public void CreditDataClassifyMethod() { DataSetLoader dataSetLoader = new DataSetLoader(); var creditData = dataSetLoader.SelectCreditData(); var data = dataSetLoader.CalculatePercent(100, creditData); DecisionTreeClassifier decisionTreeClassifier = new DecisionTreeClassifier(data.Item1, new ShannonEntropySplitter()); NaiveBayesClassifier naiveBayes = new NaiveBayesClassifier(data.Item1); var list = new List <NetML.Classification>(); Kernel kernel = new LinearKernel(); SVMClassifier SVMClassifier = new SVMClassifier(creditData, kernel, 0.001, 10.0); var neuronalCreditData = dataSetLoader.SelectNeuronalNetworksCreditData(); NeuronalNetworkClassifier neuronalNetworkClassifier = new NeuronalNetworkClassifier(neuronalCreditData, 20, 2, 20, 5000, 0.1); list.Add(decisionTreeClassifier); list.Add(naiveBayes); list.Add(SVMClassifier); //list.Add(neuronalNetworkClassifier); Classifier classifier = new Classifier(); classifier.Classify(list, creditData); }
public void AnimalClassifyMethod() { DataSetLoader dataSetLoader = new DataSetLoader(); var animals = dataSetLoader.SelectAnimals(); var data = dataSetLoader.CalculatePercent(50, animals); DecisionTreeClassifier decisionTreeClassifier = new DecisionTreeClassifier(data.Item1, new ShannonEntropySplitter()); NaiveBayesClassifier naiveBayes = new NaiveBayesClassifier(data.Item1); var list = new List <NetML.Classification>(); Kernel kernel = new LinearKernel(); SVMClassifier animalSVMClassifier = new SVMClassifier(animals, kernel, 0.001, 10.0); var neuronalAnimals = dataSetLoader.SelectNeuronalNetworkAnimals(); NeuronalNetworkClassifier neuronalNetworkClassifier = new NeuronalNetworkClassifier(neuronalAnimals, 16, 7, 16, 500, 0.1); list.Add(decisionTreeClassifier); list.Add(naiveBayes); list.Add(animalSVMClassifier); list.Add(neuronalNetworkClassifier); Classifier classifier = new Classifier(); classifier.Classify(list, data.Item2); }