/*
* 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.
*/
/*
* TimeSeriesTranslate.java
* Copyright (C) 1999 Len Trigg
*
*/
package weka.filters.unsupervised.attribute;
import weka.filters.*;
import java.io.*;
import java.util.*;
import weka.core.*;
/**
* An instance filter that assumes instances form time-series data and
* replaces attribute values in the current instance with the equivalent
* attribute attribute values of some previous (or future) instance. For
* instances where the desired value is unknown either the instance may
* be dropped, or missing values used.<p>
*
* Valid filter-specific options are:<p>
*
* -R index1,index2-index4,...<br>
* Specify list of columns to calculate new values for.
* First and last are valid indexes.
* (default none)<p>
*
* -V <br>
* Invert matching sense (i.e. calculate for all non-specified columns)<p>
*
* -I num <br>
* The number of instances forward to translate values between.
* A negative number indicates taking values from a past instance.
* (default -1) <p>
*
* -M <br>
* For instances at the beginning or end of the dataset where the translated
* values are not known, use missing values (default is to remove those
* instances). <p>
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class TimeSeriesTranslate extends AbstractTimeSeries {
/**
* 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 UnsupportedAttributeTypeException if selected
* attributes are not numeric or nominal.
*/
public boolean setInputFormat(Instances instanceInfo) throws Exception {
super.setInputFormat(instanceInfo);
// Create the output buffer
Instances outputFormat = new Instances(instanceInfo, 0);
for(int i = 0; i < instanceInfo.numAttributes(); i++) {
if (m_SelectedCols.isInRange(i)) {
if (outputFormat.attribute(i).isNominal()
|| outputFormat.attribute(i).isNumeric()) {
outputFormat.renameAttribute(i, outputFormat.attribute(i).name()
+ (m_InstanceRange < 0 ? '-' : '+')
+ Math.abs(m_InstanceRange));
} else {
throw new UnsupportedAttributeTypeException("Only numeric and nominal attributes may be "
+ " manipulated in time series.");
}
}
}
setOutputFormat(outputFormat);
return true;
}
/**
* Creates a new instance the same as one instance (the "destination")
* but with some attribute values copied from another instance
* (the "source")
*
* @param source the source instance
* @param dest the destination instance
* @return the new merged instance
*/
protected Instance mergeInstances(Instance source, Instance dest) {
Instances outputFormat = outputFormatPeek();
double[] vals = new double[outputFormat.numAttributes()];
for(int i = 0; i < vals.length; i++) {
if (m_SelectedCols.isInRange(i)) {
if (source != null) {
vals[i] = source.value(i);
} else {
vals[i] = Instance.missingValue();
}
} else {
vals[i] = dest.value(i);
}
}
Instance inst = null;
if (dest instanceof SparseInstance) {
inst = new SparseInstance(dest.weight(), vals);
} else {
inst = new Instance(dest.weight(), vals);
}
inst.setDataset(dest.dataset());
return 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 TimeSeriesTranslate(), argv);
} else {
Filter.filterFile(new TimeSeriesTranslate(), argv);
}
} catch (Exception ex) {
System.out.println(ex.getMessage());
}
}
}