LogisticGradientDescent Class

Stochastic Gradient Descent learning for Logistic Regression fitting.

Inheritance Hierarchy

SystemObject
Accord.Statistics.Models.Regression.FittingLogisticGradientDescent

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

Syntax

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public class LogisticGradientDescent : ISupervisedLearning<LogisticRegression, double[], int>, 
	ISupervisedLearning<LogisticRegression, double[], bool>, ISupervisedLearning<LogisticRegression, double[], double>, 
	IConvergenceLearning

Public Class LogisticGradientDescent
	Implements ISupervisedLearning(Of LogisticRegression, Double(), Integer), 
	ISupervisedLearning(Of LogisticRegression, Double(), Boolean), ISupervisedLearning(Of LogisticRegression, Double(), Double), 
	IConvergenceLearning

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The LogisticGradientDescent type exposes the following members.

Constructors

	Name	Description
	LogisticGradientDescent	Constructs a new Gradient Descent algorithm.
	LogisticGradientDescent(LogisticRegression)	Constructs a new Gradient Descent algorithm.

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Properties

	Name	Description
	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.
	Iterations	Obsolete. Please use MaxIterations instead.
	LearningRate	Gets or sets the algorithm learning rate. Default is 0.1.
	MaxIterations	Gets or sets the maximum number of iterations performed by the learning algorithm.
	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.
	Solution	Gets the current values for the coefficients.
	Stochastic	Gets or sets whether this algorithm should use stochastic updates or not. Default is false.
	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.

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Methods

	Name	Description
	ComputeError	Obsolete. Computes the sum-of-squared error between the model outputs and the expected outputs.
	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.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	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.)
	Run(Double, Double)	Obsolete. Runs a single pass of the gradient descent algorithm.
	Run(Double, Double)	Obsolete. Runs one iteration of the Reweighted Least Squares algorithm.
	Run(Double, Double)	Obsolete. Runs one iteration of the Reweighted Least Squares algorithm.
	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.Statistics.Models.Regression.Fitting Namespace