org.knime.base.node.mine.bfn.fuzzy
Class FuzzyBasisFunctionPredictorRow

java.lang.Object
  extended by org.knime.base.node.mine.bfn.BasisFunctionPredictorRow
      extended by org.knime.base.node.mine.bfn.fuzzy.FuzzyBasisFunctionPredictorRow

public class FuzzyBasisFunctionPredictorRow
extends BasisFunctionPredictorRow

Author:
Thomas Gabriel, University of Konstanz

Constructor Summary
  FuzzyBasisFunctionPredictorRow(ModelContentRO pp)
          Creates a new predictor as fuzzy rule.
protected FuzzyBasisFunctionPredictorRow(RowKey key, DataCell classLabel, MembershipFunction[] mem, int norm)
          Creates a new predictor as fuzzy rule.
 
Method Summary
 double compose(DataRow row, double act)
          Composes the degree of membership by using the disjunction of the tco-norm operator.
 double computeActivation(DataRow row)
          Returns the compute activation of this input vector.
 double computeDistance(DataRow row)
          
 double computeSpread()
          Returns the aggregated spread of the core.
 MutableDouble[] getMaxs()
           
 MembershipFunction getMemship(int i)
          Returns the membership for one dimension.
 MembershipFunction[] getMemships()
           
 MutableDouble[] getMins()
           
 int getNorm()
           
 int getNrMemships()
          Return number of memberships which is equivalent to the number of numeric input dimensions.
 int getNrUsedFeatures()
          
 double overlap(BasisFunctionPredictorRow bf, boolean symmetric)
          Computes the overlapping of two fuzzy basisfunction based on their core spreads.
 void save(ModelContentWO pp)
          Saves this row into a model content.
 
Methods inherited from class org.knime.base.node.mine.bfn.BasisFunctionPredictorRow
getClassLabel, getDontKnowClassDegree, getId, getNumAllCoveredPattern, getNumCorrectCoveredPattern, getNumWrongCoveredPattern, getVariance, overlapping, toString
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

FuzzyBasisFunctionPredictorRow

protected FuzzyBasisFunctionPredictorRow(RowKey key,
                                         DataCell classLabel,
                                         MembershipFunction[] mem,
                                         int norm)
Creates a new predictor as fuzzy rule.

Parameters:
key - The id for this rule.
classLabel - The class label of this rule.
mem - An array of membership functions each per dimension.
norm - A fuzzy norm to combine activations via all dimensions.

FuzzyBasisFunctionPredictorRow

public FuzzyBasisFunctionPredictorRow(ModelContentRO pp)
                               throws InvalidSettingsException
Creates a new predictor as fuzzy rule.

Parameters:
pp - Content to read rule from.
Throws:
InvalidSettingsException - If the content is invalid.
Method Detail

overlap

public double overlap(BasisFunctionPredictorRow bf,
                      boolean symmetric)
Computes the overlapping of two fuzzy basisfunction based on their core spreads.

Specified by:
overlap in class BasisFunctionPredictorRow
Parameters:
bf - the other fuzzy basis function
symmetric - if the result is proportional to both basis functions, and thus symmetric, or if it is proportional to the area of the basis function on which the function is called
Returns:
a degree of overlap normalized with the overall volume of both basis functions

getNrMemships

public int getNrMemships()
Return number of memberships which is equivalent to the number of numeric input dimensions.

Returns:
Number of membership functions.

getMemship

public MembershipFunction getMemship(int i)
Returns the membership for one dimension.

Parameters:
i - Dimension index.
Returns:
A fuzzy membership function.

getMemships

public MembershipFunction[] getMemships()
Returns:
array of fuzzy membership function

save

public void save(ModelContentWO pp)
Saves this row into a model content.

Overrides:
save in class BasisFunctionPredictorRow
Parameters:
pp - the model content to save this row to

compose

public double compose(DataRow row,
                      double act)
Composes the degree of membership by using the disjunction of the tco-norm operator.

Specified by:
compose in class BasisFunctionPredictorRow
Parameters:
row - row
act - activation
Returns:
the new activation array
See Also:
computeActivation(DataRow), Norm.computeTCoNorm(double,double)

computeActivation

public double computeActivation(DataRow row)
Returns the compute activation of this input vector.

Specified by:
computeActivation in class BasisFunctionPredictorRow
Parameters:
row - input pattern
Returns:
membership degree

computeDistance

public double computeDistance(DataRow row)

Specified by:
computeDistance in class BasisFunctionPredictorRow
Parameters:
row - to compute distance with
Returns:
computes the distance between this row and the anchor

computeSpread

public double computeSpread()
Returns the aggregated spread of the core.

Specified by:
computeSpread in class BasisFunctionPredictorRow
Returns:
the overall spread of the core regions

getNrUsedFeatures

public int getNrUsedFeatures()

Specified by:
getNrUsedFeatures in class BasisFunctionPredictorRow
Returns:
number of features that have been shrunken

getNorm

public final int getNorm()
Returns:
fuzzy norm

getMins

public final MutableDouble[] getMins()
Returns:
array of minimum bounds

getMaxs

public final MutableDouble[] getMaxs()
Returns:
array of maximum bounds


Copyright, 2003 - 2010. All rights reserved.
University of Konstanz, Germany.
Chair for Bioinformatics and Information Mining, Prof. Dr. Michael R. Berthold.
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