Serializable, DoubleConsumer, AggregatableStatistic<Mean>, StorelessUnivariateStatistic, UnivariateStatistic, WeightedEvaluation, MathArrays.Functionpublic class Mean extends AbstractStorelessUnivariateStatistic implements AggregatableStatistic<Mean>, WeightedEvaluation, Serializable
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:
m = the first valuem = 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.
| 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 | 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. |
| 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.
|
equals, hashCode, toStringaggregate, aggregateandThenclone, finalize, getClass, notify, notifyAll, wait, wait, waitaccept, incrementAll, incrementAllevaluateevaluateprotected final org.hipparchus.stat.descriptive.moment.FirstMoment moment
protected final boolean incMoment
Statistics based on (constructed from) external moments cannot be incremented or cleared.
public Mean()
public Mean(org.hipparchus.stat.descriptive.moment.FirstMoment m1)
m1 - the momentpublic Mean(Mean original) throws NullArgumentException
Mean identical
to the original.original - the Mean instance to copyNullArgumentException - if original is nullpublic void increment(double d)
Note that when Mean(FirstMoment) is used to
create a Mean, this method does nothing. In that case, the
FirstMoment should be incremented directly.
increment in interface StorelessUnivariateStatisticincrement in class AbstractStorelessUnivariateStatisticd - the new value.public void clear()
clear in interface StorelessUnivariateStatisticclear in class AbstractStorelessUnivariateStatisticpublic double getResult()
getResult in interface StorelessUnivariateStatisticgetResult in class AbstractStorelessUnivariateStatisticDouble.NaN if it
has been cleared or just instantiated.public long getN()
getN in interface StorelessUnivariateStatisticpublic void aggregate(Mean other)
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.
aggregate in interface AggregatableStatistic<Mean>other - the instance to aggregate into this instancepublic double evaluate(double[] values,
int begin,
int length)
throws MathIllegalArgumentException
Double.NaN if the designated subarray
is empty.evaluate in interface MathArrays.Functionevaluate in interface StorelessUnivariateStatisticevaluate in interface UnivariateStatisticvalues - the input arraybegin - index of the first array element to includelength - the number of elements to includeMathIllegalArgumentException - if the array is null or the array index
parameters are not validUnivariateStatistic.evaluate(double[], int, int)public double evaluate(double[] values,
double[] weights,
int begin,
int length)
throws MathIllegalArgumentException
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:
evaluate in interface WeightedEvaluationvalues - the input arrayweights - the weights arraybegin - index of the first array element to includelength - the number of elements to includeMathIllegalArgumentException - if the parameters are not validpublic Mean copy()
copy in interface StorelessUnivariateStatisticcopy in interface UnivariateStatisticcopy in class AbstractStorelessUnivariateStatisticCopyright © 2016–2018 Hipparchus.org. All rights reserved.