static void TestANNMiner(Dataset dataset) { IClassificationMeasure measure = new AccuracyMeasure(); ILearningMethod learningMethod = new BackPropagation(0.1, 10, 0.9, false); int hiddenUnitCount = dataset.Metadata.Attributes.Length * dataset.Metadata.Target.Length; IActivationFunction activationFunction = new SigmoidActivationFunction(); ISolutionQualityEvaluator <ConnectionDC> evaluator = new NNClassificationQualityEvaluator(measure, learningMethod, hiddenUnitCount, activationFunction); IHeuristicsCalculator <ConnectionDC> calculator = new DefaultHeuristicCalculator <ConnectionDC>(); ILocalSearch <ConnectionDC> localSearch = new DefaultRemovalLocalSearch <ConnectionDC>(evaluator); IComponentInvalidator <ConnectionDC> invalidator = new NNConnectorInvalidator(); Problem <ConnectionDC> problem = new Problem <ConnectionDC>(invalidator, calculator, evaluator, localSearch); NeuralNetwork network_before = null; NeuralNetwork network_final = SingleTest.CreateNeuralNet_ANNMiner(problem, hiddenUnitCount, true, false, dataset, ref network_before); double quilty_before = SingleTest.TestClassifier(network_before, dataset, measure); double quilty_final = SingleTest.TestClassifier(network_final, dataset, measure); Console.WriteLine("ANN -" + quilty_before); Console.WriteLine("ANN -" + quilty_final); }
public void PostProcessing2() { NNClassificationQualityEvaluator evaluator = this._problem.SolutionQualityEvaluator as NNClassificationQualityEvaluator; //NeuralNetwork network = evaluator.NeuralNetwork; NeuralNetwork network = evaluator.CreateNeuralNetwork(this._bestAnt.Solution); this.NetworkBeforePostProcessing = network; //this.PerformLocalSearch(this._bestAnt); int maxIterations = 1000; int colonySize = 1; int convergence = 100; int archive = 25; double q = 0.25; double segma = 0.85; int problemSize = network.Size; NNClassificationQualityEvaluator2 evaluator2 = new NNClassificationQualityEvaluator2(-5, 5, evaluator.Measure); evaluator2.LearningSet = _trainingSet; evaluator2.ValidationSet = _trainingSet; evaluator2.NeuralNetwork = network; Problem <double> problem = new Problem <double>(null, null, evaluator2, null); ACO_RNN acornn = new ACO_RNN(maxIterations, colonySize, convergence, problem, problemSize, archive, q, segma); acornn.OnPostColonyIteration += OnPostColonyIteration; acornn.TrainNetwork(network, _trainingSet); this.FinalNetwork = network; }
public override void PostProcessing() { NNClassificationQualityEvaluator evaluator = this._problem.SolutionQualityEvaluator as NNClassificationQualityEvaluator; //NeuralNetwork network = evaluator.NeuralNetwork; NeuralNetwork network = evaluator.CreateNeuralNetwork(this._bestAnt.Solution); this.NetworkBeforePostProcessing = network; //this.PerformLocalSearch(this._bestAnt); NeuralNetworks.LearningMethods.BackPropagation BP = new NeuralNetworks.LearningMethods.BackPropagation(0.01, 1000, 0.9, true); this.FinalNetwork = evaluator.CreateNeuralNetwork(this._bestAnt.Solution, BP); }
public static void RunANNMiner_QEM() { AccuracyMeasure testMeasure = new AccuracyMeasure(); foreach (string dataset in GetDatasetFolds("datasets.txt")) { //---------------------------------------- Console.WriteLine("Data Table:" + dataset); //---------------------------------------- foreach (IClassificationMeasure measure in GetMeasures()) { for (_currentFold = 0; _currentFold < _folds; _currentFold++) { //---------------------------------------- Console.WriteLine(dataset + " - Fold:" + _currentFold.ToString() + " - " + measure.ToString()); //---------------------------------------- DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold); DataMining.Data.Dataset trainingSet = tables[0]; DataMining.Data.Dataset testingSet = tables[1]; double quality_before = 0.0; double quality_final = 0.0; double size_before = 0.0; double size_final = 0.0; ILearningMethod learningMethod = new BackPropagation(0.05, 10, 0.9, false); int hiddenUnitCount = (trainingSet.Metadata.Attributes.Length + trainingSet.Metadata.Target.Length); IActivationFunction activationFunction = new SigmoidActivationFunction(); NNClassificationQualityEvaluator evaluator = new NNClassificationQualityEvaluator(measure, learningMethod, hiddenUnitCount, activationFunction); NNConnectionHeuristicCalculator calculator = new NNConnectionHeuristicCalculator(0.7); DefaultRemovalLocalSearch <ConnectionDC> localSearch = new DefaultRemovalLocalSearch <ConnectionDC>(evaluator); NNConnectorInvalidator invalidator = new NNConnectorInvalidator(); Problem <ConnectionDC> problem = new Problem <ConnectionDC>(invalidator, calculator, evaluator, localSearch); NeuralNetwork network_before = null; try { NeuralNetwork network_final = SingleTest.CreateNeuralNet_ANNMiner(problem, hiddenUnitCount, true, false, trainingSet, ref network_before); quality_before = SingleTest.TestClassifier(network_before, testingSet, testMeasure); quality_before = Math.Round(quality_before * 100, 2); quality_final = SingleTest.TestClassifier(network_final, testingSet, testMeasure); quality_final = Math.Round(quality_final * 100, 2); size_before = network_before.Size; size_final = network_final.Size; //---------------------------------------- Console.WriteLine("ANNMiner - before:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + measure.ToString() + ":" + quality_before.ToString()); Console.WriteLine("ANNMiner - final:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + measure.ToString() + ":" + quality_final.ToString()); Console.WriteLine("---------------------------------------------------"); //---------------------------------------- SaveResults(dataset, "ANNMiner - before", measure.ToString(), quality_before.ToString(), size_before.ToString()); SaveResults(dataset, "ANNMiner - final", measure.ToString(), quality_final.ToString(), size_final.ToString()); } catch (Exception ex) { LogError(ex); break; } } } Console.WriteLine("---------------------------------------------------"); Console.WriteLine("---------------------------------------------------"); Console.WriteLine("---------------------------------------------------"); } }
public static void RunGHCNN() { foreach (string dataset in GetDatasetFolds("datasets.txt")) { //---------------------------------------- Console.WriteLine("Data Table:" + dataset); //---------------------------------------- double avgQualityBefore = 0; double avgSizeBefore = 0; double avgQulityAfter = 0; double avgSizeAfter = 0; for (_currentFold = 0; _currentFold < _folds; _currentFold++) { //---------------------------------------- Console.WriteLine("Fold:" + _currentFold.ToString()); //---------------------------------------- DataMining.Data.Dataset[] tables = LoadTrainingAndTestingData(dataset, _currentFold); DataMining.Data.Dataset trainingSet = tables[0]; DataMining.Data.Dataset testingSet = tables[1]; double quality_before = 0.0; double quality_final = 0.0; double size_before = 0.0; double size_final = 0.0; IClassificationMeasure testMeasure = new AccuracyMeasure(); ILearningMethod learningMethod = new BackPropagation(_acoLearningRateNW, _acoEpochsNW, 0.9, false); //int hiddenUnitCount = trainingSet.Metadata.Attributes.Length * trainingSet.Metadata.Target.Length; int hiddenUnitCount = (trainingSet.Metadata.Attributes.Length + trainingSet.Metadata.Target.Length); IActivationFunction activationFunction = new SigmoidActivationFunction(); IClassificationMeasure trainingMeasure = new QLFunction(); NNClassificationQualityEvaluator evaluator = new NNClassificationQualityEvaluator(trainingMeasure, learningMethod, hiddenUnitCount, activationFunction); NNConnectionHeuristicCalculator calculator = new NNConnectionHeuristicCalculator(0.7); DefaultRemovalLocalSearch <ConnectionDC> localSearch = new DefaultRemovalLocalSearch <ConnectionDC>(evaluator); NNConnectorInvalidator invalidator = new NNConnectorInvalidator(); Problem <ConnectionDC> problem = new Problem <ConnectionDC>(invalidator, calculator, evaluator, localSearch); NeuralNetwork network_before = null; try { stopWatch.Reset(); stopWatch.Start(); NeuralNetwork network_final = SingleTest.CreateNeuralNet_GHCNN(problem, hiddenUnitCount, true, false, trainingSet, ref network_before); stopWatch.Stop(); quality_before = SingleTest.TestClassifier(network_before, testingSet, testMeasure); quality_before = Math.Round(quality_before * 100, 2); avgQualityBefore += quality_before; quality_final = SingleTest.TestClassifier(network_final, testingSet, testMeasure); quality_final = Math.Round(quality_final * 100, 2); avgQulityAfter += quality_final; size_before = network_before.Size; size_final = network_final.Size; avgSizeBefore += size_before; avgSizeAfter += avgSizeAfter; //---------------------------------------- Console.WriteLine("GHCNN - before:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + testMeasure.ToString() + ":" + quality_before.ToString()); Console.WriteLine("GHCNN - final:" + dataset + "- Fold:" + _currentFold.ToString() + "=>" + testMeasure.ToString() + ":" + quality_final.ToString()); Console.WriteLine("---------------------------------------------------"); //---------------------------------------- } catch (Exception ex) { LogError(ex); break; } } avgQualityBefore /= _folds; avgQulityAfter /= _folds; avgSizeBefore /= _folds; avgSizeAfter /= _folds; SaveResults(dataset, "GHCNN - before", avgQualityBefore.ToString(), avgSizeBefore.ToString(), stopWatch.ElapsedMilliseconds.ToString()); SaveResults(dataset, "GHCNN - final", avgQulityAfter.ToString(), avgSizeAfter.ToString(), stopWatch.ElapsedMilliseconds.ToString()); Console.WriteLine("---------------------------------------------------"); Console.WriteLine("---------------------------------------------------"); Console.WriteLine("---------------------------------------------------"); } }