public void Go()
{
var slow_VI = new List <myClusterable>();
slow_VI = GetData();
Console.WriteLine("Raw unclustered data:\n");
Console.WriteLine("----------------------");
ShowData(slow_VI, 1, true, false);
var c = new Clusterer();
c.Data = slow_VI;
Console.WriteLine("\nSetting numClusters to 3");
Console.WriteLine("Starting clustering using k-means algorithm");
int numClusters = c.numCentroids = 3;
int[] results = c.Cluster();
Console.WriteLine("Clustering complete\n");
Console.WriteLine("Final clustering in internal form:\n");
ShowVector(results, true);
Console.WriteLine("Raw data by cluster:\n");
ShowClustered(slow_VI, results, numClusters, 1);
Console.WriteLine("OK, done");
//Console.ReadLine();
}
public void CreateItemClusters()
{
// this is equal to the number of possible genres (in the MovieLens 1M dataset that is 18)
int numFeatures = 18;
double[,] genreFeatures = new double[Items.Count, numFeatures];
var itemIds = new List <string>();
int i = 0;
foreach (var item in Items.Values)
{
itemIds.Add(item.Id);
string genresStr = item.Properties["genres"];
var genres = genresStr.Split('|');
foreach (string g in genres)
{
genreFeatures[i, _mapper.ToInternalID(g)] = 1;
}
i++;
}
_itemsCluster = Clusterer.Cluster(itemIds, genreFeatures, NumDomains);
}
public static void Run()
{
//const string path = @"F:\thoughts\";
while (true)
{
Console.WriteLine("Write path to file(s):");
var path = Console.ReadLine();
Console.WriteLine("Where to save results?");
var savePath = Console.ReadLine();
if (path != null)
{
var fileReader = new FileReader(path);
var documents = fileReader.PerformRead();
var clusterableDocuments = new MyDocumentToClusterableEntityMapper().MapAllData(documents);
var kMeansDatas = new ClusterableEntityToKMeansEntityMapper().MapAllData(clusterableDocuments);
var clusterer = new Clusterer(7);
clusterer.Cluster(kMeansDatas, null);
var results = new ClusteredDataResultWriter().WriteResults(kMeansDatas);
if (savePath != null)
{
File.WriteAllText(savePath, results);
}
}
Console.WriteLine($"Amount of exceptions while reading: {exceptionsCount}");
Console.Read();
}
}
private async void performKMeans_Click(object sender, EventArgs e)
{
informer.Text = " ";
var task = Task.Run(() =>
{
var clusterer = new Clusterer(5);
clusterer.Cluster(data, DrawChangedData);
});
await task;
informer.Text = "Done!";
}
static void K_Means_Main(string[] args)
{
Console.WriteLine($@"\nBegin k-means clustering demo\n");
#region setup
double[][] rawData = new double[10][];
rawData[0] = new double[] { 73, 72.6 };
rawData[1] = new double[] { 61, 54.4 };
rawData[2] = new double[] { 67, 99.9 };
rawData[3] = new double[] { 68, 97.3 };
rawData[4] = new double[] { 62, 59.0 };
rawData[5] = new double[] { 75, 81.6 };
rawData[6] = new double[] { 74, 77.1 };
rawData[7] = new double[] { 66, 97.3 };
rawData[8] = new double[] { 68, 93.3 };
rawData[9] = new double[] { 61, 59.0 };
//double[][] rawData = LoadData("..\\..\\HeightWeight.txt", 10, 2, ',');
#endregion
Console.WriteLine("Raw unclustered height (in.) weight (kg.) data:\n");
Console.WriteLine(" ID Height Weight");
Console.WriteLine("----------------------");
ShowData(rawData, 1, true, false);
int numClusters = 3;
Console.WriteLine("\nSetting numClusters to " + numClusters);
Console.WriteLine("Starting clustering using k-means algorithm");
Clusterer c = new Clusterer(numClusters);
int[] clustering = c.Cluster(rawData);
Console.WriteLine("Clustering complete\n");
Console.WriteLine("Final clustering in internal form:\n");
ShowVector(clustering, true);
Console.WriteLine("Raw data by cluster:\n");
ShowClustered(rawData, clustering, numClusters, 1);
Console.WriteLine("\nEnd k-means clustering demo\n");
Console.ReadLine();
}
