javax.visrec.ml.data

Interface DataSet<E>

Type Parameters:

E - type of data set elements

All Superinterfaces:

Iterable<E>

All Known Implementing Classes:

BasicDataSet

public interface DataSet<E>
extends Iterable<E>

Generic interface for all data sets for machine learning, independent of type of elements.

Since:

1.0

Author:

Zoran Sevarac

Nested Class Summary

Nested Classes

Modifier and Type	Interface and Description
static class	DataSet.Column
static class	DataSet.ColumnType

Method Summary

Modifier and Type	Method and Description
default DataSet<E>	add(E item) Adds an element to this data set.
default DataSet<E>	addAll(DataSet<E> dataSet) Add an existing DataSet to the current DataSet
default void	clear() Clear items of the DataSet
default E	get(int index) Get an item from the DataSet
List<E>	getItems() Get a collection of the items in the DataSet
default boolean	isEmpty() Determines whether the DataSet is empty or not.
default void	shuffle() Shuffles the data set.
default void	shuffle(Random rnd) Shuffles the data set using the specified random number generator.
default int	size() Get the number of elements in DataSet
DataSet<E>[]	split(double... parts) Split data set into parts of specified sizes
default DataSet<E>[]	split(double part) Split data set in two parts, one with size of specified percentage, and other with rest of the data set
DataSet<E>[]	split(int numParts) Split dataset into specified number of equally sized parts.
DataSet<E>[]	split(int numParts, Random rnd) Split dataset into specified number of equally sized parts, using specified random generator.
DataSet<E>[]	split(Random rnd, double... parts) Split data set into parts of specified sizes using specified random generator

Methods inherited from interface java.lang.Iterable

forEach, iterator, spliterator

Method Detail

getItems

List<E> getItems()

Get a collection of the items in the DataSet

Returns:

Collection

add

default DataSet<E> add(E item)

Adds an element to this data set.

Parameters:

item - data set item to add to the data set

Returns:

current instance of DataSet

addAll

default DataSet<E> addAll(DataSet<E> dataSet)

Add an existing DataSet to the current DataSet

Parameters:

dataSet - existing DataSet

Returns:

current instance of DataSet

get

default E get(int index)

Get an item from the DataSet

Parameters:

index - index as int which corresponds with the index of the DataSet

Returns:

item from the DataSet

clear

default void clear()

Clear items of the DataSet

isEmpty

default boolean isEmpty()

Determines whether the DataSet is empty or not.

Returns:

true if the DataSet is empty, otherwise false

size

default int size()

Get the number of elements in DataSet

Returns:

size in int

split

DataSet<E>[] split(int numParts)

Split dataset into specified number of equally sized parts.

Parameters:

numParts - number of parts to be returned

Returns:

multiple DataSet in an array.

split

DataSet<E>[] split(int numParts,
                   Random rnd)

Split dataset into specified number of equally sized parts, using specified random generator.

Parameters:

numParts - number of parts/subsets to return

rnd - random number generator

Returns:

multiple DataSet in an array.

split

default DataSet<E>[] split(double part)

Split data set in two parts, one with size of specified percentage, and other with rest of the data set

Parameters:

part - specified percentage of the first DataSet

Returns:

multiple DataSet in an array.

split

DataSet<E>[] split(double... parts)

Split data set into parts of specified sizes

Parameters:

parts - specific sizes of DataSet

Returns:

array of DataSet

split

DataSet<E>[] split(Random rnd,
                   double... parts)

Split data set into parts of specified sizes using specified random generator

Parameters:

rnd - random generator

parts - specific sizes of DataSet

Returns:

array of DataSet

shuffle

default void shuffle()

Shuffles the data set.

shuffle

default void shuffle(Random rnd)

Shuffles the data set using the specified random number generator.

Parameters:

rnd - random generator