- Timestamp:
- 09/10/12 13:42:43 (12 years ago)
- Location:
- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess
- Files:
-
- 1 edited
- 4 moved
Legend:
- Unmodified
- Added
- Removed
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trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceRationalQuadraticArd.cs
r8612 r8615 30 30 namespace HeuristicLab.Algorithms.DataAnalysis { 31 31 [StorableClass] 32 [Item(Name = "CovarianceR QArd",32 [Item(Name = "CovarianceRationalQuadraticArd", 33 33 Description = "Rational quadratic covariance function with automatic relevance determination for Gaussian processes.")] 34 public sealed class CovarianceR QArd : ParameterizedNamedItem, ICovarianceFunction {34 public sealed class CovarianceRationalQuadraticArd : ParameterizedNamedItem, ICovarianceFunction { 35 35 [Storable] 36 36 private double sf2; … … 58 58 59 59 [StorableConstructor] 60 private CovarianceR QArd(bool deserializing)60 private CovarianceRationalQuadraticArd(bool deserializing) 61 61 : base(deserializing) { 62 62 } 63 63 64 private CovarianceR QArd(CovarianceRQArd original, Cloner cloner)64 private CovarianceRationalQuadraticArd(CovarianceRationalQuadraticArd original, Cloner cloner) 65 65 : base(original, cloner) { 66 66 this.scaleParameter = cloner.Clone(original.scaleParameter); … … 79 79 } 80 80 81 public CovarianceR QArd()81 public CovarianceRationalQuadraticArd() 82 82 : base() { 83 83 Name = ItemName; … … 96 96 97 97 public override IDeepCloneable Clone(Cloner cloner) { 98 return new CovarianceR QArd(this, cloner);98 return new CovarianceRationalQuadraticArd(this, cloner); 99 99 } 100 100 … … 131 131 i += hyp.Skip(i).Count(); 132 132 } 133 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceR QArd", "hyp");133 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceRationalQuadraticArd", "hyp"); 134 134 } 135 135 -
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceRationalQuadraticIso.cs
r8612 r8615 29 29 namespace HeuristicLab.Algorithms.DataAnalysis { 30 30 [StorableClass] 31 [Item(Name = "CovarianceR Qiso",31 [Item(Name = "CovarianceRationalQuadraticIso", 32 32 Description = "Isotropic rational quadratic covariance function for Gaussian processes.")] 33 public sealed class CovarianceR Qiso : ParameterizedNamedItem, ICovarianceFunction {33 public sealed class CovarianceRationalQuadraticIso : ParameterizedNamedItem, ICovarianceFunction { 34 34 [Storable] 35 35 private double sf2; … … 51 51 52 52 [StorableConstructor] 53 private CovarianceR Qiso(bool deserializing)53 private CovarianceRationalQuadraticIso(bool deserializing) 54 54 : base(deserializing) { 55 55 } 56 56 57 private CovarianceR Qiso(CovarianceRQiso original, Cloner cloner)57 private CovarianceRationalQuadraticIso(CovarianceRationalQuadraticIso original, Cloner cloner) 58 58 : base(original, cloner) { 59 59 this.sf2 = original.sf2; … … 69 69 } 70 70 71 public CovarianceR Qiso()71 public CovarianceRationalQuadraticIso() 72 72 : base() { 73 73 Name = ItemName; … … 86 86 87 87 public override IDeepCloneable Clone(Cloner cloner) { 88 return new CovarianceR Qiso(this, cloner);88 return new CovarianceRationalQuadraticIso(this, cloner); 89 89 } 90 90 … … 121 121 i++; 122 122 } 123 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceR Qiso", "hyp");123 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceRationalQuadraticIso", "hyp"); 124 124 } 125 125 -
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceSquaredExponentialArd.cs
r8612 r8615 30 30 namespace HeuristicLab.Algorithms.DataAnalysis { 31 31 [StorableClass] 32 [Item(Name = "CovarianceS Eard", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")]33 public sealed class CovarianceS Eard : ParameterizedNamedItem, ICovarianceFunction {32 [Item(Name = "CovarianceSquaredExponentialArd", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")] 33 public sealed class CovarianceSquaredExponentialArd : ParameterizedNamedItem, ICovarianceFunction { 34 34 [Storable] 35 35 private double sf2; … … 45 45 46 46 [StorableConstructor] 47 private CovarianceS Eard(bool deserializing) : base(deserializing) { }48 private CovarianceS Eard(CovarianceSEard original, Cloner cloner)47 private CovarianceSquaredExponentialArd(bool deserializing) : base(deserializing) { } 48 private CovarianceSquaredExponentialArd(CovarianceSquaredExponentialArd original, Cloner cloner) 49 49 : base(original, cloner) { 50 50 this.sf2 = original.sf2; … … 59 59 RegisterEvents(); 60 60 } 61 public CovarianceS Eard()61 public CovarianceSquaredExponentialArd() 62 62 : base() { 63 63 Name = ItemName; … … 74 74 75 75 public override IDeepCloneable Clone(Cloner cloner) { 76 return new CovarianceS Eard(this, cloner);76 return new CovarianceSquaredExponentialArd(this, cloner); 77 77 } 78 78 … … 107 107 i += hyp.Skip(i).Count(); 108 108 } 109 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for Covarianc SEard", "hyp");109 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialArd", "hyp"); 110 110 } 111 111 -
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceSquaredExponentialIso.cs
r8612 r8615 29 29 namespace HeuristicLab.Algorithms.DataAnalysis { 30 30 [StorableClass] 31 [Item(Name = "CovarianceS Eiso",31 [Item(Name = "CovarianceSquaredExponentialIso", 32 32 Description = "Isotropic squared exponential covariance function for Gaussian processes.")] 33 public sealed class CovarianceS Eiso : ParameterizedNamedItem, ICovarianceFunction {33 public sealed class CovarianceSquaredExponentialIso : ParameterizedNamedItem, ICovarianceFunction { 34 34 [Storable] 35 35 private double sf2; … … 45 45 46 46 [StorableConstructor] 47 private CovarianceS Eiso(bool deserializing)47 private CovarianceSquaredExponentialIso(bool deserializing) 48 48 : base(deserializing) { 49 49 } 50 50 51 private CovarianceS Eiso(CovarianceSEiso original, Cloner cloner)51 private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner) 52 52 : base(original, cloner) { 53 53 this.sf2 = original.sf2; … … 60 60 } 61 61 62 public CovarianceS Eiso()62 public CovarianceSquaredExponentialIso() 63 63 : base() { 64 64 Name = ItemName; … … 75 75 76 76 public override IDeepCloneable Clone(Cloner cloner) { 77 return new CovarianceS Eiso(this, cloner);77 return new CovarianceSquaredExponentialIso(this, cloner); 78 78 } 79 79 … … 104 104 i++; 105 105 } 106 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceS Eiso", "hyp");106 if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialIso", "hyp"); 107 107 } 108 108 -
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessRegression.cs
r8495 r8615 102 102 103 103 Parameters.Add(new ValueParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function to use.", new MeanConst())); 104 Parameters.Add(new ValueParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.", new CovarianceS Eiso()));104 Parameters.Add(new ValueParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.", new CovarianceSquaredExponentialIso())); 105 105 Parameters.Add(new ValueParameter<IntValue>(MinimizationIterationsParameterName, "The number of iterations for likelihood optimization with LM-BFGS.", new IntValue(20))); 106 106 Parameters.Add(new ValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
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