Changeset 16398

Timestamp:

12/18/18 19:22:41 (6 years ago)

Author:

gkronber

Message:

#2925: small changes

Location:

branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3

Files:

• Problem.cs (modified) (8 diffs)
• Vector.cs (modified) (1 diff)

branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3/Problem.cs

@@ -39 +39 @@
 
 namespace HeuristicLab.Problems.DynamicalSystemsModelling {
-
-
-
-
   // Eine weitere Möglichkeit ist spline-smoothing der Daten (über Zeit) um damit für jede Zielvariable
   // einen bereinigten (Rauschen) Wert und die Ableitung dy/dt für alle Beobachtungen zu bekommen
@@ -196 +192 @@
       // TODO: use training range as default training episode
       // TODO: write back optimized parameters to solution?
+      // TODO: optimization of starting values for latent variables in CVODES solver
 
     }
@@ -258 +255 @@
         }
       }
-      // init params randomly from Unif(-1e-5, 1e-5)
+      // init params randomly
       // theta contains parameter values for trees and then the initial values for latent variables (a separate vector for each episode)
       // inital values for latent variables are also optimized
       var theta = new double[paramNodes.Count + latentVariables.Length * episodes.Count()];
       for (int i = 0; i < theta.Length; i++)
-        theta[i] = random.NextDouble() * 2.0e-2 - 1.0e-2;
+        theta[i] = random.NextDouble() * 2.0 - 1.0;
 
       optTheta = new double[0];
@@ -388 +385 @@
         results.Add(new Result("Models", typeof(VariableCollection)));
       }
+      if(!results.ContainsKey("SNMSE")) {
+        results.Add(new Result("SNMSE", typeof(DoubleValue)));
+      }
 
       var bestIndividualAndQuality = this.GetBestIndividual(individuals, qualities);
       var trees = bestIndividualAndQuality.Item1.Values.Select(v => v.Value).OfType<ISymbolicExpressionTree>().ToArray(); // extract all trees from individual
+
+      results["SNMSE"].Value = new DoubleValue(bestIndividualAndQuality.Item2);
 
       var problemData = ProblemData;
@@ -646 +648 @@
     }
 
+    #region CVODES
+
 
     /// <summary>
@@ -769 +773 @@
       string[] calculatedVariables,
       double[] parameterValues) {
-      // we don't need to calculate a gradient here -> no nodes are selected for 
-      // --> no nodes are selected to be included in the gradient calculation
-      var nodeIdx = new Dictionary<ISymbolicExpressionTreeNode, int>();
+      // we don't need to calculate a gradient here 
       return (double t,
               IntPtr y, // N_Vector, current value of y (input)
@@ -942 +944 @@
         };
     }
+    #endregion
 
     private static void IntegrateHL(
@@ -994 +997 @@
           variableValues[kvp.Key] = newVal;
         }
-        // update nodeValues from variableValues
-        // TODO: perf using dictionary with list of nodes for each variable
-        foreach (var tree in trees) {
-          foreach (var node in tree.Root.IterateNodesPrefix().Where(n => IsVariableNode(n))) {
+
+
+        foreach(var node in nodeValues.Keys.ToArray()) {
+          if(node.SubtreeCount == 0 && !IsConstantNode(node)) {
+            // update values for (latent) variables
             var varName = node.Symbol.Name;
             nodeValues[node] = variableValues[varName];

branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3/Vector.cs

@@ -15 +15 @@
       return new Vector(res);
     }
+
     public static Vector operator -(Vector a, Vector b) {
       if (b == Zero) return a;