Changeset 5624 for branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

Timestamp:

03/07/11 18:11:23 (13 years ago)

Author:

gkronber

Message:

#1418 renamed interface for interpreter, worked on solutions and models and implemented SVM regression.

File:

• branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs (modified) (4 diffs)

branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

@@ -32 +32 @@
 using System.Collections.Generic;
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
@@ -61 +62 @@
     protected override void Run() {
       double rmsError, cvRmsError;
-      IRegressionProblemData problemData = Problem.ProblemData;
-      int samplesStart = problemData.TrainingPartitionStart.Value;
-      int samplesEnd = problemData.TrainingPartitionEnd.Value;
-      IEnumerable<string> selectedInputVariables = problemData.InputVariables.CheckedItems.Select(x => x.Value);
-      var solution = CreateLinearRegressionSolution(problemData.Dataset, problemData.TargetVariable, selectedInputVariables, samplesStart, samplesEnd, out rmsError, out cvRmsError);
+      var solution = CreateLinearRegressionSolution(Problem.ProblemData, out rmsError, out cvRmsError);
       Results.Add(new Result(LinearRegressionModelResultName, "The linear regression model.", solution));
       Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the linear regression model on the training set.", new DoubleValue(rmsError)));
@@ -71 +68 @@
     }
 
-    public static ISymbolicExpressionTree CreateLinearRegressionSolution(Dataset dataset, string targetVariable, IEnumerable<string> allowedInputVariables, int start, int end, out double rmsError, out double cvRmsError) {
-      double[,] inputMatrix = LinearRegressionUtil.PrepareInputMatrix(dataset, targetVariable, allowedInputVariables, start, end);
+    public static ISymbolicRegressionSolution CreateLinearRegressionSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError) {
+      Dataset dataset = problemData.Dataset;
+      string targetVariable = problemData.TargetVariable;
+      IEnumerable<string> allowedInputVariables = problemData.InputVariables.CheckedItems.Select(x => x.Value);
+      int samplesStart = problemData.TrainingPartitionStart.Value;
+      int samplesEnd = problemData.TrainingPartitionEnd.Value;
+
+      IEnumerable<string> selectedInputVariables = problemData.InputVariables.CheckedItems.Select(x => x.Value);
+
+      double[,] inputMatrix = LinearRegressionUtil.PrepareInputMatrix(dataset, targetVariable, allowedInputVariables, samplesStart, samplesEnd);
 
       alglib.linearmodel lm = new alglib.linearmodel();
@@ -107 +112 @@
       addition.AddSubTree(cNode);
 
-      return tree;
+      SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), problemData);
+      return solution;
     }
     #endregion