Package weka.estimators
Class KernelEstimator
- java.lang.Object
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- weka.estimators.Estimator
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- weka.estimators.KernelEstimator
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- 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: 5540 $
- Author:
- Len Trigg (trigg@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description KernelEstimator(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[]
getMeans()
Return the means of the kernels.int
getNumKernels()
Return the number of kernels in this kernel estimatordouble
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
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Constructor Detail
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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.
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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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getNumKernels
public int getNumKernels()
Return the number of kernels in this kernel estimator- Returns:
- the number of kernels
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getMeans
public double[] getMeans()
Return the means of the kernels.- Returns:
- the means of the kernels
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getWeights
public double[] getWeights()
Return the weights of the kernels.- Returns:
- the weights of the kernels
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getPrecision
public double getPrecision()
Return the precision of this kernel estimator.- Returns:
- the precision
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getStdDev
public double getStdDev()
Return the standard deviation of this kernel estimator.- Returns:
- the standard deviation
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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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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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