public void MushroomDecisionTreeTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var mushroom = dataSetLoader.SelectMushroom();
DecisionTreeClassifier decisionTreeClassifier =
new DecisionTreeClassifier(mushroom, new ShannonEntropySplitter());
decisionTreeClassifier.Train();
var mushroomTest = dataSetLoader.SelectMushroom();
var trueCounter = 0;
var counter = 0;
foreach (var item in mushroomTest)
{
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 MushroomSupportVectorMachineClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var mushroom = dataSetLoader.SelectMushroom();
for (double i = 0; i < 1; i = i + 1)
{
Kernel kernel = new LinearKernel();
SVMClassifier mushroomSVMClassifier = new SVMClassifier(mushroom, kernel, 0.001, 10.0);
mushroomSVMClassifier.Train();
var mushroomTest = dataSetLoader.SelectMushroom();
var trueCounter = 0;
var counter = 0;
foreach (var item in mushroomTest)
{
var outputValue = mushroomSVMClassifier.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(" i = {0} Data {1} - True {2} Verhältnis: {3}", i,
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
}
public void NaiveBayesMushroomTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var mushroom = dataSetLoader.SelectMushroom();
for (double i = 0; i < 1; i = i + 1)
{
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(mushroom, new LinearBayesKernel(mushroom));
var mushroomTests = dataSetLoader.SelectMushroom();
var trueCounter = 0;
var counter = 0;
foreach (var item in mushroomTests)
{
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(" i = {0} Data {1} - True {2} Verhältnis: {3}", i,
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
}
public void MushroomsNeuronalNetworkClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var mushroom = dataSetLoader.SelectNeuronalNetworksMushroom();
for (double i = 0; i < 1; i = i + 1)
{
NeuronalNetworkClassifier neuronalNetworkClassifier =
new NeuronalNetworkClassifier(mushroom, 21, 2, 21, 50, 0.2);
neuronalNetworkClassifier.Train();
var mushroomTest = dataSetLoader.SelectMushroom();
var trueCounter = 0;
var counter = 0;
foreach (var item in mushroomTest)
{
var outputValue = neuronalNetworkClassifier.ClassifiyMultibleResultValue(item.Item1);
var resultString = String.Empty;
double maxValue = 0.0;
int innerCounter = 0;
int maxItem = 0;
foreach (var value in outputValue)
{
if (value > maxValue)
{
maxValue = value;
maxItem = innerCounter;
}
innerCounter++;
}
if (maxItem == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, maxItem, (maxItem == 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()));
}
}
