/*
* 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.
*/
/*
* Normalize.java
* Copyright (C) 1999 Eibe Frank
*
*/
package weka.filters.unsupervised.attribute;
import weka.filters.*;
import java.io.*;
import java.util.*;
import weka.core.*;
/**
* Normalizes all numeric values in the given dataset. The resulting
* values are in [0,1] for the data used to compute the normalization
* intervals.
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class Normalize extends Filter implements UnsupervisedFilter {
/** The minimum values for numeric attributes. */
private double [] m_MinArray;
/** The maximum values for numeric attributes. */
private double [] m_MaxArray;
/**
* Sets the format of the input instances.
*
* @param instanceInfo an Instances object containing the input
* instance structure (any instances contained in the object are
* ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @exception Exception if the input format can't be set
* successfully
*/
public boolean setInputFormat(Instances instanceInfo)
throws Exception {
super.setInputFormat(instanceInfo);
setOutputFormat(instanceInfo);
m_MinArray = m_MaxArray = null;
return true;
}
/**
* Input an instance for filtering. Filter requires all
* training instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be
* collected with output().
* @exception IllegalStateException if no input format has been set.
*/
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (m_MinArray == null) {
bufferInput(instance);
return false;
} else {
convertInstance(instance);
return true;
}
}
/**
* Signify that this batch of input to the filter is finished.
* If the filter requires all instances prior to filtering,
* output() may now be called to retrieve the filtered instances.
*
* @return true if there are instances pending output
* @exception IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_MinArray == null) {
Instances input = getInputFormat();
// Compute minimums and maximums
m_MinArray = new double[input.numAttributes()];
m_MaxArray = new double[input.numAttributes()];
for (int i = 0; i < input.numAttributes(); i++) {
m_MinArray[i] = Double.NaN;
}
for (int j = 0; j < input.numInstances(); j++) {
double[] value = input.instance(j).toDoubleArray();
for (int i = 0; i < input.numAttributes(); i++) {
if (input.attribute(i).isNumeric()) {
if (!Instance.isMissingValue(value[i])) {
if (Double.isNaN(m_MinArray[i])) {
m_MinArray[i] = m_MaxArray[i] = value[i];
} else {
if (value[i] < m_MinArray[i]) {
m_MinArray[i] = value[i];
}
if (value[i] > m_MaxArray[i]) {
m_MaxArray[i] = value[i];
}
}
}
}
}
}
// Convert pending input instances
for(int i = 0; i < input.numInstances(); i++) {
convertInstance(input.instance(i));
}
}
// Free memory
flushInput();
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* Convert a single instance over. The converted instance is
* added to the end of the output queue.
*
* @param instance the instance to convert
*/
private void convertInstance(Instance instance) {
Instance inst = null;
if (instance instanceof SparseInstance) {
double[] newVals = new double[instance.numAttributes()];
int[] newIndices = new int[instance.numAttributes()];
double[] vals = instance.toDoubleArray();
int ind = 0;
for (int j = 0; j < instance.numAttributes(); j++) {
double value;
if (instance.attribute(j).isNumeric() &&
(!Instance.isMissingValue(vals[j]))) {
if (Double.isNaN(m_MinArray[j]) ||
(m_MaxArray[j] == m_MinArray[j])) {
value = 0;
} else {
value = (vals[j] - m_MinArray[j]) /
(m_MaxArray[j] - m_MinArray[j]);
}
if (value != 0.0) {
newVals[ind] = value;
newIndices[ind] = j;
ind++;
}
} else {
value = vals[j];
if (value != 0.0) {
newVals[ind] = value;
newIndices[ind] = j;
ind++;
}
}
}
double[] tempVals = new double[ind];
int[] tempInd = new int[ind];
System.arraycopy(newVals, 0, tempVals, 0, ind);
System.arraycopy(newIndices, 0, tempInd, 0, ind);
inst = new SparseInstance(instance.weight(), tempVals, tempInd,
instance.numAttributes());
} else {
double[] vals = instance.toDoubleArray();
for (int j = 0; j < getInputFormat().numAttributes(); j++) {
if (instance.attribute(j).isNumeric() &&
(!Instance.isMissingValue(vals[j]))) {
if (Double.isNaN(m_MinArray[j]) ||
(m_MaxArray[j] == m_MinArray[j])) {
vals[j] = 0;
} else {
vals[j] = (vals[j] - m_MinArray[j]) /
(m_MaxArray[j] - m_MinArray[j]);
}
}
}
inst = new Instance(instance.weight(), vals);
}
inst.setDataset(instance.dataset());
push(inst);
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter:
* use -h for help
*/
public static void main(String [] argv) {
try {
if (Utils.getFlag('b', argv)) {
Filter.batchFilterFile(new Normalize(), argv);
} else {
Filter.filterFile(new Normalize(), argv);
}
} catch (Exception ex) {
System.out.println(ex.getMessage());
}
}
}