Changeset 16497
- Timestamp:
- 01/04/19 12:43:26 (6 years ago)
- Location:
- branches/2288_HeuristicLab.VariableInteractionNetworks
- Files:
-
- 2 edited
Legend:
- Unmodified
- Added
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branches/2288_HeuristicLab.VariableInteractionNetworks/HeuristicLab.VariableInteractionNetworks.Views/3.3/RunCollectionVariableInteractionNetworkView.cs
r16295 r16497 150 150 151 151 var count = group.Count(); 152 foreach (var v in averageImpacts.Keys ) {152 foreach (var v in averageImpacts.Keys.ToList()) { 153 153 averageImpacts[v] /= count; 154 154 } … … 327 327 } 328 328 329 public void UpdateNetwork(VariableInteractionNetwork network) { 330 if (InvokeRequired) { 331 Invoke((Action<VariableInteractionNetwork>)UpdateNetwork, network); 332 } else { 333 graphChart.Graph = network; 334 } 335 } 336 329 337 #region events 330 338 protected override void OnContentChanged() { -
branches/2288_HeuristicLab.VariableInteractionNetworks/HeuristicLab.VariableInteractionNetworks/3.3/VariableInteractionNetwork.cs
r14655 r16497 35 35 [StorableClass] 36 36 public class VariableInteractionNetwork : DirectedGraph { 37 38 /// <summary> 39 /// Creates a simple network from a matrix of variable impacts (each row represents a target variable, each column represents an input variable) 40 /// For each target variable not more than one row can be defined (no junction nodes are build, cf. FromNmseAndVariableImpacts(..) for building more complex networks). 41 /// The network is acyclic. Values in the diagonal are ignored. 42 /// The algorithm starts with an empty network and incrementally adds next most relevant input variable for each target variable up to a given threshold 43 /// In each iteration cycles are broken by removing the weakest link. 44 /// </summary> 45 /// <param name="nmse">vector of NMSE values for each target variable</param> 46 /// <param name="variableImpacts">Variable impacts (smaller is lower impact). Row names and columns names should be set</param> 47 /// <param name="nmseThreshold">Threshold for NMSE values. Variables with a NMSE value larger than the threshold are considered as independent variables</param> 48 /// <param name="varImpactThreshold">Threshold for variable impact values. Impacts with a value smaller than the threshold are considered as independent</param> 49 /// <returns></returns> 50 public static VariableInteractionNetwork CreateSimpleNetwork(double[] nmse, DoubleMatrix variableImpacts, double nmseThreshold = 0.2, double varImpactThreshold = 0.0) { 51 if (variableImpacts.Rows != variableImpacts.Columns) throw new ArgumentException(); 52 var network = new VariableInteractionNetwork(); 53 var targets = new Dictionary<string, double>(); 54 string[] varNames = variableImpacts.RowNames.ToArray(); 55 if (nmse.Length != varNames.Length) throw new ArgumentException(); 56 57 for (int i = 0; i < varNames.Length; i++) { 58 var name = varNames[i]; 59 var varVertex = new VariableNetworkNode() {Label = name, Weight = nmse[i]}; 60 network.AddVertex(varVertex); 61 if (nmse[i] < nmseThreshold) { 62 targets.Add(name, nmse[i]); 63 } 64 } 65 66 // rel is updated (impacts which are represented in the network are set to zero) 67 var rel = variableImpacts.CloneAsMatrix(); 68 // make sure the diagonal is not considered 69 for (int i = 0; i < rel.GetLength(0); i++) rel[i, i] = double.NegativeInfinity; 70 71 var addedArcs = AddArcs(network, rel, varNames, targets, varImpactThreshold); 72 while (addedArcs.Any()) { 73 var cycles = network.FindShortestCycles().ToList(); 74 while (cycles.Any()) { 75 // delete weakest link 76 var weakestArc = cycles.SelectMany(cycle => network.ArcsForCycle(cycle)).OrderBy(a => a.Weight).First(); 77 network.RemoveArc(weakestArc); 78 79 cycles = network.FindShortestCycles().ToList(); 80 } 81 82 addedArcs = AddArcs(network, rel, varNames, targets, varImpactThreshold); 83 } 84 85 return network; 86 } 87 88 private static List<IArc> AddArcs(VariableInteractionNetwork network, double[,] impacts, string[] varNames, Dictionary<string, double> targets, double threshold = 0.0) { 89 var newArcs = new List<IArc>(); 90 for (int row = 0; row < impacts.GetLength(0); row++) { 91 if (!targets.ContainsKey(varNames[row])) continue; 92 93 var rowVector = Enumerable.Range(0, impacts.GetLength(0)).Select(col => impacts[row, col]).ToArray(); 94 var max = rowVector.Max(); 95 if (max > threshold) { 96 var idxOfMax = Array.IndexOf<double>(rowVector, max); 97 impacts[row, idxOfMax] = double.NegativeInfinity; 98 var srcName = varNames[idxOfMax]; 99 var dstName = varNames[row]; 100 var srcVertex = network.Vertices.Single(v => v.Label == srcName); 101 var dstVertex = network.Vertices.Single(v => v.Label == dstName); 102 var arc = network.AddArc(srcVertex, dstVertex); 103 arc.Weight = max; 104 newArcs.Add(arc); 105 } 106 } 107 108 return newArcs; 109 } 110 37 111 /// <summary> 38 112 /// Creates a network from a matrix of variable impacts (each row represents a target variable, each column represents an input variable) … … 262 336 } 263 337 338 public DoubleMatrix GetSimpleWeightsMatrix() { 339 var names = Vertices.OfType<VariableNetworkNode>() 340 .Select(v => v.Label) 341 .OrderBy(s => s, new NaturalStringComparer()).ToArray(); 342 var w = new double[names.Length, names.Length]; 343 344 var name2idx = new Dictionary<string, int>(); 345 for (int i = 0; i < names.Length; i++) { 346 name2idx.Add(names[i], i); 347 } 348 349 foreach (var arc in Arcs) { 350 if (arc.Target != null) { 351 var srcVarName = arc.Source.Label; 352 var dstVarName = arc.Target.Label; 353 w[name2idx[dstVarName], name2idx[srcVarName]] = arc.Weight; 354 } 355 } 356 357 return new DoubleMatrix(w, names, names); 358 } 359 264 360 public string ToGraphVizString() { 265 361 Func<string, string> NodeAndEdgeColor = (str) =>
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