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source: branches/ExcelIntegration/HeuristicLabExcel/ExcelFunctions.cs @ 11716

Last change on this file since 11716 was 11716, checked in by gkronber, 9 years ago
File size: 2.1 KB
Line 
1using System;
2using System.Collections.Generic;
3using System.Linq;
4using ExcelDna.Integration;
5using HeuristicLab.Problems.DataAnalysis;
6
7namespace HeuristicLabExcel {
8
9  public class ExcelFunctions {
10    /* Standard example from ExcelDNA */
11    [ExcelFunction(Description = "Multiplies two numbers", Category = "Useful functions")]
12    public static double MultiplyThem(double x, double y) {
13      return x * y;
14    }
15
16   
17    [ExcelFunction(Description = "Random forest", Category = "Data Analysis")]
18    public static double[,] PredictRandomForest(double[,] x, double[] y) {
19      int nRows = x.GetLength(0);
20      int nCols = x.GetLength(1);
21      if (nRows > 5000) throw new ArgumentException("y");
22      if (nCols >= nRows) throw new ArgumentException("x");
23      var inputs = Enumerable.Range(0, nCols).Select(i => "x" + i);
24      var target = "y";
25      var variables = inputs.Concat(new string[] { target });
26      // copy data
27      var xy = new double[nRows, nCols + 1];
28      for (int r = 0; r < nRows; r++) {
29        for (int c = 0; c < nCols; c++) {
30          xy[r, c] = x[r, c];
31        }
32        if (r < y.Length)
33          xy[r, nCols] = y[r];
34      }
35      var ds = new Dataset(variables, xy);
36
37      var problemData = new RegressionProblemData(ds, inputs, target);
38      problemData.TrainingPartition.Start = 0;
39      problemData.TrainingPartition.End = y.Length;
40      problemData.TestPartition.Start = y.Length;
41      problemData.TestPartition.End = nRows;
42
43      double rmsError;
44      double oobAvgRelError;
45      double oobRmsError;
46      double avgRelError;
47      var rf = HeuristicLab.Algorithms.DataAnalysis.RandomForestRegression.CreateRandomForestRegressionSolution(problemData, 100, 0.5, 0.5, 31415, out rmsError, out avgRelError, out oobRmsError, out oobAvgRelError);
48
49      // copy for output
50      var res = new double[nRows, 1];
51      var estValuesEnum = rf.EstimatedValues.GetEnumerator();
52      estValuesEnum.MoveNext();
53      for (int r = 0; r < nRows; r++) {
54        res[r, 0] = estValuesEnum.Current;
55        estValuesEnum.MoveNext();
56      }
57      return res;
58    }
59  }
60}
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