/*
* File: MultivariateRegression.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Apr 23, 2012, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.learning.algorithm.regression;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.evaluator.Evaluator;
import gov.sandia.cognition.math.matrix.Vectorizable;
/**
* A regression algorithm that maps one or more independent (input) variables
* onto multiple output variables.
* @author Kevin R. Dixon
* @since 3.4.2
* @param <InputType> The type of input data in the input-output pair that
* the learner can learn from. The {@code Evaluator} learned from the
* algorithm also takes this as the input parameter.
* @param <EvaluatorType> The type of object created by the learning algorithm.
*/
@PublicationReference(
author="Wikipedia",
title="General linear model",
type=PublicationType.WebPage,
year=2012,
url="http://en.wikipedia.org/wiki/Multivariate_regression",
notes={
"The only article on Multivariate Regression I could find only talks about the linear case.",
"This interface, however, can deal with the nonlinear case as well."
}
)
public interface MultivariateRegression<InputType, EvaluatorType extends Evaluator<? super InputType, ? extends Vectorizable>>
extends Regression<InputType,Vectorizable,EvaluatorType>
{
}