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);
}
/// <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);
}
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");
}
/// <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);
}
