Package org.hipparchus.stat.descriptive.moment

Class Mean

java.lang.Object
org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic
org.hipparchus.stat.descriptive.moment.Mean
All Implemented Interfaces:
Serializable, DoubleConsumer, AggregatableStatistic<Mean>, StorelessUnivariateStatistic, UnivariateStatistic, WeightedEvaluation, MathArrays.Function
public class Mean extends AbstractStorelessUnivariateStatistic implements AggregatableStatistic<Mean>, WeightedEvaluation, Serializable
Computes the arithmetic mean of a set of values. Uses the definitional formula:

mean = sum(x_i) / n

where n is the number of observations.

When increment(double) is used to add data incrementally from a stream of (unstored) values, the value of the statistic that getResult() returns is computed using the following recursive updating algorithm:

  1. Initialize m = the first value
  2. For each additional value, update using
    m = m + (new value - m) / (number of observations)

If UnivariateStatistic.evaluate(double[]) is used to compute the mean of an array of stored values, a two-pass, corrected algorithm is used, starting with the definitional formula computed using the array of stored values and then correcting this by adding the mean deviation of the data values from the arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing Sample Means and Variances," Robert F. Ling, Journal of the American Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866.

Returns Double.NaN if the dataset is empty. Note that Double.NaN may also be returned if the input includes NaN and / or infinite values.

Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally.

See Also:

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    protected final boolean
    incMoment
    Determines whether or not this statistic can be incremented or cleared.
    protected final org.hipparchus.stat.descriptive.moment.FirstMoment
    moment
    First moment on which this statistic is based.

  • Constructor Summary

    Constructors
    Constructor
    Description
    Mean()
    Constructs a Mean.
    Mean(org.hipparchus.stat.descriptive.moment.FirstMoment m1)
    Constructs a Mean with an External Moment.
    Mean(Mean original)
    Copy constructor, creates a new Mean identical to the original.

  • Method Summary

    Modifier and Type
    Method
    Description
    void
    aggregate(Mean other)
    Aggregates the provided instance into this instance.
    void
    clear()
    Clears the internal state of the Statistic
    Mean
    copy()
    Returns a copy of the statistic with the same internal state.
    double
    evaluate(double[] values, double[] weights, int begin, int length)
    Returns the weighted arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
    double
    evaluate(double[] values, int begin, int length)
    Returns the arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
    long
    getN()
    Returns the number of values that have been added.
    double
    getResult()
    Returns the current value of the Statistic.
    void
    increment(double d)
    Updates the internal state of the statistic to reflect the addition of the new value.

    Methods inherited from class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic

    equals, hashCode, toString

    Methods inherited from class java.lang.Object

    clone, finalize, getClass, notify, notifyAll, wait, wait, wait

    Methods inherited from interface org.hipparchus.stat.descriptive.AggregatableStatistic

    aggregate, aggregate

    Methods inherited from interface java.util.function.DoubleConsumer

    andThen

    Methods inherited from interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic

    accept, incrementAll, incrementAll

    Methods inherited from interface org.hipparchus.stat.descriptive.UnivariateStatistic

    evaluate

    Methods inherited from interface org.hipparchus.stat.descriptive.WeightedEvaluation

    evaluate

  • Field Details

    • moment

      protected final org.hipparchus.stat.descriptive.moment.FirstMoment moment
      First moment on which this statistic is based.

    • incMoment

      protected final boolean incMoment
      Determines whether or not this statistic can be incremented or cleared.

      Statistics based on (constructed from) external moments cannot be incremented or cleared.

  • Constructor Details

    • Mean

      public Mean()
      Constructs a Mean.

    • Mean

      public Mean(org.hipparchus.stat.descriptive.moment.FirstMoment m1)
      Constructs a Mean with an External Moment.
      Parameters:
      m1 - the moment

    • Mean

      public Mean(Mean original) throws NullArgumentException
      Copy constructor, creates a new Mean identical to the original.
      Parameters:
      original - the Mean instance to copy
      Throws:
      NullArgumentException - if original is null

  • Method Details

    • increment

      public void increment(double d)
      Updates the internal state of the statistic to reflect the addition of the new value.

      Note that when Mean(FirstMoment) is used to create a Mean, this method does nothing. In that case, the FirstMoment should be incremented directly.

      Specified by:
      increment in interface StorelessUnivariateStatistic
      Specified by:
      increment in class AbstractStorelessUnivariateStatistic
      Parameters:
      d - the new value.

    • clear

      public void clear()
      Clears the internal state of the Statistic
      Specified by:
      clear in interface StorelessUnivariateStatistic
      Specified by:
      clear in class AbstractStorelessUnivariateStatistic

    • getResult

      public double getResult()
      Returns the current value of the Statistic.
      Specified by:
      getResult in interface StorelessUnivariateStatistic
      Specified by:
      getResult in class AbstractStorelessUnivariateStatistic
      Returns:
      value of the statistic, Double.NaN if it has been cleared or just instantiated.

    • getN

      public long getN()
      Returns the number of values that have been added.
      Specified by:
      getN in interface StorelessUnivariateStatistic
      Returns:
      the number of values.

    • aggregate

      public void aggregate(Mean other)
      Aggregates the provided instance into this instance.

      This method can be used to combine statistics computed over partitions or subsamples - i.e., the value of this instance after this operation should be the same as if a single statistic would have been applied over the combined dataset.

      Specified by:
      aggregate in interface AggregatableStatistic<Mean>
      Parameters:
      other - the instance to aggregate into this instance

    • evaluate

      public double evaluate(double[] values, int begin, int length) throws MathIllegalArgumentException
      Returns the arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
      Specified by:
      evaluate in interface MathArrays.Function
      Specified by:
      evaluate in interface StorelessUnivariateStatistic
      Specified by:
      evaluate in interface UnivariateStatistic
      Parameters:
      values - the input array
      begin - index of the first array element to include
      length - the number of elements to include
      Returns:
      the mean of the values or Double.NaN if length = 0
      Throws:
      MathIllegalArgumentException - if the array is null or the array index parameters are not valid
      See Also:

    • evaluate

      public double evaluate(double[] values, double[] weights, int begin, int length) throws MathIllegalArgumentException
      Returns the weighted arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.

      Throws IllegalArgumentException if either array is null.

      See Mean for details on the computing algorithm. The two-pass algorithm described above is used here, with weights applied in computing both the original estimate and the correction factor.

      Throws IllegalArgumentException if any of the following are true:

      • the values array is null
      • the weights array is null
      • the weights array does not have the same length as the values array
      • the weights array contains one or more infinite values
      • the weights array contains one or more NaN values
      • the weights array contains negative values
      • the start and length arguments do not determine a valid array
      Specified by:
      evaluate in interface WeightedEvaluation
      Parameters:
      values - the input array
      weights - the weights array
      begin - index of the first array element to include
      length - the number of elements to include
      Returns:
      the mean of the values or Double.NaN if length = 0
      Throws:
      MathIllegalArgumentException - if the parameters are not valid

    • copy

      public Mean copy()
      Returns a copy of the statistic with the same internal state.
      Specified by:
      copy in interface StorelessUnivariateStatistic
      Specified by:
      copy in interface UnivariateStatistic
      Specified by:
      copy in class AbstractStorelessUnivariateStatistic
      Returns:
      a copy of the statistic