public void TestCenter()
{
var cluster = new Cluster(3);
double[] ob1 = { 2.0, 10.0, 100.0 };
double[] ob2 = { 4.0, 20.0, 200.0 };
double[] ob3 = { 6.0, 30.0, 300.0 };
cluster.Observations.Add(new BasicData(ob1));
cluster.Observations.Add(new BasicData(ob2));
cluster.Observations.Add(new BasicData(ob3));
Assert.AreEqual(3, cluster.Observations.Count);
cluster.CalculateCenter();
Assert.AreEqual(4.0, cluster.Center[0], 0.00001);
Assert.AreEqual(20.0, cluster.Center[1], 0.00001);
Assert.AreEqual(200.0, cluster.Center[2], 0.00001);
}
/// <summary>
/// Init the observations to random clusters. The Forgy method randomly chooses k observations from the
/// data set and uses these as the initial means.
/// </summary>
/// <param name="theObservations">The observations to cluster.</param>
public void InitForgy(IList<BasicData> theObservations)
{
int dimensions = FindDimensions(theObservations);
_clusters.Clear();
var usedObservations = new HashSet<int>();
for (int i = 0; i < _k; i++)
{
var cluster = new Cluster(dimensions);
_clusters.Add(cluster);
int observationIndex = -1;
while (observationIndex == -1)
{
observationIndex = _randomGeneration.NextInt(theObservations.Count);
if (usedObservations.Contains(observationIndex))
{
observationIndex = -1;
}
}
double[] observation = theObservations[observationIndex].Input;
Array.Copy(observation, 0, cluster.Center, 0, dimensions);
usedObservations.Add(observationIndex);
}
// assign all observations to a cluster
foreach (BasicData observation in theObservations)
{
Cluster cluster = FindNearestCluster(observation.Input);
cluster.Observations.Add(observation);
}
// calculate initial centers
UpdateStep();
}
public void TestDimensions()
{
var cluster = new Cluster(3);
Assert.AreEqual(true, cluster.ToString().Length > 0);
Assert.AreEqual(3, cluster.Dimensions);
}
