public void Ejecutar(int k)
{
var inicio = DateTime.Now.Ticks;
var kmeans = new KMeans().Cluster(LstDocumentos, k);
var fin = DateTime.Now.Ticks;
var transcurrido = new TimeSpan(fin - inicio).TotalMilliseconds;
while (transcurrido <= 1000)
{
var cluster = MinimoDePuntos(kmeans);
_eps = EpsilonMaximo(kmeans[cluster]);
_minPts = kmeans[cluster].Count;
LstDocumentos = new DBSCAN(_eps, _minPts, _laMatrixDeSimilitud).BuildCluster(LstDocumentos);
var res = (from c in LstDocumentos
group c by c.ClusterID
into g
select new {clusterID = g.Key, miembros = g.Select(x => x.ClusterID).Count()}).OrderBy(
d => d.clusterID).ToList();
Console.WriteLine("Eps: {0}\tMinPts: {1}\tK: {2}", _eps, _minPts, k);
foreach (var r in res)
{
Console.WriteLine("#Cluster: {0}\tMiembros: {1}", r.clusterID, r.miembros);
}
fin = DateTime.Now.Ticks;
kmeans = new KMeans().Cluster(LstDocumentos, k);
transcurrido = new TimeSpan(fin - inicio).TotalMilliseconds;
}
Console.WriteLine("Transcurrido: {0}", transcurrido);
}
private void kmeans()
{
// Retrieve the number of clusters
int k = (int)numClusters.Value;
// Load original image
Bitmap image = Properties.Resources.leaf;
// Create conversors
ImageToArray imageToArray = new ImageToArray(min: -1, max: +1);
ArrayToImage arrayToImage = new ArrayToImage(image.Width, image.Height, min: -1, max: +1);
// Transform the image into an array of pixel values
double[][] pixels; imageToArray.Convert(image, out pixels);
// Create a K-Means algorithm using given k and a
// square euclidean distance as distance metric.
KMeans kmeans = new KMeans(k, Distance.SquareEuclidean);
// Compute the K-Means algorithm until the difference in
// cluster centroids between two iterations is below 0.05
int[] idx = kmeans.Compute(pixels, 0.05);
// Replace every pixel with its corresponding centroid
pixels.ApplyInPlace((x, i) => kmeans.Clusters.Centroids[idx[i]]);
// Show resulting image in the picture box
Bitmap result; arrayToImage.Convert(pixels, out result);
pictureBox.Image = result;
}
public void Learn(double[][] observations, int amountOfClusters)
{
var kmeans = new KMeans(amountOfClusters);
_clusters = kmeans.Learn(observations);
}
static void Main(string[] args)
{
Console.SetWindowSize(100, 50);
// Read in the Online Retail feature dataset
// TODO: change the path to point to your data directory
string dataDirPath = @"\\Mac\Home\Documents\c-sharp-machine-learning\ch.6\input-data";
// Load the data into a data frame
string dataPath = Path.Combine(dataDirPath, "features.csv");
Console.WriteLine("Loading {0}\n\n", dataPath);
var ecommerceDF = Frame.ReadCsv(
dataPath,
hasHeaders: true,
inferTypes: true
);
Console.WriteLine("* Shape: {0}, {1}", ecommerceDF.RowCount, ecommerceDF.ColumnCount);
string[] features = new string[] { "NetRevenuePercentile", "AvgUnitPricePercentile", "AvgQuantityPercentile" };
Console.WriteLine("* Features: {0}\n\n", String.Join(", ", features));
var normalizedDf = Frame.CreateEmpty <int, string>();
var average = ecommerceDF.Columns[features].Sum() / ecommerceDF.RowCount;
foreach (string feature in features)
{
normalizedDf.AddColumn(feature, (ecommerceDF[feature] - average[feature]) / ecommerceDF[feature].StdDev());
}
double[][] sampleSet = BuildJaggedArray(
normalizedDf.Columns[features].ToArray2D <double>(),
normalizedDf.RowCount,
features.Length
);
// Create a new K-Means algorithm with n clusters
Accord.Math.Random.Generator.Seed = 0;
int[] numClusters = new int[] { 4, 5, 6, 7, 8 };
List <string> clusterNames = new List <string>();
List <double> silhouetteScores = new List <double>();
for (int i = 0; i < numClusters.Length; i++)
{
KMeans kmeans = new KMeans(numClusters[i]);
KMeansClusterCollection clusters = kmeans.Learn(sampleSet);
int[] labels = clusters.Decide(sampleSet);
string colname = String.Format("Cluster-{0}", numClusters[i]);
clusterNames.Add(colname);
normalizedDf.AddColumn(colname, labels);
ecommerceDF.AddColumn(colname, labels);
Console.WriteLine("\n\n\n##################### {0} ###########################", colname);
Console.WriteLine("\n\n* Centroids for {0} clusters:", numClusters[i]);
PrintCentroidsInfo(clusters.Centroids, features);
Console.WriteLine("\n");
VisualizeClusters(normalizedDf, colname, "NetRevenuePercentile", "AvgUnitPricePercentile");
VisualizeClusters(normalizedDf, colname, "AvgUnitPricePercentile", "AvgQuantityPercentile");
VisualizeClusters(normalizedDf, colname, "NetRevenuePercentile", "AvgQuantityPercentile");
for (int j = 0; j < numClusters[i]; j++)
{
GetTopNItemsPerCluster(ecommerceDF, j, colname);
}
double silhouetteScore = CalculateSilhouetteScore(normalizedDf, features, numClusters[i], colname);
Console.WriteLine("\n\n* Silhouette Score: {0}", silhouetteScore.ToString("0.0000"));
silhouetteScores.Add(silhouetteScore);
Console.WriteLine("\n\n##############################################################\n\n\n");
}
for (int i = 0; i < clusterNames.Count; i++)
{
Console.WriteLine("- Silhouette Score for {0}: {1}", clusterNames[i], silhouetteScores[i].ToString("0.0000"));
}
Console.WriteLine("\n\n\nDONE!!");
Console.ReadKey();
}