weka.core

Class Instances

java.lang.Object

java.util.AbstractCollection<E>

java.util.AbstractList<Instance>

weka.core.Instances

All Implemented Interfaces:

java.io.Serializable, java.lang.Iterable<Instance>, java.util.Collection<Instance>, java.util.List<Instance>, RevisionHandler

public class Instances
extends java.util.AbstractList<Instance>
implements java.io.Serializable, RevisionHandler

Class for handling an ordered set of weighted instances.

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.

Version:

$Revision: 15570 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static java.lang.String	ARFF_DATA The keyword used to denote the start of the arff data section
static java.lang.String	ARFF_RELATION The keyword used to denote the start of an arff header
static java.lang.String	FILE_EXTENSION The filename extension that should be used for arff files
static java.lang.String	SERIALIZED_OBJ_FILE_EXTENSION The filename extension that should be used for bin.

Constructor Summary

Constructors

Constructor and Description
Instances(Instances dataset) Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances dataset, int capacity) Constructor creating an empty set of instances.
Instances(Instances source, int first, int toCopy) Creates a new set of instances by copying a subset of another set.
Instances(java.io.Reader reader) Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(java.io.Reader reader, int capacity) Deprecated. instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.
Instances(java.lang.String name, java.util.ArrayList<Attribute> attInfo, int capacity) Creates an empty set of instances.

Method Summary

Modifier and Type	Method and Description
boolean	add(Instance instance) Adds one instance to the end of the set.
void	add(int index, Instance instance) Adds one instance at the given position in the list.
boolean	allAttributeWeightsIdentical() Returns true if all attribute weights are the same and false otherwise.
boolean	allInstanceWeightsIdentical() Returns true if all instance weights are the same and false otherwise.
Attribute	attribute(int index) Returns an attribute.
Attribute	attribute(java.lang.String name) Returns an attribute given its name.
AttributeStats	attributeStats(int index) Calculates summary statistics on the values that appear in this set of instances for a specified attribute.
double[]	attributeToDoubleArray(int index) Gets the value of all instances in this dataset for a particular attribute.
boolean	checkForAttributeType(int attType) Checks for attributes of the given type in the dataset
boolean	checkForStringAttributes() Checks for string attributes in the dataset
boolean	checkInstance(Instance instance) Checks if the given instance is compatible with this dataset.
Attribute	classAttribute() Returns the class attribute.
int	classIndex() Returns the class attribute's index.
void	compactify() Compactifies the set of instances.
void	delete() Removes all instances from the set.
void	delete(int index) Removes an instance at the given position from the set.
void	deleteAttributeAt(int position) Deletes an attribute at the given position (0 to numAttributes() - 1).
void	deleteAttributeType(int attType) Deletes all attributes of the given type in the dataset.
void	deleteStringAttributes() Deletes all string attributes in the dataset.
void	deleteWithMissing(Attribute att) Removes all instances with missing values for a particular attribute from the dataset.
void	deleteWithMissing(int attIndex) Removes all instances with missing values for a particular attribute from the dataset.
void	deleteWithMissingClass() Removes all instances with a missing class value from the dataset.
java.util.Enumeration<Attribute>	enumerateAttributes() Returns an enumeration of all the attributes.
java.util.Enumeration<Instance>	enumerateInstances() Returns an enumeration of all instances in the dataset.
boolean	equalHeaders(Instances dataset) Checks if two headers are equivalent.
java.lang.String	equalHeadersMsg(Instances dataset) Checks if two headers are equivalent.
Instance	firstInstance() Returns the first instance in the set.
Instance	get(int index) Returns the instance at the given position.
java.util.Random	getRandomNumberGenerator(long seed) Returns a random number generator.
java.lang.String	getRevision() Returns the revision string.
void	insertAttributeAt(Attribute att, int position) Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
Instance	instance(int index) Returns the instance at the given position.
double	kthSmallestValue(Attribute att, int k) Returns the kth-smallest attribute value of a numeric attribute.
double	kthSmallestValue(int attIndex, int k) Returns the kth-smallest attribute value of a numeric attribute.
Instance	lastInstance() Returns the last instance in the set.
static void	main(java.lang.String[] args) Main method for this class.
double	meanOrMode(Attribute att) Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
double	meanOrMode(int attIndex) Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
static Instances	mergeInstances(Instances first, Instances second) Merges two sets of Instances together.
int	numAttributes() Returns the number of attributes.
int	numClasses() Returns the number of class labels.
int	numDistinctValues(Attribute att) Returns the number of distinct values of a given attribute.
int	numDistinctValues(int attIndex) Returns the number of distinct values of a given attribute.
int	numInstances() Returns the number of instances in the dataset.
void	randomize(java.util.Random random) Shuffles the instances in the set so that they are ordered randomly.
boolean	readInstance(java.io.Reader reader) Deprecated. instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.
java.lang.String	relationName() Returns the relation's name.
Instance	remove(int index) Removes the instance at the given position.
void	renameAttribute(Attribute att, java.lang.String name) Renames an attribute.
void	renameAttribute(int att, java.lang.String name) Renames an attribute.
void	renameAttributeValue(Attribute att, java.lang.String val, java.lang.String name) Renames the value of a nominal (or string) attribute value.
void	renameAttributeValue(int att, int val, java.lang.String name) Renames the value of a nominal (or string) attribute value.
void	replaceAttributeAt(Attribute att, int position) Replaces the attribute at the given position (0 to numAttributes()) with the given attribute and sets all its values to be missing.
Instances	resample(java.util.Random random) Creates a new dataset of the same size as this dataset using random sampling with replacement.
Instances	resampleWithWeights(java.util.Random random) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the current instance weights.
Instances	resampleWithWeights(java.util.Random random, boolean representUsingWeights) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the current instance weights.
Instances	resampleWithWeights(java.util.Random random, boolean[] sampled) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the current instance weights.
Instances	resampleWithWeights(java.util.Random random, boolean[] sampled, boolean representUsingWeights) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the current instance weights.
Instances	resampleWithWeights(java.util.Random random, boolean[] sampled, boolean representUsingWeights, double sampleSize) Creates a new dataset from this dataset using random sampling with replacement according to current instance weights.
Instances	resampleWithWeights(java.util.Random random, double[] weights) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the given weight vector.
Instances	resampleWithWeights(java.util.Random random, double[] weights, boolean[] sampled) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the given weight vector.
Instances	resampleWithWeights(java.util.Random random, double[] weights, boolean[] sampled, boolean representUsingWeights) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the given weight vector.
Instances	resampleWithWeights(java.util.Random random, double[] weights, boolean[] sampled, boolean representUsingWeights, double sampleSize) Creates a new dataset from this dataset using random sampling with replacement according to the given weight vector.
Instance	set(int index, Instance instance) Replaces the instance at the given position.
void	setAttributeWeight(Attribute att, double weight) Sets the weight of an attribute.
void	setAttributeWeight(int att, double weight) Sets the weight of an attribute.
void	setClass(Attribute att) Sets the class attribute.
void	setClassIndex(int classIndex) Sets the class index of the set.
void	setRelationName(java.lang.String newName) Sets the relation's name.
int	size() Returns the number of instances in the dataset.
void	sort(Attribute att) Sorts the instances based on an attribute.
void	sort(int attIndex) Sorts the instances based on an attribute.
void	stableSort(Attribute att) Sorts the instances based on an attribute, using a stable sort.
void	stableSort(int attIndex) Sorts the instances based on an attribute, using a stable sort.
void	stratify(int numFolds) 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).
Instances	stringFreeStructure() Create a copy of the structure.
double	sumOfWeights() Computes the sum of all the instances' weights.
void	swap(int i, int j) Swaps two instances in the set.
static void	test(java.lang.String[] argv) Method for testing this class.
Instances	testCV(int numFolds, int numFold) Creates the test set for one fold of a cross-validation on the dataset.
java.lang.String	toString() Returns the dataset as a string in ARFF format.
java.lang.String	toSummaryString() Generates a string summarizing the set of instances.
Instances	trainCV(int numFolds, int numFold) Creates the training set for one fold of a cross-validation on the dataset.
Instances	trainCV(int numFolds, int numFold, java.util.Random random) Creates the training set for one fold of a cross-validation on the dataset.
double	variance(Attribute att) Computes the variance for a numeric attribute.
double	variance(int attIndex) Computes the variance for a numeric attribute.
double[]	variances() Computes the variance for all numeric attributes simultaneously.

Methods inherited from class java.util.AbstractList

addAll, clear, equals, hashCode, indexOf, iterator, lastIndexOf, listIterator, listIterator, subList

Methods inherited from class java.util.AbstractCollection

addAll, contains, containsAll, isEmpty, remove, removeAll, retainAll, toArray, toArray

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.util.List

addAll, contains, containsAll, isEmpty, remove, removeAll, replaceAll, retainAll, sort, spliterator, toArray, toArray

Methods inherited from interface java.util.Collection

parallelStream, removeIf, stream

Methods inherited from interface java.lang.Iterable

forEach

Field Detail

FILE_EXTENSION

public static final java.lang.String FILE_EXTENSION

The filename extension that should be used for arff files

See Also:

Constant Field Values

SERIALIZED_OBJ_FILE_EXTENSION

public static final java.lang.String SERIALIZED_OBJ_FILE_EXTENSION

The filename extension that should be used for bin. serialized instances files

See Also:

Constant Field Values

ARFF_RELATION

public static final java.lang.String ARFF_RELATION

The keyword used to denote the start of an arff header

See Also:

Constant Field Values

ARFF_DATA

public static final java.lang.String ARFF_DATA

The keyword used to denote the start of the arff data section

See Also:

Constant Field Values

Constructor Detail

Instances

public Instances(java.io.Reader reader)
          throws java.io.IOException

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).

Parameters:

reader - the reader

Throws:

java.io.IOException - if the ARFF file is not read successfully

Instances

@Deprecated
public Instances(java.io.Reader reader,
                             int capacity)
                      throws java.io.IOException

Deprecated. instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.

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).

Parameters:

reader - the reader

capacity - the capacity

Throws:

java.lang.IllegalArgumentException - if the header is not read successfully or the capacity is negative.

java.io.IOException - if there is a problem with the reader.

See Also:

ArffLoader, ConverterUtils.DataSource

Instances

public Instances(Instances dataset)

Constructor copying all instances and references to the header information from the given set of instances.

Parameters:

dataset - the set to be copied

Instances

public Instances(Instances dataset,
                 int capacity)

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.

Parameters:

dataset - the instances from which the header information is to be taken

capacity - the capacity of the new dataset

Instances

public Instances(Instances source,
                 int first,
                 int toCopy)

Creates a new set of instances by copying a subset of another set.

Parameters:

source - the set of instances from which a subset is to be created

first - the index of the first instance to be copied

toCopy - the number of instances to be copied

Throws:

java.lang.IllegalArgumentException - if first and toCopy are out of range

Instances

public Instances(java.lang.String name,
                 java.util.ArrayList<Attribute> attInfo,
                 int capacity)

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.

Parameters:

name - the name of the relation

attInfo - the attribute information

capacity - the capacity of the set

Throws:

java.lang.IllegalArgumentException - if attribute names are not unique

Method Detail

stringFreeStructure

public Instances stringFreeStructure()

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.

Returns:

a copy of the instance structure.

add

public boolean add(Instance instance)

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.

Specified by:

add in interface java.util.Collection<Instance>

Specified by:

add in interface java.util.List<Instance>

Overrides:

add in class java.util.AbstractList<Instance>

Parameters:

instance - the instance to be added

add

public void add(int index,
                Instance instance)

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.

Specified by:

add in interface java.util.List<Instance>

Overrides:

add in class java.util.AbstractList<Instance>

Parameters:

index - position where instance is to be inserted

instance - the instance to be added

allAttributeWeightsIdentical

public boolean allAttributeWeightsIdentical()

Returns true if all attribute weights are the same and false otherwise. Returns true if there are no attributes. The class attribute (if set) is skipped when this test is performed.

allInstanceWeightsIdentical

public boolean allInstanceWeightsIdentical()

Returns true if all instance weights are the same and false otherwise. Returns true if there are no instances.

attribute

public Attribute attribute(int index)

Returns an attribute.

Parameters:

index - the attribute's index (index starts with 0)

Returns:

the attribute at the given position

attribute

public Attribute attribute(java.lang.String name)

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.

Parameters:

name - the attribute's name

Returns:

the attribute with the given name, null if the attribute can't be found

checkForAttributeType

public boolean checkForAttributeType(int attType)

Checks for attributes of the given type in the dataset

Parameters:

attType - the attribute type to look for

Returns:

true if attributes of the given type are present

checkForStringAttributes

public boolean checkForStringAttributes()

Checks for string attributes in the dataset

Returns:

true if string attributes are present, false otherwise

checkInstance

public boolean checkInstance(Instance instance)

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.

Parameters:

instance - the instance to check

Returns:

true if the instance is compatible with the dataset

classAttribute

public Attribute classAttribute()

Returns the class attribute.

Returns:

the class attribute

Throws:

UnassignedClassException - if the class is not set

classIndex

public int classIndex()

Returns the class attribute's index. Returns negative number if it's undefined.

Returns:

the class index as an integer

compactify

public void compactify()

Compactifies the set of instances. Decreases the capacity of the set so that it matches the number of instances in the set.

delete

public void delete()

Removes all instances from the set.

delete

public void delete(int index)

Removes an instance at the given position from the set.

Parameters:

index - the instance's position (index starts with 0)

deleteAttributeAt

public void deleteAttributeAt(int position)

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.

Parameters:

position - the attribute's position (position starts with 0)

Throws:

java.lang.IllegalArgumentException - if the given index is out of range or the class attribute is being deleted

deleteAttributeType

public void deleteAttributeType(int attType)

Deletes all attributes of the given type in the dataset. A deep copy of the attribute information is performed before an attribute is deleted.

Parameters:

attType - the attribute type to delete

Throws:

java.lang.IllegalArgumentException - if attribute couldn't be successfully deleted (probably because it is the class attribute).

deleteStringAttributes

public void deleteStringAttributes()

Deletes all string attributes in the dataset. A deep copy of the attribute information is performed before an attribute is deleted.

Throws:

java.lang.IllegalArgumentException - if string attribute couldn't be successfully deleted (probably because it is the class attribute).

See Also:

deleteAttributeType(int)

deleteWithMissing

public void deleteWithMissing(int attIndex)

Removes all instances with missing values for a particular attribute from the dataset.

Parameters:

attIndex - the attribute's index (index starts with 0)

deleteWithMissing

public void deleteWithMissing(Attribute att)

Removes all instances with missing values for a particular attribute from the dataset.

Parameters:

att - the attribute

deleteWithMissingClass

public void deleteWithMissingClass()

Removes all instances with a missing class value from the dataset.

Throws:

UnassignedClassException - if class is not set

enumerateAttributes

public java.util.Enumeration<Attribute> enumerateAttributes()

Returns an enumeration of all the attributes. The class attribute (if set) is skipped by this enumeration.

Returns:

enumeration of all the attributes.

enumerateInstances

public java.util.Enumeration<Instance> enumerateInstances()

Returns an enumeration of all instances in the dataset.

Returns:

enumeration of all instances in the dataset

equalHeadersMsg

public java.lang.String equalHeadersMsg(Instances dataset)

Checks if two headers are equivalent. If not, then returns a message why they differ.

Parameters:

dataset - another dataset

Returns:

null if the header of the given dataset is equivalent to this header, otherwise a message with details on why they differ

equalHeaders

public boolean equalHeaders(Instances dataset)

Checks if two headers are equivalent.

Parameters:

dataset - another dataset

Returns:

true if the header of the given dataset is equivalent to this header

firstInstance

public Instance firstInstance()

Returns the first instance in the set.

Returns:

the first instance in the set

getRandomNumberGenerator

public java.util.Random getRandomNumberGenerator(long seed)

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.

Parameters:

seed - the given seed

Returns:

the random number generator

insertAttributeAt

public void insertAttributeAt(Attribute att,
                              int position)

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.

Parameters:

att - the attribute to be inserted

position - the attribute's position (position starts with 0)

Throws:

java.lang.IllegalArgumentException - if the given index is out of range

instance

public Instance instance(int index)

Returns the instance at the given position.

Parameters:

index - the instance's index (index starts with 0)

Returns:

the instance at the given position

get

public Instance get(int index)

Returns the instance at the given position.

Specified by:

get in interface java.util.List<Instance>

Specified by:

get in class java.util.AbstractList<Instance>

Parameters:

index - the instance's index (index starts with 0)

Returns:

the instance at the given position

kthSmallestValue

public double kthSmallestValue(Attribute att,
                               int k)

Returns the kth-smallest attribute value of a numeric attribute.

Parameters:

att - the Attribute object

k - the value of k

Returns:

the kth-smallest value

kthSmallestValue

public double kthSmallestValue(int attIndex,
                               int 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.

Parameters:

attIndex - the attribute's index

k - the value of k

Returns:

the kth-smallest value

lastInstance

public Instance lastInstance()

Returns the last instance in the set.

Returns:

the last instance in the set

meanOrMode

public double meanOrMode(int attIndex)

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.

Parameters:

attIndex - the attribute's index (index starts with 0)

Returns:

the mean or the mode

meanOrMode

public double meanOrMode(Attribute att)

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.

Parameters:

att - the attribute

Returns:

the mean or the mode

numAttributes

public int numAttributes()

Returns the number of attributes.

Returns:

the number of attributes as an integer

numClasses

public int numClasses()

Returns the number of class labels.

Returns:

the number of class labels as an integer if the class attribute is nominal, 1 otherwise.

Throws:

UnassignedClassException - if the class is not set

numDistinctValues

public int numDistinctValues(int attIndex)

Returns the number of distinct values of a given attribute. The value 'missing' is not counted.

Parameters:

attIndex - the attribute (index starts with 0)

Returns:

the number of distinct values of a given attribute

numDistinctValues

public int numDistinctValues(Attribute att)

Returns the number of distinct values of a given attribute. The value 'missing' is not counted.

Parameters:

att - the attribute

Returns:

the number of distinct values of a given attribute

numInstances

public int numInstances()

Returns the number of instances in the dataset.

Returns:

the number of instances in the dataset as an integer

size

public int size()

Returns the number of instances in the dataset.

Specified by:

size in interface java.util.Collection<Instance>

Specified by:

size in interface java.util.List<Instance>

Specified by:

size in class java.util.AbstractCollection<Instance>

Returns:

the number of instances in the dataset as an integer

randomize

public void randomize(java.util.Random random)

Shuffles the instances in the set so that they are ordered randomly.

Parameters:

random - a random number generator

readInstance

@Deprecated
public boolean readInstance(java.io.Reader reader)
                                 throws java.io.IOException

Deprecated. instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.

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.

Parameters:

reader - the reader

Returns:

false if end of file has been reached

Throws:

java.io.IOException - if the information is not read successfully

See Also:

ArffLoader, ConverterUtils.DataSource

replaceAttributeAt

public void replaceAttributeAt(Attribute att,
                               int position)

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.

Parameters:

att - the attribute to be inserted

position - the attribute's position (position starts with 0)

Throws:

java.lang.IllegalArgumentException - if the given index is out of range

relationName

public java.lang.String relationName()

Returns the relation's name.

Returns:

the relation's name as a string

remove

public Instance remove(int index)

Removes the instance at the given position.

Specified by:

remove in interface java.util.List<Instance>

Overrides:

remove in class java.util.AbstractList<Instance>

Parameters:

index - the instance's index (index starts with 0)

Returns:

the instance at the given position

renameAttribute

public void renameAttribute(int att,
                            java.lang.String name)

Renames an attribute. This change only affects this dataset.

Parameters:

att - the attribute's index (index starts with 0)

name - the new name

setAttributeWeight

public void setAttributeWeight(Attribute att,
                               double weight)

Sets the weight of an attribute. This change only affects this dataset.

Parameters:

att - the attribute

weight - the new weight

setAttributeWeight

public void setAttributeWeight(int att,
                               double weight)

Sets the weight of an attribute. This change only affects this dataset.

Parameters:

att - the attribute's index (index starts with 0)

weight - the new weight

renameAttribute

public void renameAttribute(Attribute att,
                            java.lang.String name)

Renames an attribute. This change only affects this dataset.

Parameters:

att - the attribute

name - the new name

renameAttributeValue

public void renameAttributeValue(int att,
                                 int val,
                                 java.lang.String name)

Renames the value of a nominal (or string) attribute value. This change only affects this dataset.

Parameters:

att - the attribute's index (index starts with 0)

val - the value's index (index starts with 0)

name - the new name

renameAttributeValue

public void renameAttributeValue(Attribute att,
                                 java.lang.String val,
                                 java.lang.String name)

Renames the value of a nominal (or string) attribute value. This change only affects this dataset.

Parameters:

att - the attribute

val - the value

name - the new name

resample

public Instances resample(java.util.Random random)

Creates a new dataset of the same size as this dataset using random sampling with replacement.

Parameters:

random - a random number generator

Returns:

the new dataset

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random)

Creates a new dataset of the same size as this dataset 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[]).

Parameters:

random - a random number generator

Returns:

the new dataset

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     boolean[] sampled)

Creates a new dataset of the same size as this dataset 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[]).

Parameters:

random - a random number generator

sampled - an array indicating what has been sampled

Returns:

the new dataset

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     boolean representUsingWeights)

Creates a new dataset of the same size as this dataset using random sampling with replacement according to the current instance weights. See also resampleWithWeights(Random, double[], boolean[]).

Parameters:

random - a random number generator

representUsingWeights - if true, copies are represented using weights in resampled data

Returns:

the new dataset

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     boolean[] sampled,
                                     boolean representUsingWeights)

Creates a new dataset of the same size as this dataset using random sampling with replacement according to the current instance weights. See also resampleWithWeights(Random, double[], boolean[]).

Parameters:

random - a random number generator

sampled - an array indicating what has been sampled

representUsingWeights - if true, copies are represented using weights in resampled data

Returns:

the new dataset

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     boolean[] sampled,
                                     boolean representUsingWeights,
                                     double sampleSize)

Creates a new dataset from this dataset using random sampling with replacement according to current instance weights. The size of the sample can be specified as a percentage of this dataset. See also resampleWithWeights(Random, double[], boolean[]).

Parameters:

random - a random number generator

sampled - an array indicating what has been sampled, can be null

representUsingWeights - if true, copies are represented using weights in resampled data

sampleSize - size of the new dataset as a percentage of the size of this dataset

Returns:

the new dataset

Throws:

java.lang.IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     double[] weights)

Creates a new dataset of the same size as this dataset 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. See also resampleWithWeights(Random, double[], boolean[]).

Parameters:

random - a random number generator

weights - the weight vector

Returns:

the new dataset

Throws:

java.lang.IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     double[] weights,
                                     boolean[] sampled)

Creates a new dataset of the same size as this dataset 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).

Parameters:

random - a random number generator

weights - the weight vector

sampled - an array indicating what has been sampled, can be null

Returns:

the new dataset

Throws:

java.lang.IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     double[] weights,
                                     boolean[] sampled,
                                     boolean representUsingWeights)

Creates a new dataset of the same size as this dataset using random sampling with replacement according to the given weight vector. 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).

Parameters:

random - a random number generator

weights - the weight vector

sampled - an array indicating what has been sampled, can be null

representUsingWeights - if true, copies are represented using weights in resampled data

Returns:

the new dataset

Throws:

java.lang.IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

resampleWithWeights

public Instances resampleWithWeights(java.util.Random random,
                                     double[] weights,
                                     boolean[] sampled,
                                     boolean representUsingWeights,
                                     double sampleSize)

Creates a new dataset from this dataset using random sampling with replacement according to the given weight vector. 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). The size of the sample can be specified as a percentage of this dataset.

Parameters:

random - a random number generator

weights - the weight vector

sampled - an array indicating what has been sampled, can be null

representUsingWeights - if true, copies are represented using weights in resampled data

sampleSize - size of the new dataset as a percentage of the size of this dataset

Returns:

the new dataset

Throws:

java.lang.IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

set

public Instance set(int index,
                    Instance instance)

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.

Specified by:

set in interface java.util.List<Instance>

Overrides:

set in class java.util.AbstractList<Instance>

Parameters:

index - position where instance is to be inserted

instance - the instance to be inserted

Returns:

the instance previously at that position

setClass

public void setClass(Attribute att)

Sets the class attribute.

Parameters:

att - attribute to be the class

setClassIndex

public void setClassIndex(int classIndex)

Sets the class index of the set. If the class index is negative there is assumed to be no class. (ie. it is undefined)

Parameters:

classIndex - the new class index (index starts with 0)

Throws:

java.lang.IllegalArgumentException - if the class index is too big or < 0

setRelationName

public void setRelationName(java.lang.String newName)

Sets the relation's name.

Parameters:

newName - the new relation name.

sort

public void sort(int attIndex)

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.

Parameters:

attIndex - the attribute's index (index starts with 0)

sort

public void sort(Attribute att)

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.

Parameters:

att - the attribute

stableSort

public void stableSort(int attIndex)

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.

Parameters:

attIndex - the attribute's index (index starts with 0)

stableSort

public void stableSort(Attribute att)

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.

Parameters:

att - the attribute

stratify

public void stratify(int numFolds)

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).

Parameters:

numFolds - the number of folds in the cross-validation

Throws:

UnassignedClassException - if the class is not set

sumOfWeights

public double sumOfWeights()

Computes the sum of all the instances' weights.

Returns:

the sum of all the instances' weights as a double

testCV

public Instances testCV(int numFolds,
                        int numFold)

Creates the test set for one fold of a cross-validation on the dataset.

Parameters:

numFolds - the number of folds in the cross-validation. Must be greater than 1.

numFold - 0 for the first fold, 1 for the second, ...

Returns:

the test set as a set of weighted instances

Throws:

java.lang.IllegalArgumentException - if the number of folds is less than 2 or greater than the number of instances.

toString

public java.lang.String toString()

Returns the dataset as a string in ARFF format. Strings are quoted if they contain whitespace characters, or if they are a question mark.

Overrides:

toString in class java.util.AbstractCollection<Instance>

Returns:

the dataset in ARFF format as a string

trainCV

public Instances trainCV(int numFolds,
                         int numFold)

Creates the training set for one fold of a cross-validation on the dataset.

Parameters:

numFolds - the number of folds in the cross-validation. Must be greater than 1.

numFold - 0 for the first fold, 1 for the second, ...

Returns:

the training set

Throws:

java.lang.IllegalArgumentException - if the number of folds is less than 2 or greater than the number of instances.

trainCV

public Instances trainCV(int numFolds,
                         int numFold,
                         java.util.Random random)

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.

Parameters:

numFolds - the number of folds in the cross-validation. Must be greater than 1.

numFold - 0 for the first fold, 1 for the second, ...

random - the random number generator

Returns:

the training set

Throws:

java.lang.IllegalArgumentException - if the number of folds is less than 2 or greater than the number of instances.

variances

public double[] variances()

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.

Returns:

the array containing the variance values

variance

public double variance(int attIndex)

Computes the variance for a numeric attribute.

Parameters:

attIndex - the numeric attribute (index starts with 0)

Returns:

the variance if the attribute is numeric

Throws:

java.lang.IllegalArgumentException - if the attribute is not numeric

variance

public double variance(Attribute att)

Computes the variance for a numeric attribute.

Parameters:

att - the numeric attribute

Returns:

the variance if the attribute is numeric

Throws:

java.lang.IllegalArgumentException - if the attribute is not numeric

attributeStats

public AttributeStats attributeStats(int index)

Calculates summary statistics on the values that appear in this set of instances for a specified attribute.

Parameters:

index - the index of the attribute to summarize (index starts with 0)

Returns:

an AttributeStats object with it's fields calculated.

attributeToDoubleArray

public double[] attributeToDoubleArray(int index)

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.

Parameters:

index - the index of the attribute.

Returns:

an array containing the value of the desired attribute for each instance in the dataset.

toSummaryString

public java.lang.String toSummaryString()

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.

Returns:

a string summarizing the dataset

swap

public void swap(int i,
                 int j)

Swaps two instances in the set.

Parameters:

i - the first instance's index (index starts with 0)

j - the second instance's index (index starts with 0)

mergeInstances

public static Instances mergeInstances(Instances first,
                                       Instances second)

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.

Parameters:

first - the first set of Instances

second - the second set of Instances

Returns:

the merged set of Instances

Throws:

java.lang.IllegalArgumentException - if the datasets are not the same size

test

public static void test(java.lang.String[] argv)

Method for testing this class.

Parameters:

argv - should contain one element: the name of an ARFF file

main

public static void main(java.lang.String[] args)

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.

Parameters:

args - the commandline parameters

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision