IterativeReweightedLeastSquaresTModel Class

Iterative Reweighted Least Squares for fitting Generalized Linear Models.

Inheritance Hierarchy

SystemObject
Accord.Statistics.Models.Regression.FittingIterativeReweightedLeastSquaresTModel
Accord.Statistics.Models.Regression.FittingIterativeReweightedLeastSquares

Namespace: Accord.Statistics.Models.Regression.Fitting
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0

Syntax

Copy

public class IterativeReweightedLeastSquares<TModel> : ISupervisedLearning<TModel, double[], double>, 
	ISupervisedLearning<TModel, double[], int>, ISupervisedLearning<TModel, double[], bool>, 
	IConvergenceLearning
where TModel : new(), GeneralizedLinearRegression

Public Class IterativeReweightedLeastSquares(Of TModel As {New, GeneralizedLinearRegression})
	Implements ISupervisedLearning(Of TModel, Double(), Double), 
	ISupervisedLearning(Of TModel, Double(), Integer), ISupervisedLearning(Of TModel, Double(), Boolean), 
	IConvergenceLearning

Request Example View Source

Type Parameters

TModel

The IterativeReweightedLeastSquaresTModel type exposes the following members.

Constructors

	Name	Description
	IterativeReweightedLeastSquaresTModel	Initializes a new instance of the IterativeReweightedLeastSquaresTModel class.

Top

Properties

	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 Newton-Raphson iteration.
	HasConverged	Gets or sets whether the algorithm has converged.
	Hessian	Gets the Hessian matrix computed in the last Newton-Raphson 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 1e-10.
	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.

Top

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.)
	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.)

Top

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.)

Top

Reference

Accord.Statistics.Models.Regression.Fitting Namespace

IterativeReweightedLeastSquaresAddLanguageSpecificTextSet("LSTCBF10167_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModelAddLanguageSpecificTextSet("LSTCBF10167_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

IterativeReweightedLeastSquaresTModel Class