public void IrisSupportVectorMachineClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectIrises();
for (double i = 0; i < 1; i = i + 1)
{
Kernel kernel = new LinearKernel();
SVMClassifier animalSVMClassifier = new SVMClassifier(irises, kernel, 0.001, 10.0);
animalSVMClassifier.Train();
var irisesTest = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = animalSVMClassifier.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 IrisDecisionTreeTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectIrises();
DecisionTreeClassifier decisionTreeClassifier =
new DecisionTreeClassifier(irises, new ShannonEntropySplitter());
decisionTreeClassifier.Train();
var animalsTest = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
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 IrisBoostingTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectIrises();
for (double i = 0; i < 1; i = i + 1)
{
BoostingAlgorithmus boostingAlgorithmus =
new BoostingAlgorithmus(10);
boostingAlgorithmus.Train(irises, new ShannonEntropySplitter());
var irisesTest = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = boostingAlgorithmus.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 NaiveBayesIrisTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irisis = dataSetLoader.SelectIrises();
for (double i = 0; i < 1; i = i + 1)
{
NaiveBayesClassifier naiveBayes =
new NaiveBayesClassifier(irisis, new LinearBayesKernel(irisis));
var irisesTests = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTests)
{
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 IrisRadialBasisFunctionNeuronalNetworkClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectNeuronalNetworkIrises();
for (double i = 0; i < 1; i = i + 1)
{
OneHiddenLayerNeuronCounter oneHiddenLayerNeuronCounter = new OneHiddenLayerNeuronCounter(4, 3, 3);
RadialBasisFunctionNeuronalNetwork radialBasisFunctionNeuronalNetworkClassifier =
new RadialBasisFunctionNeuronalNetwork(oneHiddenLayerNeuronCounter, 10000, 0.7);
radialBasisFunctionNeuronalNetworkClassifier.Train(irises);
var irisesTest = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = radialBasisFunctionNeuronalNetworkClassifier.Classify(item.Item1);
var resultString = String.Empty;
double maxValue = 0.0;
int innerCounter = 1;
int maxItem = 0;
foreach (var value in outputValue.OutputValues)
{
if (value > maxValue)
{
maxValue = value;
maxItem = innerCounter;
}
innerCounter++;
}
if (maxItem == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} {2} {3} = {4}",
item.Item2,
Convert.ToDecimal(outputValue.OutputValues[0]),
Convert.ToDecimal(outputValue.OutputValues[1]),
Convert.ToDecimal(outputValue.OutputValues[2]),
(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()));
}
}
public void kMetroidIrisTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectClusteringIrises();
kMetroidClustering kmeansClustering = new kMetroidClustering(irises, new EuclideMetric(), 100);
var clusters = kmeansClustering.Cluster(3);
var clusterCounter = 0;
Dictionary <int, int> clusterDictonary = new Dictionary <int, int>();
foreach (var cluster in clusters)
{
Debug.WriteLine(string.Format("Cluster {0} - Count {1}", clusterCounter, cluster.Count));
clusterDictonary.Add(clusterCounter, 0);
clusterCounter++;
}
var irisesTest = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = kmeansClustering.CalculateClusterAffinity(item.Item1);
Debug.WriteLine(string.Format("Value {0} - Predicted {1} - ClassificationItem {2}", item, outputValue, item.Item2 - 1));
clusterDictonary[outputValue]++;
counter++;
trueCounter++;
}
clusterCounter = 0;
foreach (var cluster in clusters)
{
var calculatedCluster = clusterDictonary[clusterCounter];
Debug.WriteLine(string.Format("Cluster {0} - Original Count {1} - Calculated Count {2}",
clusterCounter, cluster.Count, calculatedCluster));
clusterCounter++;
}
}
