io.improbable.keanu.vertices.dbl.nonprobabilistic.diff

Class PartialDerivatives

java.lang.Object

io.improbable.keanu.vertices.dbl.nonprobabilistic.diff.PartialDerivatives

public class PartialDerivatives
extends java.lang.Object

Field Summary

Fields

Modifier and Type	Field and Description
static PartialDerivatives	OF_CONSTANT

Constructor Summary

Constructors

Constructor and Description
PartialDerivatives(long id, DoubleTensor derivativeWithRespectTo)
PartialDerivatives(java.util.Map<java.lang.Long,DoubleTensor> derivativeWithRespectTo)

Method Summary

Modifier and Type	Method and Description
PartialDerivatives	add(PartialDerivatives toAdd)
java.util.Map<java.lang.Long,DoubleTensor>	asMap()
PartialDerivatives	clone()
PartialDerivatives	divideBy(double divisor)
PartialDerivatives	divideBy(DoubleTensor divisor)
static DoubleTensor	increaseRankByAppendingOnesToShape(DoubleTensor lowRankTensor, int desiredRank)
static DoubleTensor	increaseRankByPrependingOnesToShape(DoubleTensor lowRankTensor, int desiredRank)
boolean	isEmpty()
static PartialDerivatives	matrixMultiply(PartialDerivatives partials, DoubleTensor multiplier, boolean partialIsLeft)
PartialDerivatives	multiplyBy(double multiplier)
PartialDerivatives	multiplyBy(DoubleTensor multiplier)
PartialDerivatives	powerTo(double power)
void	putWithRespectTo(long id, DoubleTensor value)
PartialDerivatives	reshape(int currentRank, int[] proposedShape)
PartialDerivatives	subtract(PartialDerivatives toSubtract)
PartialDerivatives	sum(boolean reshape, int... overDimensions)
DoubleTensor	withRespectTo(long id)
DoubleTensor	withRespectTo(Vertex vertex)
static PartialDerivatives	withRespectToSelf(long withRespectTo, int[] shape)

Methods inherited from class java.lang.Object

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

Field Detail

OF_CONSTANT

public static PartialDerivatives OF_CONSTANT

Constructor Detail

PartialDerivatives

public PartialDerivatives(long id,
                          DoubleTensor derivativeWithRespectTo)

PartialDerivatives

public PartialDerivatives(java.util.Map<java.lang.Long,DoubleTensor> derivativeWithRespectTo)

Method Detail

withRespectToSelf

public static PartialDerivatives withRespectToSelf(long withRespectTo,
                                                   int[] shape)

withRespectTo

public DoubleTensor withRespectTo(Vertex vertex)

withRespectTo

public DoubleTensor withRespectTo(long id)

isEmpty

public boolean isEmpty()

asMap

public java.util.Map<java.lang.Long,DoubleTensor> asMap()

putWithRespectTo

public void putWithRespectTo(long id,
                             DoubleTensor value)

sum

public PartialDerivatives sum(boolean reshape,
                              int... overDimensions)

add

public PartialDerivatives add(PartialDerivatives toAdd)

subtract

public PartialDerivatives subtract(PartialDerivatives toSubtract)

multiplyBy

public PartialDerivatives multiplyBy(DoubleTensor multiplier)

matrixMultiply

public static PartialDerivatives matrixMultiply(PartialDerivatives partials,
                                                DoubleTensor multiplier,
                                                boolean partialIsLeft)

multiplyBy

public PartialDerivatives multiplyBy(double multiplier)

divideBy

public PartialDerivatives divideBy(DoubleTensor divisor)

divideBy

public PartialDerivatives divideBy(double divisor)

powerTo

public PartialDerivatives powerTo(double power)

clone

public PartialDerivatives clone()

Overrides:

clone in class java.lang.Object

reshape

public PartialDerivatives reshape(int currentRank,
                                  int[] proposedShape)

increaseRankByPrependingOnesToShape

public static DoubleTensor increaseRankByPrependingOnesToShape(DoubleTensor lowRankTensor,
                                                               int desiredRank)

increaseRankByAppendingOnesToShape

public static DoubleTensor increaseRankByAppendingOnesToShape(DoubleTensor lowRankTensor,
                                                              int desiredRank)