public void CountC_CountCCorrect()
{
Node node = new Node(0, 0, 0.0, 0.0, "");
Params.inputDataDimension = 3;
node.CountOfSamples = 6;
// cluster 1
ClusterX newClusterX1 = new ClusterX(node);
newClusterX1.Items.Add(new Vector(0, 0));
newClusterX1.Items.Add(new Vector(0, 0));
newClusterX1.Items.Add(new Vector(0, 0));
newClusterX1.Mean = new Vector(new double[] { 1, 2, 3 });
node.ClustersX.Add(newClusterX1);
ClusterPair clusterPair1 = new ClusterPair();
clusterPair1.X = newClusterX1;
newClusterX1.SetClusterPair(clusterPair1);
node.ClusterPairs.Add(clusterPair1);
// cluster 2
ClusterX newClusterX2 = new ClusterX(node);
newClusterX2.Items.Add(new Vector(0, 0));
newClusterX2.Items.Add(new Vector(0, 0));
newClusterX2.Mean = new Vector(new double[] { 3, 3, 4 });
node.ClustersX.Add(newClusterX2);
ClusterPair clusterPair2 = new ClusterPair();
clusterPair2.X = newClusterX2;
newClusterX2.SetClusterPair(clusterPair2);
node.ClusterPairs.Add(clusterPair2);
// cluster 3
ClusterX newClusterX3 = new ClusterX(node);
newClusterX3.Items.Add(new Vector(0, 0));
newClusterX3.Mean = new Vector(new double[] { 9, 6, 7 });
node.ClustersX.Add(newClusterX3);
ClusterPair clusterPair3 = new ClusterPair();
clusterPair3.X = newClusterX3;
newClusterX3.SetClusterPair(clusterPair3);
node.ClusterPairs.Add(clusterPair3);
Vector mean = node.GetCFromClustersX();
Assert.AreEqual(mean.Values[0], 3);
Assert.AreEqual(mean.Values[1], 3);
Assert.AreEqual(mean.Values[2], 1);
}
/// <summary>
/// Create new clusers X and Y and their cluster pair
/// </summary>
/// <param name="sample">new sample</param>
private void CreateNewClusters(Sample sample, Node parent)
{
ClusterX newClusterX = new ClusterX(sample, parent);
this.clustersX.Add(newClusterX);
ClusterY newClusterY = new ClusterY(sample, parent);
this.clustersY.Add(newClusterY);
ClusterPair clusterPair = new ClusterPair(newClusterX, newClusterY, sample);
newClusterX.SetClusterPair(clusterPair);
newClusterY.SetClusterPair(clusterPair);
clusterPair.Id = clusterPairs.Count;
clusterPair.Samples.Add(sample);
this.clusterPairs.Add(clusterPair);
}
public void CountMeanOfNode_CountCorrectMean()
{
Params.inputDataDimension = 3;
Node node = new Node(1, 1, 0.0, 0.0, "");
// cluster 1
ClusterX newClusterX1 = new ClusterX(node);
newClusterX1.Items.Add(new Vector(new double[] { 1, 2, 3 }, new double[] { 1, -2, 3 }));
newClusterX1.Items.Add(new Vector(new double[] { 2, 3, 4 }, new double[] { 2, 3, 4 }));
newClusterX1.Items.Add(new Vector(new double[] { 3, 4, 5 }, new double[] { 3, 4, 5 }));
newClusterX1.Mean = new Vector(new double[] { 1, 2, 3 });
node.ClustersX.Add(newClusterX1);
ClusterPair clusterPair1 = new ClusterPair();
clusterPair1.X = newClusterX1;
newClusterX1.SetClusterPair(clusterPair1);
node.ClusterPairs.Add(clusterPair1);
node.CountMeanAndVarianceMDF();
node.CountOfSamples = 4;
node.UpdateMeanAndVarianceMdf(new Vector(new double[] { 4, 5, 6 }, new double[] { 4, 5, 6 }));
node.CountOfSamples = 5;
node.UpdateMeanAndVarianceMdf(new Vector(new double[] { 5, 6, -7 }, new double[] { 5, 6, -7 }));
Assert.AreEqual(node.VarianceMDF[0], 2.5);
Assert.AreEqual(node.VarianceMDF[1], 9.7);
Assert.AreEqual(node.VarianceMDF[2], 27.7);
}
