private void button1_Click(object sender, EventArgs e)
{
List<ClusterPoint> points = new List<ClusterPoint>
{
new ClusterPoint(new List<double>(){0, 4}),
new ClusterPoint(new List<double>(){0, 2}),
new ClusterPoint(new List<double>(){0, 0}),
new ClusterPoint(new List<double>(){1, 3}),
new ClusterPoint(new List<double>(){1, 2}),
new ClusterPoint(new List<double>(){1, 1}),
new ClusterPoint(new List<double>(){2, 2}),
new ClusterPoint(new List<double>(){3, 2}),
new ClusterPoint(new List<double>(){4, 2}),
new ClusterPoint(new List<double>(){5, 3}),
new ClusterPoint(new List<double>(){5, 2}),
new ClusterPoint(new List<double>(){5, 1}),
new ClusterPoint(new List<double>(){6, 4}),
new ClusterPoint(new List<double>(){6, 2}),
new ClusterPoint(new List<double>(){6, 0})
};
List<ClusterCentroid> centroids = new List<ClusterCentroid>
{
new ClusterCentroid(new List<double>(){1, 2}),
new ClusterCentroid(new List<double>(){6, 2})
};
CMeansAlgorithm alg = new CMeansAlgorithm(points, centroids, 2);
int iterations = alg.Run(Math.Pow(10, -5));
double[,] matrix = alg.U;
for (int j = 0; j < points.Count; j++)
{
for (int i = 0; i < centroids.Count; i++)
{
ClusterPoint p = points[j];
Console.WriteLine("{0:00} Point: ({1};{2}) ClusterIndex: {3} Value: {4:0.000}", j + 1, p.Data[0], p.Data[1], p.ClusterIndex, matrix[j, i]);
}
}
Console.WriteLine();
Console.WriteLine("Iteration count: {0}", iterations);
Console.WriteLine();
Console.WriteLine("Please press any key to exit...");
Console.ReadLine();
}
private void cToolStripMenuItem_Click(object sender, EventArgs e)
{
List<ClusterPoint> points = new List<ClusterPoint>();
List<ClusterCentroid> centroids = new List<ClusterCentroid>();
foreach (DataRow row in m_LearningSample.Rows)
{
string tag = "";
List<double> data = new List<double>();
for(int colNum = 0; colNum<m_LearningSample.Columns.Count; colNum++)
{
if (!m_Descriptions[colNum].IsNotUse)
{
data.Add(double.Parse(row.Field<string>(colNum), CultureInfo.InvariantCulture));
}
else
{
tag = row.Field<string>(colNum);
}
}
points.Add(new ClusterPoint(data, tag));
}
Random rnd = new Random();
for (int centroidNum = 0; centroidNum < 10; centroidNum++)
{
List<double> data = new List<double>();
for (int colNum = 0; colNum < m_LearningSample.Columns.Count; colNum++)
{
if (!m_Descriptions[colNum].IsNotUse)
{
data.Add(0.0/*rnd.NextDouble()*/);
}
}
centroids.Add(new ClusterCentroid(data));
}
CMeansAlgorithm alg = new CMeansAlgorithm(points, centroids, 2);
int iterations = alg.Run(Math.Pow(10, -5));
double[,] matrix = alg.U;
for (int j = 0; j < points.Count; j++)
{
double max = -1.0;
string output = string.Empty;
for (int i = 0; i < centroids.Count; i++)
{
if(max < matrix[j, i])
{
ClusterPoint p = points[j];
StringBuilder sb = new StringBuilder();
for (int k = 0; k < p.Data.Count; k++)
{
sb.AppendFormat("{0}; ", p.Data[k]);
}
max = matrix[j, i];
output = string.Format("{0:00} Point: ({1}) ClusterIndex: {2} Value: {3} Class {4}", j + 1, sb, p.ClusterIndex, matrix[j, i], p.Tag);
}
}
Console.WriteLine(output);
}
Console.WriteLine();
Console.WriteLine("Iteration count: {0}", iterations);
}