Changeset 14936

Timestamp:

05/05/17 16:06:17 (7 years ago)

Author:

gkronber

Message:

#2699: changed namespace for KernelRidgeRegression

Location:

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression

Files:

• KernelFunctions/CicularKernel.cs (modified) (1 diff)
• KernelFunctions/GaussianKernel.cs (modified) (1 diff)
• KernelFunctions/IKernel.cs (modified) (1 diff)
• KernelFunctions/InverseMultiquadraticKernel.cs (modified) (1 diff)
• KernelFunctions/KernelBase.cs (modified) (1 diff)
• KernelFunctions/MultiquadraticKernel.cs (modified) (1 diff)
• KernelFunctions/PolysplineKernel.cs (modified) (1 diff)
• KernelFunctions/ThinPlatePolysplineKernel.cs (modified) (1 diff)
• KernelRidgeRegression.cs (modified) (1 diff)
• KernelRidgeRegressionModel.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/CicularKernel.cs

@@ -25 +25 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   [Item("CircularKernel", "A circular kernel function 2*pi*(acos(-d)-d*(1-d²)^(0.5)) where n = ||x-c|| and d = n/beta \n  As described in http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/")]

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/GaussianKernel.cs

@@ -27 +27 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   [Item("GaussianKernel", "A kernel function that uses Gaussian function exp(-n²/beta²). As described in http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/")]

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/IKernel.cs

@@ -21 +21 @@
 
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   public interface IKernel : ICovarianceFunction {
     double? Beta { get; set; } // a kernel parameter

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/InverseMultiquadraticKernel.cs

@@ -25 +25 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   [Item("InverseMultiquadraticKernel", "A kernel function that uses the inverse multi-quadratic function  1 / sqrt(1+||x-c||²/beta²). Similar to http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/ with beta as a scaling factor.")]

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/KernelBase.cs

@@ -28 +28 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   public abstract class KernelBase : ParameterizedNamedItem, IKernel {

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/MultiquadraticKernel.cs

@@ -25 +25 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   // conditionally positive definite. (need to add polynomials) see http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf 

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/PolysplineKernel.cs

@@ -27 +27 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   // conditionally positive definite. (need to add polynomials) see http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf 

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelFunctions/ThinPlatePolysplineKernel.cs

@@ -27 +27 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   // conditionally positive definite. (need to add polynomials) see http://num.math.uni-goettingen.de/schaback/teaching/sc.pdf 

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelRidgeRegression.cs

@@ -31 +31 @@
 using HeuristicLab.Problems.DataAnalysis;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [Item("Kernel Ridge Regression", "Kernelized ridge regression e.g. for radial basis function (RBF) regression.")]
   [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 100)]

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelRidgeRegressionModel.cs

@@ -28 +28 @@
 using HeuristicLab.Problems.DataAnalysis;
 
-namespace HeuristicLab.Algorithms.DataAnalysis.KernelRidgeRegression {
+namespace HeuristicLab.Algorithms.DataAnalysis {
   [StorableClass]
   [Item("KernelRidgeRegressionModel", "A kernel ridge regression model")]