weka.core.Instances Maven / Gradle / Ivy
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/*
* 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 3 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, see
*
* Typical usage:
*
*
*
* import weka.core.converters.ConverterUtils.DataSource;
* ...
*
* // Read all the instances in the file (ARFF, CSV, XRFF, ...)
* DataSource source = new DataSource(filename);
* Instances instances = source.getDataSet();
*
* // 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);
*
* ...
*
*
*
* 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 ([email protected])
* @author Len Trigg ([email protected])
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 12446 $
*/
public class Instances extends AbstractList implements Serializable,
RevisionHandler {
/** for serialization */
static final long serialVersionUID = -19412345060742748L;
/** The filename extension that should be used for arff files */
public final static String FILE_EXTENSION = ".arff";
/**
* The filename extension that should be used for bin. serialized instances
* files
*/
public final static String SERIALIZED_OBJ_FILE_EXTENSION = ".bsi";
/** The keyword used to denote the start of an arff header */
public final static String ARFF_RELATION = "@relation";
/** The keyword used to denote the start of the arff data section */
public final static String ARFF_DATA = "@data";
/** The dataset's name. */
protected/* @spec_public non_null@ */String m_RelationName;
/** The attribute information. */
protected/* @spec_public non_null@ */ArrayList m_Attributes;
/*
* public invariant (\forall int i; 0 <= i && i < m_Attributes.size();
* m_Attributes.get(i) != null);
*/
/** A map to quickly find attribute indices based on their names. */
protected HashMap m_NamesToAttributeIndices;
/** The instances. */
protected/* @spec_public non_null@ */ArrayList m_Instances;
/** The class attribute's index */
protected int m_ClassIndex;
// @ protected invariant classIndex() == m_ClassIndex;
/**
* The lines read so far in case of incremental loading. Since the
* StreamTokenizer will be re-initialized with every instance that is read, we
* have to keep track of the number of lines read so far.
*
* @see #readInstance(Reader)
*/
protected int m_Lines = 0;
/**
* 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
* @throws IOException if the ARFF file is not read successfully
*/
public Instances(/* @non_null@ */Reader reader) throws IOException {
ArffReader arff = new ArffReader(reader, 1000, false);
initialize(arff.getData(), 1000);
arff.setRetainStringValues(true);
Instance inst;
while ((inst = arff.readInstance(this)) != null) {
m_Instances.add(inst);
}
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
* @throws IllegalArgumentException if the header is not read successfully or
* the capacity is negative.
* @throws IOException if there is a problem with the reader.
* @deprecated instead of using this method in conjunction with the
* readInstance(Reader) method, one should use the
* ArffLoader or DataSource class
* instead.
* @see weka.core.converters.ArffLoader
* @see weka.core.converters.ConverterUtils.DataSource
*/
// @ requires capacity >= 0;
// @ ensures classIndex() == -1;
@Deprecated
public Instances(/* @non_null@ */Reader reader, int capacity) throws IOException {
ArffReader arff = new ArffReader(reader, 0);
Instances header = arff.getStructure();
initialize(header, capacity);
m_Lines = arff.getLineNo();
}
/**
* Constructor copying all instances and references to the header information
* from the given set of instances.
*
* @param dataset the set to be copied
*/
public Instances(/* @non_null@ */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 dataset the instances from which the header information is to be
* taken
* @param capacity the capacity of the new dataset
*/
public Instances(/* @non_null@ */Instances dataset, int capacity) {
initialize(dataset, capacity);
}
/**
* initializes with the header information of the given dataset and sets the
* capacity of the set of instances.
*
* @param dataset the dataset to use as template
* @param capacity the number of rows to reserve
*/
protected void initialize(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_NamesToAttributeIndices = dataset.m_NamesToAttributeIndices;
m_Instances = new ArrayList(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
* @throws IllegalArgumentException if first and toCopy are out of range
*/
// @ requires 0 <= first;
// @ requires 0 <= toCopy;
// @ requires first + toCopy <= source.numInstances();
public Instances(/* @non_null@ */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);
}
/**
* 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
* @throws IllegalArgumentException if attribute names are not unique
*/
public Instances(/* @non_null@ */String name,
/* @non_null@ */ArrayList attInfo, int capacity) {
// check whether the attribute names are unique
HashSet names = new HashSet();
StringBuffer nonUniqueNames = new StringBuffer();
for (Attribute att : attInfo) {
if (names.contains(att.name())) {
nonUniqueNames.append("'" + att.name() + "' ");
}
names.add(att.name());
}
if (names.size() != attInfo.size()) {
throw new IllegalArgumentException("Attribute names are not unique!"
+ " Causes: " + nonUniqueNames.toString());
}
names.clear();
m_RelationName = name;
m_ClassIndex = -1;
m_Attributes = attInfo;
m_NamesToAttributeIndices = new HashMap((int) (numAttributes() / 0.75));
for (int i = 0; i < numAttributes(); i++) {
attribute(i).setIndex(i);
m_NamesToAttributeIndices.put(attribute(i).name(), i);
}
m_Instances = new ArrayList(capacity);
}
/**
* Create a copy of the structure. If the data has string or relational
* attributes, theses are replaced by empty copies. Other attributes are left
* unmodified, but the underlying list structure holding references to the attributes
* is shallow-copied, so that other Instances objects with a reference to this list are not affected.
*
* @return a copy of the instance structure.
*/
public Instances stringFreeStructure() {
ArrayList newAtts = new ArrayList();
for (Attribute att : m_Attributes) {
if (att.type() == Attribute.STRING) {
newAtts.add(new Attribute(att.name(), (List) null, att.index()));
} else if (att.type() == Attribute.RELATIONAL) {
newAtts.add(new Attribute(att.name(), new Instances(att.relation(), 0),
att.index()));
}
}
if (newAtts.size() == 0) {
return new Instances(this, 0);
}
ArrayList atts = Utils.cast(m_Attributes.clone());
for (Attribute att : newAtts) {
atts.set(att.index(), att);
}
Instances result = new Instances(this, 0);
result.m_Attributes = atts;
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. Note: String or
* relational values are not transferred.
*
* @param instance the instance to be added
*/
@Override
public boolean add(/* @non_null@ */Instance instance) {
Instance newInstance = (Instance) instance.copy();
newInstance.setDataset(this);
m_Instances.add(newInstance);
return true;
}
/**
* Adds one instance at the given position in the list. 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. Note: String or relational values
* are not transferred.
*
* @param index position where instance is to be inserted
* @param instance the instance to be added
*/
// @ requires 0 <= index;
// @ requires index < m_Instances.size();
@Override
public void add(int index, /* @non_null@ */Instance instance) {
Instance newInstance = (Instance) instance.copy();
newInstance.setDataset(this);
m_Instances.add(index, newInstance);
}
/**
* Returns an attribute.
*
* @param index the attribute's index (index starts with 0)
* @return the attribute at the given position
*/
// @ requires 0 <= index;
// @ requires index < m_Attributes.size();
// @ ensures \result != null;
public/* @pure@ */Attribute attribute(int index) {
return m_Attributes.get(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/* @pure@ */Attribute attribute(String name) {
Integer index = m_NamesToAttributeIndices.get(name);
if (index != null) {
return attribute(index);
}
return null;
}
/**
* Checks for attributes of the given type in the dataset
*
* @param attType the attribute type to look for
* @return true if attributes of the given type are present
*/
public boolean checkForAttributeType(int attType) {
int i = 0;
while (i < m_Attributes.size()) {
if (attribute(i++).type() == attType) {
return true;
}
}
return false;
}
/**
* Checks for string attributes in the dataset
*
* @return true if string attributes are present, false otherwise
*/
public/* @pure@ */boolean checkForStringAttributes() {
return checkForAttributeType(Attribute.STRING);
}
/**
* 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.
*
* @param instance the instance to check
* @return true if the instance is compatible with the dataset
*/
public/* @pure@ */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), (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
* @throws UnassignedClassException if the class is not set
*/
// @ requires classIndex() >= 0;
public/* @pure@ */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
*/
// ensures \result == m_ClassIndex;
public/* @pure@ */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 void compactify() {
m_Instances.trimToSize();
}
/**
* Removes all instances from the set.
*/
public void delete() {
m_Instances = new ArrayList();
}
/**
* Removes an instance at the given position from the set.
*
* @param index the instance's position (index starts with 0)
*/
// @ requires 0 <= index && index < numInstances();
public void delete(int index) {
m_Instances.remove(index);
}
/**
* Deletes an attribute at the given position (0 to numAttributes()
* - 1). Attribute objects after the deletion point are copied so
* that their indices can be decremented. Creates a fresh list to
* hold the old and new attribute objects.
* @param position the attribute's position (position starts with 0)
* @throws IllegalArgumentException if the given index is out of range or the
* class attribute is being deleted
*/
// @ requires 0 <= position && position < numAttributes();
// @ requires position != classIndex();
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");
}
ArrayList newList = new ArrayList(m_Attributes.size() - 1);
HashMap newMap = new HashMap((int) ((m_Attributes.size() - 1) / 0.75));
for (int i = 0 ; i < position; i++) {
Attribute att = m_Attributes.get(i);
newList.add(att);
newMap.put(att.name(), i);
}
for (int i = position + 1; i < m_Attributes.size(); i++) {
Attribute newAtt = (Attribute) m_Attributes.get(i).copy();
newAtt.setIndex(i - 1);
newList.add(newAtt);
newMap.put(newAtt.name(), i - 1);
}
m_Attributes = newList;
m_NamesToAttributeIndices = newMap;
if (m_ClassIndex > position) {
m_ClassIndex--;
}
for (int i = 0; i < numInstances(); i++) {
instance(i).setDataset(null);
instance(i).deleteAttributeAt(position);
instance(i).setDataset(this);
}
}
/**
* Deletes all attributes of the given type in the dataset. A deep copy of the
* attribute information is performed before an attribute is deleted.
*
* @param attType the attribute type to delete
* @throws IllegalArgumentException if attribute couldn't be successfully
* deleted (probably because it is the class attribute).
*/
public void deleteAttributeType(int attType) {
int i = 0;
while (i < m_Attributes.size()) {
if (attribute(i).type() == attType) {
deleteAttributeAt(i);
} else {
i++;
}
}
}
/**
* Deletes all string attributes in the dataset. A deep copy of the attribute
* information is performed before an attribute is deleted.
*
* @throws IllegalArgumentException if string attribute couldn't be
* successfully deleted (probably because it is the class
* attribute).
* @see #deleteAttributeType(int)
*/
public void deleteStringAttributes() {
deleteAttributeType(Attribute.STRING);
}
/**
* Removes all instances with missing values for a particular attribute from
* the dataset.
*
* @param attIndex the attribute's index (index starts with 0)
*/
// @ requires 0 <= attIndex && attIndex < numAttributes();
public void deleteWithMissing(int attIndex) {
ArrayList newInstances = new ArrayList(numInstances());
for (int i = 0; i < numInstances(); i++) {
if (!instance(i).isMissing(attIndex)) {
newInstances.add(instance(i));
}
}
m_Instances = newInstances;
}
/**
* Removes all instances with missing values for a particular attribute from
* the dataset.
*
* @param att the attribute
*/
public void deleteWithMissing(/* @non_null@ */Attribute att) {
deleteWithMissing(att.index());
}
/**
* Removes all instances with a missing class value from the dataset.
*
* @throws UnassignedClassException if class is not set
*/
public 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. The class attribute (if set)
* is skipped by this enumeration.
*
* @return enumeration of all the attributes.
*/
public/* @non_null pure@ */Enumeration enumerateAttributes() {
return new WekaEnumeration(m_Attributes, m_ClassIndex);
}
/**
* Returns an enumeration of all instances in the dataset.
*
* @return enumeration of all instances in the dataset
*/
public/* @non_null pure@ */Enumeration enumerateInstances() {
return new WekaEnumeration(m_Instances);
}
/**
* Checks if two headers are equivalent. If not, then returns a message why
* they differ.
*
* @param dataset another dataset
* @return null if the header of the given dataset is equivalent to this
* header, otherwise a message with details on why they differ
*/
public String equalHeadersMsg(Instances dataset) {
// Check class and all attributes
if (m_ClassIndex != dataset.m_ClassIndex) {
return "Class index differ: " + (m_ClassIndex + 1) + " != "
+ (dataset.m_ClassIndex + 1);
}
if (m_Attributes.size() != dataset.m_Attributes.size()) {
return "Different number of attributes: " + m_Attributes.size() + " != "
+ dataset.m_Attributes.size();
}
for (int i = 0; i < m_Attributes.size(); i++) {
String msg = attribute(i).equalsMsg(dataset.attribute(i));
if (msg != null) {
return "Attributes differ at position " + (i + 1) + ":\n" + msg;
}
}
return null;
}
/**
* 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/* @pure@ */boolean equalHeaders(Instances dataset) {
return (equalHeadersMsg(dataset) == null);
}
/**
* Returns the first instance in the set.
*
* @return the first instance in the set
*/
// @ requires numInstances() > 0;
public/* @non_null pure@ */Instance firstInstance() {
return m_Instances.get(0);
}
/**
* Returns a random number generator. The initial seed of the random number
* generator depends on the given seed and the hash code of a string
* representation of a instances chosen based on the given seed.
*
* @param seed the given seed
* @return the random number generator
*/
public Random getRandomNumberGenerator(long seed) {
Random r = new Random(seed);
r.setSeed(instance(r.nextInt(numInstances())).toStringNoWeight().hashCode()
+ seed);
return r;
}
/**
* 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. Existing attribute objects at and after
* the insertion point are also copied so that their indices can be
* incremented. Creates a fresh list to hold the old and new
* attribute objects.
*
* @param att the attribute to be inserted
* @param position the attribute's position (position starts with 0)
* @throws IllegalArgumentException if the given index is out of range
*/
// @ requires 0 <= position;
// @ requires position <= numAttributes();
public void insertAttributeAt(/* @non_null@ */Attribute att, int position) {
if ((position < 0) || (position > m_Attributes.size())) {
throw new IllegalArgumentException("Index out of range");
}
if (attribute(att.name()) != null) {
throw new IllegalArgumentException("Attribute name '" + att.name()
+ "' already in use at position #" + attribute(att.name()).index());
}
att = (Attribute) att.copy();
att.setIndex(position);
ArrayList newList = new ArrayList(m_Attributes.size() + 1);
HashMap newMap = new HashMap((int) ((m_Attributes.size() + 1) / 0.75));
for (int i = 0 ; i < position; i++) {
Attribute oldAtt = m_Attributes.get(i);
newList.add(oldAtt);
newMap.put(oldAtt.name(), i);
}
newList.add(att);
newMap.put(att.name(), position);
for (int i = position; i < m_Attributes.size(); i++) {
Attribute newAtt = (Attribute) m_Attributes.get(i).copy();
newAtt.setIndex(i + 1);
newList.add(newAtt);
newMap.put(newAtt.name(), i + 1);
}
m_Attributes = newList;
m_NamesToAttributeIndices = newMap;
for (int i = 0; i < numInstances(); i++) {
instance(i).setDataset(null);
instance(i).insertAttributeAt(position);
instance(i).setDataset(this);
}
if (m_ClassIndex >= position) {
m_ClassIndex++;
}
}
/**
* Returns the instance at the given position.
*
* @param index the instance's index (index starts with 0)
* @return the instance at the given position
*/
// @ requires 0 <= index;
// @ requires index < numInstances();
public/* @non_null pure@ */Instance instance(int index) {
return m_Instances.get(index);
}
/**
* Returns the instance at the given position.
*
* @param index the instance's index (index starts with 0)
* @return the instance at the given position
*/
// @ requires 0 <= index;
// @ requires index < numInstances();
@Override
public/* @non_null pure@ */Instance get(int index) {
return m_Instances.get(index);
}
/**
* Returns the kth-smallest attribute value of a numeric attribute.
*
* @param att the Attribute object
* @param k the value of k
* @return the kth-smallest value
*/
public double kthSmallestValue(Attribute att, int k) {
return kthSmallestValue(att.index(), k);
}
/**
* Returns the kth-smallest attribute value of a numeric attribute. NOTE
* CHANGE: Missing values (NaN values) are now treated as Double.MAX_VALUE.
* Also, the order of the instances in the data is no longer affected.
*
* @param attIndex the attribute's index
* @param k the value of k
* @return the kth-smallest value
*/
public double kthSmallestValue(int attIndex, int k) {
if (!attribute(attIndex).isNumeric()) {
throw new IllegalArgumentException(
"Instances: attribute must be numeric to compute kth-smallest value.");
}
if ((k < 1) || (k > numInstances())) {
throw new IllegalArgumentException(
"Instances: value for k for computing kth-smallest value too large.");
}
double[] vals = new double[numInstances()];
for (int i = 0; i < vals.length; i++) {
double val = instance(i).value(attIndex);
if (Utils.isMissingValue(val)) {
vals[i] = Double.MAX_VALUE;
} else {
vals[i] = val;
}
}
return Utils.kthSmallestValue(vals, k);
}
/**
* Returns the last instance in the set.
*
* @return the last instance in the set
*/
// @ requires numInstances() > 0;
public/* @non_null pure@ */Instance lastInstance() {
return m_Instances.get(m_Instances.size() - 1);
}
/**
* 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 attIndex the attribute's index (index starts with 0)
* @return the mean or the mode
*/
public/* @pure@ */double meanOrMode(int attIndex) {
double result, found;
int[] counts;
if (attribute(attIndex).isNumeric()) {
result = found = 0;
for (int j = 0; j < numInstances(); j++) {
if (!instance(j).isMissing(attIndex)) {
found += instance(j).weight();
result += instance(j).weight() * instance(j).value(attIndex);
}
}
if (found <= 0) {
return 0;
} else {
return result / found;
}
} else if (attribute(attIndex).isNominal()) {
counts = new int[attribute(attIndex).numValues()];
for (int j = 0; j < numInstances(); j++) {
if (!instance(j).isMissing(attIndex)) {
counts[(int) instance(j).value(attIndex)] += instance(j).weight();
}
}
return Utils.maxIndex(counts);
} else {
return 0;
}
}
/**
* 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 the attribute
* @return the mean or the mode
*/
public/* @pure@ */double meanOrMode(Attribute att) {
return meanOrMode(att.index());
}
/**
* Returns the number of attributes.
*
* @return the number of attributes as an integer
*/
// @ ensures \result == m_Attributes.size();
public/* @pure@ */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.
* @throws UnassignedClassException if the class is not set
*/
// @ requires classIndex() >= 0;
public/* @pure@ */int numClasses() {
if (m_ClassIndex < 0) {
throw new UnassignedClassException("Class index is negative (not set)!");
}
if (!classAttribute().isNominal()) {
return 1;
} else {
return classAttribute().numValues();
}
}
/**
* Returns the number of distinct values of a given attribute. The value
* 'missing' is not counted.
*
* @param attIndex the attribute (index starts with 0)
* @return the number of distinct values of a given attribute
*/
// @ requires 0 <= attIndex;
// @ requires attIndex < numAttributes();
public/* @pure@ */int numDistinctValues(int attIndex) {
HashSet set = new HashSet(2 * numInstances());
for (Instance current : this) {
double key = current.value(attIndex);
if (!Utils.isMissingValue(key)) {
set.add(key);
}
}
return set.size();
}
/**
* Returns the number of distinct values of a given attribute. The value
* 'missing' is not counted.
*
* @param att the attribute
* @return the number of distinct values of a given attribute
*/
public/* @pure@ */int numDistinctValues(/* @non_null@ */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
*/
// @ ensures \result == m_Instances.size();
public/* @pure@ */int numInstances() {
return m_Instances.size();
}
/**
* Returns the number of instances in the dataset.
*
* @return the number of instances in the dataset as an integer
*/
// @ ensures \result == m_Instances.size();
@Override
public/* @pure@ */int size() {
return m_Instances.size();
}
/**
* Shuffles the instances in the set so that they are ordered randomly.
*
* @param random a random number generator
*/
public 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
* @throws IOException if the information is not read successfully
* @deprecated instead of using this method in conjunction with the
* readInstance(Reader) method, one should use the
* ArffLoader or DataSource class
* instead.
* @see weka.core.converters.ArffLoader
* @see weka.core.converters.ConverterUtils.DataSource
*/
@Deprecated
public boolean readInstance(Reader reader) throws IOException {
ArffReader arff = new ArffReader(reader, this, m_Lines, 1);
Instance inst = arff.readInstance(arff.getData(), false);
m_Lines = arff.getLineNo();
if (inst != null) {
add(inst);
return true;
} else {
return false;
}
}
/**
* Replaces the attribute at the given position (0 to
* numAttributes()) with the given attribute and sets all its values to
* be missing. Shallow copies the given attribute before it is
* inserted. Creates a fresh list to hold the old and new
* attribute objects.
*
* @param att the attribute to be inserted
* @param position the attribute's position (position starts with 0)
* @throws IllegalArgumentException if the given index is out of range
*/
// @ requires 0 <= position;
// @ requires position <= numAttributes();
public void replaceAttributeAt(/* @non_null@ */Attribute att, int position) {
if ((position < 0) || (position > m_Attributes.size())) {
throw new IllegalArgumentException("Index out of range");
}
// Does the new attribute have a different name?
if (!att.name().equals(m_Attributes.get(position).name())) {
// Need to check if attribute name already exists
Attribute candidate = attribute(att.name());
if ((candidate != null) && (position != candidate.index())) {
throw new IllegalArgumentException("Attribute name '" + att.name()
+ "' already in use at position #" +
attribute(att.name()).index());
}
}
att = (Attribute) att.copy();
att.setIndex(position);
ArrayList newList = new ArrayList(m_Attributes.size());
HashMap newMap = new HashMap((int) ((m_Attributes.size() + 1) / 0.75));
for (int i = 0 ; i < position; i++) {
Attribute oldAtt = m_Attributes.get(i);
newList.add(oldAtt);
newMap.put(oldAtt.name(), i);
}
newList.add(att);
newMap.put(att.name(), position);
for (int i = position + 1; i < m_Attributes.size(); i++) {
Attribute newAtt = (Attribute) m_Attributes.get(i);
newList.add(newAtt);
newMap.put(newAtt.name(), i);
}
m_Attributes = newList;
m_NamesToAttributeIndices = newMap;
for (int i = 0; i < numInstances(); i++) {
instance(i).setDataset(null);
instance(i).setMissing(position);
instance(i).setDataset(this);
}
}
/**
* Returns the relation's name.
*
* @return the relation's name as a string
*/
// @ ensures \result == m_RelationName;
public/* @pure@ */String relationName() {
return m_RelationName;
}
/**
* Removes the instance at the given position.
*
* @param index the instance's index (index starts with 0)
* @return the instance at the given position
*/
// @ requires 0 <= index;
// @ requires index < numInstances();
@Override
public Instance remove(int index) {
return m_Instances.remove(index);
}
/**
* Renames an attribute. This change only affects this dataset.
*
* @param att the attribute's index (index starts with 0)
* @param name the new name
*/
public void renameAttribute(int att, String name) {
Attribute existingAtt = attribute(name);
if (existingAtt != null) {
if (att == existingAtt.index()) {
return; // Old name is equal to new name
} else {
throw new IllegalArgumentException("Attribute name '" + name
+ "' already present at position #" + existingAtt.index());
}
}
Attribute newAtt = attribute(att).copy(name);
ArrayList newVec = new ArrayList(numAttributes());
HashMap newMap = new HashMap((int)(numAttributes() / 0.75));
for (Attribute attr : m_Attributes) {
if (attr.index() == att) {
newVec.add(newAtt);
newMap.put(name, att);
} else {
newVec.add(attr);
newMap.put(attr.name(), attr.index());
}
}
m_Attributes = newVec;
m_NamesToAttributeIndices = newMap;
}
/**
* Renames an attribute. This change only affects this dataset.
*
* @param att the attribute
* @param name the new name
*/
public 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 (index starts with 0)
* @param val the value's index (index starts with 0)
* @param name the new name
*/
public void renameAttributeValue(int att, int val, String name) {
Attribute newAtt = (Attribute) attribute(att).copy();
ArrayList newVec = new ArrayList(numAttributes());
newAtt.setValue(val, name);
for (Attribute attr : m_Attributes) {
if (attr.index() == att) {
newVec.add(newAtt);
} else {
newVec.add(attr);
}
}
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 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 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. See also
* resampleWithWeights(Random, double[], boolean[]).
*
* @param random a random number generator
* @return the new dataset
*/
public Instances resampleWithWeights(Random random) {
return resampleWithWeights(random, false);
}
/**
* 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. See also
* resampleWithWeights(Random, double[], boolean[]).
*
* @param random a random number generator
* @param sampled an array indicating what has been sampled
* @return the new dataset
*/
public Instances resampleWithWeights(Random random, boolean[] sampled) {
return resampleWithWeights(random, sampled, false);
}
/**
* 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. See also
* resampleWithWeights(Random, double[], boolean[]).
*
* @param random a random number generator
* @param representUsingWeights if true, copies are represented using weights
* in resampled data
* @return the new dataset
*/
public Instances resampleWithWeights(Random random,
boolean representUsingWeights) {
return resampleWithWeights(random, null, representUsingWeights);
}
/**
* 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. See also
* resampleWithWeights(Random, double[], boolean[]).
*
* @param random a random number generator
* @param sampled an array indicating what has been sampled
* @param representUsingWeights if true, copies are represented using weights
* in resampled data
* @return the new dataset
*/
public Instances resampleWithWeights(Random random, boolean[] sampled,
boolean representUsingWeights) {
double[] weights = new double[numInstances()];
for (int i = 0; i < weights.length; i++) {
weights[i] = instance(i).weight();
}
return resampleWithWeights(random, weights, sampled, representUsingWeights);
}
/**
* Creates a new dataset of the same size using random sampling with
* replacement according to the given weight vector. See also
* resampleWithWeights(Random, double[], boolean[]).
*
* @param random a random number generator
* @param weights the weight vector
* @return the new dataset
* @throws IllegalArgumentException if the weights array is of the wrong
* length or contains negative weights.
*/
public Instances resampleWithWeights(Random random, double[] weights) {
return resampleWithWeights(random, weights, null);
}
/**
* 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. Uses Walker's method, see pp. 232 of
* "Stochastic Simulation" by B.D. Ripley (1987).
*
* @param random a random number generator
* @param weights the weight vector
* @param sampled an array indicating what has been sampled, can be null
* @return the new dataset
* @throws IllegalArgumentException if the weights array is of the wrong
* length or contains negative weights.
*/
public Instances resampleWithWeights(Random random, double[] weights,
boolean[] sampled) {
return resampleWithWeights(random, weights, sampled, false);
}
/**
* 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. Uses Walker's method, see pp. 232 of
* "Stochastic Simulation" by B.D. Ripley (1987).
*
* @param random a random number generator
* @param weights the weight vector
* @param sampled an array indicating what has been sampled, can be null
* @param representUsingWeights if true, copies are represented using weights
* in resampled data
* @return the new dataset
* @throws IllegalArgumentException if the weights array is of the wrong
* length or contains negative weights.
*/
public Instances resampleWithWeights(Random random, double[] weights,
boolean[] sampled, boolean representUsingWeights) {
if (weights.length != numInstances()) {
throw new IllegalArgumentException("weights.length != numInstances.");
}
Instances newData = new Instances(this, numInstances());
if (numInstances() == 0) {
return newData;
}
// Walker's method, see pp. 232 of "Stochastic Simulation" by B.D. Ripley
double[] P = new double[weights.length];
System.arraycopy(weights, 0, P, 0, weights.length);
Utils.normalize(P);
double[] Q = new double[weights.length];
int[] A = new int[weights.length];
int[] W = new int[weights.length];
int M = weights.length;
int NN = -1;
int NP = M;
for (int I = 0; I < M; I++) {
if (P[I] < 0) {
throw new IllegalArgumentException("Weights have to be positive.");
}
Q[I] = M * P[I];
if (Q[I] < 1.0) {
W[++NN] = I;
} else {
W[--NP] = I;
}
}
if (NN > -1 && NP < M) {
for (int S = 0; S < M - 1; S++) {
int I = W[S];
int J = W[NP];
A[I] = J;
Q[J] += Q[I] - 1.0;
if (Q[J] < 1.0) {
NP++;
}
if (NP >= M) {
break;
}
}
// A[W[M]] = W[M];
}
for (int I = 0; I < M; I++) {
Q[I] += I;
}
// Do we need to keep track of how many copies to use?
int[] counts = null;
if (representUsingWeights) {
counts = new int[M];
}
for (int i = 0; i < numInstances(); i++) {
int ALRV;
double U = M * random.nextDouble();
int I = (int) U;
if (U < Q[I]) {
ALRV = I;
} else {
ALRV = A[I];
}
if (representUsingWeights) {
counts[ALRV]++;
} else {
newData.add(instance(ALRV));
}
if (sampled != null) {
sampled[ALRV] = true;
}
if (!representUsingWeights) {
newData.instance(newData.numInstances() - 1).setWeight(1);
}
}
// Add data based on counts if weights should represent numbers of copies.
if (representUsingWeights) {
for (int i = 0; i < counts.length; i++) {
if (counts[i] > 0) {
newData.add(instance(i));
newData.instance(newData.numInstances() - 1).setWeight(counts[i]);
}
}
}
return newData;
}
/**
* Replaces the instance at the given position. Shallow copies instance before
* it is added. Does not check if the instance is compatible with the dataset.
* Note: String or relational values are not transferred.
*
* @param index position where instance is to be inserted
* @param instance the instance to be inserted
* @return the instance previously at that position
*/
// @ requires 0 <= index;
// @ requires index < m_Instances.size();
@Override
public Instance set(int index, /* @non_null@ */Instance instance) {
Instance newInstance = (Instance) instance.copy();
Instance oldInstance = m_Instances.get(index);
newInstance.setDataset(this);
m_Instances.set(index, newInstance);
return oldInstance;
}
/**
* Sets the class attribute.
*
* @param att attribute to be the class
*/
public 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. (ie. it is undefined)
*
* @param classIndex the new class index (index starts with 0)
* @throws IllegalArgumentException if the class index is too big or < 0
*/
public void setClassIndex(int classIndex) {
if (classIndex >= numAttributes()) {
throw new IllegalArgumentException("Invalid class index: " + classIndex);
}
m_ClassIndex = classIndex;
}
/**
* Sets the relation's name.
*
* @param newName the new relation name.
*/
public void setRelationName(/* @non_null@ */String newName) {
m_RelationName = newName;
}
/**
* Sorts a nominal attribute (stable, linear-time sort). Instances
* are sorted based on the attribute label ordering specified in the header.
*
* @param attIndex the attribute's index (index starts with 0)
*/
protected void sortBasedOnNominalAttribute(int attIndex) {
// Figure out number of instances for each attribute value
// and store original list of instances away
int[] counts = new int[attribute(attIndex).numValues()];
Instance[] backup = new Instance[numInstances()];
int j = 0;
for (Instance inst : this) {
backup[j++] = inst;
if (!inst.isMissing(attIndex)) {
counts[(int)inst.value(attIndex)]++;
}
}
// Indices to figure out where to add instances
int[] indices = new int[counts.length];
int start = 0;
for (int i = 0; i < counts.length; i++) {
indices[i] = start;
start += counts[i];
}
for (Instance inst : backup) { // Use backup here
if (!inst.isMissing(attIndex)) {
m_Instances.set(indices[(int)inst.value(attIndex)]++, inst);
} else {
m_Instances.set(start++, inst);
}
}
}
/**
* 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 (index starts with 0)
*/
public void sort(int attIndex) {
if (!attribute(attIndex).isNominal()) {
// Use quicksort from Utils class for sorting
double[] vals = new double[numInstances()];
Instance[] backup = new Instance[vals.length];
for (int i = 0; i < vals.length; i++) {
Instance inst = instance(i);
backup[i] = inst;
double val = inst.value(attIndex);
if (Utils.isMissingValue(val)) {
vals[i] = Double.MAX_VALUE;
} else {
vals[i] = val;
}
}
int[] sortOrder = Utils.sortWithNoMissingValues(vals);
for (int i = 0; i < vals.length; i++) {
m_Instances.set(i, backup[sortOrder[i]]);
}
} else {
sortBasedOnNominalAttribute(attIndex);
}
}
/**
* 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 void sort(Attribute att) {
sort(att.index());
}
/**
* Sorts the instances based on an attribute, using a stable sort. 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 (index starts with 0)
*/
public void stableSort(int attIndex) {
if (!attribute(attIndex).isNominal()) {
// Use quicksort from Utils class for sorting
double[] vals = new double[numInstances()];
Instance[] backup = new Instance[vals.length];
for (int i = 0; i < vals.length; i++) {
Instance inst = instance(i);
backup[i] = inst;
vals[i] = inst.value(attIndex);
}
int[] sortOrder = Utils.stableSort(vals);
for (int i = 0; i < vals.length; i++) {
m_Instances.set(i, backup[sortOrder[i]]);
}
} else {
sortBasedOnNominalAttribute(attIndex);
}
}
/**
* Sorts the instances based on an attribute, using a stable sort. 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 void stableSort(Attribute att) {
stableSort(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
* @throws UnassignedClassException if the class is not set
*/
public void stratify(int numFolds) {
if (numFolds <= 1) {
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/* @pure@ */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
* @throws IllegalArgumentException if the number of folds is less than 2 or
* greater than the number of instances.
*/
// @ requires 2 <= numFolds && numFolds < numInstances();
// @ requires 0 <= numFold && numFold < numFolds;
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
*/
@Override
public String toString() {
StringBuffer text = new StringBuffer();
text.append(ARFF_RELATION).append(" ").append(Utils.quote(m_RelationName))
.append("\n\n");
for (int i = 0; i < numAttributes(); i++) {
text.append(attribute(i)).append("\n");
}
text.append("\n").append(ARFF_DATA).append("\n");
text.append(stringWithoutHeader());
return text.toString();
}
/**
* Returns the instances in 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
*/
protected String stringWithoutHeader() {
StringBuffer text = new StringBuffer();
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
* @throws IllegalArgumentException if the number of folds is less than 2 or
* greater than the number of instances.
*/
// @ requires 2 <= numFolds && numFolds < numInstances();
// @ requires 0 <= numFold && numFold < numFolds;
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;
}
/**
* Creates the training set for one fold of a cross-validation on the dataset.
* The data is subsequently randomized based on the given random number
* generator.
*
* @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, ...
* @param random the random number generator
* @return the training set
* @throws IllegalArgumentException if the number of folds is less than 2 or
* greater than the number of instances.
*/
// @ requires 2 <= numFolds && numFolds < numInstances();
// @ requires 0 <= numFold && numFold < numFolds;
public Instances trainCV(int numFolds, int numFold, Random random) {
Instances train = trainCV(numFolds, numFold);
train.randomize(random);
return train;
}
/**
* Computes the variance for all numeric attributes simultaneously.
* This is faster than calling variance() for each attribute.
* The resulting array has as many dimensions as there are attributes.
* Array elements corresponding to non-numeric attributes are set to 0.
*
* @return the array containing the variance values
*/
public/* @pure@ */double[] variances() {
double[] vars = new double[numAttributes()];
for (int i = 0; i < numAttributes(); i++)
vars[i] = Double.NaN;
double[] means = new double[numAttributes()];
double[] sumWeights = new double[numAttributes()];
for (int i = 0; i < numInstances(); i++) {
double weight = instance(i).weight();
for (int attIndex = 0; attIndex < numAttributes(); attIndex++) {
if (attribute(attIndex).isNumeric()) {
if (!instance(i).isMissing(attIndex)) {
double value = instance(i).value(attIndex);
if (Double.isNaN(vars[attIndex])) {
// For the first value the mean can suffer from loss of precision
// so we treat it separately and make sure the calculation stays accurate
means[attIndex] = value;
sumWeights[attIndex] = weight;
vars[attIndex] = 0;
continue;
}
double delta = weight*(value - means[attIndex]);
sumWeights[attIndex] += weight;
means[attIndex] += delta/sumWeights[attIndex];
vars[attIndex] += delta*(value - means[attIndex]);
}
}
}
}
for (int attIndex = 0; attIndex < numAttributes(); attIndex++) {
if (attribute(attIndex).isNumeric()) {
if (sumWeights[attIndex] <= 1) {
vars[attIndex] = Double.NaN;
} else {
vars[attIndex] /= sumWeights[attIndex] - 1;
if (vars[attIndex] < 0)
vars[attIndex] = 0;
}
}
}
return vars;
}
/**
* Computes the variance for a numeric attribute.
*
* @param attIndex the numeric attribute (index starts with 0)
* @return the variance if the attribute is numeric
* @throws IllegalArgumentException if the attribute is not numeric
*/
public/* @pure@ */double variance(int attIndex) {
if (!attribute(attIndex).isNumeric()) {
throw new IllegalArgumentException(
"Can't compute variance because attribute is " + "not numeric!");
}
double mean = 0;
double var = Double.NaN;
double sumWeights = 0;
for (int i = 0; i < numInstances(); i++) {
if (!instance(i).isMissing(attIndex)) {
double weight = instance(i).weight();
double value = instance(i).value(attIndex);
if (Double.isNaN(var)) {
// For the first value the mean can suffer from loss of precision
// so we treat it separately and make sure the calculation stays accurate
mean = value;
sumWeights = weight;
var = 0;
continue;
}
double delta = weight*(value - mean);
sumWeights += weight;
mean += delta/sumWeights;
var += delta*(value - mean);
}
}
if (sumWeights <= 1) {
return Double.NaN;
}
var /= sumWeights - 1;
// We don't like negative variance
if (var < 0) {
return 0;
} else {
return var;
}
}
/**
* Computes the variance for a numeric attribute.
*
* @param att the numeric attribute
* @return the variance if the attribute is numeric
* @throws IllegalArgumentException if the attribute is not numeric
*/
public/* @pure@ */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 (index starts with 0)
* @return an AttributeStats object with it's fields calculated.
*/
// @ requires 0 <= index && index < numAttributes();
public AttributeStats attributeStats(int index) {
AttributeStats result = new AttributeStats();
if (attribute(index).isNominal()) {
result.nominalCounts = new int[attribute(index).numValues()];
result.nominalWeights = new double[attribute(index).numValues()];
}
if (attribute(index).isNumeric()) {
result.numericStats = new weka.experiment.Stats();
}
result.totalCount = numInstances();
HashMap map = new HashMap(2 * result.totalCount);
for (Instance current : this) {
double key = current.value(index);
if (Utils.isMissingValue(key)) {
result.missingCount++;
} else {
double[] values = map.get(key);
if (values == null) {
values = new double[2];
values[0] = 1.0;
values[1] = current.weight();
map.put(key, values);
} else {
values[0]++;
values[1] += current.weight();
}
}
}
for (Entry entry : map.entrySet()) {
result.addDistinct(entry.getKey(), (int)entry.getValue()[0], entry.getValue()[1]);
}
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.
*/
// @ requires 0 <= index && index < numAttributes();
public/* @pure@ */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');
// Figure out how many digits we need for the index
int numDigits = (int)Math.log10((int)numAttributes()) + 1;
for (int i = 0; i < numAttributes(); i++) {
Attribute a = attribute(i);
AttributeStats as = attributeStats(i);
result.append(Utils.padLeft("" + (i + 1), numDigits)).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;
case Attribute.RELATIONAL:
result.append(Utils.padLeft("Rel", 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();
}
/**
* Copies instances from one set to the end of another one.
*
* @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
*/
// @ requires 0 <= from && from <= numInstances() - num;
// @ requires 0 <= num;
protected void copyInstances(int from, /* @non_null@ */Instances dest, int num) {
for (int i = 0; i < num; i++) {
dest.add(instance(from + i));
}
}
/**
* Returns string including all instances, their weights and their indices in
* the original dataset.
*
* @return description of instance and its weight as a string
*/
protected/* @pure@ */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();
}
/**
* Help function needed for stratification of set.
*
* @param numFolds the number of folds for the stratification
*/
protected void stratStep(int numFolds) {
ArrayList newVec = new ArrayList(m_Instances.size());
int start = 0, j;
// create stratified batch
while (newVec.size() < numInstances()) {
j = start;
while (j < numInstances()) {
newVec.add(instance(j));
j = j + numFolds;
}
start++;
}
m_Instances = newVec;
}
/**
* Swaps two instances in the set.
*
* @param i the first instance's index (index starts with 0)
* @param j the second instance's index (index starts with 0)
*/
// @ requires 0 <= i && i < numInstances();
// @ requires 0 <= j && j < numInstances();
public void swap(int i, int j) {
Instance in = m_Instances.get(i);
m_Instances.set(i, m_Instances.get(j));
m_Instances.set(j, in);
}
/**
* 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
* @throws 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
ArrayList newAttributes = new ArrayList(first.numAttributes() +
second.numAttributes());
for (Attribute att : first.m_Attributes) {
newAttributes.add(att);
}
for (Attribute att : second.m_Attributes) {
newAttributes.add((Attribute)att.copy()); // Need to copy because indices will change.
}
// 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;
}
/**
* Method for testing this class.
*
* @param argv should contain one element: the name of an ARFF file
*/
// @ requires argv != null;
// @ requires argv.length == 1;
// @ requires argv[0] != null;
public static void test(String[] argv) {
Instances instances, secondInstances, train, test, empty;
Random random = new Random(2);
Reader reader;
int start, num;
ArrayList testAtts;
ArrayList testVals;
int i, j;
try {
if (argv.length > 1) {
throw (new Exception("Usage: Instances []"));
}
// Creating set of instances from scratch
testVals = new ArrayList(2);
testVals.add("first_value");
testVals.add("second_value");
testAtts = new ArrayList(2);
testAtts.add(new Attribute("nominal_attribute", testVals));
testAtts.add(new Attribute("numeric_attribute"));
instances = new Instances("test_set", testAtts, 10);
instances.add(new DenseInstance(instances.numAttributes()));
instances.add(new DenseInstance(instances.numAttributes()));
instances.add(new DenseInstance(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, new Random(1));
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. The following calls are possible:
*
* -
*
weka.core.Instances help
* prints a short list of possible commands.
* -
*
weka.core.Instances <filename>
* prints a summary of a set of instances.
* -
*
weka.core.Instances merge <filename1> <filename2>
* merges the two datasets (must have same number of instances) and outputs
* the results on stdout.
* -
*
weka.core.Instances append <filename1> <filename2>
*
* appends the second dataset to the first one (must have same headers) and
* outputs the results on stdout.
* -
*
weka.core.Instances headers <filename1>
* <filename2>
* Compares the headers of the two datasets and prints whether they match or
* not.
* -
*
weka.core.Instances randomize <seed> <filename>
* randomizes the dataset with the given seed and outputs the result on
* stdout.
*
*
* @param args the commandline parameters
*/
public static void main(String[] args) {
try {
Instances i;
// read from stdin and print statistics
if (args.length == 0) {
DataSource source = new DataSource(System.in);
i = source.getDataSet();
System.out.println(i.toSummaryString());
}
// read file and print statistics
else if ((args.length == 1) && (!args[0].equals("-h"))
&& (!args[0].equals("help"))) {
DataSource source = new DataSource(args[0]);
i = source.getDataSet();
System.out.println(i.toSummaryString());
}
// read two files, merge them and print result to stdout
else if ((args.length == 3) && (args[0].toLowerCase().equals("merge"))) {
DataSource source1 = new DataSource(args[1]);
DataSource source2 = new DataSource(args[2]);
i = Instances
.mergeInstances(source1.getDataSet(), source2.getDataSet());
System.out.println(i);
}
// read two files, append them and print result to stdout
else if ((args.length == 3) && (args[0].toLowerCase().equals("append"))) {
DataSource source1 = new DataSource(args[1]);
DataSource source2 = new DataSource(args[2]);
String msg = source1.getStructure().equalHeadersMsg(
source2.getStructure());
if (msg != null) {
throw new Exception("The two datasets have different headers:\n"
+ msg);
}
Instances structure = source1.getStructure();
System.out.println(source1.getStructure());
while (source1.hasMoreElements(structure)) {
System.out.println(source1.nextElement(structure));
}
structure = source2.getStructure();
while (source2.hasMoreElements(structure)) {
System.out.println(source2.nextElement(structure));
}
}
// read two files and compare their headers
else if ((args.length == 3) && (args[0].toLowerCase().equals("headers"))) {
DataSource source1 = new DataSource(args[1]);
DataSource source2 = new DataSource(args[2]);
String msg = source1.getStructure().equalHeadersMsg(
source2.getStructure());
if (msg == null) {
System.out.println("Headers match");
} else {
System.out.println("Headers don't match:\n" + msg);
}
}
// read file and seed value, randomize data and print result to stdout
else if ((args.length == 3)
&& (args[0].toLowerCase().equals("randomize"))) {
DataSource source = new DataSource(args[2]);
i = source.getDataSet();
i.randomize(new Random(Integer.parseInt(args[1])));
System.out.println(i);
}
// wrong parameters or help
else {
System.err
.println("\nUsage:\n"
// help
+ "\tweka.core.Instances help\n"
+ "\t\tPrints this help\n"
// stats
+ "\tweka.core.Instances \n"
+ "\t\tOutputs dataset statistics\n"
// merge
+ "\tweka.core.Instances merge \n"
+ "\t\tMerges the datasets (must have same number of rows).\n"
+ "\t\tGenerated dataset gets output on stdout.\n"
// append
+ "\tweka.core.Instances append \n"
+ "\t\tAppends the second dataset to the first (must have same number of attributes).\n"
+ "\t\tGenerated dataset gets output on stdout.\n"
// headers
+ "\tweka.core.Instances headers \n"
+ "\t\tCompares the structure of the two datasets and outputs whether they\n"
+ "\t\tdiffer or not.\n"
// randomize
+ "\tweka.core.Instances randomize \n"
+ "\t\tRandomizes the dataset and outputs it on stdout.\n");
}
} catch (Exception ex) {
ex.printStackTrace();
System.err.println(ex.getMessage());
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 12446 $");
}
}