org.encog.ml.data.auto
Class AutoFloatDataSet

java.lang.Object
  org.encog.ml.data.auto.AutoFloatDataSet

All Implemented Interfaces:

Serializable, Iterable<MLDataPair>, MLDataSet

public class AutoFloatDataSet
extends Object
implements Serializable, MLDataSet

See Also:

Serialized Form

Nested Class Summary

class

AutoFloatDataSet.AutoFloatIterator

Constructor Summary

AutoFloatDataSet(int theInputCount, int theIdealCount, int theInputWindowSize, int theOutputWindowSize)

Method Summary

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	addColumn(float[] data)
void	close() Close this datasource and release any resources obtained by it, including any iterators created.
MLDataPair	get(int index)
int	getIdealSize()
int	getInputSize()
float	getNormalizedMax()
float	getNormalizedMin()
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	isNormalizationEnabled()
boolean	isSupervised()
Iterator<MLDataPair>	iterator()
void	loadCSV(String filename, boolean headers, CSVFormat format, int[] input, int[] ideal)
MLDataSet	openAdditional() Opens an additional instance of this dataset.
void	setNormalizationEnabled(boolean normalizationEnabled)
void	setNormalizedMax(float normalizedMax)
void	setNormalizedMin(float normalizedMin)
int	size()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AutoFloatDataSet

public AutoFloatDataSet(int theInputCount,
                        int theIdealCount,
                        int theInputWindowSize,
                        int theOutputWindowSize)

Method Detail

iterator

public Iterator<MLDataPair> iterator()

Specified by:

iterator in interface Iterable<MLDataPair>

getIdealSize

public int getIdealSize()

Specified by:

getIdealSize in interface MLDataSet

Returns:

The size of the ideal data.

getInputSize

public int getInputSize()

Specified by:

getInputSize in interface MLDataSet

Returns:

The size of the input data.

isSupervised

public boolean isSupervised()

Specified by:

isSupervised in interface MLDataSet

Returns:

True if this is a supervised training set.

getRecordCount

public long getRecordCount()

Description copied from interface: MLDataSet

Determine the total number of records in the set.

Specified by:

getRecordCount in interface MLDataSet

Returns:

The total number of records in the set.

getRecord

public void getRecord(long index,
                      MLDataPair pair)

Description copied from interface: MLDataSet

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

Specified by:

getRecord in interface MLDataSet

Parameters:

index - The index to read.

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

openAdditional

public MLDataSet openAdditional()

Description copied from interface: MLDataSet

Opens an additional instance of this dataset.

Specified by:

openAdditional in interface MLDataSet

Returns:

The new instance.

add

public void add(MLData data1)

Description copied from interface: MLDataSet

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.

Specified by:

add in interface MLDataSet

Parameters:

data1 - The data item to be added.

add

public void add(MLData inputData,
                MLData idealData)

Description copied from interface: MLDataSet

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.

Specified by:

add in interface MLDataSet

Parameters:

inputData - Input data.

idealData - Ideal data.

add

public void add(MLDataPair inputData)

Description copied from interface: MLDataSet

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.

Specified by:

add in interface MLDataSet

Parameters:

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

close

public void close()

Description copied from interface: MLDataSet

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

Specified by:

close in interface MLDataSet

size

public int size()

Specified by:

size in interface MLDataSet

get

public MLDataPair get(int index)

Specified by:

get in interface MLDataSet

addColumn

public void addColumn(float[] data)

loadCSV

public void loadCSV(String filename,
                    boolean headers,
                    CSVFormat format,
                    int[] input,
                    int[] ideal)

getNormalizedMax

public float getNormalizedMax()

Returns:

the normalizedMax

setNormalizedMax

public void setNormalizedMax(float normalizedMax)

Parameters:

normalizedMax - the normalizedMax to set

getNormalizedMin

public float getNormalizedMin()

Returns:

the normalizedMin

setNormalizedMin

public void setNormalizedMin(float normalizedMin)

Parameters:

normalizedMin - the normalizedMin to set

isNormalizationEnabled

public boolean isNormalizationEnabled()

Returns:

the normalizationEnabled

setNormalizationEnabled

public void setNormalizationEnabled(boolean normalizationEnabled)

Parameters:

normalizationEnabled - the normalizationEnabled to set