weka.estimators

Class NormalEstimator

java.lang.Object

weka.estimators.Estimator

weka.estimators.NormalEstimator

All Implemented Interfaces:

Serializable, Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, IncrementalEstimator

public class NormalEstimator
extends Estimator
implements IncrementalEstimator

Simple probability estimator that places a single normal distribution over the observed values.

Version:

$Revision: 5540 $

Author:

Len Trigg ([email protected])

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NormalEstimator(double precision) Constructor that takes a precision argument.

Method Summary

Methods

Modifier and Type	Method and Description
void	addValue(double data, double weight) Add a new data value to the current estimator.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
double	getMean() Return the value of the mean of this normal estimator.
double	getPrecision() Return the value of the precision of this normal estimator.
double	getProbability(double data) Get a probability estimate for a value
String	getRevision() Returns the revision string.
double	getStdDev() Return the value of the standard deviation of this normal estimator.
double	getSumOfWeights() Return the sum of the weights for this normal estimator.
static void	main(String[] argv) Main method for testing this class.
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

NormalEstimator

public NormalEstimator(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 String toString()

Display a representation of this estimator

Overrides:

toString in class Object

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

getMean

public double getMean()

Return the value of the mean of this normal estimator.

Returns:

the mean

getStdDev

public double getStdDev()

Return the value of the standard deviation of this normal estimator.

Returns:

the standard deviation

getPrecision

public double getPrecision()

Return the value of the precision of this normal estimator.

Returns:

the precision

getSumOfWeights

public double getSumOfWeights()

Return the sum of the weights for this normal estimator.

Returns:

the sum of the weights

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - should contain a sequence of numeric values