/// <summary>
/// Create clustering based on the density
/// </summary>
/// <param name="numOfClusters">Needed by <see cref="Kmeans"/></param>
/// <param name="initialMeans">Needed by <see cref="Kmeans"/></param>
/// <returns>
/// Clustering as <see cref="MultidimensionalArray"/>
/// [0]: x, [1]: y, [2]: data, [3]: cellToCluster (e.g. cell 0 is in cluster 1), [4]: local cell index
/// </returns>
public MultidimensionalArray CreateClustering_Density(int numOfClusters, double[] initialMeans)
{
Console.WriteLine("CreateClustering_Density: START");
double[] data = ShockFindingExtensions.GetFinalFunctionValues(input, inputExtended.ExtractSubArrayShallow(-1, 0));
Kmeans kmeans = new Kmeans(data, numOfClusters, initialMeans);
int[] cellToCluster = kmeans.Cluster();
MultidimensionalArray clustering = MultidimensionalArray.Create(data.Length, 5);
for (int i = 0; i < input.Lengths[0]; i++)
{
clustering[i, 0] = input[i, (int)inputExtended[i, 0] - 1, 0]; // x
clustering[i, 1] = input[i, (int)inputExtended[i, 0] - 1, 1]; // y
clustering[i, 2] = data[i]; // data value
clustering[i, 3] = cellToCluster[i]; // cellToCluster (e.g. cell 0 is in cluster 1)
clustering[i, 4] = inputExtended[i, 2]; // local cell index
}
_clusterings.Add(clustering);
Console.WriteLine("CreateClustering_Density: END");
return(clustering);
}
private async Task GetAllTrails()
{
trails = new List <Trail>();
var query = ParseObject.GetQuery("Trail").WhereNotEqualTo("objectId", "toto");
IEnumerable <ParseObject> results = await query.FindAsync();
foreach (var item in results)
{
Trail trail = new Trail();
trail.Distance = item.Get <float>("distance");
trail.Duration = (int)item.Get <double>("duration");
trail.Id = item.ObjectId;
trails.Add(trail);
}
Kmeans.Init(trails);
int[] clusters = Kmeans.Cluster(Kmeans.rawData, 3);
List <ParseObject> listResult = new List <ParseObject>();
for (int i = 0; i < results.Count(); i++)
{
ParseObject pObject = results.ElementAt(i);
pObject["clusterId"] = clusters[i];
listResult.Add(pObject);
await pObject.SaveAsync();
}
GetX(listResult);
}
public override void Initialize(object target, string targetInputFile)
{
base.Initialize(target, targetInputFile);
var targetBitmap = target as Bitmap;
initColors = Kmeans.GetDominanteColors(targetBitmap, k);
}
public override void Initialize(string targetInputFile)
{
base.Initialize(targetInputFile);
var targetBitmap = base.InititializeSame(targetInputFile);
initColors = Kmeans.GetDominanteColors(targetBitmap, k);
}
public void TestNumberColors()
{
Kmeans kmeans = new Kmeans();
List <Color> colors = Palette.GetColors(5, PATH, kmeans);
Assert.AreEqual(5, colors.Count);
colors = Palette.GetColors(7, PATH, kmeans);
Assert.AreEqual(7, colors.Count);
}
protected void Page_Load(object sender, EventArgs e)
{
Response.Write("\n开始 k-means 聚类（clustering）\n");
// real data likely to come from a text file or SQL
// 需要聚类的数据是两个维度，具体指某个人的身高体重
//集合中每项描述了一个人的身高（Height: inches）和体重（Weight: kilograms）
double[][] rawData = new double[20][];
rawData[0] = new double[] { 65.0, 220.0 };
rawData[1] = new double[] { 73.0, 160.0 };
rawData[2] = new double[] { 59.0, 110.0 };
rawData[3] = new double[] { 61.0, 120.0 };
rawData[4] = new double[] { 75.0, 150.0 };
rawData[5] = new double[] { 67.0, 240.0 };
rawData[6] = new double[] { 68.0, 230.0 };
rawData[7] = new double[] { 70.0, 220.0 };
rawData[8] = new double[] { 62.0, 130.0 };
rawData[9] = new double[] { 66.0, 210.0 };
rawData[10] = new double[] { 77.0, 190.0 };
rawData[11] = new double[] { 75.0, 180.0 };
rawData[12] = new double[] { 74.0, 170.0 };
rawData[13] = new double[] { 70.0, 210.0 };
rawData[14] = new double[] { 61.0, 110.0 };
rawData[15] = new double[] { 58.0, 100.0 };
rawData[16] = new double[] { 66.0, 230.0 };
rawData[17] = new double[] { 59.0, 120.0 };
rawData[18] = new double[] { 68.0, 210.0 };
rawData[19] = new double[] { 61.0, 130.0 };
Response.Write("需要聚类的数据如下:</br>");
Response.Write("    身高    体重</br>");
Response.Write("-------------------</br>");
ShowData(rawData, 2, true);
//首先需要确定划分的簇的数量
int numClusters = 3;
Response.Write("需要聚类的目标簇数: " + numClusters + "<br>");
int[] clustering = Kmeans.Cluster(rawData, numClusters); // this is it
Response.Write("K-means 聚类结束e<br>");
//Response.Write("最终划分的结果："+"<br>");
///ShowVector(clustering, true);
Response.Write("原始数据被聚类之后的结果:<br>");
ShowClustered(rawData, clustering, numClusters, 1);
//Response.Write("\nEnd k-means clustering demo\n");
}
static void Main(string[] args)
{
var parser = new Parser();
var data = parser.Parse(',', @"./Data/WineData.csv");
var iterations = 100;
var clusters = 4;
var kMeans = new Kmeans(data, iterations, clusters);
kMeans.Run();
kMeans.Print();
Console.Read();
}
public MainForm(Knn <RawImage28x28> knn, Kmeans <RawImage28x28> kmeansTrainResult)
{
InitializeComponent();
if (knn != null)
{
Knn = knn;
SetupKnn();
}
if (kmeansTrainResult != null)
{
Kmeans = kmeansTrainResult;
SetupKmeans();
}
}
public void execute()
{
List <string> documents = FileUtils.read(String.Concat(System.AppDomain.CurrentDomain.BaseDirectory, @"..\..\Data\input.txt"));
Kmeans kmeans = new Kmeans();
List <CentroidVector> centroids = kmeans.start(documents, 3);
foreach (CentroidVector centroid in centroids)
{
foreach (DocumentVector dv in centroid.getVectorCluster())
{
Console.WriteLine(dv.getLabel());
}
Console.WriteLine("\n-------------{0}-------------------\n", centroid.getVectorCluster().Count);
Thread.Sleep(100);
}
}
static void Main(string[] args)
{
int n = 2;
List <List <float> > data = new List <List <float> >();
data.Add(new List <float> {
1f, 2f
});
data.Add(new List <float> {
1f, 4f
});
data.Add(new List <float> {
1f, 0f
});
data.Add(new List <float> {
4f, 2f
});
data.Add(new List <float> {
4f, 4f
});
data.Add(new List <float> {
4f, 0f
});
data.Add(new List <float> {
5f, 6f
});
Kmeans kc = new Kmeans();
kc.KmeansCluster(n, data);
Console.WriteLine("Here's the cluster centers");
foreach (var c in kc.clusterCenter)
{
Console.WriteLine(string.Join(",", c));
}
Console.WriteLine("Here's the cluster assignment.");
for (int i = 0; i < data.Count; ++i)
{
Console.WriteLine(string.Format("{0},{1} - {2}",
data[i][0],
data[i][1],
kc.assignedCluster[i]));
}
}
void Awake()
{
var nodes = new List <Vector3>();
var center = this.box.center;
var size = this.box.size;
for (var i = 0; i < this.nodes; i++)
{
nodes.Add(new Vector3(
Random.Range(center.x - size.x / 2f, center.x + size.x / 2f),
Random.Range(center.y - size.y / 2f, center.y + size.y / 2f),
Random.Range(center.x - size.z / 2f, center.z + size.z / 2f)
));
}
this.kmeans = new Kmeans(nodes, this.clusters, this.threshold, this.initIteration /*, this.box.center, this.box.size*/);
this.Calculating = false;
for (var i = 0; i < this.clusters; i++)
{
this.colors.Add(Random.ColorHSV());
}
}
/// <summary>
/// Create clustering based on the artificial viscosity (mean values)
/// </summary>
/// <param name="inputClustering">Input data which has to be a previous clustering</param>
/// <param name="numOfClusters">Needed by <see cref="Kmeans"/></param>
/// <param name="initialMeans">Needed by <see cref="Kmeans"/></param>
/// <returns>
/// Clustering as <see cref="MultidimensionalArray"/>
/// [0]: x, [1]: y, [2]: data, [3]: cellToCluster (e.g. cell 0 is in cluster 1), [4]: local cell index
/// </returns>
public MultidimensionalArray CreateClustering_AV(MultidimensionalArray inputClustering, int numOfClusters, double[] initialMeans)
{
Console.WriteLine("CreateClustering_AV: START");
// Get AV values
var avField = this.Session.Timesteps.Last().Fields.Where(f => f.Identification == "artificialViscosity").SingleOrDefault();
int numOfPoints = inputClustering.Lengths[0];
double[] data = new double[numOfPoints];
for (int i = 0; i < data.Length; i++)
{
data[i] = avField.GetMeanValue((int)inputClustering[i, 4]);
}
// Kmeans
Kmeans kmeans = new Kmeans(data, numOfClusters, initialMeans);
int[] cellToCluster = kmeans.Cluster();
// Store values
MultidimensionalArray clustering = MultidimensionalArray.Create(data.Length, inputClustering.Lengths[1]);
for (int i = 0; i < numOfPoints; i++)
{
clustering[i, 0] = inputClustering[i, 0]; // x
clustering[i, 1] = inputClustering[i, 1]; // y
clustering[i, 2] = data[i]; // data value
clustering[i, 3] = cellToCluster[i]; // cellToCluster (e.g. cell 0 is in cluster 1)
clustering[i, 4] = inputClustering[i, 4]; // local cell index
}
_clusterings.Add(clustering);
Console.WriteLine("CreateClustering_AV: END");
return(clustering);
}
static void Main(string[] args)
{
VectorCollection <RawImage28x28> Dataset = null;
Knn <RawImage28x28> knn = null;
KmeansTrainResult <RawImage28x28> KmeansTrainResult = null;
Kmeans <RawImage28x28> kmeans = null;
//CheckFiles
if (File.Exists(MnistPixelFilePath) == false)
{
MnistPixelFilePath = Environment.CurrentDirectory + "\\train-images.idx3-ubyte";
}
if (File.Exists(MnistLabelFilePath) == false)
{
MnistLabelFilePath = Environment.CurrentDirectory + "\\train-labels.idx1-ubyte";
}
if (File.Exists(KmeansTrainResultFilePath) == false)
{
KmeansTrainResultFilePath = Environment.CurrentDirectory + "\\KmeansTrainResult.json";
}
//LoadFiles
if (File.Exists(MnistPixelFilePath) && File.Exists(MnistLabelFilePath))
{
var MnistDataSet = MnistDataSetLoader.LoadData(MnistPixelFilePath, MnistLabelFilePath);
Dataset = new VectorCollection <RawImage28x28>(MnistDataSet);
}
if (File.Exists(KmeansTrainResultFilePath))
{
KmeansTrainResult = new KmeansTrainResult <RawImage28x28>(KmeansTrainResultFilePath);
}
if (Dataset != null)
{
var knnTrainer = new KnnTrainer <RawImage28x28>(Dataset);
knn = new Knn <RawImage28x28>(knnTrainer.Train());
if (KmeansTrainResult == null)
{
Console.WriteLine("Can't find Kmeans train data.");
KmeansTrainResult = AskKmeansTrainData(false);
}
else if (KmeansTrainResult != null)
{
Console.WriteLine("Found Kmeans train data.");
Console.WriteLine("[U]se trained data or [t]rain again? [u/t]");
if (Console.ReadLine().ToLower().Contains('t'))
{
KmeansTrainResult = null;
KmeansTrainResult = AskKmeansTrainData(true);
}
}
}
else
{
Console.WriteLine("Can't find Knn train data, ignored");
Console.WriteLine("Press any key to continue.");
Console.ReadKey();
}
if (KmeansTrainResult != null)
{
kmeans = new Kmeans <RawImage28x28>(KmeansTrainResult);
}
if (kmeans == null && knn == null)
{
Console.WriteLine("Fatal error: Can not find any trained data.");
Console.WriteLine("Press any key to exit...");
Console.ReadKey();
return;
}
MainForm form = new MainForm(knn, kmeans);
Application.EnableVisualStyles();
Application.Run(form);
}
public void Run()
{
DateTime expSt = DateTime.Now;
Console.WriteLine("========================================================================");
Console.WriteLine(" Metoda porównania: " + comparators[comparatorId].GetType());
Console.WriteLine("========================================================================");
WordsStats stats = new WordsStats(Words.ComputeWords(DataStore.Instance.GetAllNews()));
stats.Compute();
List <string> experimentTopics;
if (topics != null)
{
experimentTopics = new List <string>();
experimentTopics.AddRange(topics);
}
else
{
experimentTopics = GetRandomTopics(topicCount);
}
Group initialGroup = GroupFactory.CreateGroupWithNewsFromTopics(experimentTopics);
Console.WriteLine("========================================================================");
Console.WriteLine("Topiki w grupie początkowej:");
foreach (string topic in experimentTopics)
{
Console.WriteLine(topic + " [" + Util.topicCountInGroup(topic, initialGroup) + "]");
}
Console.WriteLine("Rozmiar grupy: " + initialGroup.Count);
;
DateTime start;
TimeSpan t1, t2;
Hierarchical hr = new Hierarchical(comparators[comparatorId], stats, maxLen);
Kmeans km = new Kmeans(comparators[comparatorId], stats, maxLen);
Console.WriteLine("========================================================================");
if (!onlyKMeans)
{
start = DateTime.Now;
List <Group> hierarchicalResult = hr.Compute(initialGroup, topicCount != 0 ? topicCount : (uint)topics.Length,
Hierarchical.Distance.AVG);
t1 = (DateTime.Now - start);
PrintStats("Hierachical", t1, hierarchicalResult, 0);
}
start = DateTime.Now;
List <Group> kMeansResult = km.Compute(initialGroup, topicCount != 0 ? topicCount : (uint)topics.Length, kMeansIterations);
t2 = (DateTime.Now - start);
PrintStats("KMeans", t2, kMeansResult, kMeansIterations);
Console.WriteLine("========================================================================");
Console.WriteLine("Czas działania: " + (DateTime.Now - expSt));
}
