public void CreditDataRadialBasisFunctionNeuronalNetworkClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var creditData = dataSetLoader.SelectCreditData();
for (double i = 0; i < 1; i = i + 1)
{
OneHiddenLayerNeuronCounter oneHiddenLayerNeuronCounter = new OneHiddenLayerNeuronCounter(6, 1, 36);
RadialBasisFunctionNeuronalNetwork radialBasisFunctionNeuronalNetworkClassifier =
new RadialBasisFunctionNeuronalNetwork(oneHiddenLayerNeuronCounter, 5000, 0.7);
radialBasisFunctionNeuronalNetworkClassifier.Train(creditData);
var creditDataTest = dataSetLoader.SelectCreditData();
var trueCounter = 0;
var counter = 0;
foreach (var item in creditDataTest)
{
var outputValue = radialBasisFunctionNeuronalNetworkClassifier.Classify(item.Item1);
var value = (outputValue.OutputValues[0] > 0.5) ? 1.0 : 0.0;
if (value == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, Convert.ToDecimal(outputValue.OutputValues[0]),
(value == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format(" i = {0} Data {1} - True {2} Verhältnis: {3}", i,
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
}
public void CreditDataNaiveBayesTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var creditData = dataSetLoader.SelectCreditData();
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(creditData);
var creditDataTest = dataSetLoader.SelectCreditData();
var trueCounter = 0;
var counter = 0;
foreach (var item in creditDataTest)
{
var outputValue = naiveBayes.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
public void CreditDataDecisionTreeTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var creditData = dataSetLoader.SelectCreditData();
DecisionTreeClassifier decisionTreeClassifier =
new DecisionTreeClassifier(creditData, new ShannonEntropySplitter());
decisionTreeClassifier.Train();
var creditDataTest = dataSetLoader.SelectCreditData();
var trueCounter = 0;
var counter = 0;
foreach (var item in creditDataTest)
{
var outputValue = decisionTreeClassifier.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
public void CreditDataRegressionTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var creditData = dataSetLoader.SelectCreditData();
Regression loggistigRegression =
new Regression(creditData, new NetML.LogisticRegression.LogisticCostFunction());
loggistigRegression.Train();
var creditDataTest = dataSetLoader.SelectCreditData();
var trueCounter = 0;
var counter = 0;
foreach (var item in creditDataTest)
{
var outputValue = loggistigRegression.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
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);
}
