Java weka.core Instances fields, constructors, methods, implement or subclass

Introduction

In this page you can find the methods, fields and constructors for weka.core Instances.

The text is from its open source code.

Subclass

weka.core.Instances has subclasses.
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Field

String	SERIALIZED_OBJ_FILE_EXTENSION The filename extension that should be used for bin.
String	ARFF_RELATION The keyword used to denote the start of an arff header
String	ARFF_DATA The keyword used to denote the start of the arff data section

Constructor

	Instances(Instances source, int first, int toCopy) Creates a new set of instances by copying a subset of another set.
	Instances(String name, ArrayList attInfo, int capacity) Creates an empty set of instances.
	Instances(Reader reader) Reads an ARFF file from a reader, and assigns a weight of one to each instance.
	Instances(Instances dataset) Constructor copying all instances and references to the header information from the given set of instances.
	Instances(Reader reader, int capacity) Reads the header of an ARFF file from a reader and reserves space for the given number of instances.
	Instances(Instances dataset, int capacity) Constructor creating an empty set of instances.

Method

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.
Attribute	attribute(int index) Returns an attribute.
Attribute	attribute(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(int index) Removes an instance at the given position from the set.
void	delete() Removes all instances 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(int attIndex) Removes all instances with missing values for a particular attribute from the dataset.
void	deleteWithMissing(Attribute att) 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.
Enumeration	enumerateAttributes() Returns an enumeration of all the attributes.
Enumeration	enumerateInstances() Returns an enumeration of all instances in the dataset.
boolean	equalHeaders(Instances dataset) Checks if two headers are equivalent.
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.
Random	getRandomNumberGenerator(long seed) Returns a random number generator.
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.
double	meanOrMode(int attIndex) Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
double	meanOrMode(Attribute att) Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
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(int attIndex) Returns the number of distinct values of a given attribute.
int	numDistinctValues(Attribute att) Returns the number of distinct values of a given attribute.
int	numInstances() Returns the number of instances in the dataset.
void	randomize(Random random) Shuffles the instances in the set so that they are ordered randomly.
boolean	readInstance(Reader reader) Reads a single instance from the reader and appends it to the dataset.
String	relationName() Returns the relation's name.
Instance	remove(int index) Removes the instance at the given position.
void	renameAttribute(int att, String name) Renames an attribute.
void	renameAttribute(Attribute att, String name) Renames an attribute.
void	renameAttributeValue(int att, int val, String name) Renames the value of a nominal (or string) attribute value.
void	renameAttributeValue(Attribute att, String val, String name) Renames the value of a nominal (or string) attribute value.
Instances	resample(Random random) Creates a new dataset of the same size as this dataset using random sampling with replacement.
Instances	resampleWithWeights(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(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(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(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(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(Random random) Creates a new dataset of the same size as this dataset using random sampling with replacement according to the current instance weights.
Instance	set(int index, Instance instance) Replaces the instance at the given position.
void	setClass(Attribute att) Sets the class attribute.
void	setClassIndex(int classIndex) Sets the class index of the set.
void	setRelationName(String newName) Sets the relation's name.
int	size() Returns the number of instances in the dataset.
void	sort(int attIndex) Sorts the instances based on an attribute.
void	sort(Attribute att) Sorts the instances based on an attribute.
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.
Instances	testCV(int numFolds, int numFold) Creates the test set for one fold of a cross-validation on the dataset.
String	toString() Returns the dataset as a string in ARFF format.
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, Random random) Creates the training set for one fold of a cross-validation on the dataset.
double	variance(int attIndex) Computes the variance for a numeric attribute.
double	variance(Attribute att) Computes the variance for a numeric attribute.
double[]	variances() Computes the variance for all numeric attributes simultaneously.