Changeset 8491 for trunk/sources/HeuristicLab.Tests/HeuristicLab.Algorithms.DataAnalysis-3.4/GaussianProcessModelTest.cs
- Timestamp:
- 08/14/12 17:10:08 (12 years ago)
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- 1 edited
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trunk/sources/HeuristicLab.Tests/HeuristicLab.Algorithms.DataAnalysis-3.4/GaussianProcessModelTest.cs
r8484 r8491 86 86 Assert.AreEqual(356.6076, predTrain[1], 1e-3); 87 87 } 88 89 /*90 {91 // example from GPML book92 var hyp = new double[] {93 341.0, // mean 341 ppm94 // SE iso (long term trend)95 Math.Log(67.0 / 45.0), // length scale 67 years96 Math.Log(Math.Sqrt(66)), // magnitude 66ppm97 98 // product of SEiso and periodic99 Math.Log(90.0 / 45.0), // decay-time 90 years100 Math.Log(Math.Sqrt(2.4)), // magnitude 2.4ppm101 102 Math.Log(1.3), // smoothness103 Math.Log(1), // period 1 year104 Math.Log(Math.Sqrt(2.4)), // magnitude 2.4ppm105 106 // short term variation107 Math.Log(1.2 / 45.0), // typical length 1.2 years108 Math.Log(Math.Sqrt(0.66)), // magnitude 0.66ppm109 Math.Log(0.78), // shape (very small)110 111 // SEiso (correlated noise)112 Math.Log(1.6 / 45.0 / 12.0), // 1.6 months113 Math.Log(Math.Sqrt(0.18)), // amplitude of correlated noise 0.18ppm114 Math.Log(Math.Sqrt(0.19)), // theta11 0.19ppm noise115 };116 117 covarianceFunction.Terms.Add(new CovarianceRQiso());118 covarianceFunction.Terms.Add(new CovarianceSEiso()); // correlated noise119 var model = new GaussianProcessModel(problemData.Dataset, targetVariable, allowedInputVariables, Enumerable.Range(0, 545), hyp,120 new MeanConst(),121 covarianceFunction);122 Assert.AreEqual(-108.5, model.NegativeLogLikelihood, 1);123 }124 */125 88 } 126 89
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