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## org.apache.commons.math.stat.descriptive.moment
Class Mean

    java.lang.Object
      org.apache.commons.math.stat.descriptive.AbstractUnivariateStatistic
          org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic
              org.apache.commons.math.stat.descriptive.moment.Mean

All Implemented Interfaces:
  Serializable, StorelessUnivariateStatistic, UnivariateStatistic, WeightedEvaluation

    public class Mean

extends AbstractStorelessUnivariateStatistic
implements Serializable, WeightedEvaluation

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 `AbstractStorelessUnivariateStatistic.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 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.

Version:
  $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $
See Also:
  Serialized Form

Field Summary
`protected boolean` | `incMoment` Determines whether or not this statistic can be incremented or cleared.
`protected FirstMoment` | `moment` First moment on which this statistic is based.

Constructor Summary
`Mean()` Constructs a Mean.
`Mean(FirstMoment m1)` Constructs a Mean with an External Moment.
`Mean(Mean original)` Copy constructor, creates a new `Mean` identical to the `original`

Method Summary
`void` | `clear()` Clears the internal state of the Statistic
`Mean` | `copy()` Returns a copy of the statistic with the same internal state.
`static void` | `copy(Mean source, Mean dest)` Copies source to dest.
`double` | `evaluate(double[] values, double[] weights)` Returns the weighted arithmetic mean of the entries in the input array.
`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.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic
`equals, evaluate, hashCode, incrementAll, incrementAll`

Methods inherited from class org.apache.commons.math.stat.descriptive.AbstractUnivariateStatistic
`evaluate, getData, getDataRef, setData, setData, test, test`

Methods inherited from class java.lang.Object
`clone, finalize, getClass, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### moment

    protected FirstMoment moment

  First moment on which this statistic is based.

### incMoment

    protected 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(FirstMoment m1)

  Constructs a Mean with an External Moment.

  Parameters:
    `m1` - the moment

### Mean

    public Mean(Mean original)

  Copy constructor, creates a new `Mean` identical to the `original`

  Parameters:
    `original` - the `Mean` instance to copy

Method Detail

### increment

    public void increment(double d)

  Updates the internal state of the statistic to reflect the addition of the new value.

  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.

### evaluate

    public 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.

  Throws `IllegalArgumentException` if the array is null.

  See `Mean` for details on the computing algorithm.

  Specified by:
    `evaluate` in interface `UnivariateStatistic`
  Overrides:
    `evaluate` in class `AbstractStorelessUnivariateStatistic`
  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:
    `IllegalArgumentException` - if the array is null or the array index parameters are not valid
  See Also:
    `UnivariateStatistic.evaluate(double[], int, int)`

### evaluate

    public 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.

  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:
    `IllegalArgumentException` - if the parameters are not valid
  Since:
    2.1

### evaluate

    public double evaluate(double[] values,
                           double[] weights)

  Returns the weighted arithmetic mean of the entries in the input array.

  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

  Specified by:
    `evaluate` in interface `WeightedEvaluation`
  Parameters:
    `values` - the input array
    `weights` - the weights array
  Returns:
    the mean of the values or Double.NaN if length = 0
  Throws:
    `IllegalArgumentException` - if the parameters are not valid
  Since:
    2.1

### 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

### copy

    public static void copy(Mean source,
                            Mean dest)

  Copies source to dest.

  Neither source nor dest can be null.

  Parameters:
    `source` - Mean to copy
    `dest` - Mean to copy to
  Throws:
    `NullPointerException` - if either source or dest is null

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