java.lang.Object
- org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.Instances

All Implemented Interfaces:

java.io.Serializable
```
public class Instances
extends java.lang.Object
implements java.io.Serializable
```
Class for handling an ordered set of weighted instances.
Typical usage (code from the main() method of this class):
... // Read all the instances in the file reader = new FileReader(filename); instances = new Instances(reader); // Make the last attribute be the class instances.setClassIndex(instances.numAttributes() - 1); // Print header and instances. System.out.println("\nDataset:\n"); System.out.println(instances); ...
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: 1.58.2.6 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`static java.lang.String`	`FILE_EXTENSION`	The filename extension that should be used for arff files
`protected FastVector`	`m_Attributes`	The attribute information.
`protected int`	`m_ClassIndex`	The class attribute's index
`protected int[]`	`m_IndicesBuffer`	Buffer of indices for sparse instance
`protected FastVector`	`m_Instances`	The instances.
`protected java.lang.String`	`m_RelationName`	The dataset's name.
`protected double[]`	`m_ValueBuffer`	Buffer of values for sparse instance
`static java.lang.String`	`SERIALIZED_OBJ_FILE_EXTENSION`	The filename extension that should be used for bin.

Constructor Summary

Constructors
Constructor	Description
`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)`	Reads the header of an ARFF file from a reader and reserves space for the given number of instances.
`Instances(java.lang.String name, FastVector attInfo, int capacity)`	Creates an empty set of instances.
`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.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`add(Instance instance)`	Adds one instance to the end of the set.
`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`	`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.
`protected void`	`copyInstances(int from, Instances dest, int num)`	Copies instances from one set to the end of another one.
`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`	`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.
`java.util.Enumeration`	`enumerateAttributes()`	Returns an enumeration of all the attributes.
`java.util.Enumeration`	`enumerateInstances()`	Returns an enumeration of all instances in the dataset.
`boolean`	`equalHeaders(Instances dataset)`	Checks if two headers are equivalent.
`protected void`	`errms(java.io.StreamTokenizer tokenizer, java.lang.String theMsg)`	Throws error message with line number and last token read.
`Instance`	`firstInstance()`	Returns the first instance in the set.
`protected void`	`freshAttributeInfo()`	Replaces the attribute information by a clone of itself.
`protected void`	`getFirstToken(java.io.StreamTokenizer tokenizer)`	Gets next token, skipping empty lines.
`protected void`	`getIndex(java.io.StreamTokenizer tokenizer)`	Gets index, checking for a premature and of line.
`protected boolean`	`getInstance(java.io.StreamTokenizer tokenizer, boolean flag)`	Reads a single instance using the tokenizer and appends it to the dataset.
`protected boolean`	`getInstanceFull(java.io.StreamTokenizer tokenizer, boolean flag)`	Reads a single instance using the tokenizer and appends it to the dataset.
`protected boolean`	`getInstanceSparse(java.io.StreamTokenizer tokenizer, boolean flag)`	Reads a single instance using the tokenizer and appends it to the dataset.
`protected void`	`getLastToken(java.io.StreamTokenizer tokenizer, boolean endOfFileOk)`	Gets token and checks if its end of line.
`protected void`	`getNextToken(java.io.StreamTokenizer tokenizer)`	Gets next token, checking for a premature and of line.
`java.util.Random`	`getRandomNumberGenerator(long seed)`	Returns a random number generator.
`protected void`	`initTokenizer(java.io.StreamTokenizer tokenizer)`	Initializes the StreamTokenizer used for reading the ARFF file.
`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.
`protected java.lang.String`	`instancesAndWeights()`	Returns string including all instances, their weights and their indices in the original dataset.
`double`	`kthSmallestValue(int attIndex, int k)`	Returns the kth-smallest attribute value of a numeric attribute.
`double`	`kthSmallestValue(Attribute att, 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(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.
`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(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.
`protected int`	`partition(int attIndex, int l, int r)`	Partitions the instances around a pivot.
`protected void`	`quickSort(int attIndex, int left, int right)`	Implements quicksort according to Manber's "Introduction to Algorithms".
`void`	`randomize(java.util.Random random)`	Shuffles the instances in the set so that they are ordered randomly.
`protected void`	`readHeader(java.io.StreamTokenizer tokenizer)`	Reads and stores header of an ARFF file.
`boolean`	`readInstance(java.io.Reader reader)`	Reads a single instance from the reader and appends it to the dataset.
`protected void`	`readTillEOL(java.io.StreamTokenizer tokenizer)`	Reads and skips all tokens before next end of line token.
`java.lang.String`	`relationName()`	Returns the relation's name.
`void`	`renameAttribute(int att, java.lang.String name)`	Renames an attribute.
`void`	`renameAttribute(Attribute att, java.lang.String name)`	Renames an attribute.
`void`	`renameAttributeValue(int att, int val, java.lang.String name)`	Renames the value of a nominal (or string) attribute value.
`void`	`renameAttributeValue(Attribute att, java.lang.String val, java.lang.String name)`	Renames the value of a nominal (or string) attribute value.
`Instances`	`resample(java.util.Random random)`	Creates a new dataset of the same size using random sampling with replacement.
`Instances`	`resampleWithWeights(java.util.Random random)`	Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
`Instances`	`resampleWithWeights(java.util.Random random, double[] weights)`	Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
`protected int`	`select(int attIndex, int left, int right, int k)`	Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
`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.
`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).
`protected void`	`stratStep(int numFolds)`	Help function needed for stratification of set.
`Instances`	`stringFreeStructure()`	Create a copy of the structure, but "cleanse" string types (i.e.
`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(int attIndex)`	Computes the variance for a numeric attribute.
`double`	`variance(Attribute att)`	Computes the variance for a numeric attribute.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - FILE_EXTENSION
```
public static java.lang.String FILE_EXTENSION
```
    The filename extension that should be used for arff files
  - SERIALIZED_OBJ_FILE_EXTENSION
```
public static java.lang.String SERIALIZED_OBJ_FILE_EXTENSION
```
    The filename extension that should be used for bin. serialized instances files
  - m_RelationName
```
protected java.lang.String m_RelationName
```
    The dataset's name.
  - m_Attributes
```
protected FastVector m_Attributes
```
    The attribute information.
  - m_Instances
```
protected FastVector m_Instances
```
    The instances.
  - m_ClassIndex
```
protected int m_ClassIndex
```
    The class attribute's index
  - m_ValueBuffer
```
protected double[] m_ValueBuffer
```
    Buffer of values for sparse instance
  - m_IndicesBuffer
```
protected int[] m_IndicesBuffer
```
    Buffer of indices for sparse instance
- 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
```
public Instances(java.io.Reader reader,
                 int capacity)
          throws java.io.IOException
```
    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.
  - Instances
```
public Instances(Instances dataset)
```
    Constructor copying all instances and references to the header information from the given set of instances.
    
    Parameters:
    
    instances - 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:
    
    instances - 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,
                 FastVector 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
- Method Detail
  - stringFreeStructure
```
public Instances stringFreeStructure()
```
    Create a copy of the structure, but "cleanse" string types (i.e. doesn't contain references to the strings seen in the past).
    
    Returns:
    
    a copy of the instance structure.
  - add
```
public void 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 values are not transferred.
    
    Parameters:
    
    instance - the instance to be added
  - attribute
```
public Attribute attribute(int index)
```
    Returns an attribute.
    
    Parameters:
    
    index - the attribute's index
    
    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
  - 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.
    
    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
  - deleteAttributeAt
```
public void deleteAttributeAt(int position)
```
    Deletes an attribute at the given position (0 to numAttributes() - 1). A deep copy of the attribute information is performed before the attribute is deleted.
    
    Parameters:
    
    pos - the attribute's position
    
    Throws:
    
    java.lang.IllegalArgumentException - if the given index is out of range or the class attribute is being deleted
  - 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).
  - 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
  - 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 enumerateAttributes()
```
    Returns an enumeration of all the attributes.
    
    Returns:
    
    enumeration of all the attributes.
  - enumerateInstances
```
public java.util.Enumeration enumerateInstances()
```
    Returns an enumeration of all instances in the dataset.
    
    Returns:
    
    enumeration of all instances in the dataset
  - 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, and performs a deep copy of the existing attribute information.
    
    Parameters:
    
    att - the attribute to be inserted
    
    pos - the attribute's position
    
    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
    
    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. Note that calling this method will change the order of the data!
    
    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 that calling this method will change the order of the data! The number of non-missing values in the data must be as least as last as k for this to work.
    
    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
    
    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. Returns the number of instances if the attribute is a string attribute. The value 'missing' is not counted.
    
    Parameters:
    
    attIndex - the attribute
    
    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. Returns the number of instances if the attribute is a string 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
  - 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
```
public boolean readInstance(java.io.Reader reader)
                     throws java.io.IOException
```
    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
  - relationName
```
public java.lang.String relationName()
```
    Returns the relation's name.
    
    Returns:
    
    the relation's name as a string
  - 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
    
    name - the new name
  - 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
    
    val - the value's index
    
    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 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 using random sampling with replacement according to the current instance weights. The weights of the instances in the new dataset are set to one.
    
    Parameters:
    
    random - a random number generator
    
    Returns:
    
    the new dataset
  - resampleWithWeights
```
public Instances resampleWithWeights(java.util.Random random,
                                     double[] weights)
```
    Creates a new dataset of the same size using random sampling with replacement according to the given weight vector. The weights of the instances in the new dataset are set to one. The length of the weight vector has to be the same as the number of instances in the dataset, and all weights have to be positive.
    
    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.
  - 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
    
    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
  - 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
  - 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.lang.Object
    
    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.
  - variance
```
public double variance(int attIndex)
```
    Computes the variance for a numeric attribute.
    
    Parameters:
    
    attIndex - the numeric attribute
    
    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.
    
    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
  - getInstance
```
protected boolean getInstance(java.io.StreamTokenizer tokenizer,
                              boolean flag)
                       throws java.io.IOException
```
    Reads a single instance using the tokenizer and appends it to the dataset. Automatically expands the dataset if it is not large enough to hold the instance.
    
    Parameters:
    
    tokenizer - the tokenizer to be used
    
    flag - if method should test for carriage return after each instance
    
    Returns:
    
    false if end of file has been reached
    
    Throws:
    
    java.io.IOException - if the information is not read successfully
  - getInstanceSparse
```
protected boolean getInstanceSparse(java.io.StreamTokenizer tokenizer,
                                    boolean flag)
                             throws java.io.IOException
```
    Reads a single instance using the tokenizer and appends it to the dataset. Automatically expands the dataset if it is not large enough to hold the instance.
    
    Parameters:
    
    tokenizer - the tokenizer to be used
    
    flag - if method should test for carriage return after each instance
    
    Returns:
    
    false if end of file has been reached
    
    Throws:
    
    java.io.IOException - if the information is not read successfully
  - getInstanceFull
```
protected boolean getInstanceFull(java.io.StreamTokenizer tokenizer,
                                  boolean flag)
                           throws java.io.IOException
```
    Reads a single instance using the tokenizer and appends it to the dataset. Automatically expands the dataset if it is not large enough to hold the instance.
    
    Parameters:
    
    tokenizer - the tokenizer to be used
    
    flag - if method should test for carriage return after each instance
    
    Returns:
    
    false if end of file has been reached
    
    Throws:
    
    java.io.IOException - if the information is not read successfully
  - readHeader
```
protected void readHeader(java.io.StreamTokenizer tokenizer)
                   throws java.io.IOException
```
    Reads and stores header of an ARFF file.
    
    Parameters:
    
    tokenizer - the stream tokenizer
    
    Throws:
    
    java.io.IOException - if the information is not read successfully
  - copyInstances
```
protected void copyInstances(int from,
                             Instances dest,
                             int num)
```
    Copies instances from one set to the end of another one.
    
    Parameters:
    
    source - the source of the instances
    
    from - the position of the first instance to be copied
    
    dest - the destination for the instances
    
    num - the number of instances to be copied
  - errms
```
protected void errms(java.io.StreamTokenizer tokenizer,
                     java.lang.String theMsg)
              throws java.io.IOException
```
    Throws error message with line number and last token read.
    
    Parameters:
    
    theMsg - the error message to be thrown
    
    tokenizer - the stream tokenizer
    
    Throws:
    
    IOExcpetion - containing the error message
    
    java.io.IOException
  - freshAttributeInfo
```
protected void freshAttributeInfo()
```
    Replaces the attribute information by a clone of itself.
  - getFirstToken
```
protected void getFirstToken(java.io.StreamTokenizer tokenizer)
                      throws java.io.IOException
```
    Gets next token, skipping empty lines.
    
    Parameters:
    
    tokenizer - the stream tokenizer
    
    Throws:
    
    java.io.IOException - if reading the next token fails
  - getIndex
```
protected void getIndex(java.io.StreamTokenizer tokenizer)
                 throws java.io.IOException
```
    Gets index, checking for a premature and of line.
    
    Parameters:
    
    tokenizer - the stream tokenizer
    
    Throws:
    
    java.io.IOException - if it finds a premature end of line
  - getLastToken
```
protected void getLastToken(java.io.StreamTokenizer tokenizer,
                            boolean endOfFileOk)
                     throws java.io.IOException
```
    Gets token and checks if its end of line.
    
    Parameters:
    
    tokenizer - the stream tokenizer
    
    Throws:
    
    java.io.IOException - if it doesn't find an end of line
  - getNextToken
```
protected void getNextToken(java.io.StreamTokenizer tokenizer)
                     throws java.io.IOException
```
    Gets next token, checking for a premature and of line.
    
    Parameters:
    
    tokenizer - the stream tokenizer
    
    Throws:
    
    java.io.IOException - if it finds a premature end of line
  - initTokenizer
```
protected void initTokenizer(java.io.StreamTokenizer tokenizer)
```
    Initializes the StreamTokenizer used for reading the ARFF file.
    
    Parameters:
    
    tokenizer - the stream tokenizer
  - instancesAndWeights
```
protected java.lang.String instancesAndWeights()
```
    Returns string including all instances, their weights and their indices in the original dataset.
    
    Returns:
    
    description of instance and its weight as a string
  - partition
```
protected int partition(int attIndex,
                        int l,
                        int r)
```
    Partitions the instances around a pivot. Used by quicksort and kthSmallestValue.
    
    Parameters:
    
    attIndex - the attribute's index
    
    left - the first index of the subset
    
    right - the last index of the subset
    
    Returns:
    
    the index of the middle element
  - quickSort
```
protected void quickSort(int attIndex,
                         int left,
                         int right)
```
    Implements quicksort according to Manber's "Introduction to Algorithms".
    
    Parameters:
    
    attIndex - the attribute's index
    
    left - the first index of the subset to be sorted
    
    right - the last index of the subset to be sorted
  - readTillEOL
```
protected void readTillEOL(java.io.StreamTokenizer tokenizer)
                    throws java.io.IOException
```
    Reads and skips all tokens before next end of line token.
    
    Parameters:
    
    tokenizer - the stream tokenizer
    
    Throws:
    
    java.io.IOException
  - select
```
protected int select(int attIndex,
                     int left,
                     int right,
                     int k)
```
    Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
    
    Parameters:
    
    attIndex - the attribute's index
    
    left - the first index of the subset
    
    right - the last index of the subset
    
    k - the value of k
    
    Returns:
    
    the index of the kth-smallest element
  - stratStep
```
protected void stratStep(int numFolds)
```
    Help function needed for stratification of set.
    
    Parameters:
    
    numFolds - the number of folds for the stratification
  - swap
```
public void swap(int i,
                 int j)
```
    Swaps two instances in the set.
    
    Parameters:
    
    i - the first instance's index
    
    j - the second instance's index
  - 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 <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.
    Parameters:
    
    args - the commandline parameters

Class Instances

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

FILE_EXTENSION

SERIALIZED_OBJ_FILE_EXTENSION

m_RelationName

m_Attributes

m_Instances

m_ClassIndex

m_ValueBuffer

m_IndicesBuffer

Constructor Detail

Instances

Instances

Instances

Instances

Instances

Instances

Method Detail

stringFreeStructure

add

attribute

attribute

checkForStringAttributes

checkInstance

classAttribute

classIndex

compactify

delete

delete

deleteAttributeAt

deleteStringAttributes

deleteWithMissing

deleteWithMissing

deleteWithMissingClass

enumerateAttributes

enumerateInstances

equalHeaders

firstInstance

getRandomNumberGenerator

insertAttributeAt

instance

kthSmallestValue

kthSmallestValue

lastInstance

meanOrMode

meanOrMode

numAttributes

numClasses

numDistinctValues

numDistinctValues

numInstances

randomize

readInstance

relationName

renameAttribute

renameAttribute

renameAttributeValue

renameAttributeValue

resample

resampleWithWeights

resampleWithWeights

setClass

setClassIndex

setRelationName

sort

sort

stratify

sumOfWeights

testCV

toString

trainCV

trainCV

variance

variance

attributeStats

attributeToDoubleArray