Package weka.core

Class Instances

java.lang.Object
java.util.AbstractCollection<E>
java.util.AbstractList<Instance>
weka.core.Instances
All Implemented Interfaces:
Serializable, Iterable<Instance>, Collection<Instance>, List<Instance>, RevisionHandler
public class Instances extends AbstractList<Instance> implements 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: 15569 $
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:

  • Field Details

    • FILE_EXTENSION

      public static final String FILE_EXTENSION
      The filename extension that should be used for arff files
      See Also:

    • SERIALIZED_OBJ_FILE_EXTENSION

      public static final String SERIALIZED_OBJ_FILE_EXTENSION
      The filename extension that should be used for bin. serialized instances files
      See Also:

    • ARFF_RELATION

      public static final String ARFF_RELATION
      The keyword used to denote the start of an arff header
      See Also:

    • ARFF_DATA

      public static final String ARFF_DATA
      The keyword used to denote the start of the arff data section
      See Also:

  • Constructor Details

    • Instances

      public Instances(Reader reader) throws 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:
      IOException - if the ARFF file is not read successfully

    • Instances

      @Deprecated public Instances(Reader reader, int capacity) throws 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:
      IllegalArgumentException - if the header is not read successfully or the capacity is negative.
      IOException - if there is a problem with the reader.
      See Also:

    • 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:
      IllegalArgumentException - if first and toCopy are out of range

    • Instances

      public Instances(String name, 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:
      IllegalArgumentException - if attribute names are not unique

  • Method Details

    • 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 Collection<Instance>
      Specified by:
      add in interface List<Instance>
      Overrides:
      add in class 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 List<Instance>
      Overrides:
      add in class 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(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:
      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:
      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:
      IllegalArgumentException - if string attribute couldn't be successfully deleted (probably because it is the class attribute).
      See Also:

    • 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 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 Enumeration<Instance> enumerateInstances()
      Returns an enumeration of all instances in the dataset.
      Returns:
      enumeration of all instances in the dataset

    • equalHeadersMsg

      public 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 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:
      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 List<Instance>
      Specified by:
      get in class 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 Collection<Instance>
      Specified by:
      size in interface List<Instance>
      Specified by:
      size in class AbstractCollection<Instance>
      Returns:
      the number of instances in the dataset as an integer

    • randomize

      public void randomize(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(Reader reader) throws 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:
      IOException - if the information is not read successfully
      See Also:

    • 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:
      IllegalArgumentException - if the given index is out of range

    • relationName

      public 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 List<Instance>
      Overrides:
      remove in class 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, 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, 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, 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, String val, 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(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(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(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(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(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(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:
      IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

    • resampleWithWeights

      public 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. 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:
      IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

    • resampleWithWeights

      public 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. 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:
      IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

    • resampleWithWeights

      public Instances resampleWithWeights(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:
      IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

    • resampleWithWeights

      public Instances resampleWithWeights(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:
      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 List<Instance>
      Overrides:
      set in class 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:
      IllegalArgumentException - if the class index is too big or < 0

    • setRelationName

      public void setRelationName(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:
      IllegalArgumentException - if the number of folds is less than 2 or greater than the number of instances.

    • toString

      public 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 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:
      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, 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:
      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:
      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:
      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 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:
      IllegalArgumentException - if the datasets are not the same size

    • test

      public static void test(String[] argv)
      Method for testing this class.
      Parameters:
      argv - should contain one element: the name of an ARFF file

    • main

      public static void main(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 String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Returns:
      the revision