protected PrincipleComponentTransformation(PrincipleComponentTransformation original, Cloner cloner)
{
if (original.Variances != null)
{
Variances = original.Variances.ToArray();
}
if (original.VariableNames != null)
{
VariableNames = original.VariableNames.ToArray();
}
if (original.Deviations != null)
{
Deviations = original.Deviations.ToArray();
}
if (original.Means != null)
{
Means = original.Means.ToArray();
}
if (original.Matrix == null)
{
return;
}
Matrix = new double[original.Matrix.GetLength(0), original.Matrix.GetLength(1)];
for (var i = 0; i < original.Matrix.GetLength(0); i++)
{
for (var j = 0; j < original.Matrix.GetLength(1); j++)
{
Matrix[i, j] = original.Matrix[i, j];
}
}
}
public static PrincipleComponentTransformation CreateProjection(IDataset dataset, IEnumerable <int> rows, IEnumerable <string> variables, bool normalize = false)
{
var res = new PrincipleComponentTransformation();
res.BuildPca(dataset, rows, variables, normalize);
return(res);
}
public override IRegressionModel Build(IRegressionProblemData pd, IRandom random,
CancellationToken cancellationToken, out int numberOfParameters)
{
var pca = PrincipleComponentTransformation.CreateProjection(pd.Dataset, pd.TrainingIndices, pd.AllowedInputVariables, normalize: true);
var pcdata = pca.TransformProblemData(pd);
ComponentReducedLinearModel bestModel = null;
var bestCvrmse = double.MaxValue;
numberOfParameters = 1;
for (var i = 1; i <= Math.Min(NumberOfComponents, pd.AllowedInputVariables.Count()); i++)
{
var pd2 = (IRegressionProblemData)pcdata.Clone();
var inputs = new HashSet <string>(pca.ComponentNames.Take(i));
foreach (var v in pd2.InputVariables.CheckedItems.ToArray())
{
pd2.InputVariables.SetItemCheckedState(v.Value, inputs.Contains(v.Value.Value));
}
double rmse;
var model = PreconstructedLinearModel.CreateLinearModel(pd2, out rmse);
if (rmse > bestCvrmse)
{
continue;
}
bestModel = new ComponentReducedLinearModel(pd2.TargetVariable, model, pca);
numberOfParameters = i + 1;
bestCvrmse = rmse;
}
return(bestModel);
}
public ComponentReducedLinearModel(string targetVariable, IRegressionModel model, PrincipleComponentTransformation pca) : base(targetVariable)
{
Model = model;
Pca = pca;
}
private ComponentReducedLinearModel(ComponentReducedLinearModel original, Cloner cloner) : base(original, cloner)
{
Model = cloner.Clone(original.Model);
Pca = cloner.Clone(original.Pca);
}
