public void MeansClustering()
{
// Re-create the mouse test
double[] x = new double[3];
double[] y = new double[3];
double[] s = new double[3];
x[0] = 0.25;
y[0] = 0.75;
s[0] = 0.1;
x[1] = 0.75;
y[1] = 0.75;
s[1] = 0.1;
x[2] = 0.5;
y[2] = 0.5;
s[2] = 0.2;
MultivariateSample points = new MultivariateSample(2);
Random rng = new Random(1);
NormalDistribution d = new NormalDistribution();
for (int i = 0; i < 100; i++)
{
int k = rng.Next(3);
points.Add(x[k] + s[k] * d.GetRandomValue(rng), y[k] + s[k] * d.GetRandomValue(rng));
}
MeansClusteringResult result = points.MeansClustering(3);
Assert.IsTrue(result.Count == 3);
Assert.IsTrue(result.Dimension == 2);
}
public void MeansClustering2()
{
ColumnVector[] centers = new ColumnVector[] {
new ColumnVector(0.0, 0.0, 0.0),
new ColumnVector(2.0, 0.0, 0.0),
new ColumnVector(0.0, 2.0, 0.0),
new ColumnVector(0.0, 0.0, 2.0)
};
FrameTable table = new FrameTable();
string alphabet = "abcdefghijklmnopqrstuvwxyz";
for (int j = 0; j < 3; j++)
{
table.AddColumn <double>(alphabet[j].ToString());
}
List <int> inputAssignments = new List <int>();
List <ColumnVector> inputVectors = new List <ColumnVector>();
Random rng = new Random(2);
ContinuousDistribution dist = new NormalDistribution(0.0, 1.0);
for (int i = 0; i < 100; i++)
{
int inputAssignment = rng.Next(0, centers.Length);
inputAssignments.Add(inputAssignment);
ColumnVector inputVector = centers[inputAssignment].Copy();
for (int k = 0; k < inputVector.Dimension; k++)
{
inputVector[k] += dist.GetRandomValue(rng);
}
inputVectors.Add(inputVector);
table.AddRow <double>(inputVector);
}
MeansClusteringResult result = table.AsColumns <double>().MeansClustering(centers.Length);
//MultivariateSample s = new MultivariateSample(3);
//foreach (ColumnVector v in inputVectors) { s.Add(v); }
//MeansClusteringResult result = s.MeansClustering(centers.Length);
List <int> outputAssignments = new List <int>();
for (int i = 0; i < inputVectors.Count; i++)
{
int assignment = result.Classify(inputVectors[i]);
outputAssignments.Add(assignment);
}
// Map the output centroids to the original centroids
Dictionary <int, int> map = new Dictionary <int, int>();
for (int outputIndex = 0; outputIndex < result.Count; outputIndex++)
{
ColumnVector centroid = result.Centroid(outputIndex);
int mappedInputIndex = -1;
double mappedInputDistance = Double.MaxValue;
for (int inputIndex = 0; inputIndex < centers.Length; inputIndex++)
{
double distance = (centroid - centers[inputIndex]).Norm();
if (distance < mappedInputDistance)
{
mappedInputIndex = inputIndex;
mappedInputDistance = distance;
}
}
Assert.IsTrue(mappedInputIndex >= 0);
Assert.IsTrue(mappedInputDistance < 1.0);
map.Add(outputIndex, mappedInputIndex);
}
int correctCount = 0;
for (int i = 0; i < outputAssignments.Count; i++)
{
if (map[outputAssignments[i]] == inputAssignments[i])
{
correctCount++;
}
}
Assert.IsTrue(correctCount >= 0.50 * outputAssignments.Count);
}
