de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel
Class FooKernelFunction<O extends FeatureVector<?,?>>
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.FooKernelFunction<O>
- Type Parameters:
O
- vector type
- All Implemented Interfaces:
- DistanceFunction<O,DoubleDistance>, MeasurementFunction<O,DoubleDistance>, KernelFunction<O,DoubleDistance>, SimilarityFunction<O,DoubleDistance>, Parameterizable
public class FooKernelFunction<O extends FeatureVector<?,?>>
- extends AbstractDoubleKernelFunction<O>
Provides an experimental KernelDistanceFunction for RealVectors.
Currently only supports 2D data and x1^2 ~ x2 correlations.
- Author:
- Simon Paradies
Constructor Summary |
FooKernelFunction()
Provides a polynomial Kernel function that computes
a similarity between the two vectors V1 and V2 definded by (V1^T*V2)^max_degree |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
DEFAULT_MAX_DEGREE
public static final int DEFAULT_MAX_DEGREE
- The default max_degree.
- See Also:
- Constant Field Values
MAX_DEGREE_ID
public static final OptionID MAX_DEGREE_ID
- OptionID for
MAX_DEGREE_PARAM
MAX_DEGREE_PARAM
private final IntParameter MAX_DEGREE_PARAM
- Parameter for the maximum degree
max_degree
private int max_degree
- Degree of the polynomial kernel function
FooKernelFunction
public FooKernelFunction()
- Provides a polynomial Kernel function that computes
a similarity between the two vectors V1 and V2 definded by (V1^T*V2)^max_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<?,?>,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 an experimental kernel similarity between the given two vectors.
- Parameters:
o1
- first vectoro2
- second vector
- Returns:
- the experimental kernel similarity between the given two vectors as an
instance of
DoubleDistance
.