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

Class BinomialDistribution

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.BinomialDistribution

All Implemented Interfaces:

Op, RandomOp

public class BinomialDistribution
extends BaseRandomOp

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

dataType, 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, ownName, ownNameSetWithDefault, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
BinomialDistribution()
BinomialDistribution(@NonNull INDArray z, @NonNull INDArray probabilities) This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray
BinomialDistribution(@NonNull INDArray z, int trials, double probability) This op fills Z with binomial distribution over given trials with single given probability for all trials
BinomialDistribution(@NonNull INDArray z, int trials, @NonNull INDArray probabilities) This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray
BinomialDistribution(int trials, double probability, DataType dt, long[] shape)
BinomialDistribution(SameDiff sd, int trials, double probability, DataType dataType, long[] shape)
BinomialDistribution(SameDiff sd, int trials, double probability, 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<LongShapeDescriptor>	calculateOutputShape() Calculate the output shape for this op
List<LongShapeDescriptor>	calculateOutputShape(OpContext oc)
List<SDVariable>	doDiff(List<SDVariable> f1) The actual implementation for automatic differentiation.
boolean	isTripleArgRngOp()
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)
void	setZ(INDArray z) set z (the solution ndarray)
String	tensorflowName() The opName of this function tensorflow

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

isInPlace, opType

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

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

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

arg, arg, argNames, args, attributeAdaptersForFunction, configFieldName, diff, dup, getValue, isConfigProperties, larg, mappingsForFunction, onnxNames, outputs, outputVariable, outputVariables, outputVariablesNames, propertiesForFunction, rarg, replaceArg, 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

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

Constructor Detail

BinomialDistribution

public BinomialDistribution(SameDiff sd,
                            int trials,
                            double probability,
                            long[] shape)

BinomialDistribution

public BinomialDistribution(SameDiff sd,
                            int trials,
                            double probability,
                            DataType dataType,
                            long[] shape)

BinomialDistribution

public BinomialDistribution(int trials,
                            double probability,
                            DataType dt,
                            long[] shape)

BinomialDistribution

public BinomialDistribution()

BinomialDistribution

public BinomialDistribution(@NonNull
                            @NonNull INDArray z,
                            int trials,
                            double probability)

This op fills Z with binomial distribution over given trials with single given probability for all trials

Parameters:

z -

trials -

probability -

BinomialDistribution

public BinomialDistribution(@NonNull
                            @NonNull INDArray z,
                            int trials,
                            @NonNull
                            @NonNull INDArray probabilities)

This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray

Parameters:

z -

trials -

probabilities - array with probability value for each trial

BinomialDistribution

public BinomialDistribution(@NonNull
                            @NonNull INDArray z,
                            @NonNull
                            @NonNull INDArray probabilities)

This op fills Z with binomial distribution over given trials with probability for each trial given as probabilities INDArray

Parameters:

z -

probabilities -

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

Overrides:

onnxName in class BaseOp

Returns:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Overrides:

tensorflowName in class BaseOp

Returns:

calculateOutputShape

public List<LongShapeDescriptor> calculateOutputShape(OpContext oc)

Overrides:

calculateOutputShape in class DifferentialFunction

calculateOutputShape

public List<LongShapeDescriptor> calculateOutputShape()

Description copied from class: DifferentialFunction

Calculate the output shape for this op

Overrides:

calculateOutputShape in class BaseRandomOp

Returns:

List of output shape descriptors

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:

setZ

public void setZ(INDArray z)

Description copied from interface: Op

set z (the solution ndarray)

Specified by:

setZ in interface Op

Overrides:

setZ in class BaseOp

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

isTripleArgRngOp

public boolean isTripleArgRngOp()

Overrides:

isTripleArgRngOp in class BaseRandomOp