Changeset 16660 for branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3/Solution.cs

Timestamp:

03/07/19 17:00:39 (5 years ago)

Author:

gkronber

Message:

#2925: re-introduced partial support for latent variables

File:

• branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3/Solution.cs (modified) (3 diffs)

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

@@ -86 +86 @@
     }
 
-    public IEnumerable<double[]> Predict(IntRange episode, int forecastHorizon, out double snmse) {
+    public IEnumerable<double[]> Predict(IntRange episode, int forecastHorizon) {
       var forecastEpisode = new IntRange(episode.Start, episode.End + forecastHorizon);
       // 
@@ -92 +92 @@
       // snmse = Problem.OptimizeForEpisodes(trees, problemData, targetVars, latentVariables, random, new[] { forecastEpisode }, 100, numericIntegrationSteps, odeSolver);
 
-      var inputVariables = trees.SelectMany(t => t.IterateNodesPrefix().OfType<VariableTreeNode>().Select(n => n.VariableName)).Except(targetVars);
+      var inputVariables = trees.SelectMany(t => t.IterateNodesPrefix().OfType<VariableTreeNode>().Select(n => n.VariableName))
+        .Except(targetVars)
+        .Except(latentVariables)
+        .Distinct();
 
       var optimizationData = new Problem.OptimizationData(trees, targetVars, inputVariables.ToArray(), problemData, null, new[] { forecastEpisode }, numericIntegrationSteps, latentVariables, odeSolver);
@@ -99 +102 @@
       // var theta = Problem.ExtractParametersFromTrees(trees);
 
-      snmse = 0.0; // TODO
-      return Problem.Integrate(optimizationData).Select(p => p.Select(pi => pi.Item1).ToArray()).ToArray();
+
+      var fi = new double[forecastEpisode.Size * targetVars.Length];
+      var jac = new double[forecastEpisode.Size * targetVars.Length, optimizationData.nodeValueLookup.ParameterCount];
+      var latentValues = new double[forecastEpisode.Size, LatentVariables.Length];
+      Problem.Integrate(optimizationData, fi, jac, latentValues);
+      for (int i = 0; i < forecastEpisode.Size; i++) {
+        var res = new double[targetVars.Length + latentVariables.Length];
+        for (int j = 0; j < targetVars.Length; j++) {
+          res[j] = fi[i * targetVars.Length + j];
+        }
+        for (int j = 0; j < latentVariables.Length; j++) {
+          res[targetVars.Length + j] = latentValues[i, j];
+        }
+        yield return res;
+      }
     }
   }