com.rapidminer.operator.learner.functions.kernel.gaussianprocess
Class Regression

java.lang.Object
  com.rapidminer.operator.learner.functions.kernel.gaussianprocess.GPBase
      com.rapidminer.operator.learner.functions.kernel.gaussianprocess.Regression

public class Regression
extends GPBase

Gaussian Process Regression. REFERENCES: Lehel Csato. Gaussian Processes --- Iterative Sparse Approximations. PhD thesis, Aston University, Birmingham, UK, March 2002. TODO: - own Matrix implementation with SE-cholesky-decomposition and the ability to constrain matrix operation on sub matrices - CompositeKernel implementation for kernels based on finite basis functions

Author:

Piotr Kasprzak

Nested Class Summary

protected static class

Regression.Score Used to hold a score value with an associated index

Field Summary

Fields inherited from class com.rapidminer.operator.learner.functions.kernel.gaussianprocess.GPBase

model, parameter, problem

Constructor Summary

Regression(RegressionProblem problem, Parameter parameter)
Constructor

Method Summary

Model	learn() The hard work is done here
java.lang.String	toString() Identify the GP

Methods inherited from class com.rapidminer.operator.learner.functions.kernel.gaussianprocess.GPBase

getModel

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

Regression

public Regression(RegressionProblem problem,
                  Parameter parameter)

Constructor

Method Detail

learn

public Model learn()
            throws java.lang.Exception

The hard work is done here

Specified by:

learn in class GPBase

Throws:

java.lang.Exception

toString

public java.lang.String toString()

Identify the GP

Overrides:

toString in class java.lang.Object