Changeset 15343 for branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/SamplingMethods

Timestamp:

08/29/17 11:28:16 (7 years ago)

Author:

bwerth

Message:

#2745 added discretized EGO-version for use with IntegerVectors

Location:

branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/SamplingMethods

Files:

• LatinHyperCubeDesignCreator.cs (modified) (3 diffs)
• UniformRandomDiscreteSampling.cs (copied) (copied from branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/SamplingMethods/UniformRandomSampling.cs) (2 diffs)
• UniformRandomSampling.cs (modified) (3 diffs)

branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/SamplingMethods/LatinHyperCubeDesignCreator.cs

@@ -26 +26 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
+using HeuristicLab.Optimization;
 using HeuristicLab.Encodings.PermutationEncoding;
 using HeuristicLab.Encodings.RealVectorEncoding;
@@ -36 +37 @@
   [StorableClass]
   [Item("LatinHyperCubeDesignCreator", "A latin hypercube sampling strategy for real valued optimization")]
-  public class LatinHyperCubeDesignCreator : RealVectorCreator, IInitialSampling {
+  public class LatinHyperCubeDesignCreator : RealVectorCreator, IInitialSampling<RealVector> {
     private IValueLookupParameter<IntValue> DesignSize => (IValueLookupParameter<IntValue>)Parameters["DesignSize"];
     [Storable]
@@ -54 +55 @@
     }
 
-    public RealVector[] GetSamples(int noSamples, RealVector[] existingSamples, RealVectorEncoding encoding, IRandom random) {
-      return GetSamples(random, encoding.Length, encoding.Bounds, noSamples);
+    public RealVector[] GetSamples(int noSamples, RealVector[] existingSamples, IEncoding encoding, IRandom random) {
+      var enc = encoding as RealVectorEncoding;
+
+
+      return GetSamples(random, enc.Length, enc.Bounds, noSamples);
     }
 

branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/SamplingMethods/UniformRandomDiscreteSampling.cs

@@ -22 +22 @@
 using HeuristicLab.Common;
 using HeuristicLab.Core;
-using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Optimization;
+using HeuristicLab.Encodings.IntegerVectorEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
@@ -29 +30 @@
 
   [StorableClass]
-  [Item("UniformRandomSampling", "A uniform random sampling strategy for real valued optimization")]
-  public class UniformRandomSampling : ParameterizedNamedItem, IInitialSampling {
+  [Item("UniformRandomDiscreteSampling", "A uniform random sampling strategy for real valued optimization")]
+  public class UniformRandomDiscreteSampling : ParameterizedNamedItem, IInitialSampling<IntegerVector> {
 
     #region HL-Constructors, Serialization and Cloning 
     [StorableConstructor]
-    protected UniformRandomSampling(bool deserializing) : base(deserializing) { }
-    protected UniformRandomSampling(UniformRandomSampling original, Cloner cloner) : base(original, cloner) { }
-    public UniformRandomSampling() {
+    protected UniformRandomDiscreteSampling(bool deserializing) : base(deserializing) { }
+    protected UniformRandomDiscreteSampling(UniformRandomDiscreteSampling original, Cloner cloner) : base(original, cloner) { }
+    public UniformRandomDiscreteSampling() {
     }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new UniformRandomSampling(this, cloner);
+      return new UniformRandomDiscreteSampling(this, cloner);
     }
 
-    public RealVector[] GetSamples(int noSamples, RealVector[] existingSamples, RealVectorEncoding encoding, IRandom random) {
-      var res = new RealVector[noSamples];
+    public IntegerVector[] GetSamples(int noSamples, IntegerVector[] existingSamples, IEncoding encoding, IRandom random) {
+      var enc = encoding as IntegerVectorEncoding;
+      var res = new IntegerVector[noSamples];
       for (var i = 0; i < noSamples; i++) {
-        var r = new RealVector(encoding.Length);
+        var r = new IntegerVector(enc.Length);
         res[i] = r;
-        for (var j = 0; j < encoding.Length; j++) {
-          var b = j % encoding.Bounds.Rows;
-          r[j] = UniformRandom(encoding.Bounds[b, 0], encoding.Bounds[b, 1], random);
+        for (var j = 0; j < enc.Length; j++) {
+          var b = j % enc.Bounds.Rows;
+          r[j] = UniformRandom(enc.Bounds[b, 0], enc.Bounds[b, 1], random);
         }
       }
       return res;
     }
-    private static double UniformRandom(double min, double max, IRandom rand) {
-      return rand.NextDouble() * (max - min) + min;
+    private static int UniformRandom(int min, int max, IRandom rand) {
+      return rand.Next(min, max + 1);  //TODO check wether max is inclusive or exclusive
     }
   }

branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/SamplingMethods/UniformRandomSampling.cs

@@ -22 +22 @@
 using HeuristicLab.Common;
 using HeuristicLab.Core;
+using HeuristicLab.Optimization;
 using HeuristicLab.Encodings.RealVectorEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -30 +31 @@
   [StorableClass]
   [Item("UniformRandomSampling", "A uniform random sampling strategy for real valued optimization")]
-  public class UniformRandomSampling : ParameterizedNamedItem, IInitialSampling {
+  public class UniformRandomSampling : ParameterizedNamedItem, IInitialSampling<RealVector> {
 
     #region HL-Constructors, Serialization and Cloning 
@@ -42 +43 @@
     }
 
-    public RealVector[] GetSamples(int noSamples, RealVector[] existingSamples, RealVectorEncoding encoding, IRandom random) {
+    public RealVector[] GetSamples(int noSamples, RealVector[] existingSamples, IEncoding encoding, IRandom random) {
+      var enc = encoding as RealVectorEncoding;
       var res = new RealVector[noSamples];
       for (var i = 0; i < noSamples; i++) {
-        var r = new RealVector(encoding.Length);
+        var r = new RealVector(enc.Length);
         res[i] = r;
-        for (var j = 0; j < encoding.Length; j++) {
-          var b = j % encoding.Bounds.Rows;
-          r[j] = UniformRandom(encoding.Bounds[b, 0], encoding.Bounds[b, 1], random);
+        for (var j = 0; j < enc.Length; j++) {
+          var b = j % enc.Bounds.Rows;
+          r[j] = UniformRandom(enc.Bounds[b, 0], enc.Bounds[b, 1], random);
         }
       }