protected void Page_Load(object sender, EventArgs e)
{
string sql = "select height, weight, footsize from TblBodies";
SQLHelper sh = new SQLHelper();
DataTable dt = new DataTable();
DataSet ds = new DataSet();
try
{
sh.RunSQL(sql, ref ds);
if (ds.Tables[0] != null)
{
dt = ds.Tables[0];
if (dt.Rows.Count > 0)
{
double[][] rawData = new double[dt.Rows.Count][];
int i = 0;
int columns = dt.Columns.Count;
foreach (DataRow dr in dt.Rows)
{
if (i < dt.Rows.Count)
{
List <double> ls = new List <double>();
for (int j = 0; j < columns; j++)
{
ls.Add(double.Parse(dr[j].ToString()));
}
rawData[i] = ls.ToArray();
i++;
}
}
int numClusters = 2;
int[] clustering = ClassKmeans.Cluster(rawData, numClusters);
Response.Write("K=2时，分类如下：</br>");
ShowClustered(rawData, clustering, numClusters, 1);
numClusters = 3;
int[] clustering2 = ClassKmeans.Cluster(rawData, numClusters);
Response.Write("K=3时，分类如下：</br>");
ShowClustered(rawData, clustering2, numClusters, 1);
}
}
}
catch (Exception ex)
{
Response.Write(ex.Message);
}
finally
{
sh.Close();
}
}
protected void Page_Load(object sender, EventArgs e)
{
string sql = "select name, power, wisdom from TblGenerals";
SQLHelper sh = new SQLHelper();
DataTable dt = new DataTable();
DataSet ds = new DataSet();
try
{
sh.RunSQL(sql, ref ds);
if (ds.Tables[0] != null)
{
dt = ds.Tables[0];
if (dt.Rows.Count > 0)
{
double[][] rawData = new double[dt.Rows.Count][];
string[] general = new string[dt.Rows.Count];
int i = 0;
int columns = dt.Columns.Count;
foreach (DataRow dr in dt.Rows)
{
if (i < dt.Rows.Count)
{
List <double> ls = new List <double>();
for (int j = 1; j < columns; j++)
{
ls.Add(double.Parse(dr[j].ToString()));
}
rawData[i] = ls.ToArray();
general[i] = dr[0].ToString();
i++;
}
}
int numClusters = 2;
int[] clustering = ClassKmeans.Cluster(rawData, numClusters);
ShowClustered(general, clustering, numClusters, 1);
string output = JsonConvert.SerializeObject(rawData);
File.WriteAllText(@Server.MapPath("~/data/generalScatterJson.json"), output);
}
}
}
catch (Exception ex)
{
Response.Write(ex.Message);
}
finally
{
sh.Close();
}
}
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 = ClassKmeans.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);
}
protected void BtnCluster_Click(object sender, EventArgs e)
{
int k = int.Parse(TxtK.Text);
SQLHelper helper = new SQLHelper();
string strQuery = "SELECT id, name, military, power, wisdom, politics, charm FROM TblGenerals;";
string strCount = "SELECT COUNT(*) FROM TblGenerals;";
DataSet data = new DataSet();
ArrayList generals = new ArrayList();
try
{
helper.RunSQL(strCount, ref data);
if (data.Tables[0] != null)
{
//Response.Write(data.Tables[0].Rows[0][0]);
int count = int.Parse(data.Tables[0].Rows[0][0].ToString());
data = new DataSet();
double[][] generalProperties = new double[count][];
helper.RunSQL(strQuery, ref data);
if (data.Tables[0] != null)
{
DataTable table = data.Tables[0];
int index = 0;
foreach (DataRow row in table.Rows)
{
General general = new General(
row["name"].ToString(),
Convert.ToDouble(row["military"]),
Convert.ToDouble(row["power"]),
Convert.ToDouble(row["power"]),
Convert.ToDouble(row["politics"]));
generals.Add(general);
generalProperties[index] = general.GetData;
index++;
}
int[] clustering = ClassKmeans.Cluster(generalProperties, k);
ShowClustered(generals, clustering, k, 1);
}
}
}
catch (Exception exception)
{
Response.Write(exception.Message);
throw;
}
}
