public void BackprobpagationLanguageTest()
{
var netMLString =
"create classification backpropagation inputneurons = 16 outputneurons = 1 firsthiddenlayerneurons = 16 evolutions = 100 learningrate = 0.1 ";
NetMLParser netMLParser = new NetMLParser();
var result = netMLParser.Parse(netMLString);
NetMLCreator netMLCreator = new NetMLCreator(result);
DataSetLoader dataSetLoader = new DataSetLoader();
var data = dataSetLoader.SelectAnimals();
netMLCreator.Create(data);
netMLCreator.Train();
var testData = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in testData)
{
var outputValue = netMLCreator.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 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 SoundTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var sound = dataSetLoader.SelectSounds(@"\Sounds\Unbenannt.wma");
}
public void SanFranciscoCrimeClassificationTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var crimes = dataSetLoader.SelectCrimes();
RandomForestClassifier decisionTreeClassifier =
new RandomForestClassifier(crimes, new ShannonEntropySplitter(), new BaggingAlgorithmus(1));
decisionTreeClassifier.Train();
var crimeTests = dataSetLoader.SelectCrimes();
var trueCounter = 0;
var counter = 0;
foreach (var item in crimeTests)
{
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 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 NaivebayersLanguageTest()
{
var netMLString = "create classification naivebayers linearbayeskernel";
NetMLCreator netMLCreator = new NetMLCreator(netMLString);
DataSetLoader dataSetLoader = new DataSetLoader();
var data = dataSetLoader.SelectAnimals();
netMLCreator.Create(data);
netMLCreator.Train();
var testData = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in testData)
{
var outputValue = netMLCreator.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 SanFranciscoCrimeSVMClassificationDataSetTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var crimes = dataSetLoader.SelectCrimes();
Kernel kernel = new GaussianKernel(0.9);
SVMClassifier svmClassifier =
new SVMClassifier(crimes, kernel);
svmClassifier.Train();
var crimeTests = dataSetLoader.SelectCrimes();
var trueCounter = 0;
var counter = 0;
foreach (var item in crimeTests)
{
var outputValue = svmClassifier.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 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 AnimalDualPerceptronTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectAnimals();
for (double i = 0; i < 1; i = i + 1)
{
Kernel kernel = new LinearKernel();
DualPerceptronClassifier dualPerceptronClassifier = new DualPerceptronClassifier(animals, kernel);
dualPerceptronClassifier.Train();
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = dualPerceptronClassifier.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 AnimalBoostingTrainAndClassify8020Test()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectTrainingAnimals(90);
for (double i = 0; i < 1; i = i + 1)
{
BoostingAlgorithmus boostingAlgorithmus =
new BoostingAlgorithmus(10);
boostingAlgorithmus.Train(animals, new ShannonEntropySplitter());
var animalsTest = dataSetLoader.SelectSelectingAnimals(10);
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
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 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 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);
}
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 AnimalRadialBasisFunctionNeuronalNetworkClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectNeuronalNetworkAnimals();
for (double i = 0; i < 1; i = i + 1)
{
OneHiddenLayerNeuronCounter oneHiddenLayerNeuronCounter = new OneHiddenLayerNeuronCounter(16, 8, 32);
RadialBasisFunctionNeuronalNetwork radialBasisFunctionNeuronalNetworkClassifier =
new RadialBasisFunctionNeuronalNetwork(oneHiddenLayerNeuronCounter, 10000, 0.5);
radialBasisFunctionNeuronalNetworkClassifier.Train(animals);
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
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} {5} {6} {7} = {8}",
item.Item2,
Convert.ToDecimal(outputValue.OutputValues[0]),
Convert.ToDecimal(outputValue.OutputValues[1]),
Convert.ToDecimal(outputValue.OutputValues[2]),
Convert.ToDecimal(outputValue.OutputValues[3]),
Convert.ToDecimal(outputValue.OutputValues[4]),
Convert.ToDecimal(outputValue.OutputValues[5]),
Convert.ToDecimal(outputValue.OutputValues[6]),
(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 SanFranciscoCrimeClassificationTestDataSetTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var crimes = dataSetLoader.SelectNeuronalNetworkCrimes();
//DecisionTreeClassifier decisionTreeClassifier =
//new DecisionTreeClassifier(crimes, new ShannonEntropySplitter());
NeuronalNetworkClassifier neuronalNetworkClassifier =
new NeuronalNetworkClassifier(crimes, 2, 38, 2, 5000, 0.1);
//Kernel kernel = new LinearKernel();
//NaiveBayesClassifier naiveBayes =
// new NaiveBayesClassifier(crimes);
neuronalNetworkClassifier.Train();
var crimeTests = dataSetLoader.SelectCrimes();
var trueCounter = 0;
var counter = 0;
foreach (var item in crimeTests)
{
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("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
public void MushroomsNeuronalNetworkMachineTrainAndClassify8020Test()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var mushroom = dataSetLoader.SelectNeuronalNetworksTrainingMushroom(80);
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.SelectNeuronalNetworksSelectingMushroom(20);
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()));
}
}
public void AnimalNeuronalNetworkClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var animals = dataSetLoader.SelectNeuronalNetworkAnimals();
for (double i = 0; i < 1; i = i + 1)
{
NeuronalNetworkClassifier neuronalNetworkClassifier =
new NeuronalNetworkClassifier(animals, 16, 7, 16, 900, 0.1, NeuronalNetworkMode.Standard);
neuronalNetworkClassifier.Train();
var animalsTest = dataSetLoader.SelectAnimals();
var trueCounter = 0;
var counter = 0;
foreach (var item in animalsTest)
{
var outputValue = neuronalNetworkClassifier.ClassifiyMultibleResultValue(item.Item1);
var resultString = String.Empty;
double maxValue = 0.0;
int innerCounter = 1;
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()));
}
}
public void ClusteringLanguageTest()
{
var netMLString =
"create clustering kmetroids euclidmetric ";
NetMLParser netMLParser = new NetMLParser();
var result = netMLParser.Parse(netMLString);
NetMLCreator netMLCreator = new NetMLCreator(result);
DataSetLoader dataSetLoader = new DataSetLoader();
var irises = dataSetLoader.SelectClusteringIrises();
netMLCreator.Create(irises);
var clusters = netMLCreator.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.SelectClusteringIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = netMLCreator.CalculateClusterAffinity(item);
Debug.WriteLine(string.Format("Value {0} - Predicted {1}", item, outputValue));
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++;
}
}
public void kMeansIrisTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectClusteringIrises();
kMeansClustering kmeansClustering = new kMeansClustering(irises, new MaximumMetric());
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.SelectClusteringIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = kmeansClustering.CalculateClusterAffinity(item);
Debug.WriteLine(string.Format("Value {0} - Predicted {1}", item, outputValue));
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++;
}
//var centroids = kmeansClustering.centroids;
//var clusters = kmeansClustering.ClusterWithCentroid(4);
}