Changeset 2674
- Timestamp:
- 01/22/10 16:06:48 (15 years ago)
- Location:
- trunk/sources
- Files:
-
- 4 edited
Legend:
- Unmodified
- Added
- Removed
-
trunk/sources/HeuristicLab.GP.StructureIdentification.Networks/3.2/NetworkToFunctionTransformer.cs
r2635 r2674 30 30 using HeuristicLab.DataAnalysis; 31 31 using System.Diagnostics; 32 using HeuristicLab.Common; 32 33 33 34 namespace HeuristicLab.GP.StructureIdentification.Networks { … … 93 94 } else if (tree.Function is Flip) { 94 95 var partiallyAppliedBranch = ApplyFlips(tree.SubTrees[0]); 95 if (partiallyAppliedBranch.Function is OpenParameter) return partiallyAppliedBranch; 96 else return InvertChain(partiallyAppliedBranch); 96 if (partiallyAppliedBranch.Function is OpenParameter) { 97 var openParFunTree = (OpenParameterFunctionTree)partiallyAppliedBranch; 98 openParFunTree.Weight = 1.0 / openParFunTree.Weight; 99 return partiallyAppliedBranch; 100 } else return InvertChain(partiallyAppliedBranch); 97 101 } else { 98 102 List<IFunctionTree> subTrees = new List<IFunctionTree>(tree.SubTrees); … … 116 120 // get a list of function trees from bottom to top 117 121 List<IFunctionTree> reversedChain = new List<IFunctionTree>(currentChain.Reverse<IFunctionTree>().Skip(1)); 118 IFunctionTree openParam =currentChain.Last();122 OpenParameterFunctionTree openParam = (OpenParameterFunctionTree)currentChain.Last(); 119 123 120 124 // build new tree by inverting every function in the reversed chain and keeping f0 branches untouched. … … 136 140 } 137 141 } 142 // invert factor of openParam 143 openParam.Weight = 1.0 / openParam.Weight; 138 144 // append open param at the end 139 145 invParent.InsertSubTree(0, openParam); … … 195 201 196 202 197 private static IFunctionTree AppendLeft(IFunctionTree tree, IFunctionTree node) {198 IFunctionTree originalTree = tree;199 while (!IsBottomLeft(tree)) tree = tree.SubTrees[0];200 tree.InsertSubTree(0, node);201 return originalTree;202 }203 //private static IFunctionTree AppendLeft(IFunctionTree tree, IFunctionTree node) { 204 // IFunctionTree originalTree = tree; 205 // while (!IsBottomLeft(tree)) tree = tree.SubTrees[0]; 206 // tree.InsertSubTree(0, node); 207 // return originalTree; 208 //} 203 209 204 210 private static bool IsBottomLeft(IFunctionTree tree) { … … 265 271 combinator.AddSubTree(subTrees[i]); 266 272 } 267 if (subTrees[targetIndex].Function is Variable) return combinator;273 if (subTrees[targetIndex].Function is Variable) return MakeMultiplication(combinator, 1.0 / GetTargetVariableWeight(subTrees[targetIndex])); 268 274 else { 269 275 IFunctionTree bottomLeft; 270 276 IFunctionTree targetChain = InvertF0Chain(subTrees[targetIndex], out bottomLeft); 271 277 bottomLeft.InsertSubTree(0, combinator); 272 return targetChain; 273 } 274 } 278 return MakeMultiplication(targetChain, 1.0 / GetTargetVariableWeight(subTrees[targetIndex])); 279 } 280 } 281 } 282 283 private static IFunctionTree MakeMultiplication(IFunctionTree tree, double p) { 284 if (p.IsAlmost(1.0)) return tree; 285 var mul = (new Multiplication()).GetTreeNode(); 286 var constP = (ConstantFunctionTree)(new Constant()).GetTreeNode(); 287 constP.Value = p; 288 mul.AddSubTree(tree); 289 mul.AddSubTree(constP); 290 return mul; 291 } 292 293 private static double GetTargetVariableWeight(IFunctionTree tree) { 294 while (tree.SubTrees.Count > 0) { 295 tree = tree.SubTrees[0]; 296 } 297 return ((VariableFunctionTree)tree).Weight; 275 298 } 276 299 … … 377 400 varTreeNode.VariableName = targetVariables.Current; 378 401 varTreeNode.SampleOffset = ((OpenParameterFunctionTree)tree).SampleOffset; 379 varTreeNode.Weight = 1.0;402 varTreeNode.Weight = ((OpenParameterFunctionTree)tree).Weight; 380 403 return varTreeNode; 381 404 //} else if (matchingFunction is Power) { -
trunk/sources/HeuristicLab.GP.StructureIdentification.Networks/3.2/Symbols/OpenParameterFunctionTree.cs
r2616 r2674 30 30 public string VariableName { get; set; } 31 31 public int SampleOffset { get; set; } 32 public double Weight { get; set; } 32 33 33 34 public OpenParameterFunctionTree(OpenParameter openParameter) 34 35 : base(openParameter) { 36 this.Weight = 1.0; 35 37 } 36 38 … … 39 41 VariableName = original.VariableName; 40 42 SampleOffset = original.SampleOffset; 43 Weight = original.Weight; 41 44 } 42 45 … … 51 54 scope.AddSubScope(myVariableScope); 52 55 myVariableScope.AddVariable(CreateSampleOffsetVariable()); 56 myVariableScope.AddVariable(CreateWeightVariable()); 53 57 return new AtomicOperation(Function.Manipulator, myVariableScope); 54 58 } … … 58 62 scope.AddSubScope(myVariableScope); 59 63 myVariableScope.AddVariable(CreateSampleOffsetVariable()); 64 myVariableScope.AddVariable(CreateWeightVariable()); 60 65 return new AtomicOperation(Function.Initializer, myVariableScope); 61 66 } … … 72 77 public override XmlNode GetXmlNode(string name, XmlDocument document, IDictionary<System.Guid, IStorable> persistedObjects) { 73 78 XmlNode node = document.CreateElement(name); 79 XmlAttribute weightAttr = document.CreateAttribute("Weight"); 80 weightAttr.Value = XmlConvert.ToString(Weight); 74 81 XmlAttribute variableAttr = document.CreateAttribute("Variable"); 75 82 variableAttr.Value = VariableName; 76 83 XmlAttribute sampleOffsetAttr = document.CreateAttribute("SampleOffset"); 77 84 sampleOffsetAttr.Value = XmlConvert.ToString(SampleOffset); 85 node.Attributes.Append(weightAttr); 78 86 node.Attributes.Append(sampleOffsetAttr); 79 87 node.Attributes.Append(variableAttr); … … 83 91 public override void Populate(XmlNode node, IDictionary<System.Guid, IStorable> restoredObjects) { 84 92 base.Populate(node, restoredObjects); 93 Weight = XmlConvert.ToDouble(node.Attributes["Weight"].Value); 85 94 SampleOffset = XmlConvert.ToInt32(node.Attributes["SampleOffset"].Value); 86 95 VariableName = node.Attributes["Variable"].Value; … … 103 112 return variable; 104 113 } 114 115 private IVariable CreateWeightVariable() { 116 DoubleData data = new DoubleData(Weight); 117 data.Changed += (sender, args) => Weight = data.Data; 118 var variable = new HeuristicLab.Core.Variable(Variable.WEIGHT, data); 119 variable.ValueChanged += (sender, args) => Weight = ((DoubleData)variable.Value).Data; 120 return variable; 121 } 105 122 } 106 123 } -
trunk/sources/HeuristicLab.GP.Tests/NetworkToFunctionTransformerTest.cs
r2635 r2674 8 8 using HeuristicLab.Random; 9 9 using HeuristicLab.DataAnalysis; 10 using HeuristicLab.Common; 10 11 11 12 namespace HeuristicLab.GP.Test { … … 105 106 106 107 { 107 IFunctionTree tree = importer.Import("(open-+ (open-param - 0) (open-param - 0) (open-param- 0))");108 109 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 110 111 IFunctionTree t0 = importer.Import("(+ (variable 1.0 b 0) (variable 1.0 c 0))");112 IFunctionTree t1 = importer.Import("( - (variable 1.0 a 0) (variable 1.0 c 0))");113 IFunctionTree t2 = importer.Import("( - (variable 1.0 a 0) (variable 1.0 b 0))");114 115 CompareTrees(actualTrees.ToList(), new List<IFunctionTree>() { 116 t0, t1, t2 117 }); 118 } 119 120 { 121 IFunctionTree tree = importer.Import("(open-- (open-param - 0) (f1-+ (open-param - 0) 1.0) (f1-* (open-param- 0) 1.0))");122 123 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 124 125 IFunctionTree t0 = importer.Import("(- (+ (variable 1.0 b 0) 1.0) (* (variable 1.0 c 0) 1.0 ))");126 IFunctionTree t1 = importer.Import("( - (+ (variable 1.0 a 0) (* (variable 1.0 c 0) 1.0)) 1.0)");127 IFunctionTree t2 = importer.Import("( / (+ (variable 1.0 a 0) (+ (variable 1.0 b 0) 1.0)) 1.0)");128 129 CompareTrees(actualTrees.ToList(), new List<IFunctionTree>() { 130 t0, t1, t2 131 }); 132 } 133 134 { 135 IFunctionTree tree = importer.Import("(cycle (open-* (open-param - 0) (open-param - 0) (open-param- 0)))");136 137 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 138 139 IFunctionTree t0 = importer.Import("(* ( variable 1.0 b 0) (variable 1.0 c 0))");140 IFunctionTree t1 = importer.Import("( / (variable 1.0 a 0) (variable 1.0 c 0))");141 IFunctionTree t2 = importer.Import("(/ (variable 1.0 a 0) (variable 1.0 b 0))");142 143 144 CompareTrees(actualTrees.ToList(), new List<IFunctionTree>() { 145 t0, t1, t2 146 }); 147 } 148 149 150 { 151 IFunctionTree tree = importer.Import("(open-- (open-log (open-param - 0)) (open-exp (open-param - 0)) (open-param- 0))");108 IFunctionTree tree = importer.Import("(open-+ (open-param 1.0 - 0) (open-param 2.0 - 0) (open-param 4.0 - 0))"); 109 110 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 111 112 IFunctionTree t0 = importer.Import("(+ (variable 2.0 b 0) (variable 4.0 c 0))"); 113 IFunctionTree t1 = importer.Import("(* (- (variable 1.0 a 0) (variable 4.0 c 0)) 0.5)"); 114 IFunctionTree t2 = importer.Import("(* (- (variable 1.0 a 0) (variable 2.0 b 0)) 0.25)"); 115 116 CompareTrees(actualTrees.ToList(), new List<IFunctionTree>() { 117 t0, t1, t2 118 }); 119 } 120 121 { 122 IFunctionTree tree = importer.Import("(open-- (open-param 1.0 - 0) (f1-+ (open-param 2.0 - 0) 1.0) (f1-* (open-param 4.0 - 0) 1.0))"); 123 124 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 125 126 IFunctionTree t0 = importer.Import("(- (+ (variable 2.0 b 0) 1.0) (* (variable 4.0 c 0) 1.0 ))"); 127 IFunctionTree t1 = importer.Import("(* (- (+ (variable 1.0 a 0) (* (variable 4.0 c 0) 1.0)) 1.0 ) 0.5)"); 128 IFunctionTree t2 = importer.Import("(* (/ (+ (variable 1.0 a 0) (+ (variable 2.0 b 0) 1.0)) 1.0 ) 0.25)"); 129 130 CompareTrees(actualTrees.ToList(), new List<IFunctionTree>() { 131 t0, t1, t2 132 }); 133 } 134 135 { 136 IFunctionTree tree = importer.Import("(cycle (open-* (open-param 1.0 - 0) (open-param 2.0 - 0) (open-param 4.0 - 0)))"); 137 138 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 139 140 IFunctionTree t0 = importer.Import("(* (* (variable 4.0 b 0) (variable 1.0 c 0)) 0.5)"); 141 IFunctionTree t1 = importer.Import("(* (/ (variable 2.0 a 0) (variable 1.0 c 0)) 0.25)"); 142 IFunctionTree t2 = importer.Import("(/ (variable 2.0 a 0) (variable 4.0 b 0))"); 143 144 145 CompareTrees(actualTrees.ToList(), new List<IFunctionTree>() { 146 t0, t1, t2 147 }); 148 } 149 150 151 { 152 IFunctionTree tree = importer.Import("(open-- (open-log (open-param 1.0 - 0)) (open-exp (open-param 1.0 - 0)) (open-param 1.0 - 0))"); 152 153 153 154 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); … … 165 166 } 166 167 { 167 IFunctionTree tree = importer.Import("(open-- (open-log (open-log (open-param - 0))) (open-exp (open-exp (open-param - 0))) (f1-* (open-param- 0) 2.0))");168 IFunctionTree tree = importer.Import("(open-- (open-log (open-log (open-param 1.0 - 0))) (open-exp (open-exp (open-param 1.0 - 0))) (f1-* (open-param 1.0 - 0) 2.0))"); 168 169 169 170 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); … … 181 182 } 182 183 { 183 IFunctionTree tree = importer.Import(@"(open-- (flip (open-log (open-param - 0)))184 (flip (f1-* (open-param - 0) 2.0))185 (flip (flip (f1-* (open-param - 0) 2.0))))");186 187 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 188 189 IFunctionTree t0 = importer.Import(@"(log (- (/ (variable 1.0b 0) 2.0)190 (* (variable 1.0 c 0) 2.0)))");191 IFunctionTree t1 = importer.Import(@"(* ( + (exp (variable 1.0 a 0))192 (* (variable 1.0 c 0) 2.0)) 2.0)");193 IFunctionTree t2 = importer.Import(@"( / (+ (exp (variable 1.0 a 0))194 (/ (variable 1.0 b 0) 2.0)) 2.0)");184 IFunctionTree tree = importer.Import(@"(open-- (flip (open-log (open-param 1.0 - 0))) 185 (flip (f1-* (open-param 2.0 - 0) 2.0)) 186 (flip (flip (f1-* (open-param 4.0 - 0) 2.0))))"); 187 188 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); 189 190 IFunctionTree t0 = importer.Import(@"(log (- (/ (variable 0.5 b 0) 2.0) 191 (* (variable 4.0 c 0) 2.0)))"); 192 IFunctionTree t1 = importer.Import(@"(* (* (+ (exp (variable 1.0 a 0)) 193 (* (variable 4.0 c 0) 2.0)) 2.0) 2)"); 194 IFunctionTree t2 = importer.Import(@"(* (/ (+ (exp (variable 1.0 a 0)) 195 (/ (variable 0.5 b 0) 2.0)) 2.0) 0.25)"); 195 196 196 197 CompareTrees(actualTrees.ToList(), new List<IFunctionTree>() { … … 203 204 (flip (f1-- 204 205 (flip (f1-/ 205 (open-param - 0) 4.0)) 3.0)) 2.0))206 (open-param - 0)207 (open-param - 0)))");206 (open-param 1.0 - 0) 4.0)) 3.0)) 2.0)) 207 (open-param 1.0 - 0) 208 (open-param 1.0 - 0)))"); 208 209 // -3*4-2 x 209 210 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); … … 235 236 { 236 237 // expression with one parameter 237 IFunctionTree tree = importer.Import(@"(f1-* (open-param - 0) (variable 1.0 d 0))");238 IFunctionTree tree = importer.Import(@"(f1-* (open-param 1.0 - 0) (variable 1.0 d 0))"); 238 239 239 240 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); … … 250 251 { 251 252 // expression with one parameter 252 IFunctionTree tree = importer.Import(@"(open-log (open-param - 0))");253 IFunctionTree tree = importer.Import(@"(open-log (open-param 1.0 - 0))"); 253 254 254 255 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); … … 265 266 { 266 267 // expression with flip and one parameter 267 IFunctionTree tree = importer.Import(@"(flip (open-log (open-param - 0)))");268 IFunctionTree tree = importer.Import(@"(flip (open-log (open-param 1.0 - 0)))"); 268 269 269 270 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); … … 281 282 { 282 283 // expression with flip and one parameter 283 IFunctionTree tree = importer.Import(@"(open-param - 0)");284 IFunctionTree tree = importer.Import(@"(open-param 1.0 - 0)"); 284 285 285 286 IEnumerable<IFunctionTree> actualTrees = NetworkToFunctionTransformer_Accessor.Transform(tree, new List<string>() { "a", "b", "c" }); … … 297 298 298 299 private void CompareTrees(List<IFunctionTree> actual, List<IFunctionTree> expected) { 299 var expectedEnumerator = expected.GetEnumerator(); 300 var expectedEnumerator = expected.GetEnumerator(); 300 301 foreach (var actualTree in actual) { 301 302 if (!expectedEnumerator.MoveNext()) Assert.Fail(); … … 312 313 var actualVar = (VariableFunctionTree)a.Current; 313 314 Assert.AreEqual(expectedVar.VariableName, actualVar.VariableName); 314 Assert. AreEqual(expectedVar.Weight, actualVar.Weight);315 Assert.IsTrue(expectedVar.Weight.IsAlmost(actualVar.Weight)); 315 316 Assert.AreEqual(expectedVar.SampleOffset, actualVar.SampleOffset); 316 317 } else if (e.Current.Function is Constant) { -
trunk/sources/HeuristicLab.GP.Tests/SymbolicExpressionImporter.cs
r2627 r2674 102 102 } else if (tokens.Peek().StringValue.StartsWith(DIFFSTART)) { 103 103 tree = ParseDifferential(tokens); 104 } else if (tokens.Peek().StringValue.StartsWith(OPENPARAMSTART)) {104 } else if (tokens.Peek().StringValue.StartsWith(OPENPARAMSTART)) { 105 105 tree = ParseOpenParameter(tokens); 106 106 } else { … … 123 123 Token tok = tokens.Dequeue(); 124 124 Debug.Assert(tok.StringValue == "open-param"); 125 HeuristicLab.GP.StructureIdentification.Networks.OpenParameterFunctionTree t = (HeuristicLab.GP.StructureIdentification.Networks.OpenParameterFunctionTree)openParam.GetTreeNode(); 125 HeuristicLab.GP.StructureIdentification.Networks.OpenParameterFunctionTree t = (HeuristicLab.GP.StructureIdentification.Networks.OpenParameterFunctionTree)openParam.GetTreeNode(); 126 t.Weight = tokens.Dequeue().DoubleValue; 126 127 t.VariableName = tokens.Dequeue().StringValue; 127 128 t.SampleOffset = (int)tokens.Dequeue().DoubleValue;
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