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.

Serialized Form
• ### Field Summary

Fields
Modifier and Type Field and 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 and 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

All Methods
Modifier and Type Method and 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 org.hipparchus.stat.descriptive.WeightedEvaluation

evaluate
• ### 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 java.util.function.DoubleConsumer

andThen
• ### 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

• #### 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 StorelessUnivariateStatistic
Specified by:
evaluate in interface UnivariateStatistic
Specified by:
evaluate in interface MathArrays.Function
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
UnivariateStatistic.evaluate(double[], int, int)
• #### 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