/*
* 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.
*/
/*
* Wavelet.java
* Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.attribute;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
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.filters.Filter;
import weka.filters.MultiFilter;
import weka.filters.SimpleBatchFilter;
import java.util.Enumeration;
import java.util.Vector;
import weka.core.DenseInstance;
/**
<!-- globalinfo-start -->
* A filter for wavelet transformation.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Wikipedia (2004). Discrete wavelet transform.<br/>
* <br/>
* Kristian Sandberg (2000). The Haar wavelet transform. University of Colorado at Boulder, USA.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @misc{Wikipedia2004,
* author = {Wikipedia},
* title = {Discrete wavelet transform},
* year = {2004},
* HTTP = {http://en.wikipedia.org/wiki/Discrete_wavelet_transform}
* }
*
* @misc{Sandberg2000,
* address = {University of Colorado at Boulder, USA},
* author = {Kristian Sandberg},
* institution = {Dept. of Applied Mathematics},
* title = {The Haar wavelet transform},
* year = {2000},
* HTTP = {http://amath.colorado.edu/courses/5720/2000Spr/Labs/Haar/haar.html}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turns on output of debugging information.</pre>
*
* <pre> -A <Haar>
* The algorithm to use.
* (default: HAAR)</pre>
*
* <pre> -P <Zero>
* The padding to use.
* (default: ZERO)</pre>
*
* <pre> -F <filter specification>
* The filter to use as preprocessing step (classname and options).
* (default: MultiFilter with ReplaceMissingValues and Normalize)</pre>
*
* <pre>
* Options specific to filter weka.filters.MultiFilter ('-F'):
* </pre>
*
* <pre> -D
* Turns on output of debugging information.</pre>
*
* <pre> -F <classname [options]>
* A filter to apply (can be specified multiple times).</pre>
*
<!-- options-end -->
*
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 5547 $
*/
public class Wavelet
extends SimpleBatchFilter
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -3335106965521265631L;
/** the type of algorithm: Haar wavelet */
public static final int ALGORITHM_HAAR = 0;
/** the types of algorithm */
public static final Tag[] TAGS_ALGORITHM = {
new Tag(ALGORITHM_HAAR, "Haar")
};
/** the type of padding: Zero padding */
public static final int PADDING_ZERO = 0;
/** the types of padding */
public static final Tag[] TAGS_PADDING = {
new Tag(PADDING_ZERO, "Zero")
};
/** an optional filter for preprocessing of the data */
protected Filter m_Filter = null;
/** the type of algorithm */
protected int m_Algorithm = ALGORITHM_HAAR;
/** the type of padding */
protected int m_Padding = PADDING_ZERO;
/**
* default constructor
*/
public Wavelet() {
super();
m_Filter = new MultiFilter();
((MultiFilter) m_Filter).setFilters(
new Filter[]{
new weka.filters.unsupervised.attribute.ReplaceMissingValues(),
new weka.filters.unsupervised.attribute.Normalize()
});
}
/**
* Returns a string describing this classifier.
*
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"A filter for wavelet transformation.\n\n"
+ "For more information 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;
TechnicalInformation additional;
result = new TechnicalInformation(Type.MISC);
result.setValue(Field.AUTHOR, "Wikipedia");
result.setValue(Field.YEAR, "2004");
result.setValue(Field.TITLE, "Discrete wavelet transform");
result.setValue(Field.HTTP, "http://en.wikipedia.org/wiki/Discrete_wavelet_transform");
additional = result.add(Type.MISC);
additional.setValue(Field.AUTHOR, "Kristian Sandberg");
additional.setValue(Field.YEAR, "2000");
additional.setValue(Field.TITLE, "The Haar wavelet transform");
additional.setValue(Field.INSTITUTION, "Dept. of Applied Mathematics");
additional.setValue(Field.ADDRESS, "University of Colorado at Boulder, USA");
additional.setValue(Field.HTTP, "http://amath.colorado.edu/courses/5720/2000Spr/Labs/Haar/haar.html");
return result;
}
/**
* Gets an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector result;
Enumeration enm;
String param;
SelectedTag tag;
int i;
result = new Vector();
enm = super.listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
param = "";
for (i = 0; i < TAGS_ALGORITHM.length; i++) {
if (i > 0)
param += "|";
tag = new SelectedTag(TAGS_ALGORITHM[i].getID(), TAGS_ALGORITHM);
param += tag.getSelectedTag().getReadable();
}
result.addElement(new Option(
"\tThe algorithm to use.\n"
+ "\t(default: HAAR)",
"A", 1, "-A <" + param + ">"));
param = "";
for (i = 0; i < TAGS_PADDING.length; i++) {
if (i > 0)
param += "|";
tag = new SelectedTag(TAGS_PADDING[i].getID(), TAGS_PADDING);
param += tag.getSelectedTag().getReadable();
}
result.addElement(new Option(
"\tThe padding to use.\n"
+ "\t(default: ZERO)",
"P", 1, "-P <" + param + ">"));
result.addElement(new Option(
"\tThe filter to use as preprocessing step (classname and options).\n"
+ "\t(default: MultiFilter with ReplaceMissingValues and Normalize)",
"F", 1, "-F <filter specification>"));
if (getFilter() instanceof OptionHandler) {
result.addElement(new Option(
"",
"", 0, "\nOptions specific to filter "
+ getFilter().getClass().getName() + " ('-F'):"));
enm = ((OptionHandler) getFilter()).listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
}
return result.elements();
}
/**
* returns the options of the current setup
*
* @return the current options
*/
public String[] getOptions() {
int i;
Vector result;
String[] options;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
result.add("-A");
result.add("" + getAlgorithm().getSelectedTag().getReadable());
result.add("-P");
result.add("" + getPadding().getSelectedTag().getReadable());
result.add("-F");
if (getFilter() instanceof OptionHandler)
result.add(
getFilter().getClass().getName()
+ " "
+ Utils.joinOptions(((OptionHandler) getFilter()).getOptions()));
else
result.add(
getFilter().getClass().getName());
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Parses the options for this object. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turns on output of debugging information.</pre>
*
* <pre> -A <Haar>
* The algorithm to use.
* (default: HAAR)</pre>
*
* <pre> -P <Zero>
* The padding to use.
* (default: ZERO)</pre>
*
* <pre> -F <filter specification>
* The filter to use as preprocessing step (classname and options).
* (default: MultiFilter with ReplaceMissingValues and Normalize)</pre>
*
* <pre>
* Options specific to filter weka.filters.MultiFilter ('-F'):
* </pre>
*
* <pre> -D
* Turns on output of debugging information.</pre>
*
* <pre> -F <classname [options]>
* A filter to apply (can be specified multiple times).</pre>
*
<!-- options-end -->
*
* @param options the options to use
* @throws Exception if the option setting fails
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
String[] tmpOptions;
Filter filter;
super.setOptions(options);
tmpStr = Utils.getOption("A", options);
if (tmpStr.length() != 0)
setAlgorithm(new SelectedTag(tmpStr, TAGS_ALGORITHM));
else
setAlgorithm(new SelectedTag(ALGORITHM_HAAR, TAGS_ALGORITHM));
tmpStr = Utils.getOption("P", options);
if (tmpStr.length() != 0)
setPadding(new SelectedTag(tmpStr, TAGS_PADDING));
else
setPadding(new SelectedTag(PADDING_ZERO, TAGS_PADDING));
tmpStr = Utils.getOption("F", options);
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length != 0) {
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setFilter((Filter) Utils.forName(Filter.class, tmpStr, tmpOptions));
}
else {
filter = new MultiFilter();
((MultiFilter) filter).setFilters(
new Filter[]{
new weka.filters.unsupervised.attribute.ReplaceMissingValues(),
new weka.filters.unsupervised.attribute.Normalize()
});
setFilter(filter);
}
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String filterTipText() {
return "The preprocessing filter to use.";
}
/**
* Set the preprocessing filter (only used for setup).
*
* @param value the preprocessing filter.
*/
public void setFilter(Filter value) {
m_Filter = value;
}
/**
* Get the preprocessing filter.
*
* @return the preprocessing filter
*/
public Filter getFilter() {
return m_Filter;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String algorithmTipText() {
return "Sets the type of algorithm to use.";
}
/**
* Sets the type of algorithm to use
*
* @param value the algorithm type
*/
public void setAlgorithm(SelectedTag value) {
if (value.getTags() == TAGS_ALGORITHM) {
m_Algorithm = value.getSelectedTag().getID();
}
}
/**
* Gets the type of algorithm to use
*
* @return the current algorithm type.
*/
public SelectedTag getAlgorithm() {
return new SelectedTag(m_Algorithm, TAGS_ALGORITHM);
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String paddingTipText() {
return "Sets the type of padding to use.";
}
/**
* Sets the type of Padding to use
*
* @param value the Padding type
*/
public void setPadding(SelectedTag value) {
if (value.getTags() == TAGS_PADDING) {
m_Padding = value.getSelectedTag().getID();
}
}
/**
* Gets the type of Padding to use
*
* @return the current Padding type.
*/
public SelectedTag getPadding() {
return new SelectedTag(m_Padding, TAGS_PADDING);
}
/**
* returns the next bigger number that's a power of 2. If the number is
* already a power of 2 then this will be returned. The number will be at
* least 2^2..
*
* @param n the number to start from
* @return the next bigger number
*/
protected static int nextPowerOf2(int n) {
int exp;
exp = (int) StrictMath.ceil(StrictMath.log(n) / StrictMath.log(2.0));
exp = StrictMath.max(2, exp);
return (int) StrictMath.pow(2, exp);
}
/**
* pads the data to conform to the necessary number of attributes
*
* @param data the data to pad
* @return the padded data
*/
protected Instances pad(Instances data) {
Instances result;
int i;
int n;
String prefix;
int numAtts;
boolean isLast;
int index;
Vector<Integer> padded;
int[] indices;
FastVector atts;
// determine number of padding attributes
switch (m_Padding) {
case PADDING_ZERO:
if (data.classIndex() > -1)
numAtts = (nextPowerOf2(data.numAttributes() - 1) + 1) - data.numAttributes();
else
numAtts = nextPowerOf2(data.numAttributes()) - data.numAttributes();
break;
default:
throw new IllegalStateException(
"Padding " + new SelectedTag(m_Algorithm, TAGS_PADDING)
+ " not implemented!");
}
result = new Instances(data);
prefix = getAlgorithm().getSelectedTag().getReadable();
// any padding necessary?
if (numAtts > 0) {
// add padding attributes
isLast = (data.classIndex() == data.numAttributes() - 1);
padded = new Vector<Integer>();
for (i = 0; i < numAtts; i++) {
if (isLast)
index = result.numAttributes() - 1;
else
index = result.numAttributes();
result.insertAttributeAt(
new Attribute(prefix + "_padding_" + (i+1)),
index);
// record index
padded.add(new Integer(index));
}
// get padded indices
indices = new int[padded.size()];
for (i = 0; i < padded.size(); i++)
indices[i] = padded.get(i);
// determine number of padding attributes
switch (m_Padding) {
case PADDING_ZERO:
for (i = 0; i < result.numInstances(); i++) {
for (n = 0; n < indices.length; n++)
result.instance(i).setValue(indices[n], 0);
}
break;
}
}
// rename all attributes apart from class
data = result;
atts = new FastVector();
n = 0;
for (i = 0; i < data.numAttributes(); i++) {
n++;
if (i == data.classIndex())
atts.addElement((Attribute) data.attribute(i).copy());
else
atts.addElement(new Attribute(prefix + "_" + n));
}
// create new dataset
result = new Instances(data.relationName(), atts, data.numInstances());
result.setClassIndex(data.classIndex());
for (i = 0; i < data.numInstances(); i++)
result.add(new DenseInstance(1.0, data.instance(i).toDoubleArray()));
return result;
}
/**
* 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 pad(new Instances(inputFormat, 0));
}
/**
* processes the instances using the HAAR algorithm
*
* @param instances the data to process
* @return the modified data
* @throws Exception in case the processing goes wrong
*/
protected Instances processHAAR(Instances instances) throws Exception {
Instances result;
int i;
int n;
int j;
int clsIdx;
double[] oldVal;
double[] newVal;
int level;
int length;
double[] clsVal;
Attribute clsAtt;
clsIdx = instances.classIndex();
clsVal = null;
clsAtt = null;
if (clsIdx > -1) {
clsVal = instances.attributeToDoubleArray(clsIdx);
clsAtt = (Attribute) instances.classAttribute().copy();
instances.setClassIndex(-1);
instances.deleteAttributeAt(clsIdx);
}
result = new Instances(instances, 0);
level = (int) StrictMath.ceil(
StrictMath.log(instances.numAttributes())
/ StrictMath.log(2.0));
for (i = 0; i < instances.numInstances(); i++) {
oldVal = instances.instance(i).toDoubleArray();
newVal = new double[oldVal.length];
for (n = level; n > 0; n--) {
length = (int) StrictMath.pow(2, n - 1);
for (j = 0; j < length; j++) {
newVal[j] = (oldVal[j*2] + oldVal[j*2 + 1]) / StrictMath.sqrt(2);
newVal[j + length] = (oldVal[j*2] - oldVal[j*2 + 1]) / StrictMath.sqrt(2);
}
System.arraycopy(newVal, 0, oldVal, 0, newVal.length);
}
// add new transformed instance
result.add(new DenseInstance(1, newVal));
}
// add class again
if (clsIdx > -1) {
result.insertAttributeAt(clsAtt, clsIdx);
result.setClassIndex(clsIdx);
for (i = 0; i < clsVal.length; i++)
result.instance(i).setClassValue(clsVal[i]);
}
return result;
}
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attribute
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.NUMERIC_CLASS);
result.enable(Capability.DATE_CLASS);
result.enable(Capability.NO_CLASS);
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()
*/
public Instances process(Instances instances) throws Exception {
if (!isFirstBatchDone())
m_Filter.setInputFormat(instances);
instances = Filter.useFilter(instances, m_Filter);
switch (m_Algorithm) {
case ALGORITHM_HAAR:
return processHAAR(pad(instances));
default:
throw new IllegalStateException(
"Algorithm type '" + m_Algorithm + "' is not recognized!");
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5547 $");
}
/**
* runs the filter with the given arguments
*
* @param args the commandline arguments
*/
public static void main(String[] args) {
runFilter(new Wavelet(), args);
}
}