de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel
Class PolynomialKernelFunction<O extends FeatureVector<O,?>>
java.lang.Object
de.lmu.ifi.dbs.elki.logging.AbstractLoggable
de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizable
de.lmu.ifi.dbs.elki.distance.AbstractMeasurementFunction<O,D>
de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractDistanceFunction<O,D>
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.AbstractKernelFunction<O,DoubleDistance>
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.AbstractDoubleKernelFunction<O>
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.PolynomialKernelFunction<O>
- Type Parameters:
O
- vector type
- All Implemented Interfaces:
- DistanceFunction<O,DoubleDistance>, MeasurementFunction<O,DoubleDistance>, KernelFunction<O,DoubleDistance>, SimilarityFunction<O,DoubleDistance>, Parameterizable
public class PolynomialKernelFunction<O extends FeatureVector<O,?>>
- extends AbstractDoubleKernelFunction<O>
Provides a polynomial Kernel function that computes
a similarity between the two feature vectors V1 and V2 defined by (V1^T*V2)^degree.
- Author:
- Simon Paradies
Constructor Summary |
PolynomialKernelFunction()
Provides a polynomial Kernel function that computes
a similarity between the two feature vectors V1 and V2 defined by (V1^T*V2)^degree. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
DEFAULT_DEGREE
public static final double DEFAULT_DEGREE
- The default degree.
- See Also:
- Constant Field Values
DEGREE_ID
public static final OptionID DEGREE_ID
- OptionID for
DEGREE_PARAM
DEGREE_PARAM
private final DoubleParameter DEGREE_PARAM
degree
private double degree
- Degree of the polynomial kernel function
PolynomialKernelFunction
public PolynomialKernelFunction()
- Provides a polynomial Kernel function that computes
a similarity between the two feature vectors V1 and V2 defined by (V1^T*V2)^degree.
shortDescription
public String shortDescription()
- Description copied from class:
AbstractMeasurementFunction
- Returns the required input pattern.
- Specified by:
shortDescription
in interface Parameterizable
- Overrides:
shortDescription
in class AbstractMeasurementFunction<O extends FeatureVector<O,?>,DoubleDistance>
- Returns:
- Description of the class
setParameters
public List<String> setParameters(List<String> args)
throws ParameterException
- Description copied from class:
AbstractParameterizable
- Grabs all specified options from the option handler. Any extending class
should call this method first and return the returned array without further
changes, but after setting further required parameters. An example for
overwriting this method taking advantage from the previously (in
superclasses) defined options would be:
{
List remainingParameters = super.setParameters(args);
// set parameters for your class
// for example like this:
if(isSet(MY_PARAM_VALUE_PARAM))
{
myParamValue = getParameterValue(MY_PARAM_VALUE_PARAM);
}
.
.
.
return remainingParameters;
// or in case of attributes requesting parameters themselves
// return parameterizableAttribbute.setParameters(remainingParameters);
}
- Specified by:
setParameters
in interface Parameterizable
- Overrides:
setParameters
in class AbstractParameterizable
- Parameters:
args
- parameters to set the attributes accordingly to
- Returns:
- a list containing the unused parameters
- Throws:
ParameterException
- in case of wrong parameter-setting
similarity
public DoubleDistance similarity(O o1,
O o2)
- Provides the linear kernel similarity between the given two vectors.
- Parameters:
o1
- first vectoro2
- second vector
- Returns:
- the linear kernel similarity between the given two vectors as an
instance of
DoubleDistance
. - See Also:
DistanceFunction.distance(de.lmu.ifi.dbs.elki.data.DatabaseObject, de.lmu.ifi.dbs.elki.data.DatabaseObject)