public void GaussianProcessRegressionPerformanceTest() { ex = null; var alg = new GaussianProcessRegression(); alg.Engine = new HeuristicLab.SequentialEngine.SequentialEngine(); alg.Problem = new RegressionProblem(); var provider = new RegressionCSVInstanceProvider(); var problemData = (RegressionProblemData)provider.ImportData(@"Test Resources\co2.txt"); problemData.TargetVariableParameter.ActualValue = problemData.TargetVariableParameter.ValidValues.First(x => x.Value == "interpolated"); problemData.InputVariables.SetItemCheckedState(problemData.InputVariables.First(x => x.Value == "year"), false); problemData.InputVariables.SetItemCheckedState(problemData.InputVariables.First(x => x.Value == "month"), false); problemData.InputVariables.SetItemCheckedState(problemData.InputVariables.First(x => x.Value == "average"), false); problemData.InputVariables.SetItemCheckedState(problemData.InputVariables.First(x => x.Value == "interpolated"), false); problemData.InputVariables.SetItemCheckedState(problemData.InputVariables.First(x => x.Value == "trend"), false); problemData.InputVariables.SetItemCheckedState(problemData.InputVariables.First(x => x.Value == "#days"), false); alg.Problem.ProblemDataParameter.Value = problemData; alg.ExceptionOccurred += new EventHandler<EventArgs<Exception>>(cv_ExceptionOccurred); alg.Stopped += new EventHandler(cv_Stopped); alg.Prepare(); alg.Start(); trigger.WaitOne(); if (ex != null) throw ex; TestContext.WriteLine("Runtime: {0}", alg.ExecutionTime.ToString()); }
public void GaussianProcessModelOutputTest() { var provider = new RegressionCSVInstanceProvider(); var problemData = provider.ImportData(@"Test Resources\co2.txt"); var targetVariable = "interpolated"; var allowedInputVariables = new string[] { "decimal date" }; var rows = Enumerable.Range(0, 401); var meanFunction = new MeanConst(); var covarianceFunction = new CovarianceSum(); covarianceFunction.Terms.Add(new CovarianceSquaredExponentialIso()); var prod = new CovarianceProduct(); prod.Factors.Add(new CovarianceSquaredExponentialIso()); prod.Factors.Add(new CovariancePeriodic()); covarianceFunction.Terms.Add(prod); { var hyp = new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; var model = new GaussianProcessModel(problemData.Dataset, targetVariable, allowedInputVariables, rows, hyp, meanFunction, covarianceFunction); Assert.AreEqual(4.3170e+004, model.NegativeLogLikelihood, 1); var dHyp = model.HyperparameterGradients; Assert.AreEqual(-248.7932, dHyp[0], 1E-2); var dHypCovExpected = new double[] { -0.5550e4, -5.5533e4, -0.2511e4, -2.7625e4, -1.3033e4, 0.0289e4, -2.7625e4 }; AssertEqual(dHypCovExpected, dHyp.Skip(1).Take(7).ToArray(), 1); Assert.AreEqual(-2.0171e+003, dHyp.Last(), 1); var predTrain = model.GetEstimatedValues(problemData.Dataset, new int[] { 0, 400 }).ToArray(); Assert.AreEqual(310.5930, predTrain[0], 1e-3); Assert.AreEqual(347.9993, predTrain[1], 1e-3); var predTrainVar = model.GetEstimatedVariance(problemData.Dataset, problemData.TrainingIndices).ToArray(); } { var hyp = new double[] { 0.029973094285941, 0.455535210579926, 3.438647883940457, 1.464114485889487, 3.001788584487478, 3.815289323309630, 4.374914122810222, 3.001788584487478, 0.716427415979145 }; var model = new GaussianProcessModel(problemData.Dataset, targetVariable, allowedInputVariables, rows, hyp, meanFunction, covarianceFunction); Assert.AreEqual(872.8448, model.NegativeLogLikelihood, 1e-3); var dHyp = model.HyperparameterGradients; Assert.AreEqual(-0.0046, dHyp[0], 1e-3); var dHypCovExpected = new double[] { 0.2652, -0.2386, 0.1706, -0.1744, 0.0000, 0.0000, -0.1744 }; AssertEqual(dHypCovExpected, dHyp.Skip(1).Take(7).ToArray(), 1e-3); Assert.AreEqual(0.8621, dHyp.Last(), 1e-3); var predTrain = model.GetEstimatedValues(problemData.Dataset, new int[] { 0, 400 }).ToArray(); Assert.AreEqual(315.3692, predTrain[0], 1e-3); Assert.AreEqual(356.6076, predTrain[1], 1e-3); } }