Uses of Class
org.deeplearning4j.berkeley.Pair

Packages that use Pair

Package	Description
org.deeplearning4j.berkeley
org.deeplearning4j.models.featuredetectors.autoencoder
org.deeplearning4j.models.featuredetectors.da
org.deeplearning4j.models.featuredetectors.rbm
org.deeplearning4j.nn
org.deeplearning4j.nn.api
org.deeplearning4j.optimize.solvers
org.deeplearning4j.plot
org.deeplearning4j.util

Uses of Pair in org.deeplearning4j.berkeley

Methods in org.deeplearning4j.berkeley that return Pair

Modifier and Type	Method and Description
Pair<K,V>	CounterMap.argMax() Finds the key with maximum count.
static <S,T> Pair<S,T>	Pair.makePair(S first, T second)
static <S,T> Pair<S,T>	Pair.newPair(S first, T second)
Pair<S,F>	Pair.reverse()

Methods in org.deeplearning4j.berkeley that return types with arguments of type Pair

Modifier and Type	Method and Description
Iterator<Pair<K,V>>	CounterMap.getPairIterator()
static <S,T> Iterator<Pair<S,T>>	Iterators.zip(Iterator<S> s, Iterator<T> t)

Methods in org.deeplearning4j.berkeley with parameters of type Pair

Modifier and Type	Method and Description
int	Pair.LexicographicPairComparator.compare(Pair<F,S> pair1, Pair<F,S> pair2)
int	Pair.LexicographicPairComparator.compare(Pair<F,S> pair1, Pair<F,S> pair2)
int	Pair.DefaultLexicographicPairComparator.compare(Pair<F,S> o1, Pair<F,S> o2)
int	Pair.DefaultLexicographicPairComparator.compare(Pair<F,S> o1, Pair<F,S> o2)
int	Pair.FirstComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.FirstComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.ReverseFirstComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.ReverseFirstComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.SecondComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.SecondComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.ReverseSecondComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.ReverseSecondComparator.compare(Pair<S,T> p1, Pair<S,T> p2)
int	Pair.compareTo(Pair<F,S> o) Compares this object with the specified object for order.

Uses of Pair in org.deeplearning4j.models.featuredetectors.autoencoder

Methods in org.deeplearning4j.models.featuredetectors.autoencoder that return Pair

Modifier and Type	Method and Description
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	AutoEncoder.sampleHiddenGivenVisible(org.nd4j.linalg.api.ndarray.INDArray v) Sample hidden mean and sample given visible
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	AutoEncoder.sampleVisibleGivenHidden(org.nd4j.linalg.api.ndarray.INDArray h) Sample visible mean and sample given hidden

Methods in org.deeplearning4j.models.featuredetectors.autoencoder that return types with arguments of type Pair

Modifier and Type	Method and Description
List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	SemanticHashing.computeDeltas2()

Uses of Pair in org.deeplearning4j.models.featuredetectors.da

Methods in org.deeplearning4j.models.featuredetectors.da that return Pair

Modifier and Type	Method and Description
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	DenoisingAutoEncoder.sampleHiddenGivenVisible(org.nd4j.linalg.api.ndarray.INDArray v)
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	DenoisingAutoEncoder.sampleVisibleGivenHidden(org.nd4j.linalg.api.ndarray.INDArray h)

Uses of Pair in org.deeplearning4j.models.featuredetectors.rbm

Methods in org.deeplearning4j.models.featuredetectors.rbm that return Pair

Modifier and Type	Method and Description
Pair<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>,Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	RBM.gibbhVh(org.nd4j.linalg.api.ndarray.INDArray h) Gibbs sampling step: hidden ---> visible ---> hidden
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	RBM.sampleHiddenGivenVisible(org.nd4j.linalg.api.ndarray.INDArray v) Binomial sampling of the hidden values given visible
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	RBM.sampleVisibleGivenHidden(org.nd4j.linalg.api.ndarray.INDArray h) Guess the visible values given the hidden

Methods in org.deeplearning4j.models.featuredetectors.rbm that return types with arguments of type Pair

Modifier and Type	Method and Description
Pair<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>,Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	RBM.gibbhVh(org.nd4j.linalg.api.ndarray.INDArray h) Gibbs sampling step: hidden ---> visible ---> hidden
Pair<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>,Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	RBM.gibbhVh(org.nd4j.linalg.api.ndarray.INDArray h) Gibbs sampling step: hidden ---> visible ---> hidden

Uses of Pair in org.deeplearning4j.nn

Methods in org.deeplearning4j.nn that return Pair

Modifier and Type	Method and Description
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	BaseMultiLayerNetwork.getBackPropGradient2() Gets the back prop gradient with the r operator (gauss vector) and the associated precon matrix This is also called computeGV

Methods in org.deeplearning4j.nn that return types with arguments of type Pair

Modifier and Type	Method and Description
protected List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	BaseMultiLayerNetwork.backPropGradient() Do a back prop iteration.
protected List<Pair<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>,Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>>	BaseMultiLayerNetwork.backPropGradient2() Do a back prop iteration.
protected List<Pair<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>,Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>>	BaseMultiLayerNetwork.backPropGradient2() Do a back prop iteration.
protected List<Pair<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>,Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>>	BaseMultiLayerNetwork.backPropGradient2() Do a back prop iteration.
protected List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	BaseMultiLayerNetwork.backPropGradientR(org.nd4j.linalg.api.ndarray.INDArray v) Do a back prop iteration.
protected List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	BaseMultiLayerNetwork.computeDeltas2()
List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>>	BaseMultiLayerNetwork.unPack(org.nd4j.linalg.api.ndarray.INDArray param) Unpacks a parameter matrix in to a applyTransformToDestination of pairs(w,hbias) triples with layer wise

Method parameters in org.deeplearning4j.nn with type arguments of type Pair

Modifier and Type	Method and Description
org.nd4j.linalg.api.ndarray.INDArray	BaseMultiLayerNetwork.pack(List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> layers) Packs a set of matrices in to one vector

Uses of Pair in org.deeplearning4j.nn.api

Methods in org.deeplearning4j.nn.api that return Pair

Modifier and Type	Method and Description
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	NeuralNetwork.sampleHiddenGivenVisible(org.nd4j.linalg.api.ndarray.INDArray v) Sample hidden mean and sample given visible
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	NeuralNetwork.sampleVisibleGivenHidden(org.nd4j.linalg.api.ndarray.INDArray h) Sample visible mean and sample given hidden

Uses of Pair in org.deeplearning4j.optimize.solvers

Methods in org.deeplearning4j.optimize.solvers that return Pair

Modifier and Type	Method and Description
Pair<org.nd4j.linalg.api.ndarray.INDArray,Double>	StochasticHessianFree.cgBackTrack(List<org.nd4j.linalg.api.ndarray.INDArray> chs, org.nd4j.linalg.api.ndarray.INDArray p) Iterate through the current applyTransformToDestination of gradients and backtrack upon an optimal step that improves the current score
Pair<List<Integer>,List<org.nd4j.linalg.api.ndarray.INDArray>>	StochasticHessianFree.conjGradient(org.nd4j.linalg.api.ndarray.INDArray b, org.nd4j.linalg.api.ndarray.INDArray x0, org.nd4j.linalg.api.ndarray.INDArray preCon, int numIterations)

Uses of Pair in org.deeplearning4j.plot

Methods in org.deeplearning4j.plot that return Pair

Modifier and Type	Method and Description
Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	Tsne.hBeta(org.nd4j.linalg.api.ndarray.INDArray d, double beta) Computes a gaussian kernel given a vector of squared euclidean distances

Uses of Pair in org.deeplearning4j.util

Methods in org.deeplearning4j.util that return Pair

Modifier and Type	Method and Description
Pair<Double,org.nd4j.linalg.api.ndarray.INDArray>	Viterbi.decode(org.nd4j.linalg.api.ndarray.INDArray labels) Decodes the given labels, assuming its a binary label matrix
Pair<Double,org.nd4j.linalg.api.ndarray.INDArray>	Viterbi.decode(org.nd4j.linalg.api.ndarray.INDArray labels, boolean binaryLabelMatrix) Decodes a series of labels
Pair<K,T>	MultiDimensionalMap.Entry.getKey() Returns the key corresponding to this entry.

Methods in org.deeplearning4j.util that return types with arguments of type Pair

Modifier and Type	Method and Description
Iterator<Pair<K,V>>	MultiDimensionalSet.iterator() Returns an iterator over the elements in this applyTransformToDestination.
Set<Pair<K,T>>	MultiDimensionalMap.keySet() Returns a Set view of the keys contained in this map.

Methods in org.deeplearning4j.util with parameters of type Pair

Modifier and Type	Method and Description
boolean	MultiDimensionalSet.add(Pair<K,V> kvPair) Adds the specified element to this applyTransformToDestination if it is not already present (optional operation).
V	MultiDimensionalMap.put(Pair<K,T> key, V value) Associates the specified value with the specified key in this map (optional operation).

Method parameters in org.deeplearning4j.util with type arguments of type Pair

Modifier and Type	Method and Description
boolean	MultiDimensionalSet.addAll(Collection<? extends Pair<K,V>> c) Adds all of the elements in the specified collection to this applyTransformToDestination if they're not already present (optional operation).
void	MultiDimensionalMap.putAll(Map<? extends Pair<K,T>,? extends V> m) Copies all of the mappings from the specified map to this map (optional operation).
static void	InputSplit.splitInputs(org.nd4j.linalg.api.ndarray.INDArray inputs, org.nd4j.linalg.api.ndarray.INDArray outcomes, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> train, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> test, double split)
static void	InputSplit.splitInputs(org.nd4j.linalg.api.ndarray.INDArray inputs, org.nd4j.linalg.api.ndarray.INDArray outcomes, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> train, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> test, double split)
static void	InputSplit.splitInputs(List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> pairs, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> train, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> test, double split)
static void	InputSplit.splitInputs(List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> pairs, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> train, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> test, double split)
static void	InputSplit.splitInputs(List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> pairs, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> train, List<Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>> test, double split)

Constructor parameters in org.deeplearning4j.util with type arguments of type Pair

Constructor and Description
MultiDimensionalMap(Map<Pair<K,T>,V> backedMap)
MultiDimensionalSet(Set<Pair<K,V>> backedSet)