Changeset 1043 for branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Core/Problem.cs

Timestamp:

12/20/08 13:54:29 (16 years ago)

Author:

gkronber

Message:

worked on view for CEDMA problems and persistence of CEDMA problems to RDF store. #419 (Refactor CEDMA plugins)

File:

• branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Core/Problem.cs (modified) (6 diffs)

branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Core/Problem.cs

@@ -30 +30 @@
 
 namespace HeuristicLab.CEDMA.Core {
+
+  public enum LearningTask {
+    Classification,
+    Regression,
+    TimeSeries,
+    Clustering
+  }
+
   /// <summary>
   /// Problem describes the data mining task.
@@ -59 +67 @@
 
     private int validationSamplesStart;
-
     public int ValidationSamplesStart {
       get { return validationSamplesStart; }
       set { validationSamplesStart = value; }
     }
+
     private int validationSamplesEnd;
-
     public int ValidationSamplesEnd {
       get { return validationSamplesEnd; }
@@ -72 +79 @@
 
     private int testSamplesStart;
-
     public int TestSamplesStart {
       get { return testSamplesStart; }
       set { testSamplesStart = value; }
     }
+
     private int testSamplesEnd;
-
     public int TestSamplesEnd {
       get { return testSamplesEnd; }
@@ -95 +101 @@
 
     private bool autoRegressive;
-
     public bool AutoRegressive {
       get { return autoRegressive; }
@@ -101 +106 @@
     }
 
-    private bool timeSeries;
-
-    public bool TimeSeries {
-      get { return timeSeries; }
-      set { timeSeries = value; }
+    private LearningTask learningTask;
+    public LearningTask LearningTask {
+      get { return learningTask; }
+      set { learningTask = value; }
     }
 
@@ -118 +122 @@
       return new ProblemView(this);
     }
+
+    public override XmlNode GetXmlNode(string name, XmlDocument document, IDictionary<Guid, IStorable> persistedObjects) {
+      XmlNode node = base.GetXmlNode(name, document, persistedObjects);
+      node.AppendChild(PersistenceManager.Persist("DataSet", dataset, document, persistedObjects));
+      XmlAttribute trainingSamplesStartAttr = document.CreateAttribute("TrainingSamplesStart");
+      trainingSamplesStartAttr.Value = TrainingSamplesStart.ToString();
+      XmlAttribute trainingSamplesEndAttr = document.CreateAttribute("TrainingSamplesEnd");
+      trainingSamplesEndAttr.Value = TrainingSamplesEnd.ToString();
+      XmlAttribute validationSamplesStartAttr = document.CreateAttribute("ValidationSamplesStart");
+      validationSamplesStartAttr.Value = ValidationSamplesStart.ToString();
+      XmlAttribute validationSamplesEndAttr = document.CreateAttribute("ValidationSamplesEnd");
+      validationSamplesEndAttr.Value = ValidationSamplesEnd.ToString();
+      XmlAttribute testSamplesStartAttr = document.CreateAttribute("TestSamplesStart");
+      testSamplesStartAttr.Value = TestSamplesStart.ToString();
+      XmlAttribute testSamplesEndAttr = document.CreateAttribute("TestSamplesEnd");
+      testSamplesEndAttr.Value = TestSamplesEnd.ToString();
+      XmlAttribute learningTaskAttr = document.CreateAttribute("LearningTask");
+      learningTaskAttr.Value = LearningTask.ToString();
+      XmlAttribute autoRegressiveAttr = document.CreateAttribute("AutoRegressive");
+      autoRegressiveAttr.Value = AutoRegressive.ToString();
+
+      node.Attributes.Append(trainingSamplesStartAttr);
+      node.Attributes.Append(trainingSamplesEndAttr);
+      node.Attributes.Append(validationSamplesStartAttr);
+      node.Attributes.Append(validationSamplesEndAttr);
+      node.Attributes.Append(testSamplesStartAttr);
+      node.Attributes.Append(testSamplesEndAttr);
+      node.Attributes.Append(learningTaskAttr);
+      node.Attributes.Append(autoRegressiveAttr);
+
+      XmlElement targetVariablesElement = document.CreateElement("AllowedTargetVariables");
+      targetVariablesElement.InnerText = SemiColonSeparatedList(AllowedTargetVariables);
+      XmlElement inputVariablesElement = document.CreateElement("AllowedInputVariables");
+      inputVariablesElement.InnerText = SemiColonSeparatedList(AllowedInputVariables);
+      node.AppendChild(targetVariablesElement);
+      node.AppendChild(inputVariablesElement);
+      return node;
+    }
+
+    public override void Populate(XmlNode node, IDictionary<Guid, IStorable> restoredObjects) {
+      base.Populate(node, restoredObjects);
+      dataset = (HeuristicLab.DataAnalysis.Dataset)PersistenceManager.Restore(node.SelectSingleNode("DataSet"), restoredObjects);
+      TrainingSamplesStart = int.Parse(node.Attributes["TrainingSamplesStart"].Value);
+      TrainingSamplesEnd = int.Parse(node.Attributes["TrainingSamplesEnd"].Value);
+      ValidationSamplesStart = int.Parse(node.Attributes["ValidationSamplesStart"].Value);
+      ValidationSamplesEnd = int.Parse(node.Attributes["ValidationSamplesEnd"].Value);
+      TestSamplesStart = int.Parse(node.Attributes["TestSamplesStart"].Value);
+      TestSamplesEnd = int.Parse(node.Attributes["TestSamplesEnd"].Value);
+      LearningTask = (LearningTask)Enum.Parse(typeof(LearningTask), node.Attributes["LearningTask"].Value);
+      AutoRegressive = bool.Parse(node.Attributes["AutoRegressive"].Value);
+      allowedTargetVariables.Clear();
+      foreach (string tok in node.SelectSingleNode("AllowedTargetVariables").InnerText.Split(new string[]{";"}, StringSplitOptions.RemoveEmptyEntries))
+        allowedTargetVariables.Add(int.Parse(tok));
+      allowedInputVariables.Clear();
+      foreach (string tok in node.SelectSingleNode("AllowedInputVariables").InnerText.Split(new string[] { ";" }, StringSplitOptions.RemoveEmptyEntries))
+        allowedInputVariables.Add(int.Parse(tok));
+    }
+
+    private string SemiColonSeparatedList(List<int> xs) {
+      StringBuilder b = new StringBuilder();
+      foreach (int x in xs) {
+        b = b.Append(x).Append(";");
+      }
+      if (xs.Count > 0) b.Remove(b.Length - 1, 1); // remove last ';'
+      return b.ToString();
+    }
   }
 }