Changeset 1287 for trunk/sources/HeuristicLab.CEDMA.Core/Problem.cs

Timestamp:

03/08/09 12:48:18 (15 years ago)

Author:

gkronber

Message:

Merged change sets from CEDMA branch to trunk:

• r957:r990 r992 r1003 r1041:1044 (#419)
• r1051:1053 r1060 (#419, #224)
• r1072:1075 r1104:1106 r1108:1109 r1115:1116 r1129:1130 r1151 r1156 (#419)
• r1201:1202 (#224)
• r1208 (#487)
• r1214:1217 r1230:1231 (#419)
• r1235:1236 r1238:1239 r1245 (#224)
• r1246 (#505)
• r1251:1254 r1262:1263 (#224)
• r1265:1266, r1275:1277 (#419)

File:

• trunk/sources/HeuristicLab.CEDMA.Core/Problem.cs (copied) (copied from branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Core/Problem.cs) (2 diffs)

trunk/sources/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.
@@ -36 +44 @@
   ///  * regression, time-series or classification problem
   /// </summary>
-  public class Problem {
+  public class Problem : ItemBase {
+    private string name;
+    public string Name {
+      get { return name; }
+    }
+    private HeuristicLab.DataAnalysis.Dataset dataset;
+    public HeuristicLab.DataAnalysis.Dataset DataSet {
+      get { return dataset; }
+    }
+
+    private int trainingSamplesStart;
+    public int TrainingSamplesStart {
+      get { return trainingSamplesStart; }
+      set { trainingSamplesStart = value; }
+    }
+
+    private int trainingSamplesEnd;
+    public int TrainingSamplesEnd {
+      get { return trainingSamplesEnd; }
+      set { trainingSamplesEnd = value; }
+    }
+
+    private int validationSamplesStart;
+    public int ValidationSamplesStart {
+      get { return validationSamplesStart; }
+      set { validationSamplesStart = value; }
+    }
+
+    private int validationSamplesEnd;
+    public int ValidationSamplesEnd {
+      get { return validationSamplesEnd; }
+      set { validationSamplesEnd = value; }
+    }
+
+    private int testSamplesStart;
+    public int TestSamplesStart {
+      get { return testSamplesStart; }
+      set { testSamplesStart = value; }
+    }
+
+    private int testSamplesEnd;
+    public int TestSamplesEnd {
+      get { return testSamplesEnd; }
+      set { testSamplesEnd = value; }
+    }
+
+    private List<int> allowedInputVariables;
+    public List<int> AllowedInputVariables {
+      get { return allowedInputVariables; }
+    }
+
+    private List<int> allowedTargetVariables;
+    public List<int> AllowedTargetVariables {
+      get { return allowedTargetVariables; }
+    }
+
+    private bool autoRegressive;
+    public bool AutoRegressive {
+      get { return autoRegressive; }
+      set { autoRegressive = value; }
+    }
+
+    private int minTimeOffset;
+    public int MinTimeOffset {
+      get { return minTimeOffset; }
+      set { minTimeOffset = value; }
+    }
+
+    private int maxTimeOffset;
+    public int MaxTimeOffset {
+      get { return maxTimeOffset; }
+      set { maxTimeOffset = value; }
+    }
+
+    private LearningTask learningTask;
+    public LearningTask LearningTask {
+      get { return learningTask; }
+      set { learningTask = value; }
+    }
+
     public Problem()
       : base() {
+      dataset = new DataAnalysis.Dataset();
+      allowedInputVariables = new List<int>();
+      allowedTargetVariables = new List<int>();
+    }
+
+
+    public string GetVariableName(int index) {
+      return dataset.GetVariableName(index);
+    }
+
+    public override IView CreateView() {
+      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();
+      XmlAttribute minTimeOffsetAttr = document.CreateAttribute("MinTimeOffset");
+      minTimeOffsetAttr.Value = MinTimeOffset.ToString();
+      XmlAttribute maxTimeOffsetAttr = document.CreateAttribute("MaxTimeOffset");
+      maxTimeOffsetAttr.Value = MaxTimeOffset.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);
+      node.Attributes.Append(minTimeOffsetAttr);
+      node.Attributes.Append(maxTimeOffsetAttr);
+
+      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);
+      if (node.Attributes["MinTimeOffset"] != null)
+        MinTimeOffset = XmlConvert.ToInt32(node.Attributes["MinTimeOffset"].Value);
+      else MinTimeOffset = 0;
+      if (node.Attributes["MaxTimeOffset"] != null)
+        MaxTimeOffset = XmlConvert.ToInt32(node.Attributes["MaxTimeOffset"].Value);
+      else MaxTimeOffset = 0;
+
+      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();
     }
   }