source nor dest can be null.

      Parameters:
      source - DescriptiveStatistics to copy
      dest - DescriptiveStatistics to copy to
      Throws:
      NullArgumentException - if either source or dest is null