Package weka.estimators
Class NormalEstimator
- java.lang.Object
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- weka.estimators.Estimator
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- weka.estimators.NormalEstimator
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- 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
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Constructor Summary
Constructors Constructor Description NormalEstimator(double precision)
Constructor that takes a precision argument.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method 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 valuejava.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
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Constructor Detail
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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.
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Method Detail
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addValue
public void addValue(double data, double weight)
Add a new data value to the current estimator.- Specified by:
addValue
in interfaceIncrementalEstimator
- Overrides:
addValue
in classEstimator
- Parameters:
data
- the new data valueweight
- the weight assigned to the data value
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getProbability
public double getProbability(double data)
Get a probability estimate for a value- Specified by:
getProbability
in classEstimator
- Parameters:
data
- the value to estimate the probability of- Returns:
- the estimated probability of the supplied value
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toString
public java.lang.String toString()
Display a representation of this estimator- Overrides:
toString
in classjava.lang.Object
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classEstimator
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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getMean
public double getMean()
Return the value of the mean of this normal estimator.- Returns:
- the mean
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getStdDev
public double getStdDev()
Return the value of the standard deviation of this normal estimator.- Returns:
- the standard deviation
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getPrecision
public double getPrecision()
Return the value of the precision of this normal estimator.- Returns:
- the precision
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getSumOfWeights
public double getSumOfWeights()
Return the sum of the weights for this normal estimator.- Returns:
- the sum of the weights
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- should contain a sequence of numeric values
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