BaseGridSearchTResult, TModel, TRange, TParam, TLearner, TInput, TOutput Class

Base class for GridSearchTModel, TInput, TOutput methods.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearning.PerformanceBaseGridSearchTResult, TModel, TRange, TParam, TLearner, TInput, TOutput
      Accord.MachineLearning.PerformanceGridSearchTModel, TLearner, TInput, TOutput
      Accord.MachineLearning.PerformanceGridSearchTModel, TRange, TLearner, TInput, TOutput

Namespace: Accord.MachineLearning.Performance
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

Copy

public abstract class BaseGridSearch<TResult, TModel, TRange, TParam, TLearner, TInput, TOutput> : ParallelLearningBase, 
	ISupervisedLearning<TResult, TInput, TOutput>
where TResult : new(), GridSearchResult<TModel, TParam, TInput, TOutput>
where TModel : class, Object, ITransform<TInput, TOutput>

Public MustInherit Class BaseGridSearch(Of TResult As {New, GridSearchResult(Of TModel, TParam, TInput, TOutput)}, TModel As {Class, Object, ITransform(Of TInput, TOutput)}, TRange, TParam, TLearner, TInput, TOutput)
	Inherits ParallelLearningBase
	Implements ISupervisedLearning(Of TResult, TInput, TOutput)

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Type Parameters

TResult: The type of the object that should hold the results of the grid serach (e.g. GridSearchResultTModel, TInput, TOutput).
TModel: The type of the machine learning model whose parameters should be searched.
TRange: The type that specifies how ranges of the parameter values are represented.
TParam: The type that specifies how the value for a single parameter is represented.
TLearner: The type of the learning algorithm used to learn TModel.
TInput: The type of the input data. Default is double[].
TOutput: The type of the output data. Default is int.

The BaseGridSearchTResult, TModel, TRange, TParam, TLearner, TInput, TOutput type exposes the following members.

Constructors

	Name	Description
	BaseGridSearchTResult, TModel, TRange, TParam, TLearner, TInput, TOutput	Initializes a new instance of the BaseGridSearchTResult, TModel, TRange, TParam, TLearner, TInput, TOutput class.

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Properties

	Name	Description
	Fit	Gets or sets a LearnNewModelTLearner, TInput, TOutput, TModel function that can be used to create new machine learning models using the current learning algorithm.
	Learner	Gets or sets a CreateLearnerFromParameterTLearner, TParam function that can be used to create a TModel given training parameters.
	Loss	Gets or sets a ComputeLossTOutput, TInfo function that can be used to measure how far the actual model predictions were from the expected ground-truth.
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	ParameterRanges	The range of parameters to consider during search.
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from ParallelLearningBase.)

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Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetLengths	Inheritors of this class should return the number of possible parameter values for each parameter in the grid-search range. For example, if a problem should search parameters in the range {0, 1, ... 9} (10 values) and {-1, -2, -3 } (3 values), this method should return { 10, 3 }.
	GetParameters	Inheritors of this class should specify how to get actual values for the parameters given a index vector in the grid-search space. Those indices indicate which values should be given, e.g. if there are two parameters in the problem, the ranges of the first parameter are {10, 20, 30}, and the ranges of the second parameter are {0.1, 0.01, 0.001 }, if the index vector is { 1, 2 } this method should return { 20, 0.001 }.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Learn	Learns a model that can map the given inputs to the given outputs.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Reference

Accord.MachineLearning.Performance Namespace

Accord.MachineLearning.PerformanceGridSearch

Accord.MachineLearning.PerformanceGridSearchTInput, TOutput

Accord.MachineLearning.PerformanceGridSearchTModel, TInput, TOutput

Accord.MachineLearning.PerformanceGridSearchTModel, TLearner, TInput, TOutput

Accord.MachineLearning.PerformanceGridSearchTModel, TRange, TLearner, TInput, TOutput

Accord.MachineLearningParallelLearningBase

Accord.MachineLearningISupervisedLearningTModel, TInput, TOutput

BaseGridSearchAddLanguageSpecificTextSet("LSTEFFD956E_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TResult, TModel, TRange, TParam, TLearner, TInput, TOutputAddLanguageSpecificTextSet("LSTEFFD956E_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseGridSearchTResult, TModel, TRange, TParam, TLearner, TInput, TOutput Class