it.uniroma2.sag.kelp.data.dataset

Interface Dataset

All Known Implementing Classes:

SimpleDataset

public interface Dataset

Dataset is a set of Examples

Author:

Simone Filice

Method Summary

Methods

Modifier and Type	Method and Description
void	addExample(Example e)
List<Label>	getClassificationLabels() Returns all the classification labels in the dataset.
List<Example>	getExamples() Returns an array containing all the stored examples
Example	getNextExample() Returns the next n Examples stored in the Dataset or a fewer number if n examples are not available.
List<Example>	getNextExamples(int n) Returns the next Example stored in the Dataset
int	getNumberOfExamples() Returns the number of Examples in the dataset
int	getNumberOfNegativeExamples(Label positiveClass) Returns the number of negative Examples of a given class
int	getNumberOfPositiveExamples(Label positiveClass) Returns the number of positive Examples of a given class
Example	getRandExample()
List<Example>	getRandExamples(int k)
List<Label>	getRegressionProperties() Returns all the regression properties in the dataset.
Dataset	getShuffledDataset()
Vector	getZeroVector(String representationIdentifier) Returns a zero vector compliant with the representation identifier by representationIdentifier containings all zero
boolean	hasNextExample() Returns a boolean declaring whether there are other Examples in the dataset
void	manipulate(Manipulator... manipulators) Manipulates all the examples in the dataset accordingly to the strategies defined by the given manipulators.
void	reset() Reset the reading pointer
void	setSeed(long seed) Sets the seed of the random generator used to shuffling examples and getting random examples

Method Detail

addExample

void addExample(Example e)

getNextExample

Example getNextExample()

Returns the next n Examples stored in the Dataset or a fewer number if n examples are not available.

Returns:

the next n Examples

getNextExamples

List<Example> getNextExamples(int n)

Returns the next Example stored in the Dataset

Parameters:

n - the number of examples to be returned

Returns:

the next Example

hasNextExample

boolean hasNextExample()

Returns a boolean declaring whether there are other Examples in the dataset

Returns:

true if and only if there is at least another Example in the dataset

reset

void reset()

Reset the reading pointer

getNumberOfPositiveExamples

int getNumberOfPositiveExamples(Label positiveClass)

Returns the number of positive Examples of a given class

Parameters:

positiveClass - the class whose number of positive Examples are required

Returns:

the number of positive Examples of positiveClass

getNumberOfNegativeExamples

int getNumberOfNegativeExamples(Label positiveClass)

Returns the number of negative Examples of a given class

Parameters:

positiveClass - the class whose number of negative Examples are required

Returns:

the number of negative Examples of positiveClass

getNumberOfExamples

int getNumberOfExamples()

Returns the number of Examples in the dataset

Returns:

the number of Examples in the dataset

getClassificationLabels

List<Label> getClassificationLabels()

Returns all the classification labels in the dataset.

Returns:

the classification labels in the dataset

getRegressionProperties

List<Label> getRegressionProperties()

Returns all the regression properties in the dataset.

Returns:

the regression properties in the dataset

getExamples

List<Example> getExamples()

Returns an array containing all the stored examples

Returns:

the stored examples

getZeroVector

Vector getZeroVector(String representationIdentifier)

Returns a zero vector compliant with the representation identifier by representationIdentifier containings all zero

Parameters:

representationIdentifier - the identifier of the representation

Returns:

a zero vector compliant with the representation identifier by representationIdentifier containings all zero

getRandExample

Example getRandExample()

Returns:

a random example

getRandExamples

List<Example> getRandExamples(int k)

Parameters:

k - the number of examples to be returned

Returns:

a list containing k random examples

getShuffledDataset

Dataset getShuffledDataset()

Returns:

a Dataset containing all the examples in this Dataset in a shuffled order

setSeed

void setSeed(long seed)

Sets the seed of the random generator used to shuffling examples and getting random examples

Parameters:

seed - the seed of the random generator

manipulate

void manipulate(Manipulator... manipulators)

Manipulates all the examples in the dataset accordingly to the strategies defined by the given manipulators.

NOTE: If more than one manipulator is adopted, they will be applied in the same order of the manipulator in the array

Parameters:

manipulators - the manipulators that must be applied to all the examples in the dataset