public static void IterationalReportGenerationFunction(int ClusterNumber, int iteration, string algorithm, List <TestCentroid> result, int elementCount, int iterationCount, Stopwatch clusterization_stopwatch)
{
int j = iteration;
switch (algorithm)
{
case "KMeans":
string KMeans_label_resul_path = @"F:\Magistry files\data\test\testData\" + ClusterNumber.ToString() + "Clusters\\KMeans_label_result" + ClusterNumber.ToString() + "clust" + j.ToString() + ".txt";
string K_means_report_path = @"F:\Magistry files\reports\test\KMeans_report" + ClusterNumber.ToString() + "clust" + j.ToString() + ".txt";
int[] KMeans_label_matrix = new int[elementCount];
KMeans_label_matrix = Tests.Label_Matrix.Label_Matrix_Extractions(result, KMeans_label_resul_path);
RaportGeneration.VoidRaportGenerationFunction(algorithm, result, ClusterNumber, iterationCount, clusterization_stopwatch, K_means_report_path);
break;
case "KmeansPP":
string KMeansPP_label_resul_path = @"F:\Magistry files\data\test\testData\" + ClusterNumber.ToString() + "Clusters\\KMeansPP_label_result" + ClusterNumber.ToString() + "clust" + j.ToString() + ".txt";
string K_meansPP_report_path = @"F:\Magistry files\reports\test\KMeansPP_report" + ClusterNumber.ToString() + "clust" + j.ToString() + ".txt";
int[] KMeansPP_label_matrix = new int[elementCount];
KMeansPP_label_matrix = Tests.Label_Matrix.Label_Matrix_Extractions(result, KMeansPP_label_resul_path);
RaportGeneration.VoidRaportGenerationFunction(algorithm, result, ClusterNumber, iterationCount, clusterization_stopwatch, K_meansPP_report_path);
break;
/*
* case "FuzzyKMeans":
* string FuzzyKMeans_label_resul_path = @"F:\Magistry files\data\test\testData\" + ClusterNumber.ToString()+"Clusters\\FuzzyKMeans_label_result"+ ClusterNumber.ToString()+"clust"+j.ToString()+".txt";
* string Fuzzy_K_means_report_path = @"F:\Magistry files\reports\test\Fuzzy_KMeans_report" + ClusterNumber.ToString() +"clust"+j.ToString()+".txt";
* int[] FuzzyKMeans_label_matrix = new int[elementCount];
* FuzzyKMeans_label_matrix = Tests.Label_Matrix.Label_Matrix_Extractions(result, FuzzyKMeans_label_resul_path);
* RaportGeneration.VoidRaportGenerationFunction(algorithm, result, ClusterNumber, iterationCount, clusterization_stopwatch, Fuzzy_K_means_report_path);
* break;
*/
}
}
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 Gravitational_clustering_test_Click(object sender, RoutedEventArgs e)
{
clustResultTxtBox.Document.Blocks.Clear();
var clusterization_stopwatch = Stopwatch.StartNew();
string message = null;
string path_data = @"F:\Magistry files\test_data\s1_data.txt";
List <DocumentVectorTest> vSpace = TestDocVectorCreator.CreatingTheDocVectorCollection(path_data);
List <DocumentVectorTest> normilized_vSpace = TestDocVectorCreator.NormalizationDocumentVectors(vSpace);
int M = 500;
float G = 6.67408313131313131F * (float)Math.Pow(10, (-6));
float deltaG = 0.001F;
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_results11.txt";
string Gravitational_report_path = @"F:\Magistry files\reports\Gravitational_reports11.txt";
List <TestCentroid> result = new List <TestCentroid>(normilized_vSpace.Count);
var result1 = Tests.Gravitational.GravitationalAlg(normilized_vSpace, G, deltaG, M, epsilon);
var get_Clusters = Tests.Gravitational.GetClustersTest(result1, alpha, normilized_vSpace);
int[] label_matrix = Tests.Label_Matrix.Label_Matrix_Extractions(get_Clusters, gravitational_label_resul_path);
clusterization_stopwatch.Stop();
message = RaportGeneration.RaportGenerationFunction(get_Clusters, get_Clusters.Count, M, clusterization_stopwatch, Gravitational_report_path);
clustResultTxtBox.AppendText(message);
}
/*
* private void AHC_Click(object sender, RoutedEventArgs e)
* {
* 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 iterationCount = 100;
* var result = Primitive_Clustering_Hierarhical_Alg.Hierarchical_Clusterization(vSpace, iterationCount);
* List<string> docs = Tests.DocClasses.SurveyAndMeasurementsClassOfDocuments_ListCreations();
* List<List<string>> ClassCollection = Tests.DocClasses.ListOfClasses();
* var distance = Tests.InterclusterDistances.d_centroids(result);
* var min_centroid_distances = Tests.InterclusterDistances.d_min_centroids(result);
* var max_intracluster_d = Tests.IntraclusterDistances.d_max(result);
* var min_intracluster_d = Tests.IntraclusterDistances.d_min(result);
* var median_intracluster_d = Tests.IntraclusterDistances.d_sr(result);
* var Recall_result = Tests.Recall.Recall_Calculating(result, docs);
* var Precision_result = Tests.Precision.Precision_Calculating(result, docs);
* var Purity = Tests.Purity.Purity_Calculating(result, ClassCollection, vSpace);
* var Fmeasure = Tests.F1Measure.F1_Measure_Calculating(result, ClassCollection);
* var GMeasure = Tests.F1Measure.G_Measure_Calculating(result, ClassCollection);
* var NMI = Tests.NormilizedMutualInformation.NMI_Calculating(result, ClassCollection, vSpace);
* }
*/
#endregion
private void Kmeans_test(object sender, RoutedEventArgs e)
{
#region usual_KMeans_alg_invoke
clustResultTxtBox.Document.Blocks.Clear();
var clusterization_stopwatch = Stopwatch.StartNew();
string message = null;
string path_data = @"F:\Magistry files\test_data\s1_data.txt";
string KMeansPP_label_resul_path = @"F:\Magistry files\data\test\testData\20Clusters\KMeans_label_result20clust5.txt";
string K_means_report_path = @"F:\Magistry files\reports\KMeans_report20clust5.txt";
int clusterNumber = Convert.ToInt32(txtboxClusterNumber.Text);
int iterationCount = 500;
iterationCount = Convert.ToInt32(txtboxIterationCount.Text);
List <DocumentVectorTest> vSpace = TestDocVectorCreator.CreatingTheDocVectorCollection(path_data);
List <DocumentVectorTest> normilized_vSpace = TestDocVectorCreator.NormalizationDocumentVectors(vSpace);
List <TestCentroid> firstCentroidList = new List <TestCentroid>();
firstCentroidList = Test_KMeans.CentroidCalculationsForKMeans(normilized_vSpace, clusterNumber);
List <TestCentroid> resultSet = Tests.NewTestKMeansClustering.Cluster(vSpace, clusterNumber, iterationCount);
int[] KMeans_label_matrix = new int[vSpace.Count];
KMeans_label_matrix = Tests.Label_Matrix.Label_Matrix_Extractions(resultSet, KMeansPP_label_resul_path);
clusterization_stopwatch.Stop();
message = RaportGeneration.RaportGenerationFunction(resultSet, clusterNumber, iterationCount, clusterization_stopwatch, K_means_report_path);
clustResultTxtBox.AppendText(message);
#endregion
#region Iterational_test_Kmeans
/*
* int iteration = 5;
* string algorithmFlag = "KMeans";
* int clusterNumbers = Convert.ToInt32(txtboxClusterNumber.Text);
* int algiterationCount = 500;
* algiterationCount = Convert.ToInt32(txtboxIterationCount.Text);
* string test_path_data = @"F:\Magistry files\test_data\s1_data.txt";
* List<DocumentVectorTest>vSpaces = TestDocVectorCreator.CreatingTheDocVectorCollection(test_path_data);
* List<DocumentVectorTest> normilized_vSpaces = TestDocVectorCreator.NormalizationDocumentVectors(vSpaces);
* List<TestCentroid> firstCentroidLists = new List<TestCentroid>();
*
* for (int j = 5; j <= clusterNumbers;)
* {
* for (int i = 1; i <= iteration; i++)
* {
* clustResultTxtBox.Document.Blocks.Clear();
* var clusterization_stopwatchs = Stopwatch.StartNew();
* firstCentroidLists = Test_KMeans.CentroidCalculationsForKMeans(normilized_vSpaces, j);
* List<TestCentroid> IterationresultSets = Tests.NewTestKMeansClustering.Cluster(vSpaces, j, algiterationCount);
* clusterization_stopwatchs.Stop();
* Logic.IterationalReport.IterationalReportGenerationFunction(j, i, algorithmFlag, IterationresultSets, vSpaces.Count, algiterationCount, clusterization_stopwatchs);
* }
* j +=5;
* }
*/
#endregion
}
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 FuzzyKMeansTest_Click(object sender, RoutedEventArgs e)
{
#region usual_KMeansPP_alg_invoke
clustResultTxtBox.Document.Blocks.Clear();
var clusterization_stopwatch = Stopwatch.StartNew();
string message = null;
string path_data = @"F:\Magistry files\test_data\s1_data.txt";
List <DocumentVectorTest> vSpace = TestDocVectorCreator.CreatingTheDocVectorCollection(path_data);
float fuzziness = 2f;
float epsilon = 0.1f;
int clusterNumber = Convert.ToInt32(txtboxClusterNumber.Text);
List <TestCentroid> resultSet = new List <TestCentroid>(clusterNumber);
List <TestCentroid> resultSetRTest = new List <TestCentroid>();
//List<DocumentVectorTest> normilized_vSpace = TestDocVectorCreator.NormalizationDocumentVectors(vSpace);
float[,] Result_fcm;
//List<string> docs = Tests.DocClasses.SurveyAndMeasurementsClassOfDocuments_ListCreations();
var result = Tests.FuzzyKmeansTest.Fcm(vSpace, clusterNumber, epsilon, fuzziness);
Result_fcm = result.Item1;
int iterationCount = result.Item2;
resultSet = Tests.FuzzyKmeansTest.CreateClusterSet(clusterNumber);
var resultSetArray = Tests.FuzzyKmeansTest.AssignDocsToClusters(Result_fcm, clusterNumber, vSpace, resultSet);
//resultSetRTest = Tests.FuzzyKmeansTest.AssignDocsToClusters(Result_fcm, clusterNumber, vSpace);
string FuzzyKMeans_label_resul_path = @"F:\Magistry files\data\testData\FuzzyKMeans_label_result.txt";
string Fuzzy_K_means_report_path = @"F:\Magistry files\reports\Fuzzy_KMeans_report.txt";
//string RTest_FuzzyKMeans_label_resul_path = @"F:\Magistry files\data\testData\5Clusters\RTest_FuzzyKMeans_label_result5clust1.txt";
string[] string_label_matrix = new string[resultSetArray.Item1.Length];
for (int k = 0; k < resultSetArray.Item1.Length; k++)
{
string_label_matrix[k] = resultSetArray.Item1[k].ToString();
}
var result_Centroid_Set = resultSetArray.Item2;
System.IO.File.WriteAllLines(FuzzyKMeans_label_resul_path, string_label_matrix);
//string membership_matrix_file_path = @"F:\Magistry files\data\R_membership_matrix.txt";
//var memberShipMatrix = Tests.MemberShipMatrixReader.ReadDataFromFile(membership_matrix_file_path, vSpace.Count, clusterNumber);
//resultSet = Tests.FuzzyKmeansTest.CreateClusterSet(clusterNumber);
//var resultSetArray1 = Tests.FuzzyKmeansTest.AssignDocsToClusters(memberShipMatrix, clusterNumber, vSpace, resultSet);
//for (int k = 0; k < resultSetArray.Item1.Length; k++)
//string_label_matrix[k] = resultSetArray1.Item1[k].ToString();
//System.IO.File.WriteAllLines(RTest_FuzzyKMeans_label_resul_path, string_label_matrix);
clusterization_stopwatch.Stop();
message = RaportGeneration.RaportGenerationFunction(resultSet, clusterNumber, iterationCount, clusterization_stopwatch, Fuzzy_K_means_report_path);
clustResultTxtBox.AppendText(message);
#endregion
#region Iterational_test_FuzzyKmeans
/*
* int iteration = 5;
* float fuzzinesss = 2f;
* float epsilons = 0.1f;
* int clusterNumbers = Convert.ToInt32(txtboxClusterNumber.Text);
* int algiterationCount = 500;
* string algorithm = "FuzzyKMeans";
* algiterationCount = Convert.ToInt32(txtboxIterationCount.Text);
* string test_path_data = @"F:\Magistry files\test_data\s1_data.txt";
* List<TestCentroid> resultSets = new List<TestCentroid>(clusterNumbers);
* List<DocumentVectorTest> vSpaces = TestDocVectorCreator.CreatingTheDocVectorCollection(test_path_data);
* List<DocumentVectorTest> normilized_vSpaces = TestDocVectorCreator.NormalizationDocumentVectors(vSpaces);
*
* for (int j = 5; j <= clusterNumbers;)
* {
* for (int i = 1; i <= iteration; i++)
* {
*
* clustResultTxtBox.Document.Blocks.Clear();
* var clusterizations_stopwatchs = Stopwatch.StartNew();
* float[,] Result_fcms;
* var results = Tests.FuzzyKmeansTest.Fcm(vSpaces, j, epsilons, fuzzinesss);
* Result_fcms = results.Item1;
* int AlgiterationCounts = results.Item2;
* resultSets = Tests.FuzzyKmeansTest.CreateClusterSet(j);
* var resultSetArrays = Tests.FuzzyKmeansTest.AssignDocsToClusters(Result_fcms, j, vSpaces, resultSets);
* string[] string_label_matrixs = new string[resultSetArrays.Item1.Length];
* for (int k = 0; k < resultSetArrays.Item1.Length; k++)
* string_label_matrixs[k] = resultSetArrays.Item1[k].ToString();
* string FuzzyKMeans_label_resul_paths = @"F:\Magistry files\data\test\testData\" + j.ToString() + "Clusters\\FuzzyKMeans_label_result" + j.ToString() + "clust" + i.ToString() + ".txt";
* string Fuzzy_K_means_report_paths = @"F:\Magistry files\reports\test\Fuzzy_KMeans_report" + j.ToString() + "clust" + i.ToString() + ".txt";
* clusterizations_stopwatchs.Stop();
* using (StreamWriter w = File.AppendText(FuzzyKMeans_label_resul_paths))
* {
* for (int k = 0; k < string_label_matrixs.Length; k++)
* w.WriteLine(string_label_matrixs[k]);
* }
* RaportGeneration.VoidRaportGenerationFunction(algorithm, resultSetArrays.Item2, j, AlgiterationCounts, clusterizations_stopwatchs, Fuzzy_K_means_report_paths);
* }
* j += 5;
* }
*/
#endregion
}
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);
}
