private MeanConst(MeanConst original, Cloner cloner)
: base(original, cloner)
{
}
private MeanConst(MeanConst original, Cloner cloner)
: base(original, cloner) {
}
// Does not produce the same result for the same seed when using parallel engine (see below)!
public override double Evaluate(ISymbolicExpressionTree tree, IRandom random)
{
var meanFunction = new MeanConst();
var problemData = ProblemData;
var ds = problemData.Dataset;
var targetVariable = problemData.TargetVariable;
var allowedInputVariables = problemData.AllowedInputVariables.ToArray();
var nVars = allowedInputVariables.Length;
var trainingRows = problemData.TrainingIndices.ToArray();
// use the same covariance function for each restart
var covarianceFunction = TreeToCovarianceFunction(tree);
// allocate hyperparameters
var hyperParameters = new double[meanFunction.GetNumberOfParameters(nVars) + covarianceFunction.GetNumberOfParameters(nVars) + 1]; // mean + cov + noise
double[] bestHyperParameters = new double[hyperParameters.Length];
var bestObjValue = new double[1] {
double.MinValue
};
// data that is necessary for the objective function
var data = Tuple.Create(ds, targetVariable, allowedInputVariables, trainingRows, (IMeanFunction)meanFunction, covarianceFunction, bestObjValue);
for (int t = 0; t < Restarts; t++)
{
var prevBest = bestObjValue[0];
var prevBestHyperParameters = new double[hyperParameters.Length];
Array.Copy(bestHyperParameters, prevBestHyperParameters, bestHyperParameters.Length);
// initialize hyperparameters
hyperParameters[0] = ds.GetDoubleValues(targetVariable).Average(); // mean const
// Evaluate might be called concurrently therefore access to random has to be synchronized.
// However, results of multiple runs with the same seed will be different when using the parallel engine.
lock (syncRoot) {
for (int i = 0; i < covarianceFunction.GetNumberOfParameters(nVars); i++)
{
hyperParameters[1 + i] = random.NextDouble() * 2.0 - 1.0;
}
}
hyperParameters[hyperParameters.Length - 1] = 1.0; // s² = exp(2), TODO: other inits better?
// use alglib.bfgs for hyper-parameter optimization ...
double epsg = 0;
double epsf = 0.00001;
double epsx = 0;
double stpmax = 1;
int maxits = ConstantOptIterations;
alglib.mincgstate state;
alglib.mincgreport rep;
alglib.mincgcreate(hyperParameters, out state);
alglib.mincgsetcond(state, epsg, epsf, epsx, maxits);
alglib.mincgsetstpmax(state, stpmax);
alglib.mincgoptimize(state, ObjectiveFunction, null, data);
alglib.mincgresults(state, out bestHyperParameters, out rep);
if (rep.terminationtype < 0)
{
// error -> restore previous best quality
bestObjValue[0] = prevBest;
Array.Copy(prevBestHyperParameters, bestHyperParameters, prevBestHyperParameters.Length);
}
}
UpdateBestSoFar(bestObjValue[0], bestHyperParameters, meanFunction, covarianceFunction);
return(bestObjValue[0]);
}
