private void KMeansPP(object sender, RoutedEventArgs e)
{
clustResultTxtBox.Document.Blocks.Clear();
var clusterization_stopwatch = Stopwatch.StartNew();
string message = null;
string algorithm = " k-means++;";
string PPKMeans_label_resul_path = @"F:\Magistry files\data\PPKMeans_label_result4.txt";
string PPK_means_report_path = @"F:\Magistry files\reports\PPKMeans_report4.txt";
List <string> docCollection = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoading();
Dictionary <int, string> docCollectionDictionary = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoadingToDictionary();
HashSet <string> termCollection = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.getTermCollection();
Dictionary <string, int> wordIndex1 = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.DocumentsContainsTermToDictionary(docCollectionDictionary, termCollection);
List <DocumentVector> vSpace1 = VectorSpaceModel.DocumentCollectionProcessingDictionary(docCollectionDictionary);
int totalIteration = 500;
int clusterNumber = 5;
clusterNumber = Convert.ToInt32(txtboxClusterNumber.Text);
List <Centroid> firstCentroidList = new List <Centroid>();
firstCentroidList = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.InitialCentroidCalculation.CentroidCalculationsForTestKMeansPP(vSpace1, clusterNumber);
List <Centroid> resultSet = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.KMeans.KMeansClustering(vSpace1, clusterNumber, totalIteration, firstCentroidList);
clusterization_stopwatch.Stop();
int[] PPKMeans_label_matrix = new int[vSpace1.Count];
PPKMeans_label_matrix = Tests.Label_Matrix.ReleaseVersion_Label_Matrix_Extractions(resultSet, PPKMeans_label_resul_path);
message = RaportGeneration.ReleaseRaportGenerationFunction(resultSet, clusterNumber, totalIteration, clusterization_stopwatch, PPK_means_report_path, algorithm);
//clustResultTxtBox.AppendText(message);
invokeFilesToVisualizationGenerator(resultSet, algorithm);
}
private void FuzzyKMeans_Click(object sender, RoutedEventArgs e)
{
clustResultTxtBox.Document.Blocks.Clear();
var clusterization_stopwatch = Stopwatch.StartNew();
string message = null;
string algorithm = " Fuzzy c-Means;";
List <string> docCollection = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoading();
Dictionary <int, string> docCollectionDictionary = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoadingToDictionary();
HashSet <string> termCollection = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.getTermCollection();
Dictionary <string, int> wordIndex = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.DocumentsContainsTermToDictionary(docCollectionDictionary, termCollection);
List <DocumentVector> vSpace = VectorSpaceModel.DocumentCollectionProcessingDictionary(docCollectionDictionary);
string Fuzzy_K_means_clusterization_result = @"F:\Magistry files\Fuzzy_KMeans_result6.txt";
string Fuzzy_K_means_label_result = @"F:\Magistry files\FCM_label_result6.txt";
string Fuzzy_K_means_report_path = @"F:\Magistry files\reports\FCM_report6.txt";
float fuzziness = 0.5f;
float epsilon = 0.003f;
int clusterNumber = 5;
int totalIteration = 0;
clusterNumber = Convert.ToInt32(txtboxClusterNumber.Text);
List <Centroid> resultSet = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.FuzzyCMeans.CreateClusterSet(clusterNumber);
float[,] Result_fcm;
/*
* Result_fcm = FuzzyKMeans.Fcm(vSpace, clusterNumber, epsilon, fuzziness, termCollection);
* FuzzyKMeans.WriteSimilarityArrayToFile(Result_fcm, Fuzzy_K_means_clusterization_result);
* resultSet = FuzzyKMeans.AssignDocsToClusters(Result_fcm, clusterNumber,vSpace);
* FuzzyKMeans.Show_clusters(vSpace, Result_fcm, clusterNumber);
*/
var result = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.FuzzyCMeans.Fcm(vSpace, clusterNumber, epsilon, fuzziness);
Result_fcm = result.Item1;
totalIteration = result.Item2;
Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.FuzzyCMeans.WriteSimilarityArrayToFile(Result_fcm, Fuzzy_K_means_clusterization_result);
var assignedResult = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.FuzzyCMeans.AssignDocsToClusters(Result_fcm, clusterNumber, vSpace, resultSet);
clusterization_stopwatch.Stop();
int[] FuzzyKMeans_label_matrix = new int[vSpace.Count];
int[] FuzzyKMeans_label_matrix1 = assignedResult.Item1;
resultSet = assignedResult.Item2;
FuzzyKMeans_label_matrix = Tests.Label_Matrix.ReleaseVersion_Label_Matrix_Extractions(resultSet, Fuzzy_K_means_label_result);
message = RaportGeneration.ReleaseRaportGenerationFunction(resultSet, clusterNumber, totalIteration, clusterization_stopwatch, Fuzzy_K_means_report_path, algorithm);
//clustResultTxtBox.AppendText(message);
invokeFilesToVisualizationGenerator(resultSet, algorithm);
}
private void KMeans(object sender, RoutedEventArgs e)
{
clustResultTxtBox.Document.Blocks.Clear();
var clusterization_stopwatch = Stopwatch.StartNew();
string message = null;
string algorithm = " k-means;";
string PKMeans_label_resul_path = @"F:\Magistry files\data\PKMeans_label_result6.txt";
string K_means_report_path = @"F:\Magistry files\reports\PKMeans_report6.txt";
#region OldDataGeneration
/*
* List<string> docCollection = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoading();
* HashSet<string> termCollection = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.getTermCollection();
* Dictionary<string, int> wordIndex = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.DocumentsContainsTerm(docCollection, termCollection);
* List<DocumentVector> vSpace = VectorSpaceModel.DocumentCollectionProcessing(docCollection);
*/
#endregion
Dictionary <int, string> docCollectionDictionary = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoadingToDictionary();
HashSet <string> termCollection = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.getTermCollection();
Dictionary <string, int> wordIndex = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.DocumentsContainsTermToDictionary(docCollectionDictionary, termCollection);
List <DocumentVector> vSpace = VectorSpaceModel.DocumentCollectionProcessingDictionary(docCollectionDictionary);
int totalIteration = 500;
int clusterNumber = 5;
clusterNumber = Convert.ToInt32(txtboxClusterNumber.Text);
List <Centroid> firstCentroidList = new List <Centroid>();
#region OldClusteringAlgorithm
//firstCentroidList = CentroidCalculationClass.CentroidCalculationsForKMeans(vSpace, clusterNumber);
//List<Centroid> resultSet = Logic.ClusteringAlgorithms.Algorithms.KMeansPPImplementations.MyKmeansPPInterpritationcs.NewKMeansClusterization(clusterNumber, docCollection, totalIteration, vSpace, wordIndex, firstCentroidList);
#endregion
firstCentroidList = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.KMeans.CentroidCalculationsForKMeans(vSpace, clusterNumber);
List <Centroid> resultSet = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.KMeans.KMeansClustering(vSpace, clusterNumber, totalIteration, firstCentroidList);
clusterization_stopwatch.Stop();
int[] PKMeans_label_matrix = new int[vSpace.Count];
PKMeans_label_matrix = Tests.Label_Matrix.ReleaseVersion_Label_Matrix_Extractions(resultSet, PKMeans_label_resul_path);
#region tests_metrics
//List<string> docs = Tests.DocClasses.SurveyAndMeasurementsClassOfDocuments_ListCreations();
//List<List<string>> ClassCollection = Tests.DocClasses.ListOfClasses();
/*
* var distance = Tests.InterclusterDistances.d_centroids(resultSet);
* var min_centroid_distances = Tests.InterclusterDistances.d_min_centroids(resultSet);
* var max_intracluster_d = Tests.IntraclusterDistances.d_max(resultSet);
* var min_intracluster_d = Tests.IntraclusterDistances.d_min(resultSet);
*
* var median_intracluster_d = Tests.IntraclusterDistances.d_sr(resultSet);
* //string DistanceMetricsFilePath = @"F:\Magistry files\distanceMetrics\KmeansDistanceMetrics1.txt";
* //for(int iK=0; iK<clusterNumber; iK++)
* //{
* // for (int jK = 0; jK < clusterNumber; jK++)
* // {
* // File.WriteAllText(DistanceMetricsFilePath, distance[iK, jK].ToString());
* // }
* //}
*
*
* /*
* var Recall_result = Tests.Recall.Recall_Calculating(resultSet, docs);
* var Precision_result = Tests.Precision.Precision_Calculating(resultSet, docs);
* var Purity = Tests.Purity.Purity_Calculating(resultSet, ClassCollection, vSpace);
* var Fmeasure = Tests.F1Measure.F1_Measure_Calculating(resultSet, ClassCollection);
* var GMeasure = Tests.F1Measure.G_Measure_Calculating(resultSet, ClassCollection);
* var NMI = Tests.NormilizedMutualInformation.NMI_Calculating(resultSet, ClassCollection, vSpace);
* var Entropy = Tests.Entropy.Enthropy_Calculating(resultSet, ClassCollection);
*/
#endregion
message = RaportGeneration.ReleaseRaportGenerationFunction(resultSet, clusterNumber, totalIteration, clusterization_stopwatch, K_means_report_path, algorithm);
//clustResultTxtBox.AppendText(message);
invokeFilesToVisualizationGenerator(resultSet, algorithm);
}
private void Gravitational_Click(object sender, RoutedEventArgs e)
{
#region gravitational_old_working_code
/*
* List<string> docCollection = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoading();
* HashSet<string> termCollection = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.getTermCollection();
* Dictionary<string, int> wordIndex = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.DocumentsContainsTerm(docCollection, termCollection);
* List<DocumentVector> vSpace = VectorSpaceModel.DocumentCollectionProcessing(docCollection);
* int M = 1000000;
* float G = -1.28171817154F; //G=1*e-4 according to 3.2.2 in article
* float deltaG = 0.01F;
* float epsilon = -3.28171817154F;//epsilon=1*e-6 according to 3.2.2 in article or 10^(-4)= 0.0001F;
* //float epsilon = 0.6F;
* float alpha = 0.06F;
* var result1 = Logic.ClusteringAlgorithms.Algorithms.GravitationalClusteringAlgorithm.Gravitational(vSpace, G, deltaG, M, epsilon);
* //var result2 = GravitationalClusteringAlgorithm.GetClusters(result1, alpha, vSpace);
* List<string> docs = Tests.DocClasses.SurveyAndMeasurementsClassOfDocuments_ListCreations();
* List<List<string>> ClassCollection = Tests.DocClasses.ListOfClasses();
* var distance = Tests.InterclusterDistances.d_centroids(result1);
* var min_centroid_distances = Tests.InterclusterDistances.d_min_centroids(result1);
* var max_intracluster_d = Tests.IntraclusterDistances.d_max(result1);
* var min_intracluster_d = Tests.IntraclusterDistances.d_min(result1);
* var median_intracluster_d = Tests.IntraclusterDistances.d_sr(result1);
* var Recall_result = Tests.Recall.Recall_Calculating(result1, docs);
* var Precision_result = Tests.Precision.Precision_Calculating(result1, docs);
* var Purity = Tests.Purity.Purity_Calculating(result1, ClassCollection, vSpace);
* var Fmeasure = Tests.F1Measure.F1_Measure_Calculating(result1, ClassCollection);
* var GMeasure = Tests.F1Measure.G_Measure_Calculating(result1, ClassCollection);
* var NMI = Tests.NormilizedMutualInformation.NMI_Calculating(result1, ClassCollection, vSpace);
*/
#endregion
clustResultTxtBox.Document.Blocks.Clear();
var clusterization_stopwatch = Stopwatch.StartNew();
string message = null;
string algorithm = " Gravitational clustering algorithm;";
Dictionary <int, string> docCollectionDictionary = Logic.ClusteringAlgorithms.Used_functions.CreateDocumentCollection2.GenerateDocumentCollection_withoutLazyLoadingToDictionary();
HashSet <string> termCollection = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.getTermCollection();
Dictionary <string, int> wordIndex = Logic.ClusteringAlgorithms.Used_functions.TFIDF2ndrealization.DocumentsContainsTermToDictionary(docCollectionDictionary, termCollection);
List <DocumentVector> vSpace = VectorSpaceModel.DocumentCollectionProcessingDictionary(docCollectionDictionary);
int M = 500;
//float G = 7 * (float)Math.Pow(10, (-6));
//float G = -1.28171817154F; //G=1*e-4 according to 3.2.2 in article
float G = 6.67408313131313131F * (float)Math.Pow(10, (-6));
float deltaG = 0.001F;
//float epsilon = -3.28171817154F;//epsilon=1*e-6 according to 3.2.2 in article or 10^(-4)= 0.0001F;
float epsilon = 0.1F;
float alpha = 0.06F;
int clusterNumber = 6;
clusterNumber = Convert.ToInt32(txtboxClusterNumber.Text);
M = Convert.ToInt32(txtboxIterationCount.Text);
string gravitational_label_resul_path = @"F:\Magistry files\data\Gravitational_label_result5.txt";
string Gravitational_report_path = @"F:\Magistry files\reports\Gravitational_report5.txt";
List <Centroid> result = new List <Centroid>(vSpace.Count);
var results = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.GravitationalClusteringAlgorithm.GravitationalAlgorithm(vSpace, G, deltaG, M, epsilon);
var get_Clusters = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.GravitationalClusteringAlgorithm.GetClusters(results, alpha, vSpace);
List <Centroid> resultSet = Logic.ClusteringAlgorithms.WorkedAlgorithmsFromTest.GravitationalClusteringAlgorithm.RemoveSameElementsFromClusters(get_Clusters);
int[] label_matrix = Tests.Label_Matrix.ReleaseVersion_Label_Matrix_Extractions(get_Clusters, gravitational_label_resul_path);
clusterization_stopwatch.Stop();
message = RaportGeneration.ReleaseRaportGenerationFunction(get_Clusters, get_Clusters.Count, M, clusterization_stopwatch, Gravitational_report_path, algorithm);
//clustResultTxtBox.AppendText(message);
invokeFilesToVisualizationGenerator(resultSet, algorithm);
}