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Accord.NET (logo) HiddenGradientDescentLearningT Class
Stochastic Gradient Descent learning algorithm for Hidden Conditional Hidden Fields.
Inheritance Hierarchy
SystemObject
  Accord.Statistics.Models.Fields.LearningHiddenGradientDescentLearningT

Namespace:  Accord.Statistics.Models.Fields.Learning
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.4.0
Syntax
public class HiddenGradientDescentLearning<T> : ISupervisedLearning<HiddenConditionalRandomField<T>, T[], int>, 
	IHiddenConditionalRandomFieldLearning<T>, IConvergenceLearning, IDisposable
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Type Parameters

T
The type of the observations.

The HiddenGradientDescentLearningT type exposes the following members.

Constructors
  NameDescription
Public methodHiddenGradientDescentLearningT
Initializes a new instance of the HiddenGradientDescentLearningT class.
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Properties
  NameDescription
Public propertyIterations
Gets or sets the maximum number of iterations performed by the learning algorithm.
Public propertyLearningRate
Gets or sets the learning rate to use as the gradient descent step size. Default value is 1e-1.
Public propertyModel
Gets or sets the model being trained.
Public propertyRegularization
Gets or sets the amount of the parameter weights which should be included in the objective function. Default is 0 (do not include regularization).
Public propertyStochastic
Gets or sets a value indicating whether this HiddenGradientDescentLearningT should use stochastic gradient updates.
Public propertyToken
Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.
Public propertyTolerance
Gets or sets the maximum change in the average log-likelihood after an iteration of the algorithm used to detect convergence.
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Methods
  NameDescription
Public methodDispose
Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
Protected methodDispose(Boolean)
Releases unmanaged and - optionally - managed resources
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize (Overrides ObjectFinalize.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodLearn
Learns a model that can map the given inputs to the given outputs.
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Protected methodOnProgressChanged
Raises the [E:ProgressChanged] event.
Public methodReset
Resets the step size.
Public methodRun(T, Int32)
Runs one iteration of the learning algorithm with the specified input training observation and corresponding output label.
Public methodRun(T, Int32) Obsolete.
Runs the learning algorithm with the specified input training observations and corresponding output labels.
Public methodRunEpoch
Runs the learning algorithm with the specified input training observations and corresponding output labels.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Events
  NameDescription
Public eventProgressChanged
Occurs when the current learning progress has changed.
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
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.)
Public Extension MethodToTOverloaded.
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 Matrix.)
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Examples

For an example on how to learn Hidden Conditional Random Fields, please see the Hidden Resilient Gradient Learning page. All learning algorithms can be utilized in a similar manner.

See Also