Changeset 5649 for branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear

Timestamp:

03/10/11 10:00:09 (14 years ago)

Author:

gkronber

Message:

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

File:

• branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs (modified) (5 diffs)

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

@@ -42 +42 @@
   [StorableClass]
   public sealed class LinearRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
-    private const string LinearRegressionModelResultName = "LinearRegressionModel";
+    private const string LinearRegressionModelResultName = "Linear regression solution";
 
     [StorableConstructor]
@@ -51 +51 @@
     public LinearRegression()
       : base() {
+      Problem = new RegressionProblem();
     }
     [StorableHook(HookType.AfterDeserialization)]
@@ -63 +64 @@
       double rmsError, 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)));
-      Results.Add(new Result("Estimated root mean square error (cross-validation)", "The estimated root of the mean of squared errors of the linear regression model via cross validation.", new DoubleValue(cvRmsError)));
+      Results.Add(new Result(LinearRegressionModelResultName, "The linear regression solution.", solution));
+      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)));
+      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)));
     }
 
@@ -71 +72 @@
       Dataset dataset = problemData.Dataset;
       string targetVariable = problemData.TargetVariable;
-      IEnumerable<string> allowedInputVariables = problemData.InputVariables.CheckedItems.Select(x => x.Value);
+      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
       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);
@@ -87 +86 @@
       int retVal = 1;
       alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
-      if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression model");
+      if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression solution");
       rmsError = ar.rmserror;
       cvRmsError = ar.cvrmserror;