fouriertransform.java

一个很好的LIBSVM的JAVA源码。对于要研究和改进SVM算法的学者。可以参考。来自数据挖掘工具YALE工具包。

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2001-2004
 *      Simon Fischer, Ralf Klinkenberg, Ingo Mierswa, 
 *          Katharina Morik, Oliver Ritthoff
 *      Artificial Intelligence Unit
 *      Computer Science Department
 *      University of Dortmund
 *      44221 Dortmund,  Germany
 *  email: [e-mail]
 *  web:   http://yale.cs.uni-dortmund.de/
 *
 *  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., 59 Temple Place, Suite 330, Boston, MA 02111-1307
 *  USA.
 */
package edu.udo.cs.yale.operator.preprocessing;

import edu.udo.cs.yale.operator.Operator;
import edu.udo.cs.yale.operator.IOObject;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.ExampleReader;
import edu.udo.cs.yale.example.ExampleTable;
import edu.udo.cs.yale.example.MemoryExampleTable;
import edu.udo.cs.yale.example.SimpleExampleSet;
import edu.udo.cs.yale.example.DataRowReader;
import edu.udo.cs.yale.example.DataRow;
import edu.udo.cs.yale.tools.math.*;
import edu.udo.cs.yale.tools.Ontology;

import java.util.Collections;
import java.util.SortedMap;
import java.util.TreeMap;
import java.util.Iterator;
import java.util.List;
import java.util.LinkedList;

/** Creates a new example set consisting of the result of a fourier transformation for each attribute
 *  of the input example set.
 *
 *  @version $Id: FourierTransform.java,v 1.3 2004/09/09 12:00:53 ingomierswa Exp $
 */
public class FourierTransform extends Operator {

    private static final Class[] INPUT_CLASSES =  { ExampleSet.class };
    private static final Class[] OUTPUT_CLASSES = { ExampleSet.class };

    public IOObject[] apply() throws OperatorException {
	// init
	ExampleSet exampleSet = (ExampleSet)getInput(ExampleSet.class);
	List attributes = new LinkedList();

	// create new example table
	int numberOfNewExamples = FastFourierTransform.getGreatestPowerOf2LessThan(exampleSet.getSize()) / 2;
	ExampleTable exampleTable = new MemoryExampleTable(new LinkedList(), numberOfNewExamples);

	// create id attribute (for frequency)
	Attribute idAttribute = new Attribute("Id", Ontology.INTEGER, Ontology.SINGLE_VALUE, 
					      Attribute.UNDEFINED_BLOCK_NR, null);
	exampleTable.addAttribute(idAttribute);
	//attributes.add(idAttribute);
	DataRowReader drr = exampleTable.getDataReader();
	int k = 0;
	while (drr.hasNext()) {
	    DataRow dataRow = drr.next();
	    dataRow.set(idAttribute, FastFourierTransform.convertFrequency(k++, numberOfNewExamples, exampleSet.getSize()));
	}

	// create fft values
	double totalMaxEvidence = Double.NEGATIVE_INFINITY;
	Attribute label = exampleSet.getLabel();
	FastFourierTransform fft = new FastFourierTransform(WindowFunction.BLACKMAN_HARRIS);
	SpectrumFilter filter = new SpectrumFilter(SpectrumFilter.NONE);
	for (int i = 0; i < exampleSet.getNumberOfAttributes(); i++) {
	    Attribute current = exampleSet.getAttribute(i);
	    if (!current.isNominal()) {
		Complex[] result = fft.getFourierTransform(exampleSet, label, current);
		Peak[] spectrum  =  filter.filter(result, exampleSet.getSize());
		// create new attribute and fill table with values
		Attribute newAttribute = new Attribute("fft(" + current.getName() + ")", 
						       Ontology.REAL, Ontology.SINGLE_VALUE, 
						       Attribute.UNDEFINED_BLOCK_NR, null);
		exampleTable.addAttribute(newAttribute);
		attributes.add(newAttribute);
		fillTable(exampleTable, newAttribute, spectrum);
	    }
	}

	ExampleSet resultSet = new SimpleExampleSet(exampleTable, attributes);
	resultSet.setIdAttribute(idAttribute);
	resultSet.recalculateAllAttributeStatistics();

	return new IOObject[] { resultSet };
    }

    /** Fills the table with the length of the given complex numbers in the column of the attribute. */
    private void fillTable(ExampleTable table, Attribute attribute, Peak[] values) {
	DataRowReader reader = table.getDataReader();
	int k = 0;
	while (reader.hasNext()) {
	    DataRow dataRow = reader.next();
	    dataRow.set(attribute, values[k++].getMagnitude());
	}
    }

    public Class[] getInputClasses() { return INPUT_CLASSES; }
    public Class[] getOutputClasses() { return OUTPUT_CLASSES; }

}