private GaussianProcessModel(GaussianProcessModel original, Cloner cloner)
: base(original, cloner)
{
this.meanFunction = cloner.Clone(original.meanFunction);
this.covarianceFunction = cloner.Clone(original.covarianceFunction);
if (original.inputScaling != null)
{
this.inputScaling = cloner.Clone(original.inputScaling);
}
this.trainingDataset = cloner.Clone(original.trainingDataset);
this.negativeLogLikelihood = original.negativeLogLikelihood;
this.targetVariable = original.targetVariable;
this.sqrSigmaNoise = original.sqrSigmaNoise;
if (original.meanParameter != null)
{
this.meanParameter = (double[])original.meanParameter.Clone();
}
if (original.covarianceParameter != null)
{
this.covarianceParameter = (double[])original.covarianceParameter.Clone();
}
// shallow copies of arrays because they cannot be modified
this.trainingRows = original.trainingRows;
this.allowedInputVariables = original.allowedInputVariables;
this.alpha = original.alpha;
this.l = original.l;
this.x = original.x;
}
public GaussianProcessModel(IDataset ds, string targetVariable, IEnumerable <string> allowedInputVariables, IEnumerable <int> rows,
IEnumerable <double> hyp, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction,
bool scaleInputs = true)
: base()
{
this.name = ItemName;
this.description = ItemDescription;
this.meanFunction = (IMeanFunction)meanFunction.Clone();
this.covarianceFunction = (ICovarianceFunction)covarianceFunction.Clone();
this.targetVariable = targetVariable;
this.allowedInputVariables = allowedInputVariables.ToArray();
int nVariables = this.allowedInputVariables.Length;
meanParameter = hyp
.Take(this.meanFunction.GetNumberOfParameters(nVariables))
.ToArray();
covarianceParameter = hyp.Skip(this.meanFunction.GetNumberOfParameters(nVariables))
.Take(this.covarianceFunction.GetNumberOfParameters(nVariables))
.ToArray();
sqrSigmaNoise = Math.Exp(2.0 * hyp.Last());
try {
CalculateModel(ds, rows, scaleInputs);
} catch (alglib.alglibexception ae) {
// wrap exception so that calling code doesn't have to know about alglib implementation
throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
}
}
public void ClearState()
{
meanFunc = null;
covFunc = null;
bestQ = double.NegativeInfinity;
bestHyperParameters = null;
}
// cloning
private GaussianProcessCovarianceOptimizationProblem(GaussianProcessCovarianceOptimizationProblem original, Cloner cloner)
: base(original, cloner)
{
bestQ = original.bestQ;
meanFunc = cloner.Clone(original.meanFunc);
covFunc = cloner.Clone(original.covFunc);
if (bestHyperParameters != null)
{
bestHyperParameters = new double[original.bestHyperParameters.Length];
Array.Copy(original.bestHyperParameters, bestHyperParameters, bestHyperParameters.Length);
}
}
// updates the overall best quality and overall best model for Analyze()
private void UpdateBestSoFar(double bestQ, double[] bestHyperParameters, IMeanFunction meanFunc, ICovarianceFunction covFunc)
{
lock (problemStateLocker) {
if (bestQ > this.bestQ)
{
this.bestQ = bestQ;
this.bestHyperParameters = new double[bestHyperParameters.Length];
Array.Copy(bestHyperParameters, this.bestHyperParameters, this.bestHyperParameters.Length);
this.meanFunc = meanFunc;
this.covFunc = covFunc;
}
}
}
private void TestMeanFunction(IMeanFunction mf, double hypValue, double[] expectedMean, double[][] expectedGradients) {
var x = GetData();
var xt = GetDataTest();
int nHyp = mf.GetNumberOfParameters(x.GetLength(1));
var hyp = Enumerable.Repeat(hypValue, nHyp).ToArray();
var meanFunction = mf.GetParameterizedMeanFunction(hyp, Enumerable.Range(0, x.GetLength(1)).ToArray());
var m = Enumerable.Range(0, xt.GetLength(0)).Select(i => meanFunction.Mean(xt, i)).ToArray();
AssertEqual(expectedMean, m);
for (int k = 0; k < nHyp; k++) {
var g = Enumerable.Range(0, xt.GetLength(0)).Select(i => meanFunction.Gradient(x, i, k)).ToArray();
AssertEqual(expectedGradients[k], g);
}
}
public static IGaussianProcessModel Create(IRegressionProblemData problemData, double[] hyperparameter, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction, bool scaleInputs = true) {
return new GaussianProcessModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, hyperparameter, meanFunction, covarianceFunction, scaleInputs);
}
public GaussianProcessModel(IDataset ds, string targetVariable, IEnumerable<string> allowedInputVariables, IEnumerable<int> rows,
IEnumerable<double> hyp, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction,
bool scaleInputs = true)
: base(targetVariable) {
this.name = ItemName;
this.description = ItemDescription;
this.meanFunction = (IMeanFunction)meanFunction.Clone();
this.covarianceFunction = (ICovarianceFunction)covarianceFunction.Clone();
this.allowedInputVariables = allowedInputVariables.ToArray();
int nVariables = this.allowedInputVariables.Length;
meanParameter = hyp
.Take(this.meanFunction.GetNumberOfParameters(nVariables))
.ToArray();
covarianceParameter = hyp.Skip(this.meanFunction.GetNumberOfParameters(nVariables))
.Take(this.covarianceFunction.GetNumberOfParameters(nVariables))
.ToArray();
sqrSigmaNoise = Math.Exp(2.0 * hyp.Last());
try {
CalculateModel(ds, rows, scaleInputs);
}
catch (alglib.alglibexception ae) {
// wrap exception so that calling code doesn't have to know about alglib implementation
throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
}
}
private GaussianProcessModel(GaussianProcessModel original, Cloner cloner)
: base(original, cloner) {
this.meanFunction = cloner.Clone(original.meanFunction);
this.covarianceFunction = cloner.Clone(original.covarianceFunction);
if (original.inputScaling != null)
this.inputScaling = cloner.Clone(original.inputScaling);
this.trainingDataset = cloner.Clone(original.trainingDataset);
this.negativeLogLikelihood = original.negativeLogLikelihood;
this.sqrSigmaNoise = original.sqrSigmaNoise;
if (original.meanParameter != null) {
this.meanParameter = (double[])original.meanParameter.Clone();
}
if (original.covarianceParameter != null) {
this.covarianceParameter = (double[])original.covarianceParameter.Clone();
}
// shallow copies of arrays because they cannot be modified
this.trainingRows = original.trainingRows;
this.allowedInputVariables = original.allowedInputVariables;
this.alpha = original.alpha;
this.l = original.l;
this.x = original.x;
}
public static IGaussianProcessModel Create(IClassificationProblemData problemData, double[] hyperparameter, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction, bool scaleInputs = true)
{
return(new GaussianProcessModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, hyperparameter, meanFunction, covarianceFunction, scaleInputs));
}