package weka.core;
import java.io.*;
import java.text.ParseException;
import java.util.*;
/**
* Class for handling an ordered set of weighted instances. <p>
*
* Typical usage (code from the main() method of this class): <p>
*
* <code>
* ... <br>
*
* // Read all the instances in the file <br>
* reader = new FileReader(filename); <br>
* instances = new Instances(reader); <br><br>
*
* // Make the last attribute be the class <br>
* instances.setClassIndex(instances.numAttributes() - 1); <br><br>
*
* // Print header and instances. <br>
* System.out.println("\nDataset:\n"); <br>
* System.out.println(instances); <br><br>
*
* ... <br>
* </code><p>
*
* All methods that change a set of instances are safe, ie. a change
* of a set of instances does not affect any other sets of
* instances. All methods that change a datasets's attribute
* information clone the dataset before it is changed.
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 1.2 $
*/
public class Instances implements Serializable {
/** The filename extension that should be used for arff files */
public static String FILE_EXTENSION = ".arff";
/** The dataset's name. */
protected String m_RelationName;
/** The attribute information. */
protected FastVector m_Attributes;
/** The instances. */
protected FastVector m_Instances;
/** Index in ranges for MIN and MAX and WIDTH */
public static int R_MIN = 0;
public static int R_MAX = 1;
public static int R_WIDTH = 2;
/** The class attribute's index */
protected int m_ClassIndex;
/** Buffer of values for sparse instance */
protected double[] m_ValueBuffer;
/** Buffer of indices for sparse instance */
protected int[] m_IndicesBuffer;
/** Ranges of instances */
protected double[][] m_Ranges;
/**
* Reads an ARFF file from a reader, and assigns a weight of one to each instance. Lets the index of the class attribute be undefined (negative).
* @param reader the reader
* @exception IOException if the ARFF file is not read successfully
*/
public Instances(Reader reader) throws IOException {
StreamTokenizer tokenizer;
tokenizer = new StreamTokenizer(reader);
initTokenizer(tokenizer);
readHeader(tokenizer);
m_ClassIndex = -1;
m_Instances = new FastVector(1000);
while (getInstance(tokenizer, true)) {};
compactify();
}
/**
* Reads the header of an ARFF file from a reader and reserves space for the given number of instances. Lets the class index be undefined (negative).
* @param reader the reader
* @param capacity the capacity
* @exception IllegalArgumentException if the header is not read successfully or the capacity is negative.
* @exception IOException if there is a problem with the reader.
*/
public Instances(Reader reader, int capacity) throws IOException {
StreamTokenizer tokenizer;
if(capacity<0)throw new IllegalArgumentException("Capacity has to be positive!");
tokenizer = new StreamTokenizer(reader);
initTokenizer(tokenizer);
readHeader(tokenizer);
m_ClassIndex = -1;
m_Instances = new FastVector(capacity);
}
/**
* Constructor copying all instances and references to
* the header information from the given set of instances.
*
* @param instances the set to be copied
*/
public Instances(Instances dataset) {
this(dataset, dataset.numInstances());
dataset.copyInstances(0, this, dataset.numInstances());
}
/**
* Constructor creating an empty set of instances. Copies references
* to the header information from the given set of instances. Sets
* the capacity of the set of instances to 0 if its negative.
*
* @param instances the instances from which the header
* information is to be taken
* @param capacity the capacity of the new dataset
*/
public Instances(Instances dataset, int capacity) {
if(capacity<0)capacity=0;
// Strings only have to be "shallow" copied because
// they can't be modified.
m_ClassIndex = dataset.m_ClassIndex;
m_RelationName = dataset.m_RelationName;
m_Attributes = dataset.m_Attributes;
m_Instances = new FastVector(capacity);
}
/**
* Creates a new set of instances by copying a subset of another set.
* @param source the set of instances from which a subset is to be created
* @param first the index of the first instance to be copied
* @param toCopy the number of instances to be copied
* @exception IllegalArgumentException if first and toCopy are out of range
*/
public Instances(Instances source, int first, int toCopy) {
this(source, toCopy);
if ((first < 0) || ((first + toCopy) > source.numInstances())) {
throw new IllegalArgumentException("Parameters first and/or toCopy out of range");
}
source.copyInstances(first, this, toCopy);
}
public Instances(Instances source,int [] indices){
this(source,indices.length);
source.copyInstances(indices,this);
}
/**
* Creates an empty set of instances. Uses the given
* attribute information. Sets the capacity of the set of
* instances to 0 if its negative. Given attribute information
* must not be changed after this constructor has been used.
* @param name the name of the relation
* @param attInfo the attribute information
* @param capacity the capacity of the set
*/
public Instances(String name, FastVector attInfo, int capacity) {
m_RelationName = name;
m_ClassIndex = -1;
m_Attributes = attInfo;
for(int i=0;i<numAttributes();i++)attribute(i).setIndex(i);
m_Instances = new FastVector(capacity);
}
public Instances(String name,FastVector attInfo){
this(name,attInfo,0);
}
public Instances(String name){
this(name,new FastVector());
}
public Instances copy(){
Instances instances=new Instances(this,0);
Instance instance;
for(int i=0,ii=numInstances();i<ii;i++){
instance=instance(i).deepcopy();
instance.setDataset(instances);
instances.m_Instances.addElement(instance);
}
return instances;
}
/**
* Create a copy of the structure, but "cleanse" string types (i.e.
* doesn't contain references to the strings seen in the past).
*
* @return a copy of the instance structure.
*/
public Instances stringFreeStructure() {
FastVector atts = (FastVector)m_Attributes.copy();
for (int i = 0 ; i < atts.size(); i++) {
Attribute att = (Attribute)atts.elementAt(i);
if (att.type() == Attribute.STRING) {
atts.setElementAt(new Attribute(att.name(), (FastVector)null), i);
}
}
Instances result = new Instances(relationName(), atts, 0);
result.m_ClassIndex = m_ClassIndex;
return result;
}
/**
* Adds one instance to the end of the set.
* Shallow copies instance before it is added. Increases the
* size of the dataset if it is not large enough. Does not
* check if the instance is compatible with the dataset.
*
* @param instance the instance to be added
*/
public final void add(Instance instance) {
Instance newInstance = (Instance)instance.copy();
newInstance.setDataset(this);
m_Instances.addElement(newInstance);
}
/**
* Adds one instance to the end of the set, with given weight.
* Shallow copies instance before it is added. Increases the size of
* the dataset if it is not large enough. Does not check if the
* instance is compatible with the dataset.
*
* @param instance the instance to be added */
public final void add(Instance instance, double weight) {
Instance newInstance = (Instance)instance.copy();
newInstance.setDataset(this);
newInstance.setWeight(weight);
m_Instances.addElement(newInstance);
}
/**
* Returns an attribute.
*
* @param index the attribute's index
* @return the attribute at the given position
*/
public final Attribute attribute(int index) {
return (Attribute) m_Attributes.elementAt(index);
}
/**
* Returns an attribute given its name. If there is more than
* one attribute with the same name, it returns the first one.
* Returns null if the attribute can't be found.
*
* @param name the attribute's name
* @return the attribute with the given name, null if the
* attribute can't be found
*/
public final Attribute attribute(String name) {
for (int i = 0; i < numAttributes(); i++) {
if (attribute(i).name().equals(name)) {
return attribute(i);
}
}
return null;
}
/**
* Checks for string attributes in the dataset
*
* @return true if string attributes are present, false otherwise
*/
public boolean checkForStringAttributes() {
int i = 0;
while (i < m_Attributes.size()) {
if (attribute(i++).isString()) {
return true;
}
}
return false;
}
//=============================BEGIN EDIT mbilenko==========================
/**
* Checks for nominal attributes in the dataset
*
* @return true if nominal attributes are present, false otherwise. Class attribute
* is not checked (it may be nominal).
*
*/
public boolean checkForNominalAttributes() {
int i = 0;
while (i < m_Attributes.size()) {
if (i != m_ClassIndex && attribute(i).isNominal()) {
return true;
}
i++;
}
return false;
}
//=============================END EDIT mbilenko==========================
/**
* Checks if the given instance is compatible
* with this dataset. Only looks at the size of
* the instance and the ranges of the values for
* nominal and string attributes.
*
* @return true if the instance is compatible with the dataset
*/
public final boolean checkInstance(Instance instance) {
if (instance.numAttributes() != numAttributes()) {
return false;
}
for (int i = 0; i < numAttributes(); i++) {
if (instance.isMissing(i)) {
continue;
} else if (attribute(i).isNominal() ||
attribute(i).isString()) {
if (!(Utils.eq(instance.value(i),
(double)(int)instance.value(i)))) {
return false;
} else if (Utils.sm(instance.value(i), 0) ||
Utils.gr(instance.value(i),
attribute(i).numValues())) {
return false;
}
}
}
return true;
}
/**
* Returns the class attribute.
*
* @return the class attribute
* @exception UnassignedClassException if the class is not set
*/
public final Attribute classAttribute() {
if (m_ClassIndex < 0) {
throw new UnassignedClassException("Class index is negative (not set)!");
}
return attribute(m_ClassIndex);
}
/**
* Returns the class attribute's index. Returns negative number
* if it's undefined.
*
* @return the class index as an integer
*/
public final int classIndex() {
return m_ClassIndex;
}
/**
* Compactifies the set of instances. Decreases the capacity of
* the set so that it matches the number of instances in the set.
*/
public final void compactify() {
m_Instances.trimToSize();
}
/**
* Removes all instances from the set.
*/
public final void delete() {
m_Instances = new FastVector();
}
/**
* Removes an instance at the given position from the set.
*
* @param index the instance's position
*/
public final void delete(int index) {
m_Instances.removeElementAt(index);
}
/**
* Deletes an attribute at the given position
* (0 to numAttributes() - 1). A deep copy of the attribute
* information is performed before the attribute is deleted.
*
* @param pos the attribute's position
* @exception IllegalArgumentException if the given index is out of range or the
* class attribute is being deleted
*/
public void deleteAttributeAt(int position) {
if ((position < 0) || (position >= m_Attributes.size())) {
throw new IllegalArgumentException("Index out of range");
}
if (position == m_ClassIndex) {
throw new IllegalArgumentException("Can't delete class attribute");
}
freshAttributeInfo();
if (m_ClassIndex > position) {
m_ClassIndex--;
}
m_Attributes.removeElementAt(position);
for (int i = position; i < m_Attributes.size(); i++) {
Attribute current = (Attribute)m_Attributes.elementAt(i);
current.setIndex(current.index() - 1);
}
for (int i = 0; i < numInstances(); i++) {
instance(i).forceDeleteAttributeAt(position);
}
}
//=============================BEGIN EDIT sugato==========================
/**
* Deletes attribute at position classIndex
*
* @author Sugato Basu
*/
public void deleteClassAttribute() {
freshAttributeInfo();
m_Attributes.removeElementAt(m_ClassIndex);
for (int i = m_ClassIndex; i < m_Attributes.size(); i++) {
Attribute current = (Attribute)m_Attributes.elementAt(i);
current.setIndex(current.index() - 1);
}
for (int i = 0; i < numInstances(); i++) {
instance(i).forceDeleteAttributeAt(m_ClassIndex);
}
m_ClassIndex = -1;
}
//=============================END EDIT sugato==========================
/**
* Deletes all string attributes in the dataset. A deep copy of the attribute
* information is performed before an attribute is deleted.
*
* @exception IllegalArgumentException if string attribute couldn't be
* successfully deleted (probably because it is the class attribute).
*/
public void deleteStringAttributes() {
int i = 0;
while (i < m_Attributes.size()) {
if (attribute(i).isString()) {
deleteAttributeAt(i);
} else {
i++;
}
}
}
/**
* Removes all instances with missing values for a particular
* attribute from the dataset.
*
* @param attIndex the attribute's index
*/
public final void deleteWithMissing(int attIndex) {
FastVector newInstances = new FastVector(numInstances());
for (int i = 0; i < numInstances(); i++) {
if (!instance(i).isMissing(attIndex)) {
newInstances.addElement(instance(i));
}
}
m_Instances = newInstances;
}
/**
* Removes all instances with missing values for a particular
* attribute from the dataset.
*
* @param att the attribute
*/
public final void deleteWithMissing(Attribute att) {
deleteWithMissing(att.index());
}
/**
* Removes all instances with a missing class value
* from the dataset.
*
* @exception UnassignedClassException if class is not set
*/
public final void deleteWithMissingClass() {
if (m_ClassIndex < 0) {
throw new UnassignedClassException("Class index is negative (not set)!");
}
deleteWithMissing(m_ClassIndex);
}
/**
* Returns an enumeration of all the attributes.
* @return enumeration of all the attributes.
*/
public Enumeration enumerateAttributes() {
return m_Attributes.elements(m_ClassIndex);
}
/**
* Returns an enumeration of all instances in the dataset.
* @return enumeration of all instances in the dataset
*/
public final Enumeration enumerateInstances() {
return m_Instances.elements();
}
/**
* Checks if two headers are equivalent.
*
* @param dataset another dataset
* @return true if the header of the given dataset is equivalent
* to this header
*/
public final boolean equalHeaders(Instances dataset){
// Check class and all attributes
if (m_ClassIndex != dataset.m_ClassIndex) {
return false;
}
if (m_Attributes.size() != dataset.m_Attributes.size()) {
return false;
}
for (int i = 0; i < m_Attributes.size(); i++) {
if (!(attribute(i).equals(dataset.attribute(i)))) {
return false;
}
}
return true;
}
/**
* Returns the first instance in the set.
*
* @return the first instance in the set
*/
public final Instance firstInstance() {
return (Instance)m_Instances.firstElement();
}
/**
* Inserts an attribute at the given position (0 to
* numAttributes()) and sets all values to be missing.
* Shallow copies the attribute before it is inserted, and performs
* a deep copy of the existing attribute information.
* @param att the attribute to be inserted
* @param pos the attribute's position
* @exception IllegalArgumentException if the given index is out of range
*/
public void insertAttributeAt(Attribute att, int position) {
if((position<0)||(position>m_Attributes.size()))throw new IllegalArgumentException("Index out of range");
att = (Attribute)att.copy();
freshAttributeInfo();
att.setIndex(position);
m_Attributes.insertElementAt(att, position);
for (int i = position + 1; i < m_Attributes.size(); i++) {
Attribute current = (Attribute)m_Attributes.elementAt(i);
current.setIndex(current.index() + 1);
}
for (int i = 0; i < numInstances(); i++) {
instance(i).forceInsertAttributeAt(position);
}
if (m_ClassIndex >= position) {
m_ClassIndex++;
}
}
public void appendAttribute(Attribute att){
insertAttributeAt(att,numAttributes());
}
/**
* Returns the instance at the given position.
* @param index the instance's index
* @return the instance at the given position
*/
public final Instance instance(int index) {
return (Instance)m_Instances.elementAt(index);
}
/**
* Returns the last instance in the set.
*
* @return the last instance in the set
*/
public final Instance lastInstance() {
return (Instance)m_Instances.lastElement();
}
/**
* Returns the mean (mode) for a numeric (nominal) attribute as
* a floating-point value. Returns 0 if the attribute is neither nominal nor
* numeric. If all values are missing it returns zero.
*
* @param att(Index) the attribute('s index)
* @return the mean or the mode
*/
public final double mean(int attIndex){
double found=0,result=0;
for(int i=0;i<numInstances();i++)if(!instance(i).isMissing(attIndex)){
found+=instance(i).weight();
result+=instance(i).weight()*instance(i).value(attIndex);
}
if(found!=0)result/=found;
return result;
}
public final double mean(Attribute att){
return mean(att.index());
}
public final Instance mean(){
double [] vals=new double[numAttributes()];
for(int i=0,ii=numAttributes();i<ii;i++){
vals[i]=mean(i);
}
return new Instance(numInstances(),vals);
}
public final double mode(int attIndex){
int [] counts=new int[attribute(attIndex).numValues()];
for(int i=0;i<numInstances();i++)if(!instance(i).isMissing(attIndex)){
counts[(int)instance(i).value(attIndex)]+=instance(i).weight();
}
return (double)Utils.maxIndex(counts);
}
public final double mode(Attribute att){
return mode(att.index());
}
public final Instance mode(){
double [] vals=new double[numAttributes()];
for(int i=0,ii=numAttributes();i<ii;i++){
vals[i]=mode(i);
}
return new Instance(numInstances(),vals);
}
public final double meanOrMode(int attIndex) {
if (attribute(attIndex).isNumeric()) {
return mean(attIndex);
} else if (attribute(attIndex).isNominal()) {
return mode(attIndex);
} else {
return 0;
}
}
public final double meanOrMode(Attribute att) {
return meanOrMode(att.index());
}
public final Instance meanOrMode(){
double [] vals=new double[numAttributes()];
for(int i=0,ii=numAttributes();i<ii;i++){
vals[i]=meanOrMode(i);
}
return new Instance(numInstances(),vals);
}
/**
* Returns the number of attributes.
*
* @return the number of attributes as an integer
*/
public final int numAttributes() {
return m_Attributes.size();
}
/**
* Returns the number of class labels.
*
* @return the number of class labels as an integer if the class
* attribute is nominal, 1 otherwise.
* @exception UnassignedClassException if the class is not set
*/
public final int numClasses() {
if (m_ClassIndex < 0) {
throw new UnassignedClassException("Class index is negative (not set)!");
}
if (!classAttribute().isNominal()) {
return 1;
} else {
return classAttribute().numValues();
}
}
public final String[] classes(){
return classAttribute().values();
}
public final double[] allClasses(){
double[] classes=new double[numInstances()];
for(int i=0;i<classes.length;i++)classes[i]=instance(i).classValue();
return classes;
}
/**
* Returns the number of distinct values of a given attribute.
* Returns the number of instances if the attribute is a
* string attribute. The value 'missing' is not counted.
*
* @param attIndex the attribute
* @return the number of distinct values of a given attribute
*/
public final int numDistinctValues(int attIndex) {
if (attribute(attIndex).isNumeric()) {
double [] attVals = attributeToDoubleArray(attIndex);
int [] sorted = Utils.sort(attVals);
double prev = 0;
int counter = 0;
for (int i = 0; i < sorted.length; i++) {
Instance current = instance(sorted[i]);
if (current.isMissing(attIndex)) {
break;
}
if ((i == 0) ||
Utils.gr(current.value(attIndex), prev)) {
prev = current.value(attIndex);
counter++;
}
}
return counter;
} else {
return attribute(attIndex).numValues();
}
}
/**
* Returns the number of distinct values of a given attribute.
* Returns the number of instances if the attribute is a
* string attribute. The value 'missing' is not counted.
* @param att the attribute
* @return the number of distinct values of a given attribute
*/
public final int numDistinctValues(Attribute att) {
return numDistinctValues(att.index());
}
/**
* Returns the number of instances in the dataset.
* @return the number of instances in the dataset as an integer
*/
public final int numInstances() {
return m_Instances.size();
}
public final int numInstancesWithClass(double value){
int c=0;
for(int i=0,ii=numInstances();i<ii;i++)if(instance(i).classValue()==value)c++;
return c;
}
public final int numInstancesWithClass(String string){
return numInstancesWithClass(classAttribute().index(string));
}
public final int [] indicesWithClass(double value){
int [] indices=new int[numInstancesWithClass(value)];
for(int i=0,ii=numInstances(),c=0;i<ii;i++)if(instance(i).classValue()==value)indices[c++]=i;
return indices;
}
public final int [] indicesWithClass(String string){
return indicesWithClass(classAttribute().index(string));
}
/**
* Shuffles the instances in the set so that they are ordered randomly.
* @param random a random number generator
*/
public final void randomize(Random random){
for(int j=numInstances()-1;j>0;j--)swap(j,random.nextInt(j+1));
}
/**
* Reads a single instance from the reader and appends it
* to the dataset. Automatically expands the dataset if it
* is not large enough to hold the instance. This method does
* not check for carriage return at the end of the line.
* @param reader the reader
* @return false if end of file has been reached
* @exception IOException if the information is not read successfully
*/
public final boolean readInstance(Reader reader)throws IOException{
StreamTokenizer tokenizer = new StreamTokenizer(reader);
initTokenizer(tokenizer);
return getInstance(tokenizer, false);
}
/**
* Returns the relation's name.
* @return the relation's name as a string
*/
public final String relationName() {
return m_RelationName;
}
/**
* Renames an attribute. This change only affects this dataset.
* @param att the attribute's index
* @param name the new name
*/
public final void renameAttribute(int att, String name) {
Attribute newAtt = attribute(att).copy(name);
FastVector newVec = new FastVector(numAttributes());
for(int i=0;i<numAttributes();i++)if(i==att)newVec.addElement(newAtt);else newVec.addElement(attribute(i));
m_Attributes = newVec;
}
/**
* Renames an attribute. This change only affects this
* dataset.
*
* @param att the attribute
* @param name the new name
*/
public final void renameAttribute(Attribute att, String name) {
renameAttribute(att.index(), name);
}
/**
* Renames the value of a nominal (or string) attribute value. This
* change only affects this dataset.
*
* @param att the attribute's index
* @param val the value's index
* @param name the new name
*/
public final void renameAttributeValue(int att, int val, String name) {
Attribute newAtt = (Attribute)attribute(att).copy();
FastVector newVec = new FastVector(numAttributes());
newAtt.setValue(val, name);
for (int i = 0; i < numAttributes(); i++) {
if (i == att) {
newVec.addElement(newAtt);
} else {
newVec.addElement(attribute(i));
}
}
m_Attributes = newVec;
}
/**
* Renames the value of a nominal (or string) attribute value. This
* change only affects this dataset.
*
* @param att the attribute
* @param val the value
* @param name the new name
*/
public final void renameAttributeValue(Attribute att, String val,
String name) {
int v = att.indexOfValue(val);
if (v == -1) throw new IllegalArgumentException(val + " not found");
renameAttributeValue(att.index(), v, name);
}
/**
* Creates a new dataset of the same size using random sampling
* with replacement.
*
* @param random a random number generator
* @return the new dataset
*/
public final Instances resample(Random random) {
Instances newData = new Instances(this, numInstances());
while (newData.numInstances() < numInstances()) {
newData.add(instance(random.nextInt(numInstances())));
}
return newData;
}
/**
* Creates a new dataset of the same size using random sampling
* with replacement according to the current instance weights. The
* weights of the instances in the new dataset are set to one.
*
* @param random a random number generator
* @return the new dataset
*/
public final Instances resampleWithWeights(Random random) {
double [] weights = new double[numInstances()];
for (int i = 0; i < weights.length; i++) {
weights[i] = instance(i).weight();
}
return resampleWithWeights(random, weights);
}
/**
* Creates a new dataset of the same size using random sampling
* with replacement according to the given weight vector. The
* weights of the instances in the new dataset are set to one.
* The length of the weight vector has to be the same as the
* number of instances in the dataset, and all weights have to
* be positive.
*
* @param random a random number generator
* @param weights the weight vector
* @return the new dataset
* @exception IllegalArgumentException if the weights array is of the wrong
* length or contains negative weights.
*/
public final Instances resampleWithWeights(Random random,
double[] weights) {
if (weights.length != numInstances()) {
throw new IllegalArgumentException("weights.length != numInstances.");
}
Instances newData = new Instances(this, numInstances());
double[] probabilities = new double[numInstances()];
double sumProbs = 0, sumOfWeights = Utils.sum(weights);
for (int i = 0; i < numInstances(); i++) {
sumProbs += random.nextDouble();
probabilities[i] = sumProbs;
}
Utils.normalize(probabilities, sumProbs / sumOfWeights);
// Make sure that rounding errors don't mess things up
probabilities[numInstances() - 1] = sumOfWeights;
int k = 0; int l = 0;
sumProbs = 0;
while ((k < numInstances() && (l < numInstances()))) {
if (weights[l] < 0) {
throw new IllegalArgumentException("Weights have to be positive.");
}
sumProbs += weights[l];
while ((k < numInstances()) &&
(probabilities[k] <= sumProbs)) {
newData.add(instance(l));
newData.instance(k).setWeight(1);
k++;
}
l++;
}
return newData;
}
/**
* Sets the class attribute.
*
* @param att attribute to be the class
*/
public final void setClass(Attribute att) {
m_ClassIndex = att.index();
}
/**
* Sets the class index of the set.
* If the class index is negative there is assumed to be no class.
* @param classIndex the new class index
* @exception IllegalArgumentException if the class index is too big or < 0
*/
public final void setClassIndex(int classIndex) {
if (classIndex >= numAttributes()) {
throw new IllegalArgumentException("Invalid class index: " + classIndex);
}
if(classIndex<0)classIndex+=numAttributes();
m_ClassIndex = classIndex;
}
public final void setClassIndex(Integer classIndex){
setClassIndex(classIndex.intValue());
}
public final void setClassIndex(Object classIndex){
setClassIndex((Integer)classIndex);
}
public final void setRelationName(String newName) {
m_RelationName = newName;
}
/**
* Sorts the instances based on an attribute. For numeric attributes,
* instances are sorted in ascending order. For nominal attributes,
* instances are sorted based on the attribute label ordering
* specified in the header. Instances with missing values for the
* attribute are placed at the end of the dataset.
*
* @param attIndex the attribute's index
*/
public final void sort(int attIndex) {
int i,j;
// move all instances with missing values to end
j = numInstances() - 1;
i = 0;
while (i <= j) {
if (instance(j).isMissing(attIndex)) {
j--;
} else {
if (instance(i).isMissing(attIndex)) {
swap(i,j);
j--;
}
i++;
}
}
quickSort(attIndex, 0, j);
}
/**
* Sorts the instances based on an attribute. For numeric attributes,
* instances are sorted into ascending order. For nominal attributes,
* instances are sorted based on the attribute label ordering
* specified in the header. Instances with missing values for the
* attribute are placed at the end of the dataset.
*
* @param att the attribute
*/
public final void sort(Attribute att) {
sort(att.index());
}
/**
* Stratifies a set of instances according to its class values
* if the class attribute is nominal (so that afterwards a
* stratified cross-validation can be performed).
*
* @param numFolds the number of folds in the cross-validation
* @exception UnassignedClassException if the class is not set
*/
public final void stratify(int numFolds) {
if (numFolds <= 0) {
throw new IllegalArgumentException("Number of folds must be greater than 1");
}
if (m_ClassIndex < 0) {
throw new UnassignedClassException("Class index is negative (not set)!");
}
if (classAttribute().isNominal()) {
// sort by class
int index = 1;
while (index < numInstances()) {
Instance instance1 = instance(index - 1);
for (int j = index; j < numInstances(); j++) {
Instance instance2 = instance(j);
if ((instance1.classValue() == instance2.classValue()) ||
(instance1.classIsMissing() &&
instance2.classIsMissing())) {
swap(index,j);
index++;
}
}
index++;
}
stratStep(numFolds);
}
}
/**
* Computes the sum of all the instances' weights.
*
* @return the sum of all the instances' weights as a double
*/
public final double sumOfWeights() {
double sum = 0;
for (int i = 0; i < numInstances(); i++) {
sum += instance(i).weight();
}
return sum;
}
/**
* Creates the test set for one fold of a cross-validation on
* the dataset.
*
* @param numFolds the number of folds in the cross-validation. Must
* be greater than 1.
* @param numFold 0 for the first fold, 1 for the second, ...
* @return the test set as a set of weighted instances
* @exception IllegalArgumentException if the number of folds is less than 2
* or greater than the number of instances.
*/
public Instances testCV(int numFolds, int numFold) {
int numInstForFold, first, offset;
Instances test;
if (numFolds < 2) {
throw new IllegalArgumentException("Number of folds must be at least 2!");
}
if (numFolds > numInstances()) {
throw new IllegalArgumentException("Can't have more folds than instances!");
}
numInstForFold = numInstances() / numFolds;
if (numFold < numInstances() % numFolds){
numInstForFold++;
offset = numFold;
}else
offset = numInstances() % numFolds;
test = new Instances(this, numInstForFold);
first = numFold * (numInstances() / numFolds) + offset;
copyInstances(first, test, numInstForFold);
return test;
}
/**
* Returns the dataset as a string in ARFF format. Strings
* are quoted if they contain whitespace characters, or if they
* are a question mark.
*
* @return the dataset in ARFF format as a string
*/
public final String toString() {
StringBuffer text = new StringBuffer();
text.append("@relation " + Utils.quote(m_RelationName) + "\n\n");
for (int i = 0; i < numAttributes(); i++) {
text.append(attribute(i) + "\n");
}
text.append("\n@data\n");
for (int i = 0; i < numInstances(); i++) {
text.append(instance(i));
if (i < numInstances() - 1) {
text.append('\n');
}
}
return text.toString();
}
/**
* Creates the training set for one fold of a cross-validation
* on the dataset.
*
* @param numFolds the number of folds in the cross-validation. Must
* be greater than 1.
* @param numFold 0 for the first fold, 1 for the second, ...
* @return the training set as a set of weighted
* instances
* @exception IllegalArgumentException if the number of folds is less than 2
* or greater than the number of instances.
*/
public Instances trainCV(int numFolds, int numFold) {
int numInstForFold, first, offset;
Instances train;
if (numFolds < 2) {
throw new IllegalArgumentException("Number of folds must be at least 2!");
}
if (numFolds > numInstances()) {
throw new IllegalArgumentException("Can't have more folds than instances!");
}
numInstForFold = numInstances() / numFolds;
if (numFold < numInstances() % numFolds) {
numInstForFold++;
offset = numFold;
}else
offset = numInstances() % numFolds;
train = new Instances(this, numInstances() - numInstForFold);
first = numFold * (numInstances() / numFolds) + offset;
copyInstances(0, train, first);
copyInstances(first + numInstForFold, train,
numInstances() - first - numInstForFold);
return train;
}
/**
* Computes the variance for a numeric attribute.
*
* @param attIndex the numeric attribute
* @return the variance if the attribute is numeric
* @exception IllegalArgumentException if the attribute is not numeric
*/
public final double variance(int attIndex) {
double sum = 0, sumSquared = 0, sumOfWeights = 0;
if (!attribute(attIndex).isNumeric()) {
throw new IllegalArgumentException("Can't compute variance because attribute is " +
"not numeric!");
}
for (int i = 0; i < numInstances(); i++) {
if (!instance(i).isMissing(attIndex)) {
sum += instance(i).weight() *
instance(i).value(attIndex);
sumSquared += instance(i).weight() *
instance(i).value(attIndex) *
instance(i).value(attIndex);
sumOfWeights += instance(i).weight();
}
}
if (Utils.smOrEq(sumOfWeights, 1)) {
return 0;
}
double result = (sumSquared - (sum * sum / sumOfWeights)) /
(sumOfWeights - 1);
// We don't like negative variance
if (result < 0) {
return 0;
} else {
return result;
}
}
/**
* Computes the variance for a numeric attribute.
*
* @param att the numeric attribute
* @return the variance if the attribute is numeric
* @exception IllegalArgumentException if the attribute is not numeric
*/
public final double variance(Attribute att) {
return variance(att.index());
}
/**
* Calculates summary statistics on the values that appear in this
* set of instances for a specified attribute.
*
* @param index the index of the attribute to summarize.
* @return an AttributeStats object with it's fields calculated.
*/
public AttributeStats attributeStats(int index) {
AttributeStats result = new AttributeStats();
if (attribute(index).isNominal()) {
result.nominalCounts = new int [attribute(index).numValues()];
}
if (attribute(index).isNumeric()) {
result.numericStats = new weka.experiment.Stats();
}
result.totalCount = numInstances();
double [] attVals = attributeToDoubleArray(index);
int [] sorted = Utils.sort(attVals);
int currentCount = 0;
double prev = Instance.missingValue();
for (int j = 0; j < numInstances(); j++) {
Instance current = instance(sorted[j]);
if (current.isMissing(index)) {
result.missingCount = numInstances() - j;
break;
}
if (Utils.eq(current.value(index), prev)) {
currentCount++;
} else {
result.addDistinct(prev, currentCount);
currentCount = 1;
prev = current.value(index);
}
}
result.addDistinct(prev, currentCount);
result.distinctCount--; // So we don't count "missing" as a value
return result;
}
/**
* Gets the value of all instances in this dataset for a particular
* attribute. Useful in conjunction with Utils.sort to allow iterating
* through the dataset in sorted order for some attribute.
*
* @param index the index of the attribute.
* @return an array containing the value of the desired attribute for
* each instance in the dataset.
*/
public double [] attributeToDoubleArray(int index) {
double [] result = new double[numInstances()];
for (int i = 0; i < result.length; i++) {
result[i] = instance(i).value(index);
}
return result;
}
/**
* Generates a string summarizing the set of instances. Gives a breakdown
* for each attribute indicating the number of missing/discrete/unique
* values and other information.
*
* @return a string summarizing the dataset
*/
public String toSummaryString() {
StringBuffer result = new StringBuffer();
result.append("Relation Name: ").append(relationName()).append('\n');
result.append("Num Instances: ").append(numInstances()).append('\n');
result.append("Num Attributes: ").append(numAttributes()).append('\n');
result.append('\n');
result.append(Utils.padLeft("", 5)).append(Utils.padRight("Name", 25));
result.append(Utils.padLeft("Type", 5)).append(Utils.padLeft("Nom", 5));
result.append(Utils.padLeft("Int", 5)).append(Utils.padLeft("Real", 5));
result.append(Utils.padLeft("Missing", 12));
result.append(Utils.padLeft("Unique", 12));
result.append(Utils.padLeft("Dist", 6)).append('\n');
for (int i = 0; i < numAttributes(); i++) {
Attribute a = attribute(i);
AttributeStats as = attributeStats(i);
result.append(Utils.padLeft("" + (i + 1), 4)).append(' ');
result.append(Utils.padRight(a.name(), 25)).append(' ');
long percent;
switch (a.type()) {
case Attribute.NOMINAL:
result.append(Utils.padLeft("Nom", 4)).append(' ');
percent = Math.round(100.0 * as.intCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
result.append(Utils.padLeft("" + 0, 3)).append("% ");
percent = Math.round(100.0 * as.realCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
break;
case Attribute.NUMERIC:
result.append(Utils.padLeft("Num", 4)).append(' ');
result.append(Utils.padLeft("" + 0, 3)).append("% ");
percent = Math.round(100.0 * as.intCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
percent = Math.round(100.0 * as.realCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
break;
case Attribute.DATE:
result.append(Utils.padLeft("Dat", 4)).append(' ');
result.append(Utils.padLeft("" + 0, 3)).append("% ");
percent = Math.round(100.0 * as.intCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
percent = Math.round(100.0 * as.realCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
break;
case Attribute.STRING:
result.append(Utils.padLeft("Str", 4)).append(' ');
percent = Math.round(100.0 * as.intCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
result.append(Utils.padLeft("" + 0, 3)).append("% ");
percent = Math.round(100.0 * as.realCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
break;
default:
result.append(Utils.padLeft("???", 4)).append(' ');
result.append(Utils.padLeft("" + 0, 3)).append("% ");
percent = Math.round(100.0 * as.intCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
percent = Math.round(100.0 * as.realCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
break;
}
result.append(Utils.padLeft("" + as.missingCount, 5)).append(" /");
percent = Math.round(100.0 * as.missingCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
result.append(Utils.padLeft("" + as.uniqueCount, 5)).append(" /");
percent = Math.round(100.0 * as.uniqueCount / as.totalCount);
result.append(Utils.padLeft("" + percent, 3)).append("% ");
result.append(Utils.padLeft("" + as.distinctCount, 5)).append(' ');
result.append('\n');
}
return result.toString();
}
/**
* Reads a single instance using the tokenizer and appends it
* to the dataset. Automatically expands the dataset if it
* is not large enough to hold the instance.
*
* @param tokenizer the tokenizer to be used
* @param flag if method should test for carriage return after
* each instance
* @return false if end of file has been reached
* @exception IOException if the information is not read
* successfully
*/
protected boolean getInstance(StreamTokenizer tokenizer,
boolean flag)
throws IOException {
// Check if any attributes have been declared.
if (m_Attributes.size() == 0) {
errms(tokenizer,"no header information available");
}
// Check if end of file reached.
getFirstToken(tokenizer);
if (tokenizer.ttype == StreamTokenizer.TT_EOF) {
return false;
}
// Parse instance
if (tokenizer.ttype == '{') {
return getInstanceSparse(tokenizer, flag);
} else {
return getInstanceFull(tokenizer, flag);
}
}
/**
* Reads a single instance using the tokenizer and appends it
* to the dataset. Automatically expands the dataset if it
* is not large enough to hold the instance.
*
* @param tokenizer the tokenizer to be used
* @param flag if method should test for carriage return after
* each instance
* @return false if end of file has been reached
* @exception IOException if the information is not read
* successfully
*/
protected boolean getInstanceSparse(StreamTokenizer tokenizer,
boolean flag)
throws IOException {
int valIndex, numValues = 0, maxIndex = -1;
// Get values
do {
// Get index
getIndex(tokenizer);
if (tokenizer.ttype == '}') {
break;
}
// Is index valid?
try{
m_IndicesBuffer[numValues] = Integer.valueOf(tokenizer.sval).intValue();
} catch (NumberFormatException e) {
errms(tokenizer,"index number expected");
}
if (m_IndicesBuffer[numValues] <= maxIndex) {
errms(tokenizer,"indices have to be ordered");
}
if ((m_IndicesBuffer[numValues] < 0) ||
(m_IndicesBuffer[numValues] >= numAttributes())) {
errms(tokenizer,"index out of bounds");
}
maxIndex = m_IndicesBuffer[numValues];
// Get value;
getNextToken(tokenizer);
// Check if value is missing.
if (tokenizer.ttype == '?') {
m_ValueBuffer[numValues] = Instance.missingValue();
} else {
// Check if token is valid.
if (tokenizer.ttype != StreamTokenizer.TT_WORD) {
errms(tokenizer,"not a valid value");
}
switch (attribute(m_IndicesBuffer[numValues]).type()) {
case Attribute.NOMINAL:
// Check if value appears in header.
valIndex =
attribute(m_IndicesBuffer[numValues]).indexOfValue(tokenizer.sval);
if (valIndex == -1) {
errms(tokenizer,"nominal value not declared in header");
}
m_ValueBuffer[numValues] = (double)valIndex;
break;
case Attribute.NUMERIC:
// Check if value is really a number.
try{
m_ValueBuffer[numValues] = Double.valueOf(tokenizer.sval).
doubleValue();
} catch (NumberFormatException e) {
errms(tokenizer,"number expected");
}
break;
case Attribute.STRING:
m_ValueBuffer[numValues] =
attribute(m_IndicesBuffer[numValues]).addStringValue(tokenizer.sval);
break;
case Attribute.DATE:
try {
m_ValueBuffer[numValues] =
attribute(m_IndicesBuffer[numValues]).parseDate(tokenizer.sval);
} catch (ParseException e) {
errms(tokenizer,"unparseable date: " + tokenizer.sval);
}
break;
default:
errms(tokenizer,"unknown attribute type in column " + m_IndicesBuffer[numValues]);
}
}
numValues++;
} while (true);
if (flag) {
getLastToken(tokenizer,true);
}
// Add instance to dataset
double[] tempValues = new double[numValues];
int[] tempIndices = new int[numValues];
System.arraycopy(m_ValueBuffer, 0, tempValues, 0, numValues);
System.arraycopy(m_IndicesBuffer, 0, tempIndices, 0, numValues);
add(new SparseInstance(1, tempValues, tempIndices, numAttributes()));
return true;
}
/**
* Reads a single instance using the tokenizer and appends it
* to the dataset. Automatically expands the dataset if it
* is not large enough to hold the instance.
*
* @param tokenizer the tokenizer to be used
* @param flag if method should test for carriage return after
* each instance
* @return false if end of file has been reached
* @exception IOException if the information is not read
* successfully
*/
protected boolean getInstanceFull(StreamTokenizer tokenizer,
boolean flag)
throws IOException {
double[] instance = new double[numAttributes()];
int index;
// Get values for all attributes.
for (int i = 0; i < numAttributes(); i++){
// Get next token
if (i > 0) {
getNextToken(tokenizer);
}
// Check if value is missing.
if (tokenizer.ttype == '?') {
instance[i] = Instance.missingValue();
} else {
// Check if token is valid.
if (tokenizer.ttype != StreamTokenizer.TT_WORD) {
errms(tokenizer,"not a valid value");
}
switch (attribute(i).type()) {
case Attribute.NOMINAL:
// Check if value appears in header.
index = attribute(i).indexOfValue(tokenizer.sval);
if (index == -1) {
errms(tokenizer,"nominal value not declared in header");
}
instance[i] = (double)index;
break;
case Attribute.NUMERIC:
// Check if value is really a number.
try{
instance[i] = Double.valueOf(tokenizer.sval).
doubleValue();
} catch (NumberFormatException e) {
errms(tokenizer,"number expected");
}
break;
case Attribute.STRING:
instance[i] = attribute(i).addStringValue(tokenizer.sval);
break;
case Attribute.DATE:
try {
instance[i] = attribute(i).parseDate(tokenizer.sval);
} catch (ParseException e) {
errms(tokenizer,"unparseable date: " + tokenizer.sval);
}
break;
default:
errms(tokenizer,"unknown attribute type in column " + i);
}
}
}
if (flag) {
getLastToken(tokenizer,true);
}
// Add instance to dataset
add(new Instance(1, instance));
return true;
}
/**
* Reads and stores header of an ARFF file.
*
* @param tokenizer the stream tokenizer
* @exception IOException if the information is not read
* successfully
*/
protected void readHeader(StreamTokenizer tokenizer)
throws IOException {
String attributeName;
FastVector attributeValues;
int i;
// Get name of relation.
getFirstToken(tokenizer);
if (tokenizer.ttype == StreamTokenizer.TT_EOF) {
errms(tokenizer,"premature end of file");
}
if (tokenizer.sval.equalsIgnoreCase("@relation")){
getNextToken(tokenizer);
m_RelationName = tokenizer.sval;
getLastToken(tokenizer,false);
} else {
errms(tokenizer,"keyword @relation expected");
}
// Create vectors to hold information temporarily.
m_Attributes = new FastVector();
// Get attribute declarations.
getFirstToken(tokenizer);
if (tokenizer.ttype == StreamTokenizer.TT_EOF) {
errms(tokenizer,"premature end of file");
}
while (tokenizer.sval.equalsIgnoreCase("@attribute")) {
// Get attribute name.
getNextToken(tokenizer);
attributeName = tokenizer.sval;
getNextToken(tokenizer);
// Check if attribute is nominal.
if (tokenizer.ttype == StreamTokenizer.TT_WORD) {
// Attribute is real, integer, or string.
if (tokenizer.sval.equalsIgnoreCase("real") ||
tokenizer.sval.equalsIgnoreCase("integer") ||
tokenizer.sval.equalsIgnoreCase("numeric")) {
m_Attributes.addElement(new Attribute(attributeName,
numAttributes()));
readTillEOL(tokenizer);
} else if (tokenizer.sval.equalsIgnoreCase("string")) {
m_Attributes.
addElement(new Attribute(attributeName, (FastVector)null,
numAttributes()));
readTillEOL(tokenizer);
} else if (tokenizer.sval.equalsIgnoreCase("date")) {
String format = null;
if (tokenizer.nextToken() != StreamTokenizer.TT_EOL) {
if ((tokenizer.ttype != StreamTokenizer.TT_WORD) &&
(tokenizer.ttype != '\'') &&
(tokenizer.ttype != '\"')) {
errms(tokenizer,"not a valid date format");
}
format = tokenizer.sval;
readTillEOL(tokenizer);
} else {
tokenizer.pushBack();
}
m_Attributes.addElement(new Attribute(attributeName, format,
numAttributes()));
} else {
errms(tokenizer,"no valid attribute type or invalid "+
"enumeration");
}
} else {
// Attribute is nominal.
attributeValues = new FastVector();
tokenizer.pushBack();
// Get values for nominal attribute.
if (tokenizer.nextToken() != '{') {
errms(tokenizer,"{ expected at beginning of enumeration");
}
while (tokenizer.nextToken() != '}') {
if (tokenizer.ttype == StreamTokenizer.TT_EOL) {
errms(tokenizer,"} expected at end of enumeration");
} else {
attributeValues.addElement(tokenizer.sval);
}
}
if (attributeValues.size() == 0) {
errms(tokenizer,"no nominal values found");
}
m_Attributes.
addElement(new Attribute(attributeName, attributeValues,
numAttributes()));
}
getLastToken(tokenizer,false);
getFirstToken(tokenizer);
if (tokenizer.ttype == StreamTokenizer.TT_EOF)
errms(tokenizer,"premature end of file");
}
// Check if data part follows. We can't easily check for EOL.
if (!tokenizer.sval.equalsIgnoreCase("@data")) {
errms(tokenizer,"keyword @data expected");
}
// Check if any attributes have been declared.
if (m_Attributes.size() == 0) {
errms(tokenizer,"no attributes declared");
}
// Allocate buffers in case sparse instances have to be read
m_ValueBuffer = new double[numAttributes()];
m_IndicesBuffer = new int[numAttributes()];
}
/**
* Copies instances from one set to the end of another one.
* @param source the source of the instances
* @param from the position of the first instance to be copied
* @param dest the destination for the instances
* @param num the number of instances to be copied
*/
private void copyInstances(int from, Instances dest, int num) {
for (int i = 0; i < num; i++) {
dest.add(instance(from + i));
}
}
private void copyInstances(int [] indices,Instances dest){
for(int i=0;i<indices.length;i++)dest.add(instance(indices[i]));
}
/**
* Throws error message with line number and last token read.
*
* @param theMsg the error message to be thrown
* @param tokenizer the stream tokenizer
* @throws IOExcpetion containing the error message
*/
private void errms(StreamTokenizer tokenizer, String theMsg)
throws IOException {
throw new IOException(theMsg + ", read " + tokenizer.toString());
}
/**
* Replaces the attribute information by a clone of
* itself.
*/
private void freshAttributeInfo() {
m_Attributes = (FastVector) m_Attributes.copyElements();
}
/**
* Gets next token, skipping empty lines.
*
* @param tokenizer the stream tokenizer
* @exception IOException if reading the next token fails
*/
private void getFirstToken(StreamTokenizer tokenizer)
throws IOException {
while (tokenizer.nextToken() == StreamTokenizer.TT_EOL){};
if ((tokenizer.ttype == '\'') ||
(tokenizer.ttype == '"')) {
tokenizer.ttype = StreamTokenizer.TT_WORD;
} else if ((tokenizer.ttype == StreamTokenizer.TT_WORD) &&
(tokenizer.sval.equals("?"))){
tokenizer.ttype = '?';
}
}
/**
* Gets index, checking for a premature and of line.
*
* @param tokenizer the stream tokenizer
* @exception IOException if it finds a premature end of line
*/
private void getIndex(StreamTokenizer tokenizer) throws IOException {
if (tokenizer.nextToken() == StreamTokenizer.TT_EOL) {
errms(tokenizer,"premature end of line");
}
if (tokenizer.ttype == StreamTokenizer.TT_EOF) {
errms(tokenizer,"premature end of file");
}
}
/**
* Gets token and checks if its end of line.
*
* @param tokenizer the stream tokenizer
* @exception IOException if it doesn't find an end of line
*/
private void getLastToken(StreamTokenizer tokenizer, boolean endOfFileOk)
throws IOException {
if ((tokenizer.nextToken() != StreamTokenizer.TT_EOL) &&
((tokenizer.ttype != StreamTokenizer.TT_EOF) || !endOfFileOk)) {
errms(tokenizer,"end of line expected");
}
}
/**
* Gets next token, checking for a premature and of line.
*
* @param tokenizer the stream tokenizer
* @exception IOException if it finds a premature end of line
*/
private void getNextToken(StreamTokenizer tokenizer)
throws IOException {
if (tokenizer.nextToken() == StreamTokenizer.TT_EOL) {
errms(tokenizer,"premature end of line");
}
if (tokenizer.ttype == StreamTokenizer.TT_EOF) {
errms(tokenizer,"premature end of file");
} else if ((tokenizer.ttype == '\'') ||
(tokenizer.ttype == '"')) {
tokenizer.ttype = StreamTokenizer.TT_WORD;
} else if ((tokenizer.ttype == StreamTokenizer.TT_WORD) &&
(tokenizer.sval.equals("?"))){
tokenizer.ttype = '?';
}
}
/**
* Initializes the StreamTokenizer used for reading the ARFF file.
*
* @param tokenizer the stream tokenizer
*/
private void initTokenizer(StreamTokenizer tokenizer){
tokenizer.resetSyntax();
tokenizer.whitespaceChars(0, ' ');
tokenizer.wordChars(' '+1,'\u00FF');
tokenizer.whitespaceChars(',',',');
tokenizer.commentChar('%');
tokenizer.quoteChar('"');
tokenizer.quoteChar('\'');
tokenizer.ordinaryChar('{');
tokenizer.ordinaryChar('}');
tokenizer.eolIsSignificant(true);
}
/**
* Returns string including all instances, their weights and
* their indices in the original dataset.
*
* @return description of instance and its weight as a string
*/
private String instancesAndWeights(){
StringBuffer text = new StringBuffer();
for (int i = 0; i < numInstances(); i++) {
text.append(instance(i) + " " + instance(i).weight());
if (i < numInstances() - 1) {
text.append("\n");
}
}
return text.toString();
}
/**
* Implements quicksort.
*
* @param attIndex the attribute's index
* @param lo0 the first index of the subset to be sorted
* @param hi0 the last index of the subset to be sorted
*/
private void quickSort(int attIndex, int lo0, int hi0) {
int lo = lo0, hi = hi0;
double mid, midPlus, midMinus;
if (hi0 > lo0) {
// Arbitrarily establishing partition element as the
// midpoint of the array.
mid = instance((lo0 + hi0) / 2).value(attIndex);
midPlus = mid + 1e-6;
midMinus = mid - 1e-6;
// loop through the array until indices cross
while(lo <= hi) {
// find the first element that is greater than or equal to
// the partition element starting from the left Index.
while ((instance(lo).value(attIndex) <
midMinus) && (lo < hi0)) {
++lo;
}
// find an element that is smaller than or equal to
// the partition element starting from the right Index.
while ((instance(hi).value(attIndex) >
midPlus) && (hi > lo0)) {
--hi;
}
// if the indexes have not crossed, swap
if(lo <= hi) {
swap(lo,hi);
++lo;
--hi;
}
}
// If the right index has not reached the left side of array
// must now sort the left partition.
if(lo0 < hi) {
quickSort(attIndex,lo0,hi);
}
// If the left index has not reached the right side of array
// must now sort the right partition.
if(lo < hi0) {
quickSort(attIndex,lo,hi0);
}
}
}
/**
* Reads and skips all tokens before next end of line token.
*
* @param tokenizer the stream tokenizer
*/
private void readTillEOL(StreamTokenizer tokenizer)
throws IOException {
while (tokenizer.nextToken() != StreamTokenizer.TT_EOL) {};
tokenizer.pushBack();
}
/**
* Help function needed for stratification of set.
*
* @param numFolds the number of folds for the stratification
*/
private void stratStep (int numFolds){
FastVector newVec = new FastVector(m_Instances.capacity());
int start = 0, j;
// create stratified batch
while (newVec.size() < numInstances()) {
j = start;
while (j < numInstances()) {
newVec.addElement(instance(j));
j = j + numFolds;
}
start++;
}
m_Instances = newVec;
}
/**
* Swaps two instances in the set.
*
* @param i the first instance's index
* @param j the second instance's index
*/
private void swap(int i, int j){
m_Instances.swap(i, j);
}
/**
* Merges two sets of Instances together. The resulting set will have
* all the attributes of the first set plus all the attributes of the
* second set. The number of instances in both sets must be the same.
*
* @param first the first set of Instances
* @param second the second set of Instances
* @return the merged set of Instances
* @exception IllegalArgumentException if the datasets are not the same size
*/
public static Instances mergeInstances(Instances first, Instances second) {
if (first.numInstances() != second.numInstances()) {
throw new IllegalArgumentException("Instance sets must be of the same size");
}
// Create the vector of merged attributes
FastVector newAttributes = new FastVector();
for (int i = 0; i < first.numAttributes(); i++) {
newAttributes.addElement(first.attribute(i));
}
for (int i = 0; i < second.numAttributes(); i++) {
newAttributes.addElement(second.attribute(i));
}
// Create the set of Instances
Instances merged = new Instances(first.relationName() + '_'
+ second.relationName(),
newAttributes,
first.numInstances());
// Merge each instance
for (int i = 0; i < first.numInstances(); i++) {
merged.add(first.instance(i).mergeInstance(second.instance(i)));
}
return merged;
}
/**
* Initializes the ranges using all instances of the dataset.
* Sets m_Ranges.
* @return the ranges
*/
public double [][] initializeRanges() {
int numAtt = this.numAttributes();
double [][] ranges = new double [numAtt][3];
if (this.numInstances() <= 0) {
initializeRangesEmpty(numAtt, ranges);
return ranges;
}
else
// initialize ranges using the first instance
updateRangesFirst(this.instance(0), numAtt, ranges);
// update ranges, starting from the second
for (int i = 1; i < this.numInstances(); i++) {
updateRanges(this.instance(i), numAtt, ranges);
}
m_Ranges = ranges;
return ranges;
}
/**
* Initializes the ranges of a subset of the instances of this dataset.
* Therefore m_Ranges is not set.
* @param instList list of indexes of the subset
* @return the ranges
*/
public double [][] initializeRanges(int[] instList) {
int numAtt = this.numAttributes();
double [][] ranges = new double [numAtt][3];
if (this.numInstances() <= 0) {
initializeRangesEmpty(numAtt, ranges);
return ranges;
}
else {
// initialize ranges using the first instance
updateRangesFirst(this.instance(instList[0]), numAtt, ranges);
// update ranges, starting from the second
for (int i = 1; i < instList.length; i++) {
updateRanges(this.instance(instList[i]), numAtt, ranges);
}
}
return ranges;
}
/**
* Used to initialize the ranges.
* @param numAtt number of attributes in the model
* @param ranges low, high and width values for all attributes
*/
public void initializeRangesEmpty(int numAtt,
double[][] ranges) {
for (int j = 0; j < numAtt; j++) {
ranges[j][R_MIN] = Double.MAX_VALUE;
ranges[j][R_MAX] = Double.MIN_VALUE;
ranges[j][R_WIDTH] = Double.MIN_VALUE;
}
}
/**
* Used to initialize the ranges. For this the values of the first
* instance is used to save time.
* Sets low and high to the values of the first instance and
* width to zero.
* @param instance the new instance
* @param numAtt number of attributes in the model
* @param ranges low, high and width values for all attributes
*/
public void updateRangesFirst(Instance instance, int numAtt,
double[][] ranges) {
for (int j = 0; j < numAtt; j++) {
if (!instance.isMissing(j)) {
ranges[j][R_MIN] = instance.value(j);
ranges[j][R_MAX] = instance.value(j);
ranges[j][R_WIDTH] = 0.0;
}
else { // if value was missing
ranges[j][R_MIN] = Double.MIN_VALUE;
ranges[j][R_MAX] = Double.MAX_VALUE;
ranges[j][R_WIDTH] = Double.MAX_VALUE;
}
}
}
/**
* Updates the minimum and maximum and width values for all the attributes
* based on a new instance.
* @param instance the new instance
* @param numAtt number of attributes in the model
* @param ranges low, high and width values for all attributes
*/
private void updateRanges(Instance instance, int numAtt,
double [][] ranges) {
// updateRangesFirst must have been called on ranges
for (int j = 0; j < numAtt; j++) {
double value = instance.value(j);
if (!instance.isMissing(j)) {
if (value < ranges[j][R_MIN]) {
ranges[j][R_MIN] = value;
ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
} else {
if (instance.value(j) > ranges[j][R_MAX]) {
ranges[j][R_MAX] = value;
ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
}
}
}
}
}
/**
* prints the ranges.
* @param instance the new instance
* @param numAtt number of attributes in the model
* @param ranges low, high and width values for all attributes
*/
public static void printRanges(double [][] ranges) {
OOPSS("printRanges");
// updateRangesFirst must have been called on ranges
for (int j = 0; j < ranges.length; j++) {
OOPSS(" "+j+"-MIN "+ranges[j][R_MIN]);
OOPSS(" "+j+"-MAX "+ranges[j][R_MAX]);
OOPSS(" "+j+"-WIDTH "+ranges[j][R_WIDTH]);
}
}
/**
* Updates the ranges given a new instance.
* @param instance the new instance
* @param numAtt number of attributes in the model
* @param ranges low, high and width values for all attributes
*
public void updateRanges(Instance instance, double [][] ranges) {
int numAtt = numAttributes();
// updateRangesFirst must have been called on ranges
for (int j = 0; j < numAtt; j++) {
double value = instance.value(j);
if (!instance.isMissing(j)) {
if (value < ranges[j][R_MIN]) {
ranges[j][R_MIN] = value;
ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
} else {
if (instance.value(j) > ranges[j][R_MAX]) {
ranges[j][R_MAX] = value;
ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
}
}
}
}
}*/
/**
* Updates the ranges given a new instance.
* @param instance the new instance
* @param ranges low, high and width values for all attributes
*/
public static double [][] updateRanges(Instance instance,
double [][] ranges) {
// updateRangesFirst must have been called on ranges
for (int j = 0; j < ranges.length; j++) {
double value = instance.value(j);
if (!instance.isMissing(j)) {
if (value < ranges[j][R_MIN]) {
ranges[j][R_MIN] = value;
ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
} else {
if (instance.value(j) > ranges[j][R_MAX]) {
ranges[j][R_MAX] = value;
ranges[j][R_WIDTH] = ranges[j][R_MAX] - ranges[j][R_MIN];
}
}
}
}
return ranges;
}
/**
* Test if an instance is within the given ranges.
* @param instance the instance
* @param ranges the ranges the instance is tested to be in
* @return true if instance is within the ranges
*/
public static boolean inRanges(Instance instance, double [][] ranges) {
boolean isIn = true;
// updateRangesFirst must have been called on ranges
for (int j = 0; isIn && (j < ranges.length); j++) {
if (!instance.isMissing(j)) {
double value = instance.value(j);
isIn = value <= ranges[j][R_MAX];
if (isIn) isIn = value >= ranges[j][R_MIN];
}
}
return isIn;
}
/**
* Prints a range to standard output
* @param ranges the ranges to print
*/
public static void printRanges(Instances model,
double[][] ranges) {
System.out.println("printRanges");
for (int j = 0; j < model.numAttributes(); j++) {
System.out.print("Attribute "+ j +" MIN: " + ranges[j][R_MIN]);
System.out.print(" MAX: " + ranges[j][R_MAX]);
System.out.print(" WIDTH: " + ranges[j][R_WIDTH]);
System.out.println(" ");
}
}
/**
* Check if ranges are set.
* @return true if ranges are set
*/
public boolean rangesSet() {
return (m_Ranges != null);
}
/**
* Method to get the ranges.
* @return the ranges
*/
public double[][] getRanges() throws Exception {
if (m_Ranges == null)
throw new Exception("Ranges not yet set.");
return m_Ranges;
}
/**
* Used for debug println's.
* @param output string that is printed
*/
private void OOPS(String output) {
System.out.println(output);
}
/**
* Used for debug println's.
* @param output string that is printed
*/
private static void OOPSS(String output) {
System.out.println(output);
}
/**
* Method for testing this class.
*
* @param argv should contain one element: the name of an ARFF file
*/
public static void test(String [] argv) {
Instances instances, secondInstances, train, test, transformed, empty;
Instance instance;
Random random = new Random(2);
Reader reader;
int start, num;
double newWeight;
FastVector testAtts, testVals;
int i,j;
try{
if (argv.length > 1) {
throw (new Exception("Usage: Instances [<filename>]"));
}
// Creating set of instances from scratch
testVals = new FastVector(2);
testVals.addElement("first_value");
testVals.addElement("second_value");
testAtts = new FastVector(2);
testAtts.addElement(new Attribute("nominal_attribute", testVals));
testAtts.addElement(new Attribute("numeric_attribute"));
instances = new Instances("test_set", testAtts, 10);
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.setClassIndex(0);
System.out.println("\nSet of instances created from scratch:\n");
System.out.println(instances);
if (argv.length == 1) {
String filename = argv[0];
reader = new FileReader(filename);
// Read first five instances and print them
System.out.println("\nFirst five instances from file:\n");
instances = new Instances(reader, 1);
instances.setClassIndex(instances.numAttributes() - 1);
i = 0;
while ((i < 5) && (instances.readInstance(reader))) {
i++;
}
System.out.println(instances);
// Read all the instances in the file
reader = new FileReader(filename);
instances = new Instances(reader);
// Make the last attribute be the class
instances.setClassIndex(instances.numAttributes() - 1);
// Print header and instances.
System.out.println("\nDataset:\n");
System.out.println(instances);
System.out.println("\nClass index: "+instances.classIndex());
}
// Test basic methods based on class index.
System.out.println("\nClass name: "+instances.classAttribute().name());
System.out.println("\nClass index: "+instances.classIndex());
System.out.println("\nClass is nominal: " +
instances.classAttribute().isNominal());
System.out.println("\nClass is numeric: " +
instances.classAttribute().isNumeric());
System.out.println("\nClasses:\n");
for (i = 0; i < instances.numClasses(); i++) {
System.out.println(instances.classAttribute().value(i));
}
System.out.println("\nClass values and labels of instances:\n");
for (i = 0; i < instances.numInstances(); i++) {
Instance inst = instances.instance(i);
System.out.print(inst.classValue() + "\t");
System.out.print(inst.toString(inst.classIndex()));
if (instances.instance(i).classIsMissing()) {
System.out.println("\tis missing");
} else {
System.out.println();
}
}
// Create random weights.
System.out.println("\nCreating random weights for instances.");
for (i = 0; i < instances.numInstances(); i++) {
instances.instance(i).setWeight(random.nextDouble());
}
// Print all instances and their weights (and the sum of weights).
System.out.println("\nInstances and their weights:\n");
System.out.println(instances.instancesAndWeights());
System.out.print("\nSum of weights: ");
System.out.println(instances.sumOfWeights());
// Insert an attribute
secondInstances = new Instances(instances);
Attribute testAtt = new Attribute("Inserted");
secondInstances.insertAttributeAt(testAtt, 0);
System.out.println("\nSet with inserted attribute:\n");
System.out.println(secondInstances);
System.out.println("\nClass name: "
+ secondInstances.classAttribute().name());
// Delete the attribute
secondInstances.deleteAttributeAt(0);
System.out.println("\nSet with attribute deleted:\n");
System.out.println(secondInstances);
System.out.println("\nClass name: "
+ secondInstances.classAttribute().name());
// Test if headers are equal
System.out.println("\nHeaders equal: "+
instances.equalHeaders(secondInstances) + "\n");
// Print data in internal format.
System.out.println("\nData (internal values):\n");
for (i = 0; i < instances.numInstances(); i++) {
for (j = 0; j < instances.numAttributes(); j++) {
if (instances.instance(i).isMissing(j)) {
System.out.print("? ");
} else {
System.out.print(instances.instance(i).value(j) + " ");
}
}
System.out.println();
}
// Just print header
System.out.println("\nEmpty dataset:\n");
empty = new Instances(instances, 0);
System.out.println(empty);
System.out.println("\nClass name: "+empty.classAttribute().name());
// Create copy and rename an attribute and a value (if possible)
if (empty.classAttribute().isNominal()) {
Instances copy = new Instances(empty, 0);
copy.renameAttribute(copy.classAttribute(), "new_name");
copy.renameAttributeValue(copy.classAttribute(),
copy.classAttribute().value(0),
"new_val_name");
System.out.println("\nDataset with names changed:\n" + copy);
System.out.println("\nOriginal dataset:\n" + empty);
}
// Create and prints subset of instances.
start = instances.numInstances() / 4;
num = instances.numInstances() / 2;
System.out.print("\nSubset of dataset: ");
System.out.println(num + " instances from " + (start + 1)
+ ". instance");
secondInstances = new Instances(instances, start, num);
System.out.println("\nClass name: "
+ secondInstances.classAttribute().name());
// Print all instances and their weights (and the sum of weights).
System.out.println("\nInstances and their weights:\n");
System.out.println(secondInstances.instancesAndWeights());
System.out.print("\nSum of weights: ");
System.out.println(secondInstances.sumOfWeights());
// Create and print training and test sets for 3-fold
// cross-validation.
System.out.println("\nTrain and test folds for 3-fold CV:");
if (instances.classAttribute().isNominal()) {
instances.stratify(3);
}
for (j = 0; j < 3; j++) {
train = instances.trainCV(3,j);
test = instances.testCV(3,j);
// Print all instances and their weights (and the sum of weights).
System.out.println("\nTrain: ");
System.out.println("\nInstances and their weights:\n");
System.out.println(train.instancesAndWeights());
System.out.print("\nSum of weights: ");
System.out.println(train.sumOfWeights());
System.out.println("\nClass name: "+train.classAttribute().name());
System.out.println("\nTest: ");
System.out.println("\nInstances and their weights:\n");
System.out.println(test.instancesAndWeights());
System.out.print("\nSum of weights: ");
System.out.println(test.sumOfWeights());
System.out.println("\nClass name: "+test.classAttribute().name());
}
// Randomize instances and print them.
System.out.println("\nRandomized dataset:");
instances.randomize(random);
// Print all instances and their weights (and the sum of weights).
System.out.println("\nInstances and their weights:\n");
System.out.println(instances.instancesAndWeights());
System.out.print("\nSum of weights: ");
System.out.println(instances.sumOfWeights());
// Sort instances according to first attribute and
// print them.
System.out.print("\nInstances sorted according to first attribute:\n ");
instances.sort(0);
// Print all instances and their weights (and the sum of weights).
System.out.println("\nInstances and their weights:\n");
System.out.println(instances.instancesAndWeights());
System.out.print("\nSum of weights: ");
System.out.println(instances.sumOfWeights());
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* Main method for this class -- just prints a summary of a set
* of instances.
*
* @param argv should contain one element: the name of an ARFF file
*/
public static void main(String [] args) {
try {
Reader r = null;
if (args.length > 1) {
throw (new Exception("Usage: Instances <filename>"));
} else if (args.length == 0) {
r = new BufferedReader(new InputStreamReader(System.in));
} else {
r = new BufferedReader(new FileReader(args[0]));
}
Instances i = new Instances(r);
System.out.println(i.toSummaryString());
} catch (Exception ex) {
ex.printStackTrace();
System.err.println(ex.getMessage());
}
}
}