// wrap an actual model in a surrograte
public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed,
ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu,
IGradientBoostedTreesModel model)
: this(trainingProblemData, seed, lossFunction, iterations, maxSize, r, m, nu)
{
this.actualModel = model;
}
// forward message to actual model (recalculate model first if necessary)
public override IEnumerable <double> GetEstimatedValues(IDataset dataset, IEnumerable <int> rows)
{
if (actualModel == null)
{
actualModel = RecalculateModel();
}
return(actualModel.GetEstimatedValues(dataset, rows));
}
private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner)
: base(original, cloner) {
if (original.actualModel != null) this.actualModel = cloner.Clone(original.actualModel);
this.trainingProblemData = cloner.Clone(original.trainingProblemData);
this.lossFunction = cloner.Clone(original.lossFunction);
this.seed = original.seed;
this.iterations = original.iterations;
this.maxSize = original.maxSize;
this.r = original.r;
this.m = original.m;
this.nu = original.nu;
}
private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner)
: base(original, cloner)
{
if (original.actualModel != null)
{
this.actualModel = cloner.Clone(original.actualModel);
}
this.trainingProblemData = cloner.Clone(original.trainingProblemData);
this.lossFunction = cloner.Clone(original.lossFunction);
this.seed = original.seed;
this.iterations = original.iterations;
this.maxSize = original.maxSize;
this.r = original.r;
this.m = original.m;
this.nu = original.nu;
}
private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner)
: base(original, cloner)
{
IGradientBoostedTreesModel clonedModel = null;
if (original.actualModel.IsValueCreated)
{
clonedModel = cloner.Clone(original.ActualModel);
}
actualModel = new Lazy <IGradientBoostedTreesModel>(CreateLazyInitFunc(clonedModel)); // only capture clonedModel in the closure
this.trainingProblemData = cloner.Clone(original.trainingProblemData);
this.lossFunction = cloner.Clone(original.lossFunction);
this.seed = original.seed;
this.iterations = original.iterations;
this.maxSize = original.maxSize;
this.r = original.r;
this.m = original.m;
this.nu = original.nu;
}
private Func <IGradientBoostedTreesModel> CreateLazyInitFunc(IGradientBoostedTreesModel clonedModel)
{
return(() => {
return clonedModel ?? RecalculateModel();
});
}
// wrap an actual model in a surrograte
public GradientBoostedTreesModelSurrogate(IGradientBoostedTreesModel model, IRegressionProblemData trainingProblemData, uint seed,
ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu)
: this(trainingProblemData, seed, lossFunction, iterations, maxSize, r, m, nu)
{
actualModel = new Lazy <IGradientBoostedTreesModel>(() => model);
}
// wrap an actual model in a surrograte
public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed,
ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu,
IGradientBoostedTreesModel model)
: this(trainingProblemData, seed, lossFunction, iterations, maxSize, r, m, nu) {
this.actualModel = model;
}
// forward message to actual model (recalculate model first if necessary)
public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
if (actualModel == null) actualModel = RecalculateModel();
return actualModel.GetEstimatedValues(dataset, rows);
}
