/*
* 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.
*/
/*
* Discretize.java
* Copyright (C) 1999 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.DenseInstance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.Range;
import weka.core.RevisionUtils;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;
import weka.filters.UnsupervisedFilter;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by simple binning. Skips the class attribute if set.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -unset-class-temporarily
* Unsets the class index temporarily before the filter is
* applied to the data.
* (default: no)</pre>
*
* <pre> -B <num>
* Specifies the (maximum) number of bins to divide numeric attributes into.
* (default = 10)</pre>
*
* <pre> -M <num>
* Specifies the desired weight of instances per bin for
* equal-frequency binning. If this is set to a positive
* number then the -B option will be ignored.
* (default = -1)</pre>
*
* <pre> -F
* Use equal-frequency instead of equal-width discretization.</pre>
*
* <pre> -O
* Optimize number of bins using leave-one-out estimate
* of estimated entropy (for equal-width discretization).
* If this is set then the -B option will be ignored.</pre>
*
* <pre> -R <col1,col2-col4,...>
* Specifies list of columns to Discretize. First and last are valid indexes.
* (default: first-last)</pre>
*
* <pre> -V
* Invert matching sense of column indexes.</pre>
*
* <pre> -D
* Output binary attributes for discretized attributes.</pre>
*
<!-- options-end -->
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 6567 $
*/
public class Discretize
extends PotentialClassIgnorer
implements UnsupervisedFilter, WeightedInstancesHandler {
/** for serialization */
static final long serialVersionUID = -1358531742174527279L;
/** Stores which columns to Discretize */
protected Range m_DiscretizeCols = new Range();
/** The number of bins to divide the attribute into */
protected int m_NumBins = 10;
/** The desired weight of instances per bin */
protected double m_DesiredWeightOfInstancesPerInterval = -1;
/** Store the current cutpoints */
protected double [][] m_CutPoints = null;
/** Output binary attributes for discretized attributes. */
protected boolean m_MakeBinary = false;
/** Find the number of bins using cross-validated entropy. */
protected boolean m_FindNumBins = false;
/** Use equal-frequency binning if unsupervised discretization turned on */
protected boolean m_UseEqualFrequency = false;
/** The default columns to discretize */
protected String m_DefaultCols;
/** Constructor - initialises the filter */
public Discretize() {
m_DefaultCols = "first-last";
setAttributeIndices("first-last");
}
/**
* Another constructor, sets the attribute indices immediately
*
* @param cols the attribute indices
*/
public Discretize(String cols) {
m_DefaultCols = cols;
setAttributeIndices(cols);
}
/**
* Gets an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector result = new Vector();
Enumeration enm = super.listOptions();
while (enm.hasMoreElements())
result.add(enm.nextElement());
result.addElement(new Option(
"\tSpecifies the (maximum) number of bins to divide numeric"
+ " attributes into.\n"
+ "\t(default = 10)",
"B", 1, "-B <num>"));
result.addElement(new Option(
"\tSpecifies the desired weight of instances per bin for\n"
+ "\tequal-frequency binning. If this is set to a positive\n"
+ "\tnumber then the -B option will be ignored.\n"
+ "\t(default = -1)",
"M", 1, "-M <num>"));
result.addElement(new Option(
"\tUse equal-frequency instead of equal-width discretization.",
"F", 0, "-F"));
result.addElement(new Option(
"\tOptimize number of bins using leave-one-out estimate\n"+
"\tof estimated entropy (for equal-width discretization).\n"+
"\tIf this is set then the -B option will be ignored.",
"O", 0, "-O"));
result.addElement(new Option(
"\tSpecifies list of columns to Discretize. First"
+ " and last are valid indexes.\n"
+ "\t(default: first-last)",
"R", 1, "-R <col1,col2-col4,...>"));
result.addElement(new Option(
"\tInvert matching sense of column indexes.",
"V", 0, "-V"));
result.addElement(new Option(
"\tOutput binary attributes for discretized attributes.",
"D", 0, "-D"));
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -unset-class-temporarily
* Unsets the class index temporarily before the filter is
* applied to the data.
* (default: no)</pre>
*
* <pre> -B <num>
* Specifies the (maximum) number of bins to divide numeric attributes into.
* (default = 10)</pre>
*
* <pre> -M <num>
* Specifies the desired weight of instances per bin for
* equal-frequency binning. If this is set to a positive
* number then the -B option will be ignored.
* (default = -1)</pre>
*
* <pre> -F
* Use equal-frequency instead of equal-width discretization.</pre>
*
* <pre> -O
* Optimize number of bins using leave-one-out estimate
* of estimated entropy (for equal-width discretization).
* If this is set then the -B option will be ignored.</pre>
*
* <pre> -R <col1,col2-col4,...>
* Specifies list of columns to Discretize. First and last are valid indexes.
* (default: first-last)</pre>
*
* <pre> -V
* Invert matching sense of column indexes.</pre>
*
* <pre> -D
* Output binary attributes for discretized attributes.</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 {
super.setOptions(options);
setMakeBinary(Utils.getFlag('D', options));
setUseEqualFrequency(Utils.getFlag('F', options));
setFindNumBins(Utils.getFlag('O', options));
setInvertSelection(Utils.getFlag('V', options));
String weight = Utils.getOption('M', options);
if (weight.length() != 0) {
setDesiredWeightOfInstancesPerInterval((new Double(weight)).doubleValue());
} else {
setDesiredWeightOfInstancesPerInterval(-1);
}
String numBins = Utils.getOption('B', options);
if (numBins.length() != 0) {
setBins(Integer.parseInt(numBins));
} else {
setBins(10);
}
String convertList = Utils.getOption('R', options);
if (convertList.length() != 0) {
setAttributeIndices(convertList);
} else {
setAttributeIndices(m_DefaultCols);
}
if (getInputFormat() != null) {
setInputFormat(getInputFormat());
}
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
Vector result;
String[] options;
int i;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
if (getMakeBinary())
result.add("-D");
if (getUseEqualFrequency())
result.add("-F");
if (getFindNumBins())
result.add("-O");
if (getInvertSelection())
result.add("-V");
result.add("-B");
result.add("" + getBins());
result.add("-M");
result.add("" + getDesiredWeightOfInstancesPerInterval());
if (!getAttributeIndices().equals("")) {
result.add("-R");
result.add(getAttributeIndices());
}
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enableAllAttributes();
result.enable(Capability.MISSING_VALUES);
// class
result.enableAllClasses();
result.enable(Capability.MISSING_CLASS_VALUES);
if (!getMakeBinary())
result.enable(Capability.NO_CLASS);
return result;
}
/**
* 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
* @throws Exception if the input format can't be set successfully
*/
public boolean setInputFormat(Instances instanceInfo) throws Exception {
if (m_MakeBinary && m_IgnoreClass) {
throw new IllegalArgumentException("Can't ignore class when " +
"changing the number of attributes!");
}
super.setInputFormat(instanceInfo);
m_DiscretizeCols.setUpper(instanceInfo.numAttributes() - 1);
m_CutPoints = null;
if (getFindNumBins() && getUseEqualFrequency()) {
throw new IllegalArgumentException("Bin number optimization in conjunction "+
"with equal-frequency binning not implemented.");
}
// If we implement loading cutfiles, then load
//them here and set the output format
return false;
}
/**
* Input an instance for filtering. Ordinarily the instance is processed
* and made available for output immediately. Some filters require all
* instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be
* collected with output().
* @throws IllegalStateException if no input format has been defined.
*/
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_CutPoints != null) {
convertInstance(instance);
return true;
}
bufferInput(instance);
return false;
}
/**
* Signifies 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
* @throws IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_CutPoints == null) {
calculateCutPoints();
setOutputFormat();
// If we implement saving cutfiles, save the cuts here
// Convert pending input instances
for(int i = 0; i < getInputFormat().numInstances(); i++) {
convertInstance(getInputFormat().instance(i));
}
}
flushInput();
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "An instance filter that discretizes a range of numeric"
+ " attributes in the dataset into nominal attributes."
+ " Discretization is by simple binning. Skips the class"
+ " attribute if set.";
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String findNumBinsTipText() {
return "Optimize number of equal-width bins using leave-one-out. Doesn't " +
"work for equal-frequency binning";
}
/**
* Get the value of FindNumBins.
*
* @return Value of FindNumBins.
*/
public boolean getFindNumBins() {
return m_FindNumBins;
}
/**
* Set the value of FindNumBins.
*
* @param newFindNumBins Value to assign to FindNumBins.
*/
public void setFindNumBins(boolean newFindNumBins) {
m_FindNumBins = newFindNumBins;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String makeBinaryTipText() {
return "Make resulting attributes binary.";
}
/**
* Gets whether binary attributes should be made for discretized ones.
*
* @return true if attributes will be binarized
*/
public boolean getMakeBinary() {
return m_MakeBinary;
}
/**
* Sets whether binary attributes should be made for discretized ones.
*
* @param makeBinary if binary attributes are to be made
*/
public void setMakeBinary(boolean makeBinary) {
m_MakeBinary = makeBinary;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String desiredWeightOfInstancesPerIntervalTipText() {
return "Sets the desired weight of instances per interval for " +
"equal-frequency binning.";
}
/**
* Get the DesiredWeightOfInstancesPerInterval value.
* @return the DesiredWeightOfInstancesPerInterval value.
*/
public double getDesiredWeightOfInstancesPerInterval() {
return m_DesiredWeightOfInstancesPerInterval;
}
/**
* Set the DesiredWeightOfInstancesPerInterval value.
* @param newDesiredNumber The new DesiredNumber value.
*/
public void setDesiredWeightOfInstancesPerInterval(double newDesiredNumber) {
m_DesiredWeightOfInstancesPerInterval = newDesiredNumber;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String useEqualFrequencyTipText() {
return "If set to true, equal-frequency binning will be used instead of" +
" equal-width binning.";
}
/**
* Get the value of UseEqualFrequency.
*
* @return Value of UseEqualFrequency.
*/
public boolean getUseEqualFrequency() {
return m_UseEqualFrequency;
}
/**
* Set the value of UseEqualFrequency.
*
* @param newUseEqualFrequency Value to assign to UseEqualFrequency.
*/
public void setUseEqualFrequency(boolean newUseEqualFrequency) {
m_UseEqualFrequency = newUseEqualFrequency;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String binsTipText() {
return "Number of bins.";
}
/**
* Gets the number of bins numeric attributes will be divided into
*
* @return the number of bins.
*/
public int getBins() {
return m_NumBins;
}
/**
* Sets the number of bins to divide each selected numeric attribute into
*
* @param numBins the number of bins
*/
public void setBins(int numBins) {
m_NumBins = numBins;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String invertSelectionTipText() {
return "Set attribute selection mode. If false, only selected"
+ " (numeric) attributes in the range will be discretized; if"
+ " true, only non-selected attributes will be discretized.";
}
/**
* Gets whether the supplied columns are to be removed or kept
*
* @return true if the supplied columns will be kept
*/
public boolean getInvertSelection() {
return m_DiscretizeCols.getInvert();
}
/**
* Sets whether selected columns should be removed or kept. If true the
* selected columns are kept and unselected columns are deleted. If false
* selected columns are deleted and unselected columns are kept.
*
* @param invert the new invert setting
*/
public void setInvertSelection(boolean invert) {
m_DiscretizeCols.setInvert(invert);
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String attributeIndicesTipText() {
return "Specify range of attributes to act on."
+ " This is a comma separated list of attribute indices, with"
+ " \"first\" and \"last\" valid values. Specify an inclusive"
+ " range with \"-\". E.g: \"first-3,5,6-10,last\".";
}
/**
* Gets the current range selection
*
* @return a string containing a comma separated list of ranges
*/
public String getAttributeIndices() {
return m_DiscretizeCols.getRanges();
}
/**
* Sets which attributes are to be Discretized (only numeric
* attributes among the selection will be Discretized).
*
* @param rangeList a string representing the list of attributes. Since
* the string will typically come from a user, attributes are indexed from
* 1. <br>
* eg: first-3,5,6-last
* @throws IllegalArgumentException if an invalid range list is supplied
*/
public void setAttributeIndices(String rangeList) {
m_DiscretizeCols.setRanges(rangeList);
}
/**
* Sets which attributes are to be Discretized (only numeric
* attributes among the selection will be Discretized).
*
* @param attributes an array containing indexes of attributes to Discretize.
* Since the array will typically come from a program, attributes are indexed
* from 0.
* @throws IllegalArgumentException if an invalid set of ranges
* is supplied
*/
public void setAttributeIndicesArray(int [] attributes) {
setAttributeIndices(Range.indicesToRangeList(attributes));
}
/**
* Gets the cut points for an attribute
*
* @param attributeIndex the index (from 0) of the attribute to get the cut points of
* @return an array containing the cutpoints (or null if the
* attribute requested has been discretized into only one interval.)
*/
public double [] getCutPoints(int attributeIndex) {
if (m_CutPoints == null) {
return null;
}
return m_CutPoints[attributeIndex];
}
/** Generate the cutpoints for each attribute */
protected void calculateCutPoints() {
m_CutPoints = new double [getInputFormat().numAttributes()] [];
for(int i = getInputFormat().numAttributes() - 1; i >= 0; i--) {
if ((m_DiscretizeCols.isInRange(i)) &&
(getInputFormat().attribute(i).isNumeric()) &&
(getInputFormat().classIndex() != i)) {
if (m_FindNumBins) {
findNumBins(i);
} else if (!m_UseEqualFrequency) {
calculateCutPointsByEqualWidthBinning(i);
} else {
calculateCutPointsByEqualFrequencyBinning(i);
}
}
}
}
/**
* Set cutpoints for a single attribute.
*
* @param index the index of the attribute to set cutpoints for
*/
protected void calculateCutPointsByEqualWidthBinning(int index) {
// Scan for max and min values
double max = 0, min = 1, currentVal;
Instance currentInstance;
for(int i = 0; i < getInputFormat().numInstances(); i++) {
currentInstance = getInputFormat().instance(i);
if (!currentInstance.isMissing(index)) {
currentVal = currentInstance.value(index);
if (max < min) {
max = min = currentVal;
}
if (currentVal > max) {
max = currentVal;
}
if (currentVal < min) {
min = currentVal;
}
}
}
double binWidth = (max - min) / m_NumBins;
double [] cutPoints = null;
if ((m_NumBins > 1) && (binWidth > 0)) {
cutPoints = new double [m_NumBins - 1];
for(int i = 1; i < m_NumBins; i++) {
cutPoints[i - 1] = min + binWidth * i;
}
}
m_CutPoints[index] = cutPoints;
}
/**
* Set cutpoints for a single attribute.
*
* @param index the index of the attribute to set cutpoints for
*/
protected void calculateCutPointsByEqualFrequencyBinning(int index) {
// Copy data so that it can be sorted
Instances data = new Instances(getInputFormat());
// Sort input data
data.sort(index);
// Compute weight of instances without missing values
double sumOfWeights = 0;
for (int i = 0; i < data.numInstances(); i++) {
if (data.instance(i).isMissing(index)) {
break;
} else {
sumOfWeights += data.instance(i).weight();
}
}
double freq;
double[] cutPoints = new double[m_NumBins - 1];
if (getDesiredWeightOfInstancesPerInterval() > 0) {
freq = getDesiredWeightOfInstancesPerInterval();
cutPoints = new double[(int)(sumOfWeights / freq)];
} else {
freq = sumOfWeights / m_NumBins;
cutPoints = new double[m_NumBins - 1];
}
// Compute break points
double counter = 0, last = 0;
int cpindex = 0, lastIndex = -1;
for (int i = 0; i < data.numInstances() - 1; i++) {
// Stop if value missing
if (data.instance(i).isMissing(index)) {
break;
}
counter += data.instance(i).weight();
sumOfWeights -= data.instance(i).weight();
// Do we have a potential breakpoint?
if (data.instance(i).value(index) <
data.instance(i + 1).value(index)) {
// Have we passed the ideal size?
if (counter >= freq) {
// Is this break point worse than the last one?
if (((freq - last) < (counter - freq)) && (lastIndex != -1)) {
cutPoints[cpindex] = (data.instance(lastIndex).value(index) +
data.instance(lastIndex + 1).value(index)) / 2;
counter -= last;
last = counter;
lastIndex = i;
} else {
cutPoints[cpindex] = (data.instance(i).value(index) +
data.instance(i + 1).value(index)) / 2;
counter = 0;
last = 0;
lastIndex = -1;
}
cpindex++;
freq = (sumOfWeights + counter) / ((cutPoints.length + 1) - cpindex);
} else {
lastIndex = i;
last = counter;
}
}
}
// Check whether there was another possibility for a cut point
if ((cpindex < cutPoints.length) && (lastIndex != -1)) {
cutPoints[cpindex] = (data.instance(lastIndex).value(index) +
data.instance(lastIndex + 1).value(index)) / 2;
cpindex++;
}
// Did we find any cutpoints?
if (cpindex == 0) {
m_CutPoints[index] = null;
} else {
double[] cp = new double[cpindex];
for (int i = 0; i < cpindex; i++) {
cp[i] = cutPoints[i];
}
m_CutPoints[index] = cp;
}
}
/**
* Optimizes the number of bins using leave-one-out cross-validation.
*
* @param index the attribute index
*/
protected void findNumBins(int index) {
double min = Double.MAX_VALUE, max = -Double.MAX_VALUE, binWidth = 0,
entropy, bestEntropy = Double.MAX_VALUE, currentVal;
double[] distribution;
int bestNumBins = 1;
Instance currentInstance;
// Find minimum and maximum
for (int i = 0; i < getInputFormat().numInstances(); i++) {
currentInstance = getInputFormat().instance(i);
if (!currentInstance.isMissing(index)) {
currentVal = currentInstance.value(index);
if (currentVal > max) {
max = currentVal;
}
if (currentVal < min) {
min = currentVal;
}
}
}
// Find best number of bins
for (int i = 0; i < m_NumBins; i++) {
distribution = new double[i + 1];
binWidth = (max - min) / (i + 1);
// Compute distribution
for (int j = 0; j < getInputFormat().numInstances(); j++) {
currentInstance = getInputFormat().instance(j);
if (!currentInstance.isMissing(index)) {
for (int k = 0; k < i + 1; k++) {
if (currentInstance.value(index) <=
(min + (((double)k + 1) * binWidth))) {
distribution[k] += currentInstance.weight();
break;
}
}
}
}
// Compute cross-validated entropy
entropy = 0;
for (int k = 0; k < i + 1; k++) {
if (distribution[k] < 2) {
entropy = Double.MAX_VALUE;
break;
}
entropy -= distribution[k] * Math.log((distribution[k] - 1) /
binWidth);
}
// Best entropy so far?
if (entropy < bestEntropy) {
bestEntropy = entropy;
bestNumBins = i + 1;
}
}
// Compute cut points
double [] cutPoints = null;
if ((bestNumBins > 1) && (binWidth > 0)) {
cutPoints = new double [bestNumBins - 1];
for(int i = 1; i < bestNumBins; i++) {
cutPoints[i - 1] = min + binWidth * i;
}
}
m_CutPoints[index] = cutPoints;
}
/**
* Set the output format. Takes the currently defined cutpoints and
* m_InputFormat and calls setOutputFormat(Instances) appropriately.
*/
protected void setOutputFormat() {
if (m_CutPoints == null) {
setOutputFormat(null);
return;
}
FastVector attributes = new FastVector(getInputFormat().numAttributes());
int classIndex = getInputFormat().classIndex();
for(int i = 0; i < getInputFormat().numAttributes(); i++) {
if ((m_DiscretizeCols.isInRange(i))
&& (getInputFormat().attribute(i).isNumeric())
&& (getInputFormat().classIndex() != i)) {
if (!m_MakeBinary) {
FastVector attribValues = new FastVector(1);
if (m_CutPoints[i] == null) {
attribValues.addElement("'All'");
} else {
for(int j = 0; j <= m_CutPoints[i].length; j++) {
if (j == 0) {
attribValues.addElement("'(-inf-"
+ Utils.doubleToString(m_CutPoints[i][j], 6) + "]'");
} else if (j == m_CutPoints[i].length) {
attribValues.addElement("'("
+ Utils.doubleToString(m_CutPoints[i][j - 1], 6)
+ "-inf)'");
} else {
attribValues.addElement("'("
+ Utils.doubleToString(m_CutPoints[i][j - 1], 6) + "-"
+ Utils.doubleToString(m_CutPoints[i][j], 6) + "]'");
}
}
}
attributes.addElement(new Attribute(getInputFormat().
attribute(i).name(),
attribValues));
} else {
if (m_CutPoints[i] == null) {
FastVector attribValues = new FastVector(1);
attribValues.addElement("'All'");
attributes.addElement(new Attribute(getInputFormat().
attribute(i).name(),
attribValues));
} else {
if (i < getInputFormat().classIndex()) {
classIndex += m_CutPoints[i].length - 1;
}
for(int j = 0; j < m_CutPoints[i].length; j++) {
FastVector attribValues = new FastVector(2);
attribValues.addElement("'(-inf-"
+ Utils.doubleToString(m_CutPoints[i][j], 6) + "]'");
attribValues.addElement("'("
+ Utils.doubleToString(m_CutPoints[i][j], 6) + "-inf)'");
attributes.addElement(new Attribute(getInputFormat().
attribute(i).name() + "_" + (j+1),
attribValues));
}
}
}
} else {
attributes.addElement(getInputFormat().attribute(i).copy());
}
}
Instances outputFormat =
new Instances(getInputFormat().relationName(), attributes, 0);
outputFormat.setClassIndex(classIndex);
setOutputFormat(outputFormat);
}
/**
* Convert a single instance over. The converted instance is added to
* the end of the output queue.
*
* @param instance the instance to convert
*/
protected void convertInstance(Instance instance) {
int index = 0;
double [] vals = new double [outputFormatPeek().numAttributes()];
// Copy and convert the values
for(int i = 0; i < getInputFormat().numAttributes(); i++) {
if (m_DiscretizeCols.isInRange(i) &&
getInputFormat().attribute(i).isNumeric() &&
(getInputFormat().classIndex() != i)) {
int j;
double currentVal = instance.value(i);
if (m_CutPoints[i] == null) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else {
vals[index] = 0;
}
index++;
} else {
if (!m_MakeBinary) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else {
for (j = 0; j < m_CutPoints[i].length; j++) {
if (currentVal <= m_CutPoints[i][j]) {
break;
}
}
vals[index] = j;
}
index++;
} else {
for (j = 0; j < m_CutPoints[i].length; j++) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else if (currentVal <= m_CutPoints[i][j]) {
vals[index] = 0;
} else {
vals[index] = 1;
}
index++;
}
}
}
} else {
vals[index] = instance.value(i);
index++;
}
}
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(getOutputFormat());
copyValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 6567 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter: use -h for help
*/
public static void main(String [] argv) {
runFilter(new Discretize(), argv);
}
}