IterativeReweightedLeastSquaresTModel Class 
Namespace: Accord.Statistics.Models.Regression.Fitting
public class IterativeReweightedLeastSquares<TModel> : ISupervisedLearning<TModel, double[], double>, ISupervisedLearning<TModel, double[], int>, ISupervisedLearning<TModel, double[], bool>, IConvergenceLearning where TModel : new(), GeneralizedLinearRegression
The IterativeReweightedLeastSquaresTModel type exposes the following members.
Name  Description  

IterativeReweightedLeastSquaresTModel 
Initializes a new instance of the IterativeReweightedLeastSquaresTModel class.

Name  Description  

ComputeStandardErrors 
Gets or sets a value indicating whether standard
errors should be computed in the next iteration.
 
CurrentIteration 
Gets the current iteration number.
 
Gradient 
Gets the Gradient vector computed in
the last NewtonRaphson iteration.
 
HasConverged 
Gets or sets whether the algorithm has converged.
 
Hessian 
Gets the Hessian matrix computed in
the last NewtonRaphson iteration.
 
Iterations  Obsolete.
Please use MaxIterations instead.
 
MaxIterations 
Gets or sets the maximum number of iterations
performed by the learning algorithm.
 
Model 
Gets or sets the regression model being learned.
 
Parameters 
Gets the total number of parameters in the model.
 
Previous 
Gets the previous values for the coefficients which were
in place before the last learning iteration was performed.
 
Regularization 
Gets or sets the regularization value to be
added in the objective function. Default is
1e10.
 
Solution 
Gets the current values for the coefficients.
 
Token 
Gets or sets a cancellation token that can be used to
stop the learning algorithm while it is running.
 
Tolerance 
Gets or sets the tolerance value used to determine
whether the algorithm has converged.
 
Updates 
Gets the last parameter updates in the last iteration.

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.)  
GetInformationMatrix 
Gets the information matrix used to update the regression
weights in the last call to Learn(Double, Double, Double)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
Initialize 
Initializes this instance.
 
Learn(Double, Boolean, Double) 
Learns a model that can map the given inputs to the given outputs.
 
Learn(Double, Double, Double) 
Learns a model that can map the given inputs to the given outputs.
 
Learn(Double, Int32, Double) 
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.) 
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.) 