org.nd4j.linalg.api.ops.random.impl

Class BernoulliDistribution

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.api.ops.BaseOp

org.nd4j.linalg.api.ops.random.BaseRandomOp

org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution

All Implemented Interfaces:

Op, RandomOp

public class BernoulliDistribution
extends BaseRandomOp

BernoulliDistribution implementation

Author:

[email protected]

Nested Class Summary

Nested classes/interfaces inherited from interface org.nd4j.linalg.api.ops.Op

Op.Type

Field Summary

Fields inherited from class org.nd4j.linalg.api.ops.random.BaseRandomOp

shape

Fields inherited from class org.nd4j.linalg.api.ops.BaseOp

dimensionz, extraArgz, x, xVertexId, y, yVertexId, z, zVertexId

Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction

dimensions, extraArgs, inPlace, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
BernoulliDistribution()
BernoulliDistribution(INDArray z, double prob) This op fills Z with bernoulli trial results, so 0, or 1, depending by common probability
BernoulliDistribution(INDArray z, INDArray prob) This op fills Z with bernoulli trial results, so 0, or 1, each element will have it's own success probability defined in prob array
BernoulliDistribution(SameDiff sd, double prob, long[] shape)

Method Summary

Modifier and Type	Method and Description
List<DataType>	calculateOutputDataTypes(List<DataType> inputDataTypes) Calculate the data types for the output arrays.
List<SDVariable>	doDiff(List<SDVariable> f1) The actual implementation for automatic differentiation.
String	onnxName() The opName of this function in onnx
String	opName() The name of the op
int	opNum() The number of the op (mainly for old legacy XYZ ops like Op)
String	tensorflowName() The opName of this function tensorflow

Methods inherited from class org.nd4j.linalg.api.ops.random.BaseRandomOp

calculateOutputShape, isInPlace, opType

Methods inherited from class org.nd4j.linalg.api.ops.BaseOp

defineDimensions, dimensions, equals, extraArgs, extraArgsBuff, extraArgsDataBuff, getFinalResult, getNumOutputs, getOpType, hashCode, initFromOnnx, initFromTensorFlow, outputVariables, setX, setY, setZ, toCustomOp, toString, x, y, z

Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setPropertiesForFunction, setValueFor, tensorflowNames

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.nd4j.linalg.api.ops.Op

extraArgs, extraArgsBuff, extraArgsDataBuff, setExtraArgs, setX, setY, setZ, toCustomOp, x, y, z

Constructor Detail

BernoulliDistribution

public BernoulliDistribution(SameDiff sd,
                             double prob,
                             long[] shape)

BernoulliDistribution

public BernoulliDistribution()

BernoulliDistribution

public BernoulliDistribution(@NonNull
                             INDArray z,
                             double prob)

This op fills Z with bernoulli trial results, so 0, or 1, depending by common probability

Parameters:

z -

BernoulliDistribution

public BernoulliDistribution(@NonNull
                             INDArray z,
                             @NonNull
                             INDArray prob)

This op fills Z with bernoulli trial results, so 0, or 1, each element will have it's own success probability defined in prob array

Parameters:

prob - array with probabilities

z -

Method Detail

opNum

public int opNum()

Description copied from class: DifferentialFunction

The number of the op (mainly for old legacy XYZ ops like Op)

Specified by:

opNum in interface Op

Overrides:

opNum in class DifferentialFunction

Returns:

opName

public String opName()

Description copied from class: DifferentialFunction

The name of the op

Specified by:

opName in interface Op

Overrides:

opName in class DifferentialFunction

Returns:

the opName of this operation

onnxName

public String onnxName()

Description copied from class: DifferentialFunction

The opName of this function in onnx

Specified by:

onnxName in class DifferentialFunction

Returns:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Specified by:

tensorflowName in class DifferentialFunction

Returns:

doDiff

public List<SDVariable> doDiff(List<SDVariable> f1)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Specified by:

doDiff in class DifferentialFunction

Returns:

calculateOutputDataTypes

public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes)

Description copied from class: DifferentialFunction

Calculate the data types for the output arrays. Though datatypes can also be inferred from DifferentialFunction.calculateOutputShape(), this method differs in that it does not require the input arrays to be populated. This is important as it allows us to do greedy datatype inference for the entire net - even if arrays are not available.

Overrides:

calculateOutputDataTypes in class BaseRandomOp

Parameters:

inputDataTypes - The data types of the inputs

Returns:

The data types of the outputs