/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* KernelFilter.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.attribute;
import weka.classifiers.functions.supportVector.Kernel;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.classifiers.functions.supportVector.RBFKernel;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.DenseInstance;
import weka.core.Instances;
import weka.core.MathematicalExpression;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SingleIndex;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.AllFilter;
import weka.filters.Filter;
import weka.filters.SimpleBatchFilter;
import weka.filters.UnsupervisedFilter;
import java.io.File;
import java.util.Enumeration;
import java.util.HashMap;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Converts the given set of predictor variables into a kernel matrix. The class value remains unchangedm, as long as the preprocessing filter doesn't change it.<br/>
* By default, the data is preprocessed with the Center filter, but the user can choose any filter (NB: one must be careful that the filter does not alter the class attribute unintentionally). With weka.filters.AllFilter the preprocessing gets disabled.<br/>
* <br/>
* For more information regarding preprocessing the data, see:<br/>
* <br/>
* K.P. Bennett, M.J. Embrechts: An Optimization Perspective on Kernel Partial Least Squares Regression. In: Advances in Learning Theory: Methods, Models and Applications, 227-249, 2003.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Bennett2003,
* author = {K.P. Bennett and M.J. Embrechts},
* booktitle = {Advances in Learning Theory: Methods, Models and Applications},
* editor = {J. Suykens et al.},
* pages = {227-249},
* publisher = {IOS Press, Amsterdam, The Netherlands},
* series = {NATO Science Series, Series III: Computer and System Sciences},
* title = {An Optimization Perspective on Kernel Partial Least Squares Regression},
* volume = {190},
* year = {2003}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turns on output of debugging information.</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* Turning them off assumes that data is purely numeric, doesn't
* contain any missing values, and has a nominal class. Turning them
* off also means that no header information will be stored if the
* machine is linear. Finally, it also assumes that no instance has
* a weight equal to 0.
* (default: checks on)</pre>
*
* <pre> -F <filename>
* The file to initialize the filter with (optional).</pre>
*
* <pre> -C <num>
* The class index for the file to initialize with,
* First and last are valid (optional, default: last).</pre>
*
* <pre> -K <classname and parameters>
* The Kernel to use.
* (default: weka.classifiers.functions.supportVector.PolyKernel)</pre>
*
* <pre> -kernel-factor
* Defines a factor for the kernel.
* - RBFKernel: a factor for gamma
* Standardize: 1/(2*N)
* Normalize..: 6/N
* Available parameters are:
* N for # of instances, A for # of attributes
* (default: 1)</pre>
*
* <pre> -P <classname and parameters>
* The Filter used for preprocessing (use weka.filters.AllFilter
* to disable preprocessing).
* (default: weka.filters.unsupervised.attribute.Center)</pre>
*
* <pre>
* Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
* </pre>
*
* <pre> -D
* Enables debugging output (if available) to be printed.
* (default: off)</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* (default: checks on)</pre>
*
* <pre> -C <num>
* The size of the cache (a prime number), 0 for full cache and
* -1 to turn it off.
* (default: 250007)</pre>
*
* <pre> -E <num>
* The Exponent to use.
* (default: 1.0)</pre>
*
* <pre> -L
* Use lower-order terms.
* (default: no)</pre>
*
* <pre>
* Options specific to preprocessing filter weka.filters.unsupervised.attribute.Center:
* </pre>
*
* <pre> -unset-class-temporarily
* Unsets the class index temporarily before the filter is
* applied to the data.
* (default: no)</pre>
*
<!-- options-end -->
*
* @author Jonathan Miles (jdm18@cs.waikato.ac.nz)
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 5987 $
*/
public class KernelFilter
extends SimpleBatchFilter
implements UnsupervisedFilter, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 213800899640387499L;
/** The number of instances in the training data. */
protected int m_NumTrainInstances;
/** Kernel to use **/
protected Kernel m_Kernel = new PolyKernel();
/** the Kernel which is actually used for computation */
protected Kernel m_ActualKernel = null;
/** Turn off all checks and conversions? Turning them off assumes
that data is purely numeric, doesn't contain any missing values,
and has a nominal class. Turning them off also means that
no header information will be stored if the machine is linear.
Finally, it also assumes that no instance has a weight equal to 0.*/
protected boolean m_checksTurnedOff;
/** The filter used to make attributes numeric. */
protected NominalToBinary m_NominalToBinary;
/** The filter used to get rid of missing values. */
protected ReplaceMissingValues m_Missing;
/** The dataset to initialize the filter with */
protected File m_InitFile = new File(System.getProperty("user.dir"));
/** the class index for the file to initialized with
* @see #m_InitFile */
protected SingleIndex m_InitFileClassIndex = new SingleIndex("last");
/** whether the filter was initialized */
protected boolean m_Initialized = false;
/** optimizes the kernel with this formula
* (A = # of attributes, N = # of instances)*/
protected String m_KernelFactorExpression = "1";
/** the calculated kernel factor
* @see #m_KernelFactorExpression */
protected double m_KernelFactor = 1.0;
/** for centering/standardizing the data */
protected Filter m_Filter = new Center();
/** for centering/standardizing the data (the actual filter to use) */
protected Filter m_ActualFilter = null;
/**
* Returns a string describing this filter.
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Converts the given set of predictor variables into a kernel matrix. "
+ "The class value remains unchangedm, as long as the preprocessing "
+ "filter doesn't change it.\n"
+ "By default, the data is preprocessed with the Center filter, but the "
+ "user can choose any filter (NB: one must be careful that the filter "
+ "does not alter the class attribute unintentionally). With "
+ "weka.filters.AllFilter the preprocessing gets disabled.\n\n"
+ "For more information regarding preprocessing the data, see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "K.P. Bennett and M.J. Embrechts");
result.setValue(Field.TITLE, "An Optimization Perspective on Kernel Partial Least Squares Regression");
result.setValue(Field.YEAR, "2003");
result.setValue(Field.EDITOR, "J. Suykens et al.");
result.setValue(Field.BOOKTITLE, "Advances in Learning Theory: Methods, Models and Applications");
result.setValue(Field.PAGES, "227-249");
result.setValue(Field.PUBLISHER, "IOS Press, Amsterdam, The Netherlands");
result.setValue(Field.SERIES, "NATO Science Series, Series III: Computer and System Sciences");
result.setValue(Field.VOLUME, "190");
return result;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector result;
Enumeration enm;
result = new Vector();
enm = super.listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
result.addElement(new Option(
"\tTurns off all checks - use with caution!\n"
+ "\tTurning them off assumes that data is purely numeric, doesn't\n"
+ "\tcontain any missing values, and has a nominal class. Turning them\n"
+ "\toff also means that no header information will be stored if the\n"
+ "\tmachine is linear. Finally, it also assumes that no instance has\n"
+ "\ta weight equal to 0.\n"
+ "\t(default: checks on)",
"no-checks", 0, "-no-checks"));
result.addElement(new Option(
"\tThe file to initialize the filter with (optional).",
"F", 1, "-F <filename>"));
result.addElement(new Option(
"\tThe class index for the file to initialize with,\n"
+ "\tFirst and last are valid (optional, default: last).",
"C", 1, "-C <num>"));
result.addElement(new Option(
"\tThe Kernel to use.\n"
+ "\t(default: weka.classifiers.functions.supportVector.PolyKernel)",
"K", 1, "-K <classname and parameters>"));
result.addElement(new Option(
"\tDefines a factor for the kernel.\n"
+ "\t\t- RBFKernel: a factor for gamma\n"
+ "\t\t\tStandardize: 1/(2*N)\n"
+ "\t\t\tNormalize..: 6/N\n"
+ "\tAvailable parameters are:\n"
+ "\t\tN for # of instances, A for # of attributes\n"
+ "\t(default: 1)",
"kernel-factor", 0, "-kernel-factor"));
result.addElement(new Option(
"\tThe Filter used for preprocessing (use weka.filters.AllFilter\n"
+ "\tto disable preprocessing).\n"
+ "\t(default: " + Center.class.getName() + ")",
"P", 1, "-P <classname and parameters>"));
// kernel options
result.addElement(new Option(
"",
"", 0, "\nOptions specific to kernel "
+ getKernel().getClass().getName() + ":"));
enm = ((OptionHandler) getKernel()).listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
// filter options
if (getPreprocessing() instanceof OptionHandler) {
result.addElement(new Option(
"",
"", 0, "\nOptions specific to preprocessing filter "
+ getPreprocessing().getClass().getName() + ":"));
enm = ((OptionHandler) getPreprocessing()).listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
}
return result.elements();
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
int i;
Vector result;
String[] options;
String tmpStr;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
if (getChecksTurnedOff())
result.add("-no-checks");
if ((getInitFile() != null) && getInitFile().isFile()) {
result.add("-F");
result.add("" + getInitFile().getAbsolutePath());
result.add("-C");
result.add("" + getInitFileClassIndex());
}
result.add("-K");
result.add("" + getKernel().getClass().getName() + " " + Utils.joinOptions(getKernel().getOptions()));
result.add("-kernel-factor");
result.add("" + getKernelFactorExpression());
result.add("-P");
tmpStr = getPreprocessing().getClass().getName();
if (getPreprocessing() instanceof OptionHandler)
tmpStr += " " + Utils.joinOptions(((OptionHandler) getPreprocessing()).getOptions());
result.add("" + tmpStr);
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turns on output of debugging information.</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* Turning them off assumes that data is purely numeric, doesn't
* contain any missing values, and has a nominal class. Turning them
* off also means that no header information will be stored if the
* machine is linear. Finally, it also assumes that no instance has
* a weight equal to 0.
* (default: checks on)</pre>
*
* <pre> -F <filename>
* The file to initialize the filter with (optional).</pre>
*
* <pre> -C <num>
* The class index for the file to initialize with,
* First and last are valid (optional, default: last).</pre>
*
* <pre> -K <classname and parameters>
* The Kernel to use.
* (default: weka.classifiers.functions.supportVector.PolyKernel)</pre>
*
* <pre> -kernel-factor
* Defines a factor for the kernel.
* - RBFKernel: a factor for gamma
* Standardize: 1/(2*N)
* Normalize..: 6/N
* Available parameters are:
* N for # of instances, A for # of attributes
* (default: 1)</pre>
*
* <pre> -P <classname and parameters>
* The Filter used for preprocessing (use weka.filters.AllFilter
* to disable preprocessing).
* (default: weka.filters.unsupervised.attribute.Center)</pre>
*
* <pre>
* Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
* </pre>
*
* <pre> -D
* Enables debugging output (if available) to be printed.
* (default: off)</pre>
*
* <pre> -no-checks
* Turns off all checks - use with caution!
* (default: checks on)</pre>
*
* <pre> -C <num>
* The size of the cache (a prime number), 0 for full cache and
* -1 to turn it off.
* (default: 250007)</pre>
*
* <pre> -E <num>
* The Exponent to use.
* (default: 1.0)</pre>
*
* <pre> -L
* Use lower-order terms.
* (default: no)</pre>
*
* <pre>
* Options specific to preprocessing filter weka.filters.unsupervised.attribute.Center:
* </pre>
*
* <pre> -unset-class-temporarily
* Unsets the class index temporarily before the filter is
* applied to the data.
* (default: no)</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
String[] tmpOptions;
setChecksTurnedOff(Utils.getFlag("no-checks", options));
tmpStr = Utils.getOption('F', options);
if (tmpStr.length() != 0)
setInitFile(new File(tmpStr));
else
setInitFile(null);
tmpStr = Utils.getOption('C', options);
if (tmpStr.length() != 0)
setInitFileClassIndex(tmpStr);
else
setInitFileClassIndex("last");
tmpStr = Utils.getOption('K', options);
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length != 0) {
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setKernel(Kernel.forName(tmpStr, tmpOptions));
}
tmpStr = Utils.getOption("kernel-factor", options);
if (tmpStr.length() != 0)
setKernelFactorExpression(tmpStr);
else
setKernelFactorExpression("1");
tmpStr = Utils.getOption("P", options);
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length != 0) {
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setPreprocessing((Filter) Utils.forName(Filter.class, tmpStr, tmpOptions));
}
else {
setPreprocessing(new Center());
}
super.setOptions(options);
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String initFileTipText() {
return "The dataset to initialize the filter with.";
}
/**
* Gets the file to initialize the filter with, can be null.
*
* @return the file
*/
public File getInitFile() {
return m_InitFile;
}
/**
* Sets the file to initialize the filter with, can be null.
*
* @param value the file
*/
public void setInitFile(File value) {
m_InitFile = value;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String initFileClassIndexTipText() {
return "The class index of the dataset to initialize the filter with (first and last are valid).";
}
/**
* Gets the class index of the file to initialize the filter with.
*
* @return the class index
*/
public String getInitFileClassIndex() {
return m_InitFileClassIndex.getSingleIndex();
}
/**
* Sets class index of the file to initialize the filter with.
*
* @param value the class index
*/
public void setInitFileClassIndex(String value) {
m_InitFileClassIndex.setSingleIndex(value);
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String kernelTipText() {
return "The kernel to use.";
}
/**
* Gets the kernel to use.
*
* @return the kernel
*/
public Kernel getKernel() {
return m_Kernel;
}
/**
* Sets the kernel to use.
*
* @param value the kernel
*/
public void setKernel(Kernel value) {
m_Kernel = value;
}
/**
* Disables or enables the checks (which could be time-consuming). Use with
* caution!
*
* @param value if true turns off all checks
*/
public void setChecksTurnedOff(boolean value) {
m_checksTurnedOff = value;
}
/**
* Returns whether the checks are turned off or not.
*
* @return true if the checks are turned off
*/
public boolean getChecksTurnedOff() {
return m_checksTurnedOff;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String checksTurnedOffTipText() {
return "Turns time-consuming checks off - use with caution.";
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String kernelFactorExpressionTipText() {
return "The factor for the kernel, with A = # of attributes and N = # of instances.";
}
/**
* Gets the expression for the kernel.
*
* @return the expression
*/
public String getKernelFactorExpression() {
return m_KernelFactorExpression;
}
/**
* Sets the expression for the kernel.
*
* @param value the file
*/
public void setKernelFactorExpression(String value) {
m_KernelFactorExpression = value;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String preprocessingTipText() {
return "Sets the filter to use for preprocessing (use the AllFilter for no preprocessing).";
}
/**
* Sets the filter to use for preprocessing (use the AllFilter for no
* preprocessing)
*
* @param value the preprocessing filter
*/
public void setPreprocessing(Filter value) {
m_Filter = value;
m_ActualFilter = null;
}
/**
* Gets the filter used for preprocessing
*
* @return the current preprocessing filter.
*/
public Filter getPreprocessing() {
return m_Filter;
}
/**
* resets the filter, i.e., m_NewBatch to true and m_FirstBatchDone to
* false.
*/
protected void reset() {
super.reset();
m_Initialized = false;
}
/**
* Determines the output format based on the input format and returns
* this. In case the output format cannot be returned immediately, i.e.,
* immediateOutputFormat() returns false, then this method will be called
* from batchFinished().
*
* @param inputFormat the input format to base the output format on
* @return the output format
* @throws Exception in case the determination goes wrong
* @see #hasImmediateOutputFormat()
* @see #batchFinished()
*/
protected Instances determineOutputFormat(Instances inputFormat) throws Exception {
return new Instances(inputFormat);
}
/**
* initializes the filter with the given dataset, i.e., the kernel gets
* built. Needs to be called before the first call of Filter.useFilter or
* batchFinished(), if not the -F option (or setInitFile(File) is used).
*
* @param instances the data to initialize with
* @throws Exception if building of kernel fails
*/
public void initFilter(Instances instances) throws Exception {
HashMap symbols;
// determine kernel factor
symbols = new HashMap();
symbols.put("A", new Double(instances.numAttributes()));
symbols.put("N", new Double(instances.numInstances()));
m_KernelFactor = MathematicalExpression.evaluate(getKernelFactorExpression(), symbols);
// init filters
if (!m_checksTurnedOff) {
m_Missing = new ReplaceMissingValues();
m_Missing.setInputFormat(instances);
instances = Filter.useFilter(instances, m_Missing);
}
else {
m_Missing = null;
}
if (getKernel().getCapabilities().handles(Capability.NUMERIC_ATTRIBUTES)) {
boolean onlyNumeric = true;
if (!m_checksTurnedOff) {
for (int i = 0; i < instances.numAttributes(); i++) {
if (i != instances.classIndex()) {
if (!instances.attribute(i).isNumeric()) {
onlyNumeric = false;
break;
}
}
}
}
if (!onlyNumeric) {
m_NominalToBinary = new NominalToBinary();
m_NominalToBinary.setInputFormat(instances);
instances = Filter.useFilter(instances, m_NominalToBinary);
}
else {
m_NominalToBinary = null;
}
}
else {
m_NominalToBinary = null;
}
if ((m_Filter != null) && (m_Filter.getClass() != AllFilter.class)) {
m_ActualFilter = Filter.makeCopy(m_Filter);
m_ActualFilter.setInputFormat(instances);
instances = Filter.useFilter(instances, m_ActualFilter);
}
else {
m_ActualFilter = null;
}
m_NumTrainInstances = instances.numInstances();
// set factor for kernel
m_ActualKernel = Kernel.makeCopy(m_Kernel);
if (m_ActualKernel instanceof RBFKernel)
((RBFKernel) m_ActualKernel).setGamma(
m_KernelFactor * ((RBFKernel) m_ActualKernel).getGamma());
// build kernel
m_ActualKernel.buildKernel(instances);
m_Initialized = true;
}
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result;
if (getKernel() == null) {
result = super.getCapabilities();
result.disableAll();
} else {
result = getKernel().getCapabilities();
}
result.setMinimumNumberInstances(0);
return result;
}
/**
* Processes the given data (may change the provided dataset) and returns
* the modified version. This method is called in batchFinished().
*
* @param instances the data to process
* @return the modified data
* @throws Exception in case the processing goes wrong
* @see #batchFinished()
*/
protected Instances process(Instances instances) throws Exception {
// initializing necessary?
if (!m_Initialized) {
// do we have a file to initialize with?
if ((getInitFile() != null) && getInitFile().isFile()) {
DataSource source = new DataSource(getInitFile().getAbsolutePath());
Instances data = source.getDataSet();
m_InitFileClassIndex.setUpper(data.numAttributes() - 1);
data.setClassIndex(m_InitFileClassIndex.getIndex());
initFilter(data);
}
else {
initFilter(instances);
}
}
// apply filters
if (m_Missing != null)
instances = Filter.useFilter(instances, m_Missing);
if (m_NominalToBinary != null)
instances = Filter.useFilter(instances, m_NominalToBinary);
if (m_ActualFilter != null)
instances = Filter.useFilter(instances, m_ActualFilter);
// backup class attribute and remove it
double[] classes = instances.attributeToDoubleArray(instances.classIndex());
int classIndex = instances.classIndex();
instances.setClassIndex(-1);
instances.deleteAttributeAt(classIndex);
// generate new header
FastVector atts = new FastVector();
for (int j = 0; j < m_NumTrainInstances; j++)
atts.addElement(new Attribute("Kernel " + j));
atts.addElement(new Attribute("Class"));
Instances result = new Instances("Kernel", atts, 0);
result.setClassIndex(result.numAttributes() - 1);
// compute matrix
for (int i = 0; i < instances.numInstances(); i++) {
double[] k = new double[m_NumTrainInstances + 1];
for (int j = 0; j < m_NumTrainInstances; j++) {
double v = m_ActualKernel.eval(-1, j, instances.instance(i));
k[j] = v;
}
k[k.length - 1] = classes[i];
// create new instance
Instance in = new DenseInstance(1.0, k);
result.add(in);
}
if (!isFirstBatchDone())
setOutputFormat(result);
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5987 $");
}
/**
* runs the filter with the given arguments
*
* @param args the commandline arguments
*/
public static void main(String[] args) {
runFilter(new KernelFilter(), args);
}
}