Changeset 15352

Timestamp:

09/07/17 08:22:47 (7 years ago)

Author:

gkronber

Message:

#2789 testing alglib RBF and splines

Location:

branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental

Files:

• HeuristicLab.Algorithms.DataAnalysis.Experimental.csproj (modified) (1 diff)
• RBF.cs (added)
• Splines.cs (added)
• SymbolicRegressionConstantOptimizationEvaluator.cs (modified) (2 diffs)

branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/HeuristicLab.Algorithms.DataAnalysis.Experimental.csproj

@@ -131 +131 @@
   <ItemGroup>
     <Compile Include="ForwardSelection.cs" />
+    <Compile Include="RBF.cs" />
+    <Compile Include="Splines.cs" />
     <Compile Include="LinearRegressionCombinations.cs" />
     <Compile Include="NonlinearRegression.cs" />

branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/SymbolicRegressionConstantOptimizationEvaluator.cs

@@ -223 +223 @@
       var target = problemData.TargetVariable;
 
-      // only count constraints
-      var constraintRows = Enumerable.Range(0, problemData.Dataset.Rows).Where(rIdx => !string.IsNullOrEmpty(ds.GetStringValue("f(x) constraint-type", rIdx)));
+      // determine rows with constraints by checking of any constraint column contains a value
+      var constraintColNames = parameterEntries
+                .Select(e => e.variableName)
+                .Select(name => string.Format("df/d({0})", name))
+                .ToArray();
+      var constraintRows = Enumerable.Range(0, problemData.Dataset.Rows)
+                .Where(rIdx => constraintColNames.Any(name => !double.IsNaN(ds.GetDoubleValue(name, rIdx))));
 
       double[,] constraintX = new double[constraintRows.Count(), parameters.Count]; // inputs for constraint values
@@ -271 +276 @@
       int updateFreq = 10;
       try {
-        // alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state);
-        // alglib.lsfitsetcond(state, 0.0, maxIterations);
-        // //alglib.lsfitsetgradientcheck(state, 0.001);
-        // alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, null, null);
-        // alglib.lsfitresults(state, out retVal, out c, out rep);
         alglib.minnlccreate(c, out state);
         alglib.minnlcsetalgoaul(state, rho, outeriters);