All Superinterfaces:

Iterable<DataSet>, Serializable

All Known Implementing Classes:

DataSet
```
public interface DataSet
extends Iterable<DataSet>, Serializable
```

Method Summary

All Methods Instance Methods Abstract Methods Deprecated Methods
Modifier and Type	Method	Description
`void`	`addFeatureVector(INDArray toAdd)`
`void`	`addFeatureVector(INDArray feature, int example)`
`void`	`addRow(DataSet d, int i)`
`List<DataSet>`	`asList()`	Extract each example in the DataSet into its own DataSet object, and return all of them as a list
`List<DataSet>`	`batchBy(int num)`
`List<DataSet>`	`batchByNumLabels()`
`void`	`binarize()`
`void`	`binarize(double cutoff)`
`DataSet`	`copy()`	Create a copy of the DataSet
`List<DataSet>`	`dataSetBatches(int num)`	Deprecated. prefer `batchBy(int)`
`void`	`detach()`	This method detaches this DataSet from current Workspace (if any)
`void`	`divideBy(int num)`	Divide the features by a scalar
`INDArray`	`exampleMaxs()`
`INDArray`	`exampleMeans()`
`INDArray`	`exampleSums()`
`void`	`filterAndStrip(int[] labels)`
`DataSet`	`filterBy(int[] labels)`
`DataSet`	`get(int i)`
`DataSet`	`get(int[] i)`
`List<String>`	`getColumnNames()`
`List<Serializable>`	`getExampleMetaData()`	Get the example metadata, or null if no metadata has been set
`<T extends Serializable> List<T>`	`getExampleMetaData(Class<T> metaDataType)`	Get the example metadata, or null if no metadata has been set Note: this method results in an unchecked cast - care should be taken when using this!
`INDArray`	`getFeatures()`	Returns the features array for the DataSet
`INDArray`	`getFeaturesMaskArray()`	Input mask array: a mask array for input, where each value is in {0,1} in order to specify whether an input is actually present or not.
`String`	`getLabelName(int idx)`
`List<String>`	`getLabelNames()`	Deprecated.
`List<String>`	`getLabelNames(INDArray idxs)`
`List<String>`	`getLabelNamesList()`
`INDArray`	`getLabels()`
`INDArray`	`getLabelsMaskArray()`	Labels (output) mask array: a mask array for input, where each value is in {0,1} in order to specify whether an output is actually present or not.
`long`	`getMemoryFootprint()`	This method returns memory used by this DataSet
`DataSet`	`getRange(int from, int to)`
`boolean`	`hasMaskArrays()`	Whether the labels or input (features) mask arrays are present for this DataSet
`String`	`id()`
`boolean`	`isEmpty()`
`DataSetIterator`	`iterateWithMiniBatches()`	Deprecated.
`Iterator<DataSet>`	`iterator()`
`Map<Integer,Double>`	`labelCounts()`	Calculate and return a count of each label, by index.
`void`	`load(File from)`	Load the contents of the DataSet from the specified File.
`void`	`load(InputStream from)`	Load the contents of the DataSet from the specified InputStream.
`void`	`migrate()`	This method migrates this DataSet into current Workspace (if any)
`void`	`multiplyBy(double num)`	Multiply the features by a scalar
`void`	`normalize()`	Normalize this DataSet to mean 0, stdev 1 per input.
`void`	`normalizeZeroMeanZeroUnitVariance()`	Deprecated. Use `NormalizerStandardize`
`int`	`numExamples()`	Number of examples in the DataSet
`int`	`numInputs()`	Number of input values - i.e., size of the features INDArray per example
`int`	`numOutcomes()`	Returns the number of outcomes (size of the labels array for each example)
`int`	`outcome()`
`DataSet`	`reshape(int rows, int cols)`
`void`	`roundToTheNearest(int roundTo)`
`DataSet`	`sample(int numSamples)`
`DataSet`	`sample(int numSamples, boolean withReplacement)`
`DataSet`	`sample(int numSamples, Random rng)`
`DataSet`	`sample(int numSamples, Random rng, boolean withReplacement)`
`void`	`save(File to)`	Save this DataSet to a file.
`void`	`save(OutputStream to)`	Write the contents of this DataSet to the specified OutputStream
`void`	`scale()`
`void`	`scaleMinAndMax(double min, double max)`
`void`	`setColumnNames(List<String> columnNames)`
`void`	`setExampleMetaData(List<? extends Serializable> exampleMetaData)`	Set the metadata for this DataSet By convention: the metadata can be any serializable object, one per example in the DataSet
`void`	`setFeatures(INDArray features)`	Set the features array for the DataSet
`void`	`setFeaturesMaskArray(INDArray inputMask)`	Set the features mask array in this DataSet
`void`	`setLabelNames(List<String> labelNames)`
`void`	`setLabels(INDArray labels)`
`void`	`setLabelsMaskArray(INDArray labelsMask)`	Set the labels mask array in this data set
`void`	`setNewNumberOfLabels(int labels)`
`void`	`setOutcome(int example, int label)`
`void`	`shuffle()`	Shuffle the order of the rows in the DataSet.
`List<DataSet>`	`sortAndBatchByNumLabels()`
`void`	`sortByLabel()`
`SplitTestAndTrain`	`splitTestAndTrain(double fractionTrain)`	SplitV the DataSet into two DataSets randomly
`SplitTestAndTrain`	`splitTestAndTrain(int numHoldout)`
`SplitTestAndTrain`	`splitTestAndTrain(int numHoldout, Random rnd)`
`void`	`squishToRange(double min, double max)`
`MultiDataSet`	`toMultiDataSet()`
`void`	`validate()`

Methods inherited from interface java.lang.Iterable
forEach, spliterator

- Method Detail
  - getRange
```
DataSet getRange(int from,
                 int to)
```
  - load
```
void load(InputStream from)
```
    Load the contents of the DataSet from the specified InputStream. The current contents of the DataSet (if any) will be replaced.
    The InputStream should contain a DataSet that has been serialized with save(OutputStream)
    
    Parameters:
    
    from - InputStream to load the DataSet from
  - load
```
void load(File from)
```
    Load the contents of the DataSet from the specified File. The current contents of the DataSet (if any) will be replaced.
    The InputStream should contain a DataSet that has been serialized with save(File)
    
    Parameters:
    
    from - File to load the DataSet from
  - save
```
void save(OutputStream to)
```
    Write the contents of this DataSet to the specified OutputStream
    
    Parameters:
    
    to - OutputStream to save the DataSet to
  - save
```
void save(File to)
```
    Save this DataSet to a file. Can be loaded again using
    
    Parameters:
    
    to - File to sa
  - iterateWithMiniBatches
```
@Deprecated
DataSetIterator iterateWithMiniBatches()
```
    Deprecated.
  - id
```
String id()
```
  - getFeatures
```
INDArray getFeatures()
```
    Returns the features array for the DataSet
    
    Returns:
    
    features array
  - setFeatures
```
void setFeatures(INDArray features)
```
    Set the features array for the DataSet
    
    Parameters:
    
    features - Features to set
  - labelCounts
```
Map<Integer,Double> labelCounts()
```
    Calculate and return a count of each label, by index. Assumes labels are a one-hot INDArray, for classification
    
    Returns:
    
    Map of countsn
  - copy
```
DataSet copy()
```
    Create a copy of the DataSet
    
    Returns:
    
    Copy of the DataSet
  - reshape
```
DataSet reshape(int rows,
                int cols)
```
  - multiplyBy
```
void multiplyBy(double num)
```
    Multiply the features by a scalar
  - divideBy
```
void divideBy(int num)
```
    Divide the features by a scalar
  - shuffle
```
void shuffle()
```
    Shuffle the order of the rows in the DataSet. Note that this generally won't make any difference in practice unless the DataSet is later split.
  - squishToRange
```
void squishToRange(double min,
                   double max)
```
  - scaleMinAndMax
```
void scaleMinAndMax(double min,
                    double max)
```
  - scale
```
void scale()
```
  - addFeatureVector
```
void addFeatureVector(INDArray toAdd)
```
  - addFeatureVector
```
void addFeatureVector(INDArray feature,
                      int example)
```
  - normalize
```
void normalize()
```
    Normalize this DataSet to mean 0, stdev 1 per input. This calculates statistics based on the values in a single DataSet only. For normalization over multiple DataSet objects, use NormalizerStandardize
  - binarize
```
void binarize()
```
  - binarize
```
void binarize(double cutoff)
```
  - normalizeZeroMeanZeroUnitVariance
```
@Deprecated
void normalizeZeroMeanZeroUnitVariance()
```
    Deprecated.
    Use NormalizerStandardize
  - numInputs
```
int numInputs()
```
    Number of input values - i.e., size of the features INDArray per example
  - validate
```
void validate()
```
  - outcome
```
int outcome()
```
  - setNewNumberOfLabels
```
void setNewNumberOfLabels(int labels)
```
  - setOutcome
```
void setOutcome(int example,
                int label)
```
  - get
```
DataSet get(int i)
```
  - get
```
DataSet get(int[] i)
```
  - batchBy
```
List<DataSet> batchBy(int num)
```
  - filterBy
```
DataSet filterBy(int[] labels)
```
  - filterAndStrip
```
void filterAndStrip(int[] labels)
```
  - dataSetBatches
```
@Deprecated
List<DataSet> dataSetBatches(int num)
```
    Deprecated.
    prefer batchBy(int)
  - sortAndBatchByNumLabels
```
List<DataSet> sortAndBatchByNumLabels()
```
  - batchByNumLabels
```
List<DataSet> batchByNumLabels()
```
  - asList
```
List<DataSet> asList()
```
    Extract each example in the DataSet into its own DataSet object, and return all of them as a list
    
    Returns:
    
    List of DataSet objects, each with 1 example only
  - splitTestAndTrain
```
SplitTestAndTrain splitTestAndTrain(int numHoldout,
                                    Random rnd)
```
  - splitTestAndTrain
```
SplitTestAndTrain splitTestAndTrain(int numHoldout)
```
  - getLabels
```
INDArray getLabels()
```
  - setLabels
```
void setLabels(INDArray labels)
```
  - sortByLabel
```
void sortByLabel()
```
  - addRow
```
void addRow(DataSet d,
            int i)
```
  - exampleSums
```
INDArray exampleSums()
```
  - exampleMaxs
```
INDArray exampleMaxs()
```
  - exampleMeans
```
INDArray exampleMeans()
```
  - sample
```
DataSet sample(int numSamples)
```
  - sample
```
DataSet sample(int numSamples,
               Random rng)
```
  - sample
```
DataSet sample(int numSamples,
               boolean withReplacement)
```
  - sample
```
DataSet sample(int numSamples,
               Random rng,
               boolean withReplacement)
```
  - roundToTheNearest
```
void roundToTheNearest(int roundTo)
```
  - numOutcomes
```
int numOutcomes()
```
    Returns the number of outcomes (size of the labels array for each example)
  - numExamples
```
int numExamples()
```
    Number of examples in the DataSet
  - getLabelNames
```
@Deprecated
List<String> getLabelNames()
```
    Deprecated.
  - getLabelNamesList
```
List<String> getLabelNamesList()
```
  - getLabelName
```
String getLabelName(int idx)
```
  - getLabelNames
```
List<String> getLabelNames(INDArray idxs)
```
  - setLabelNames
```
void setLabelNames(List<String> labelNames)
```
  - getColumnNames
```
List<String> getColumnNames()
```
  - setColumnNames
```
void setColumnNames(List<String> columnNames)
```
  - splitTestAndTrain
```
SplitTestAndTrain splitTestAndTrain(double fractionTrain)
```
    SplitV the DataSet into two DataSets randomly
    
    Parameters:
    
    fractionTrain - Fraction (in range 0 to 1) of examples to be returned in the training DataSet object
  - iterator
```
Iterator<DataSet> iterator()
```
    Specified by:
    
    iterator in interface Iterable<DataSet>
  - getFeaturesMaskArray
```
INDArray getFeaturesMaskArray()
```
    Input mask array: a mask array for input, where each value is in {0,1} in order to specify whether an input is actually present or not. Typically used for situations such as RNNs with variable length inputs
    
    Returns:
    
    Input mask array
  - setFeaturesMaskArray
```
void setFeaturesMaskArray(INDArray inputMask)
```
    Set the features mask array in this DataSet
  - getLabelsMaskArray
```
INDArray getLabelsMaskArray()
```
    Labels (output) mask array: a mask array for input, where each value is in {0,1} in order to specify whether an output is actually present or not. Typically used for situations such as RNNs with variable length inputs or many- to-one situations.
    
    Returns:
    
    Labels (output) mask array
  - setLabelsMaskArray
```
void setLabelsMaskArray(INDArray labelsMask)
```
    Set the labels mask array in this data set
  - hasMaskArrays
```
boolean hasMaskArrays()
```
    Whether the labels or input (features) mask arrays are present for this DataSet
  - setExampleMetaData
```
void setExampleMetaData(List<? extends Serializable> exampleMetaData)
```
    Set the metadata for this DataSet
    By convention: the metadata can be any serializable object, one per example in the DataSet
    
    Parameters:
    
    exampleMetaData - Example metadata to set
  - getExampleMetaData
```
<T extends Serializable> List<T> getExampleMetaData(Class<T> metaDataType)
```
    Get the example metadata, or null if no metadata has been set
    Note: this method results in an unchecked cast - care should be taken when using this!
    
    Type Parameters:
    
    T - Type of metadata
    
    Parameters:
    
    metaDataType - Class of the metadata (used for opType information)
    
    Returns:
    
    List of metadata objects
  - getExampleMetaData
```
List<Serializable> getExampleMetaData()
```
    Get the example metadata, or null if no metadata has been set
    
    Returns:
    
    List of metadata instances
  - getMemoryFootprint
```
long getMemoryFootprint()
```
    This method returns memory used by this DataSet
    
    Returns:
  - migrate
```
void migrate()
```
    This method migrates this DataSet into current Workspace (if any)
  - detach
```
void detach()
```
    This method detaches this DataSet from current Workspace (if any)
  - isEmpty
```
boolean isEmpty()
```
    Returns:
    
    true if the DataSet object is empty (no features, labels, or masks)
  - toMultiDataSet
```
MultiDataSet toMultiDataSet()
```

Interface DataSet

Method Summary

Methods inherited from interface java.lang.Iterable

Method Detail

getRange

load

load

save

save

iterateWithMiniBatches

id

getFeatures

setFeatures

labelCounts

copy

reshape

multiplyBy

divideBy

shuffle

squishToRange

scaleMinAndMax

scale

addFeatureVector

addFeatureVector

normalize

binarize

binarize

normalizeZeroMeanZeroUnitVariance

numInputs

validate

outcome

setNewNumberOfLabels

setOutcome

get

get

batchBy

filterBy

filterAndStrip

dataSetBatches

sortAndBatchByNumLabels

batchByNumLabels

asList

splitTestAndTrain

splitTestAndTrain

getLabels

setLabels

sortByLabel

addRow

exampleSums

exampleMaxs

exampleMeans

sample

sample

sample

sample

roundToTheNearest

numOutcomes

numExamples

getLabelNames

getLabelNamesList

getLabelName

getLabelNames

setLabelNames

getColumnNames

setColumnNames

splitTestAndTrain

iterator

getFeaturesMaskArray

setFeaturesMaskArray

getLabelsMaskArray

setLabelsMaskArray

hasMaskArrays

setExampleMetaData

getExampleMetaData

getExampleMetaData

getMemoryFootprint

migrate

detach

isEmpty

toMultiDataSet