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Changeset 14107 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4

Timestamp:

07/18/16 17:45:45 (8 years ago)

Author:

pfleck

Message:

#2631

Added an additional function to ALGLIB that returns the prediction for each tree of a decision forest.
Implemented IConfidenceRegression/Solution for RandomForestModel/Solution using the variance of the predictions from the trees.

Location:

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4

Files:

r12012	r14107
29	29	/// Interface to represent a random forest model for either regression or classification
30	30	/// </summary>
31		public interface IRandomForestModel : IRegressionModel, IClassificationModel {
	31	public interface IRandomForestModel : IConfidenceRegressionModel, IClassificationModel {
32	32	}
33	33	}

r12012	r14107
28	28	/// Interface to represent a random forest regression solution
29	29	/// </summary>
30		public interface IRandomForestRegressionSolution : IRegressionSolution {
	30	public interface IRandomForestRegressionSolution : IConfidenceRegressionSolution {
31	31	new IRandomForestModel Model { get; }
32	32	}

-                      r13941
+                      r14107
+    }
+    public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
+      double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, AllowedInputVariables, rows);
+      AssertInputMatrix(inputData);
+      int n = inputData.GetLength(0);
+      int columns = inputData.GetLength(1);
+      double[] x = new double[columns];
+      double[] ys = new double[columns];
+      for (int row = 0; row < n; row++) {
+        for (int column = 0; column < columns; column++) {
+          x[column] = inputData[row, column];
+        }
+        alglib.dforest.dfprocessraw(RandomForest.innerobj, x, ref ys);
+        yield return ys.VariancePop();
+      }
+    }
     public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, AllowedInputVariables, rows);

r13941	r14107
31	31	[Item("RandomForestRegressionSolution", "Represents a random forest solution for a regression problem which can be visualized in the GUI.")]
32	32	[StorableClass]
33		public sealed class RandomForestRegressionSolution : RegressionSolution, IRandomForestRegressionSolution {
	33	public sealed class RandomForestRegressionSolution : ConfidenceRegressionSolution, IRandomForestRegressionSolution {
34	34
35	35	public new IRandomForestModel Model {

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