public static List <KMeansModel> ReadAllModels()
{
List <KMeansModel> models = new List <KMeansModel>();
if (!Directory.Exists(ModelsDirectoryPath))
{
return(models);
}
List <string> paths = Directory.GetFiles(ModelsDirectoryPath).ToList();
paths.Sort();
Console.WriteLine();
Console.WriteLine("Read models order:");
paths.ForEach(Console.WriteLine);
foreach (string path in paths)
{
try
{
KMeansModel model = ReadKMeansModel(path);
models.Add(model);
}
catch (JsonReaderException)
{
Console.WriteLine($"An error occured while reading json at path {path}");
}
}
return(models);
}
/**
* <summary> Training algorithm for K-Means classifier. K-Means finds the mean of each class for training.</summary>
*
* <param name="trainSet"> Training data given to the algorithm.</param>
* <param name="parameters">distanceMetric: distance metric used to calculate the distance between two instances.</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var priorDistribution = trainSet.ClassDistribution();
var classMeans = new InstanceList.InstanceList();
var classLists = trainSet.DivideIntoClasses();
for (var i = 0; i < classLists.Size(); i++)
{
classMeans.Add(classLists.Get(i).Average());
}
model = new KMeansModel(priorDistribution, classMeans, ((KMeansParameter)parameters).GetDistanceMetric());
}
public static void SaveKMeansModel(KMeansModel model, DateTime dateTime, int order)
{
if (!Directory.Exists(ModelsDirectoryPath))
{
Directory.CreateDirectory(ModelsDirectoryPath);
}
int clustersCount = model.Centroids.Count;
string date = GetFormattedDate(dateTime);
string fileName = $"(model_k_means)(ord_{order})(k_{clustersCount})(date_{date}).json";
string filePath = $"{ModelsDirectoryPath}/{fileName}";
SaveObject(filePath, model);
}
