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

Class GaussianDistribution

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.GaussianDistribution

All Implemented Interfaces:

Op, RandomOp

public class GaussianDistribution
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
GaussianDistribution()
GaussianDistribution(double mean, double stddev, DataType datatype, long... shape)
GaussianDistribution(@NonNull INDArray z) This op fills Z with random values within -1.0..0..1.0
GaussianDistribution(@NonNull INDArray z, double stddev) This op fills Z with random values within stddev..0..stddev
GaussianDistribution(@NonNull INDArray z, double mean, double stddev) This op fills Z with random values within stddev..mean..stddev boundaries
GaussianDistribution(@NonNull INDArray z, @NonNull INDArray means, double stddev)
GaussianDistribution(SameDiff sd, double mean, double stddev, DataType dataType, long[] shape)
GaussianDistribution(SameDiff sd, double mean, double stddev, 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)
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, setZ, 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, setZ, toCustomOp, x, y, z

Constructor Detail

GaussianDistribution

public GaussianDistribution(SameDiff sd,
                            double mean,
                            double stddev,
                            long[] shape)

GaussianDistribution

public GaussianDistribution(SameDiff sd,
                            double mean,
                            double stddev,
                            DataType dataType,
                            long[] shape)

GaussianDistribution

public GaussianDistribution()

GaussianDistribution

public GaussianDistribution(double mean,
                            double stddev,
                            DataType datatype,
                            long... shape)

GaussianDistribution

public GaussianDistribution(@NonNull
                            @NonNull INDArray z,
                            double mean,
                            double stddev)

This op fills Z with random values within stddev..mean..stddev boundaries

Parameters:

z -

mean -

stddev -

GaussianDistribution

public GaussianDistribution(@NonNull
                            @NonNull INDArray z,
                            @NonNull
                            @NonNull INDArray means,
                            double stddev)

GaussianDistribution

public GaussianDistribution(@NonNull
                            @NonNull INDArray z)

This op fills Z with random values within -1.0..0..1.0

Parameters:

z -

GaussianDistribution

public GaussianDistribution(@NonNull
                            @NonNull INDArray z,
                            double stddev)

This op fills Z with random values within stddev..0..stddev

Parameters:

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

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:

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:

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

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