public GbmState(IRegressionProblemData problemData, ILossFunction lossFunction, uint randSeed, int maxSize, double r, double m, double nu) {
// default settings for MaxSize, Nu and R
this.maxSize = maxSize;
this.nu = nu;
this.r = r;
this.m = m;
this.randSeed = randSeed;
random = new MersenneTwister(randSeed);
this.problemData = problemData;
this.trainingRows = problemData.TrainingIndices.ToArray();
this.testRows = problemData.TestIndices.ToArray();
this.lossFunction = lossFunction;
int nRows = trainingRows.Length;
y = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, trainingRows).ToArray();
treeBuilder = new RegressionTreeBuilder(problemData, random);
activeIdx = Enumerable.Range(0, nRows).ToArray();
var zeros = Enumerable.Repeat(0.0, nRows).ToArray();
double f0 = lossFunction.LineSearch(y, zeros, activeIdx, 0, nRows - 1); // initial constant value (mean for squared errors)
pred = Enumerable.Repeat(f0, nRows).ToArray();
predTest = Enumerable.Repeat(f0, testRows.Length).ToArray();
pseudoRes = new double[nRows];
models = new List<IRegressionModel>();
weights = new List<double>();
// add constant model
models.Add(new ConstantModel(f0, problemData.TargetVariable));
weights.Add(1.0);
}
public GbmState(IRegressionProblemData problemData, ILossFunction lossFunction, uint randSeed, int maxSize, double r, double m, double nu)
{
// default settings for MaxSize, Nu and R
this.maxSize = maxSize;
this.nu = nu;
this.r = r;
this.m = m;
this.randSeed = randSeed;
random = new MersenneTwister(randSeed);
this.problemData = problemData;
this.trainingRows = problemData.TrainingIndices.ToArray();
this.testRows = problemData.TestIndices.ToArray();
this.lossFunction = lossFunction;
int nRows = trainingRows.Length;
y = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, trainingRows).ToArray();
treeBuilder = new RegressionTreeBuilder(problemData, random);
activeIdx = Enumerable.Range(0, nRows).ToArray();
var zeros = Enumerable.Repeat(0.0, nRows).ToArray();
double f0 = lossFunction.LineSearch(y, zeros, activeIdx, 0, nRows - 1); // initial constant value (mean for squared errors)
pred = Enumerable.Repeat(f0, nRows).ToArray();
predTest = Enumerable.Repeat(f0, testRows.Length).ToArray();
pseudoRes = new double[nRows];
models = new List <IRegressionModel>();
weights = new List <double>();
// add constant model
models.Add(new ConstantModel(f0, problemData.TargetVariable));
weights.Add(1.0);
}