MachineLearningSweepJob Constructors

Definition

Overloads

MachineLearningSweepJob(MachineLearningObjective, SamplingAlgorithm, BinaryData, MachineLearningTrialComponent)

Initializes a new instance of MachineLearningSweepJob.

MachineLearningSweepJob(BinaryData, SamplingAlgorithm, MachineLearningObjective, MachineLearningTrialComponent)

Initializes a new instance of MachineLearningSweepJob.

MachineLearningSweepJob(MachineLearningObjective, SamplingAlgorithm, BinaryData, MachineLearningTrialComponent)

Source:
MachineLearningSweepJob.cs
Source:
MachineLearningSweepJob.cs

Initializes a new instance of MachineLearningSweepJob.

public MachineLearningSweepJob (Azure.ResourceManager.MachineLearning.Models.MachineLearningObjective objective, Azure.ResourceManager.MachineLearning.Models.SamplingAlgorithm samplingAlgorithm, BinaryData searchSpace, Azure.ResourceManager.MachineLearning.Models.MachineLearningTrialComponent trial);
new Azure.ResourceManager.MachineLearning.Models.MachineLearningSweepJob : Azure.ResourceManager.MachineLearning.Models.MachineLearningObjective * Azure.ResourceManager.MachineLearning.Models.SamplingAlgorithm * BinaryData * Azure.ResourceManager.MachineLearning.Models.MachineLearningTrialComponent -> Azure.ResourceManager.MachineLearning.Models.MachineLearningSweepJob
Public Sub New (objective As MachineLearningObjective, samplingAlgorithm As SamplingAlgorithm, searchSpace As BinaryData, trial As MachineLearningTrialComponent)

Parameters

objective
MachineLearningObjective

[Required] Optimization objective.

samplingAlgorithm
SamplingAlgorithm

[Required] The hyperparameter sampling algorithm Please note SamplingAlgorithm is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include BayesianSamplingAlgorithm, GridSamplingAlgorithm and RandomSamplingAlgorithm.

searchSpace
BinaryData

[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter.

trial
MachineLearningTrialComponent

[Required] Trial component definition.

Exceptions

objective, samplingAlgorithm, searchSpace or trial is null.

Applies to

MachineLearningSweepJob(BinaryData, SamplingAlgorithm, MachineLearningObjective, MachineLearningTrialComponent)

Initializes a new instance of MachineLearningSweepJob.

public MachineLearningSweepJob (BinaryData searchSpace, Azure.ResourceManager.MachineLearning.Models.SamplingAlgorithm samplingAlgorithm, Azure.ResourceManager.MachineLearning.Models.MachineLearningObjective objective, Azure.ResourceManager.MachineLearning.Models.MachineLearningTrialComponent trial);
new Azure.ResourceManager.MachineLearning.Models.MachineLearningSweepJob : BinaryData * Azure.ResourceManager.MachineLearning.Models.SamplingAlgorithm * Azure.ResourceManager.MachineLearning.Models.MachineLearningObjective * Azure.ResourceManager.MachineLearning.Models.MachineLearningTrialComponent -> Azure.ResourceManager.MachineLearning.Models.MachineLearningSweepJob
Public Sub New (searchSpace As BinaryData, samplingAlgorithm As SamplingAlgorithm, objective As MachineLearningObjective, trial As MachineLearningTrialComponent)

Parameters

searchSpace
BinaryData

[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter.

samplingAlgorithm
SamplingAlgorithm

[Required] The hyperparameter sampling algorithm Please note SamplingAlgorithm is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include BayesianSamplingAlgorithm, GridSamplingAlgorithm and RandomSamplingAlgorithm.

objective
MachineLearningObjective

[Required] Optimization objective.

trial
MachineLearningTrialComponent

[Required] Trial component definition.

Exceptions

searchSpace, samplingAlgorithm, objective or trial is null.

Applies to