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HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression

Timestamp:

06/13/13 11:20:25 (11 years ago)

Author:

gkronber

Message:

#1967: fixed generation of GPR problem instances (sampling from Gaussian processes) to work together with the current trunk version

Location:

branches/HeuristicLab.Problems.GaussianProcessTuning

Files:

: 17 edited

Legend:

: Unmodified
: Added
: Removed

branches/HeuristicLab.Problems.GaussianProcessTuning

Property svn:ignore

old	new
6	6	bin
7	7	x64
	8	Release
	9	TestResults

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcess2dPeriodic.cs

r9214	r9622
83	83	.Concat(new double[] { Math.Log(Math.Sqrt(0.01)) })
84	84	.ToArray(),
85		n~~ull~~);
	85	new int[] { 0, 1});
86	86
87	87	var mt = new MersenneTwister(31415);

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessPolyTen.cs

-                      r9112
+                      r9622
           -5.0 // noise
         };
       var covarianceFunction = new CovarianceSum();
 …
       covarianceFunction.Terms.Add(t5);
       var cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, null);
+      var cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 10));

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessRegressionDemo.cs

r9338	r9622
48	48	public GaussianProcessRegressionDemo(string name, ICovarianceFunction covarianceFunction, double[] hyp) {
49	49	this.name = name;
50		cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, n~~ull~~);
	50	cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, new int[] { 0 });
51	51	}
52	52

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessRegressionInstance.cs

r9124	r9622
48	48	public GaussianProcessRegressionInstance(string name, ICovarianceFunction covarianceFunction, double[] hyp) {
49	49	this.name = name;
50		cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, ~~null~~);
	50	cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 1));
51	51	}
52	52

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessRegressionInstance1D.cs

r9212	r9622
49	49	public GaussianProcessRegressionInstance1D(string name, ICovarianceFunction covarianceFunction, double[] hyp) {
50	50	this.name = name;
51		cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, ~~null~~);
	51	cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 1));
52	52	}
53	53

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessRegressionInstance2D.cs

r9212	r9622
49	49	public GaussianProcessRegressionInstance2D(string name, ICovarianceFunction covarianceFunction, double[] hyp) {
50	50	this.name = name;
51		cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, ~~null~~);
	51	cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 2));
52	52	}
53	53

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso.cs

r9112	r9622
59	59	covarianceFunction.GetParameterizedCovarianceFunction(
60	60	new double[] { Math.Log(0.1), Math.Log(Math.Sqrt(1)), Math.Log(Math.Sqrt(0.01)) },
61		~~null~~);
	61	Enumerable.Range(0, 1));
62	62
63	63	var mt = new MersenneTwister(31415);

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso1.cs

r9112	r9622
66	66	covFun.Terms.Add(new CovarianceNoise());
67	67
68		var cov = covFun.GetParameterizedCovarianceFunction(hyp, ~~null~~);
	68	var cov = covFun.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 2));
69	69	var mt = new MersenneTwister();
70	70	var target = Util.SampleGaussianProcess(mt, cov, data);

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso2.cs

r9112	r9622
70	70	covFun.Terms.Add(m2);
71	71	covFun.Terms.Add(new CovarianceNoise());
72		var cov = covFun.GetParameterizedCovarianceFunction(hyp, ~~null~~);
	72	var cov = covFun.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 4));
73	73
74	74	var mt = new MersenneTwister();

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso3.cs

r9112	r9622
75	75	covFun.Terms.Add(m3);
76	76	covFun.Terms.Add(new CovarianceNoise());
77		var cov = covFun.GetParameterizedCovarianceFunction(hyp, ~~null~~);
	77	var cov = covFun.GetParameterizedCovarianceFunction(hyp, Enumerable.Range(0, 6));
78	78
79	79	var mt = new MersenneTwister();

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso4.cs

r9112	r9622
66	66	covFun.Terms.Add(new CovarianceNoise());
67	67
68		var cov = covFun.GetParameterizedCovarianceFunction(hyp, n~~ull~~);
	68	var cov = covFun.GetParameterizedCovarianceFunction(hyp, new int[] { 0, 6, 8 });
69	69	var mt = new MersenneTwister();
70	70	var target = Util.SampleGaussianProcess(mt, cov, data);

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso5.cs

r9112	r9622
69	69	covFun.Terms.Add(m2);
70	70	covFun.Terms.Add(new CovarianceNoise());
71		var cov = covFun.GetParameterizedCovarianceFunction(hyp, n~~ull~~);
	71	var cov = covFun.GetParameterizedCovarianceFunction(hyp, new int[] { 0, 2, 5, 6, 8, 9 });
72	72
73	73	var mt = new MersenneTwister();

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIso6.cs

r9112	r9622
80	80	covFun.Terms.Add(m4);
81	81	covFun.Terms.Add(new CovarianceNoise());
82		var cov = covFun.GetParameterizedCovarianceFunction(hyp, n~~ull~~);
	82	var cov = covFun.GetParameterizedCovarianceFunction(hyp, new int[] { 0, 1, 2, 3, 4, 5, 6, 8 });
83	83
84	84

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIsoDependentNoise.cs

r9112	r9622
66	66	Math.Log(Math.Sqrt(0.01)) // noise
67	67	};
68		var cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, n~~ull~~);
	68	var cov = covarianceFunction.GetParameterizedCovarianceFunction(hyp, new int[] { 0 });
69	69
70	70	var mt = new MersenneTwister(31415);

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSumOfRQIso.cs

r9124	r9622
71	71	covFun.Terms.Add(new CovarianceNoise());
72	72
73		var cov = covFun.GetParameterizedCovarianceFunction(hyp, n~~ull~~);
	73	var cov = covFun.GetParameterizedCovarianceFunction(hyp, new int[] { 0 });
74	74	var mt = new MersenneTwister();
75	75	var target = Util.SampleGaussianProcess(mt, cov, data);

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSumOfSEIso.cs

r9124	r9622
70	70	covFun.Terms.Add(new CovarianceNoise());
71	71
72		var cov = covFun.GetParameterizedCovarianceFunction(hyp, n~~ull~~);
	72	var cov = covFun.GetParameterizedCovarianceFunction(hyp, new int[] { 0 });
73	73	var mt = new MersenneTwister();
74	74	var target = Util.SampleGaussianProcess(mt, cov, data);

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Changeset 9622 for branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression

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