org.encog.ml.data

Interface MLDataSet

All Superinterfaces:

java.lang.Iterable<MLDataPair>

All Known Subinterfaces:

MLSequenceSet, NeuralDataSet

All Known Implementing Classes:

AutoFloatDataSet, BasicMLDataSet, BasicMLSequenceSet, BasicNeuralDataSet, BatchDataSet, BufferedMLDataSet, CSVNeuralDataSet, EnsembleDataSet, FoldedDataSet, ImageMLDataSet, MarketMLDataSet, MatrixMLDataSet, SQLNeuralDataSet, TemporalMLDataSet, VersatileMLDataSet

public interface MLDataSet
extends java.lang.Iterable<MLDataPair>

An interface designed to abstract classes that store machine learning data. This interface is designed to provide EngineDataSet objects. These can be used to train machine learning methods using both supervised and unsupervised training. Some implementations of this interface are memory based. That is they store the entire contents of the dataset in memory. Other implementations of this interface are not memory based. These implementations read in data as it is needed. This allows very large datasets to be used. Typically the add methods are not supported on non-memory based datasets.

Method Summary

Modifier and Type	Method and Description
void	add(MLData data1) Add a object to the dataset.
void	add(MLData inputData, MLData idealData) Add a set of input and ideal data to the dataset.
void	add(MLDataPair inputData) Add a an object to the dataset.
void	close() Close this datasource and release any resources obtained by it, including any iterators created.
MLDataPair	get(int index)
int	getIdealSize()
int	getInputSize()
void	getRecord(long index, MLDataPair pair) Read an individual record, specified by index, in random order.
long	getRecordCount() Determine the total number of records in the set.
boolean	isSupervised()
MLDataSet	openAdditional() Opens an additional instance of this dataset.
int	size()

Methods inherited from interface java.lang.Iterable

forEach, iterator, spliterator

Method Detail

getIdealSize

int getIdealSize()

Returns:

The size of the ideal data.

getInputSize

int getInputSize()

Returns:

The size of the input data.

isSupervised

boolean isSupervised()

Returns:

True if this is a supervised training set.

getRecordCount

long getRecordCount()

Determine the total number of records in the set.

Returns:

The total number of records in the set.

getRecord

void getRecord(long index,
               MLDataPair pair)

Read an individual record, specified by index, in random order.

Parameters:

index - The index to read.

pair - The pair that the record will be copied into.

openAdditional

MLDataSet openAdditional()

Opens an additional instance of this dataset.

Returns:

The new instance.

add

void add(MLData data1)

Add a object to the dataset. This is used with unsupervised training, as no ideal output is provided. Note: not all implemenations support the add methods.

Parameters:

data1 - The data item to be added.

add

void add(MLData inputData,
         MLData idealData)

Add a set of input and ideal data to the dataset. This is used with supervised training, as ideal output is provided. Note: not all implementations support the add methods.

Parameters:

inputData - Input data.

idealData - Ideal data.

add

void add(MLDataPair inputData)

Add a an object to the dataset. This is used with unsupervised training, as no ideal output is provided. Note: not all implementations support the add methods.

Parameters:

inputData - A MLDataPair object that contains both input and ideal data.

close

void close()

Close this datasource and release any resources obtained by it, including any iterators created.

size

int size()

get

MLDataPair get(int index)