weka.estimators
Class KernelEstimator

java.lang.Object
  weka.estimators.Estimator
      weka.estimators.KernelEstimator

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, IncrementalEstimator

public class KernelEstimator
extends Estimator
implements IncrementalEstimator

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

Version:

$Revision: 5490 $

Author:

Len Trigg ([email protected])

See Also:

Serialized Form

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.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
double[]	getMeans() Return the means of the kernels.
int	getNumKernels() Return the number of kernels in this kernel estimator
double	getPrecision() Return the precision of this kernel estimator.
double	getProbability(double data) Get a probability estimate for a value.
java.lang.String	getRevision() Returns the revision string.
double	getStdDev() Return the standard deviation of this kernel estimator.
double[]	getWeights() Return the weights of the kernels.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	toString() Display a representation of this estimator

Methods inherited from class weka.estimators.Estimator

addValues, addValues, addValues, addValues, buildEstimator, buildEstimator, clone, debugTipText, equals, forName, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions, testCapabilities

Methods inherited from class java.lang.Object

getClass, hashCode, notify, notifyAll, wait, wait, wait

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

addValue

public void addValue(double data,
                     double weight)

Add a new data value to the current estimator.

Specified by:

addValue in interface IncrementalEstimator

Overrides:

addValue in class 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 class 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

Overrides:

toString in class java.lang.Object

getNumKernels

public int getNumKernels()

Return the number of kernels in this kernel estimator

Returns:

the number of kernels

getMeans

public double[] getMeans()

Return the means of the kernels.

Returns:

the means of the kernels

getWeights

public double[] getWeights()

Return the weights of the kernels.

Returns:

the weights of the kernels

getPrecision

public double getPrecision()

Return the precision of this kernel estimator.

Returns:

the precision

getStdDev

public double getStdDev()

Return the standard deviation of this kernel estimator.

Returns:

the standard deviation

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class Estimator

Returns:

the capabilities of this classifier

See Also:

Capabilities

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Estimator

Returns:

the revision

main

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

Main method for testing this class.

Parameters:

argv - should contain a sequence of numeric values