ForwardBackwardGradientT Class 
Namespace: Accord.Statistics.Models.Fields.Learning
public class ForwardBackwardGradient<T> : ParallelLearningBase, IHiddenRandomFieldGradient, IDisposable
The ForwardBackwardGradientT type exposes the following members.
Name  Description  

ForwardBackwardGradientT 
Initializes a new instance of the ForwardBackwardGradientT class.
 
ForwardBackwardGradientT(HiddenConditionalRandomFieldT) 
Initializes a new instance of the ForwardBackwardGradientT class.

Name  Description  

Inputs 
Gets or sets the inputs to be used in the next
call to the Objective or Gradient functions.
 
LastError 
Gets the error computed in the last call
to the gradient or objective functions.
 
Model 
Gets the model being trained.
 
Outputs 
Gets or sets the outputs to be used in the next
call to the Objective or Gradient functions.
 
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)  
Parameters 
Gets or sets the current parameter
vector for the model being learned.
 
Regularization 
Gets or sets the amount of the parameter weights
which should be included in the objective function.
Default is 0 (do not include regularization).
 
Token 
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.) 
Name  Description  

Dispose 
Performs applicationdefined tasks associated with freeing,
releasing, or resetting unmanaged resources.
 
Dispose(Boolean) 
Releases unmanaged and  optionally  managed resources
 
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize 
Releases unmanaged resources and performs other cleanup operations before
the ForwardBackwardGradientT is reclaimed by garbage
collection.
(Overrides ObjectFinalize.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
Gradient 
Computes the gradient using the input/outputs stored in this object.
This method is threadsafe.
 
Gradient(Double) 
Computes the gradient using the input/outputs stored in this object.
This method is not thread safe.
 
Gradient(Double, T, Int32) 
Computes the gradient (vector of derivatives) vector for
the cost function, which may be used to guide optimization.
 
Gradient(Double, T, Int32) 
Computes the gradient (vector of derivatives) vector for
the cost function, which may be used to guide optimization.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
Objective 
Computes the objective (cost) function for the Hidden
Conditional Random Field (negative loglikelihood) using
the input/outputs stored in this object.
 
Objective(Double) 
Computes the objective (cost) function for the Hidden
Conditional Random Field (negative loglikelihood) using
the input/outputs stored in this object.
 
Objective(Double, T, Int32) 
Computes the objective (cost) function for the Hidden
Conditional Random Field (negative loglikelihood).
 
ToString  Returns a string that represents the current object. (Inherited from Object.) 
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.) 