de.lmu.ifi.dbs.elki.math

Class Mean

java.lang.Object

de.lmu.ifi.dbs.elki.math.Mean

Direct Known Subclasses:

MeanVariance

public class Mean
extends java.lang.Object

Compute the mean using a numerically stable online algorithm.

This class can repeatedly be fed with data using the put() methods, the resulting values for mean can be queried at any time using getMean().

The high-precision function is based on:

P. M. Neely
Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients
Communications of the ACM 9(7), 1966

Since:

0.2

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
protected double	n Weight sum (number of samples).
protected double	sum Sum of all values.

Constructor Summary

Constructors

Constructor and Description
Mean() Empty constructor
Mean(Mean other) Constructor from other instance

Method Summary

Modifier and Type	Method and Description
double	getCount() Get the number of points the average is based on.
double	getMean() Return mean
static double	highPrecision(double... data) Static helper function, with extra precision
static Mean[]	newArray(int dimensionality) Create and initialize a new array of MeanVariance
static double	of(double... data) Static helper function.
void	put(double val) Add a single value with weight 1.0
Mean	put(double[] vals) Add values with weight 1.0
Mean	put(double[] vals, double[] weights) Add values with weight 1.0
void	put(double val, double weight) Add data with a given weight.
void	put(Mean other) Join the data of another MeanVariance instance.
void	reset() Reset the value.
java.lang.String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

sum

protected double sum

Sum of all values.

n

protected double n

Weight sum (number of samples).

Constructor Detail

Mean

public Mean()

Empty constructor

Mean

public Mean(Mean other)

Constructor from other instance

Parameters:

other - other instance to copy data from.

Method Detail

put

public void put(double val)

Add a single value with weight 1.0

Parameters:

val - Value

put

public void put(double val,
                double weight)

Add data with a given weight.

Parameters:

val - data

weight - weight

put

public void put(Mean other)

Join the data of another MeanVariance instance.

Parameters:

other - Data to join with

put

public Mean put(double[] vals)

Add values with weight 1.0

Parameters:

vals - Values

Returns:

this

put

public Mean put(double[] vals,
                double[] weights)

Add values with weight 1.0

Parameters:

vals - Values

Returns:

this

getCount

public double getCount()

Get the number of points the average is based on.

Returns:

number of data points

getMean

public double getMean()

Return mean

Returns:

mean

newArray

public static Mean[] newArray(int dimensionality)

Create and initialize a new array of MeanVariance

Parameters:

dimensionality - Dimensionality

Returns:

New and initialized Array

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

reset

public void reset()

Reset the value.

of

public static double of(double... data)

Static helper function.

Parameters:

data - Data to compute the mean for.

Returns:

Mean

highPrecision

@Reference(authors="P. M. Neely",
           title="Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients",
           booktitle="Communications of the ACM 9(7), 1966",
           url="https://doi.org/10.1145/365719.365958",
           bibkey="doi:10.1145/365719.365958")
public static double highPrecision(double... data)

Static helper function, with extra precision

Parameters:

data - Data to compute the mean for.

Returns:

Mean