weka.estimators
Class KernelEstimator

java.lang.Object
  weka.estimators.KernelEstimator

All Implemented Interfaces:

Estimator, java.io.Serializable

public class KernelEstimator

extends java.lang.Object

implements Estimator

Simple kernel density estimator. Uses one gaussian kernel per observed data value.

Version:

$Revision: 1.5 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

private boolean	m_AllWeightsOne Whether we can optimise the kernel summation
private int	m_NumValues Number of values stored in m_Weights and m_Values so far
private double	m_Precision The precision of data values
private double	m_StandardDev The standard deviation
private double	m_SumOfWeights The sum of the weights so far
private double[]	m_Values Vector containing all of the values seen
private double[]	m_Weights Vector containing the associated weights
private static double	MAX_ERROR Maximum percentage error permitted in probability calculations

Constructor Summary

KernelEstimator(double precision)
Constructor that takes a precision argument.

Method Summary

void	addValue(double data, double weight) Add a new data value to the current estimator.
private int	findNearestValue(double key) Execute a binary search to locate the nearest data value
double	getProbability(double data) Get a probability estimate for a value.
static void	main(java.lang.String[] argv) Main method for testing this class.
private double	round(double data) Round a data value using the defined precision for this estimator
java.lang.String	toString() Display a representation of this estimator

Methods inherited from class java.lang.Object

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

Field Detail

m_Values

private double[] m_Values

Vector containing all of the values seen

m_Weights

private double[] m_Weights

Vector containing the associated weights

m_NumValues

private int m_NumValues

Number of values stored in m_Weights and m_Values so far

m_SumOfWeights

private double m_SumOfWeights

The sum of the weights so far

m_StandardDev

private double m_StandardDev

The standard deviation

m_Precision

private double m_Precision

The precision of data values

m_AllWeightsOne

private boolean m_AllWeightsOne

Whether we can optimise the kernel summation

MAX_ERROR

private static double MAX_ERROR

Maximum percentage error permitted in probability calculations

Constructor Detail

KernelEstimator

public KernelEstimator(double precision)

Constructor that takes a precision argument.

Parameters:

precision - the precision to which numeric values are given. For example, if the precision is stated to be 0.1, the values in the interval (0.25,0.35] are all treated as 0.3.

Method Detail

findNearestValue

private int findNearestValue(double key)

Execute a binary search to locate the nearest data value

Returns:

the index of the nearest data value

round

private double round(double data)

Round a data value using the defined precision for this estimator

Parameters:

data - the value to round

Returns:

the rounded data value

addValue

public void addValue(double data,
                     double weight)

Add a new data value to the current estimator.

Specified by:

addValue in interface Estimator

Parameters:

data - the new data value

weight - the weight assigned to the data value

getProbability

public double getProbability(double data)

Get a probability estimate for a value.

Specified by:

getProbability in interface Estimator

Parameters:

data - the value to estimate the probability of

Returns:

the estimated probability of the supplied value

toString

public java.lang.String toString()

Display a representation of this estimator

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain a sequence of numeric values