private static void Main(string[] args)
{
if (args.Length < 3 || args.Length > 6)
{
Console.WriteLine(Help);
return;
}
var parser = new EventsParser();
IList <EventModel> events = null;
try
{
events = parser.ParseFromCSV(args[0]);
}
catch (FormatException e)
{
Console.WriteLine(e.Message);
Console.WriteLine(Strings.WrongFileFormat);
Console.WriteLine(Strings.Tweets);
return;
}
if (events == null)
{
Console.WriteLine(Strings.FileNotFound);
return;
}
ISymptomAlgorithm symtopmAlgorithm;
IDetectionMethod method;
var isFastTest = false;
switch (args[1].ToLower())
{
case "bow":
symtopmAlgorithm = new BagOfWords();
break;
case "tf-idf":
symtopmAlgorithm = new TermFrequencyInverseDocumentFrequency();
break;
case "ft":
isFastTest = true;
symtopmAlgorithm = new FastTest(args[2]);
break;
default:
Console.WriteLine(Strings.SymptomAlgorithmError);
Console.WriteLine(Strings.Bow);
return;
}
switch (args[isFastTest ? 3 : 2].ToLower())
{
case "mdm":
method = new MinimalDistanceMethod();
break;
case "knn":
if (args.Length < 4 || !int.TryParse(args[isFastTest ? 4 : 3], out var k) || k <= 0)
{
Console.WriteLine(Strings.PositiveIntegerError);
Console.WriteLine(Strings.KNN);
return;
}
method = new KNearestNeighbors(k);
break;
default:
Console.WriteLine(Strings.DetectionMethodError);
Console.WriteLine(Strings.MDM);
return;
}
Console.WriteLine("Generating symtoms...");
symtopmAlgorithm.GenerateSymptoms(events);
Console.WriteLine("Symptoms generated successfully.");
Console.WriteLine($"There are { events.Count(s => s.SymptomModel != null) } symptoms.");
Console.WriteLine("\nMaking etalons...");
var symptomLength = events[0].SymptomModel.Length;
var etalons = new List <ClusterModel>();
foreach (var e in events)
{
var exist = etalons.Where(etalon => e.EventType == etalon.Type).FirstOrDefault();
if (exist != null)
{
exist.Events.Add(e);
continue;
}
etalons.Add(new ClusterModel
{
Type = e.EventType,
Events = new List <EventModel> {
e
}
});
}
etalons.ForEach(e => Console.WriteLine($"Created etalon { e.Type }"));
Console.WriteLine("\nDetection...");
var clusters = method.Detect(events, etalons);
Console.WriteLine("Detecting done.");
var output = args.Last();
if (output.LastIndexOf('.') > 0 && output.Substring(output.LastIndexOf('.')).ToLower().CompareTo(".csv") == 0)
{
Console.WriteLine("\nWriting result into csv file...\n");
var classifiedEvents = clusters.Select(c => c.Clone() as ClusterModel).ToList();
classifiedEvents.ForEach(c => c.Events.ForEach(e => e.EventType = c.Type));
try
{
parser.ParseIntoCSV(output, classifiedEvents.SelectMany(c => c.Events.Select(e => e)).ToList());
}
catch (Exception e)
{
Console.WriteLine(e.Message);
return;
}
}
else
{
Console.WriteLine(Environment.NewLine + Strings.OutputError + Environment.NewLine);
}
Console.WriteLine("Results:");
double correctResults = clusters.Sum(c => c.Events.Count(e => e.EventType == c.Type));
double all = clusters.Sum(c => c.Events.Count);
var precision = correctResults / all;
var recall = (correctResults / Math.Abs((all - correctResults))) / 100;
var fMeasure = (2 * (precision * recall)) / (precision + recall);
Console.WriteLine($"Precision: { precision }\nRecall: { recall }\nF-measure: { fMeasure }\n");
foreach (var c in clusters)
{
foreach (var e in c.Events)
{
if (e.EventType != c.Type)
{
Console.WriteLine($"Cluster { c.Type } contain wrong classified { e.EventType }");
}
}
}
}