Index: /branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/ConstantsOptimization/LMConstantsOptimizer.cs
===================================================================
--- /branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/ConstantsOptimization/LMConstantsOptimizer.cs (revision 17392)
+++ /branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/ConstantsOptimization/LMConstantsOptimizer.cs (revision 17393)
@@ -47,5 +47,5 @@
/// The rows for which the data should be extracted.
/// A flag to determine whether linear scaling should be applied during the optimization
- /// The maximum number of iterations of the Levenberg-Marquard algorithm.
+ /// The maximum number of iterations of the Levenberg-Marquardt algorithm.
///
public static double OptimizeConstants(ISymbolicExpressionTree tree,
@@ -63,6 +63,6 @@
throw new NotSupportedException("Could not convert symbolic expression tree to an AutoDiff term due to not supported symbols used in the tree.");
- //Variables of the symbolic expression tree correspond to parameters in the term
- //Hence if no parameters are present we can't do anything and R² stays the same.
+ // Variables of the symbolic expression tree correspond to parameters in the term.
+ // Hence if no parameters are present we can't do anything and R² stays the same.
if (term.Parameters.Count == 0) return 0.0;
@@ -80,5 +80,5 @@
///
- /// Optimizes the numeric coefficents of an AutoDiff Term using the Levenberg-Marquard algorithm.
+ /// Optimizes the numeric coefficents of an AutoDiff Term using the Levenberg-Marquardt algorithm.
///
/// The AutoDiff term for which the numeric coefficients should be optimized.
@@ -86,6 +86,6 @@
/// The input data for the optimization.
/// The target values for the optimization.
- /// The maximum number of iterations of the Levenberg-Marquard
- /// The opitmized constants.
+ /// The maximum number of iterations of the Levenberg-Marquardt
+ /// The optimized constants.
/// An optional callback for detailed analysis that is called in each algorithm iteration.
/// The R² of the term evaluated on the input data x and the target data y using the optimized constants