Changeset 17204 for branches/2994-AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Extensions/ConstrainedNLS.cs

Timestamp:

08/13/19 09:29:11 (5 years ago)

Author:

gkronber

Message:

#2994: added parameter for gradient checks and experimented with preconditioning

File:

• branches/2994-AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Extensions/ConstrainedNLS.cs (modified) (7 diffs)

branches/2994-AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Extensions/ConstrainedNLS.cs

@@ -38 +38 @@
       get { return (IFixedValueParameter<DoubleValue>)Parameters["MaxTime"]; }
     }
+    public IFixedValueParameter<BoolValue> CheckGradientParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters["CheckGradient"]; }
+    }
     public int Iterations { get { return IterationsParameter.Value.Value; } set { IterationsParameter.Value.Value = value; } }
 
@@ -48 +51 @@
       get { return ModelStructureParameter.Value.Value; }
       set { ModelStructureParameter.Value.Value = value; }
+    }
+    public bool CheckGradient {
+      get { return CheckGradientParameter.Value.Value; }
+      set { CheckGradientParameter.Value.Value = value; }
     }
 
@@ -64 +71 @@
       Parameters.Add(new FixedValueParameter<DoubleValue>("FuncToleranceAbs", new DoubleValue(0)));
       Parameters.Add(new FixedValueParameter<DoubleValue>("MaxTime", new DoubleValue(10)));
+      Parameters.Add(new FixedValueParameter<BoolValue>("CheckGradient", "Flag to indicate whether the gradient should be checked using numeric approximation", new BoolValue(false)));
+
+      CheckGradientParameter.Hidden = true;
     }
 
     public ConstrainedNLS(ConstrainedNLS original, Cloner cloner) : base(original, cloner) {
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    public void AfterDeserializationHook() {
+      if (!Parameters.ContainsKey("CheckGradient")) {
+        Parameters.Add(new FixedValueParameter<BoolValue>("CheckGradient", "Flag to indicate whether the gradient should be checked using numeric approximation", new BoolValue(false)));
+
+        CheckGradientParameter.Hidden = true;
+      }
     }
 
@@ -89 +108 @@
       Results.AddOrUpdateResult("Evaluations", functionEvaluations);
       var bestError = new DoubleValue(double.MaxValue);
+      var curError = new DoubleValue(double.MaxValue);
       Results.AddOrUpdateResult("Best error", bestError);
+      Results.AddOrUpdateResult("Current error", curError);
       Results.AddOrUpdateResult("Tree", tree);
       var qualitiesTable = new DataTable("Qualities");
@@ -112 +133 @@
 
       var state = new ConstrainedNLSInternal(Solver.Value, tree, Iterations, problem.ProblemData, FuncToleranceRel, FuncToleranceAbs, MaxTime);
+      if (CheckGradient) state.CheckGradient = true;
+      int idx = 0;
+      foreach(var constraintTree in state.constraintTrees) {
+        Results.AddOrUpdateResult($"Constraint {idx++}", constraintTree);
+      }
 
       // we use a listener model here to get state from the solver      
@@ -123 +149 @@
       bestQualityRow.Values.Add(bestError.Value);
 
-
-      Results.AddOrUpdateResult("Best solution", CreateSolution(state.BestTree, problem.ProblemData));
+      Results.AddOrUpdateResult("Best solution", CreateSolution((ISymbolicExpressionTree)state.BestTree.Clone(), problem.ProblemData));
+      Results.AddOrUpdateResult("Best solution constraint values", new DoubleArray(state.BestConstraintValues));
 
 
@@ -132 +158 @@
         functionEvaluations.Value++;
         bestError.Value = state.BestError;
+        curError.Value = state.CurError;
         curQualityRow.Values.Add(state.CurError);
         bestQualityRow.Values.Add(bestError.Value);