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);
}