Free cookie consent management tool by TermsFeed Policy Generator

source: branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs @ 5649

Last change on this file since 5649 was 5649, checked in by gkronber, 14 years ago

#1418 Implemented classes for classification based on a discriminant function and thresholds and implemented interfaces and base classes for clustering.

File size: 5.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Optimization;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Problems.DataAnalysis;
31using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
32using System.Collections.Generic;
33using HeuristicLab.Problems.DataAnalysis.Symbolic;
34using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
35
36namespace HeuristicLab.Algorithms.DataAnalysis {
37  /// <summary>
38  /// Linear regression data analysis algorithm.
39  /// </summary>
40  [Item("Linear Regression", "Linear regression data analysis algorithm.")]
41  [Creatable("Data Analysis")]
42  [StorableClass]
43  public sealed class LinearRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
44    private const string LinearRegressionModelResultName = "Linear regression solution";
45
46    [StorableConstructor]
47    private LinearRegression(bool deserializing) : base(deserializing) { }
48    private LinearRegression(LinearRegression original, Cloner cloner)
49      : base(original, cloner) {
50    }
51    public LinearRegression()
52      : base() {
53      Problem = new RegressionProblem();
54    }
55    [StorableHook(HookType.AfterDeserialization)]
56    private void AfterDeserialization() { }
57
58    public override IDeepCloneable Clone(Cloner cloner) {
59      return new LinearRegression(this, cloner);
60    }
61
62    #region linear regression
63    protected override void Run() {
64      double rmsError, cvRmsError;
65      var solution = CreateLinearRegressionSolution(Problem.ProblemData, out rmsError, out cvRmsError);
66      Results.Add(new Result(LinearRegressionModelResultName, "The linear regression solution.", solution));
67      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the linear regression solution on the training set.", new DoubleValue(rmsError)));
68      Results.Add(new Result("Estimated root mean square error (cross-validation)", "The estimated root of the mean of squared errors of the linear regression solution via cross validation.", new DoubleValue(cvRmsError)));
69    }
70
71    public static ISymbolicRegressionSolution CreateLinearRegressionSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError) {
72      Dataset dataset = problemData.Dataset;
73      string targetVariable = problemData.TargetVariable;
74      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
75      int samplesStart = problemData.TrainingPartitionStart.Value;
76      int samplesEnd = problemData.TrainingPartitionEnd.Value;
77
78      double[,] inputMatrix = LinearRegressionUtil.PrepareInputMatrix(dataset, targetVariable, allowedInputVariables, samplesStart, samplesEnd);
79
80      alglib.linearmodel lm = new alglib.linearmodel();
81      alglib.lrreport ar = new alglib.lrreport();
82      int nRows = inputMatrix.GetLength(0);
83      int nFeatures = inputMatrix.GetLength(1) - 1;
84      double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant
85
86      int retVal = 1;
87      alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
88      if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression solution");
89      rmsError = ar.rmserror;
90      cvRmsError = ar.cvrmserror;
91
92      alglib.lrunpack(lm, out coefficients, out nFeatures);
93
94      ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
95      ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
96      tree.Root.AddSubTree(startNode);
97      ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
98      startNode.AddSubTree(addition);
99
100      int col = 0;
101      foreach (string column in allowedInputVariables) {
102        VariableTreeNode vNode = (VariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
103        vNode.VariableName = column;
104        vNode.Weight = coefficients[col];
105        addition.AddSubTree(vNode);
106        col++;
107      }
108
109      ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode();
110      cNode.Value = coefficients[coefficients.Length - 1];
111      addition.AddSubTree(cNode);
112
113      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), problemData);
114      return solution;
115    }
116    #endregion
117  }
118}
Note: See TracBrowser for help on using the repository browser.