Package org.hipparchus.stat.descriptive.moment

Class 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:
    Serialized Form

    • Field Summary

      Fields 
      Modifier and Type Field Description
      protected boolean incMoment
      Determines whether or not this statistic can be incremented or cleared.
      protected 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.

    • Field Detail

      • 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 Detail

      • 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 Detail

      • 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,
                       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