weka.estimators
Class NormalEstimator

java.lang.Object
  extended by weka.estimators.Estimator
      extended by weka.estimators.NormalEstimator
All Implemented Interfaces:
java.io.Serializable, java.lang.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 (trigg@cs.waikato.ac.nz)
See Also:
Serialized Form

Constructor Summary
NormalEstimator(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 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
 java.lang.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(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

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 java.lang.String toString()
Display a representation of this estimator

Overrides:
toString in class java.lang.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 java.lang.String getRevision()
Returns the revision string.

Specified by:
getRevision in interface RevisionHandler
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