public Cluster(Node parent)
{
this.clusterPair = null;
this.items = new List<Vector>();
this.mean = null;
this.parent = parent;
}
public ClusterX(SerializationInfo info, StreamingContext context)
: base(info, context)
{
child = (Node)info.GetValue("child", typeof(Node));
covarianceMatrixMDF = (ILArray<double>)info.GetValue("covarianceMatrixMDF", typeof(ILArray<double>));
label = (double)info.GetValue("label", typeof(double));
}
// The special constructor is used to deserialize values.
public Cluster(SerializationInfo info, StreamingContext context)
{
clusterPair = (ClusterPair)info.GetValue("clusterPair", typeof(ClusterPair));
items = (List<Vector>)info.GetValue("items", typeof(List<Vector>));
mean = (Vector)info.GetValue("mean", typeof(Vector));
meanMDF = (ILArray<double>)info.GetValue("meanMDF", typeof(ILArray<double>));
parent = (Node)info.GetValue("parent", typeof(Node));
}
public ClusterY(Sample sample, Node parent)
: base(sample, parent)
{
this.dimension = Params.outputDataDimension;
this.items.Add(new Vector(sample.Y.Values.ToArray(), sample.Label, this.items.Count + 1));
this.mean = new Vector(sample.Y.Values.ToArray());
}
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);
}
public ClusterX(Sample sample, Node parent)
: base(sample, parent)
{
this.child = null;
this.dimension = Params.inputDataDimension;
items.Add(new Vector(sample.X.Values.ToArray(), sample.Label, 1));
this.mean = new Vector(sample.X.Values.ToArray());
//#warning TODO count covariance matrix
//this.covarianceMatrix = ILMath.zeros(Params.inputDataDimension, Params.inputDataDimension);
}
// The special constructor is used to deserialize values.
public Node(SerializationInfo info, StreamingContext context)
{
id = (int)info.GetValue("id", typeof(int));
parent = (Node)info.GetValue("parent", typeof(Node));
//samples = (Samples)info.GetValue("samples", typeof(Samples));
clustersX = (List<ClusterX>)info.GetValue("clustersX", typeof(List<ClusterX>));
clustersY = (List<ClusterY>)info.GetValue("clustersY", typeof(List<ClusterY>));
clusterPairs = (List<ClusterPair>)info.GetValue("clusterPairs", typeof(List<ClusterPair>));
isLeafNode = (bool)info.GetValue("isLeafNode", typeof(bool));
isPlastic = (bool)info.GetValue("isPlastic", typeof(bool));
countOfSamples = (int)info.GetValue("countOfSamples", typeof(int));
path = (string)info.GetValue("path", typeof(string));
gSOManifold = (ILArray<double>)info.GetValue("gSOManifold", typeof(ILArray<double>));
covarianceMatrixMeanMDF = (ILArray<double>)info.GetValue("covarianceMatrixMeanMDF", typeof(ILArray<double>));
covarianceMatrixMean = (ILArray<double>)info.GetValue("covarianceMatrixMean", typeof(ILArray<double>));
c = (ILArray<double>)info.GetValue("c", typeof(ILArray<double>));
children = (List<Node>)info.GetValue("children", typeof(List<Node>));
}
public void CountGSOManifold_ProvideCorrectCounting()
{
Params.inputDataDimension = 4;
Node node = new Node(0.0, 0.0, 0.0, 0.0, "");
Sample s1 = new Sample(new double[] { 1, 0, 2, 1 }, 1, 0);
Sample s2 = new Sample(new double[] { -1, 1, 0, -1 }, 1, 0);
Sample s3 = new Sample(new double[] { 2, 1, 1, 1 }, 1, 0);
node.ClustersX.Add(new ClusterX(s1, null));
node.ClustersX.Add(new ClusterX(s2, null));
node.ClustersX.Add(new ClusterX(s3, null));
List<Vector> scatterVectors = new List<Vector>();
scatterVectors.Add(new Vector(new double[] { 1, 0, 2, 1 }));
scatterVectors.Add(new Vector(new double[] { -1, 1, 0, -1 }));
scatterVectors.Add(new Vector(new double[] { 2, 1, 1, 1 }));
ILArray<double> array = node.GetManifold(scatterVectors);
}
public Node(double deltaX, double deltaY, double blx, double bly, string savePath)
{
this.clustersX = new List<ClusterX>();
this.clustersY = new List<ClusterY>();
//this.samples = new Samples();
this.clusterPairs = new List<ClusterPair>();
this.isLeafNode = true;
this.parent = null;
this.countOfSamples = 0;
this.samples = new List<Sample>();
this.children = new List<Node>(Params.q);
this.path = savePath + "root\\";
this.deltaX = deltaX;
this.deltaY = deltaY;
this.blx = Math.Min(Math.Round(blx), Params.blxMax);
this.bly = Math.Min(Math.Round(bly), Params.blyMax);
this.IsPlastic = true;
}
public void GetScatterVectors_GetCorrectVectors()
{
Params.inputDataDimension = 3;
Node node = new Node(0.0, 0.0, 0.0, 0.0, "");
Sample s1 = new Sample(new double[] { 1, 2, 3 }, 1, 0);
Sample s2 = new Sample(new double[] { 2, 3, 4 }, 1, 0);
Sample s3 = new Sample(new double[] { 3, 4, 5 }, 1, 0);
node.ClustersX.Add(new ClusterX(s1, null));
node.ClustersX.Add(new ClusterX(s2, null));
node.ClustersX.Add(new ClusterX(s3, null));
List<Vector> scatterVectors = node.GetScatterVectors();
Assert.AreEqual(scatterVectors[0].Values[0], 0);
Assert.AreEqual(scatterVectors[0].Values[1], 0);
Assert.AreEqual(scatterVectors[0].Values[2], 0);
Assert.AreEqual(scatterVectors[1].Values[0], 1);
Assert.AreEqual(scatterVectors[1].Values[1], 1);
Assert.AreEqual(scatterVectors[1].Values[2], 1);
}
// The special constructor is used to deserialize values.
public Tree(SerializationInfo info, StreamingContext context)
{
// Reset the property value using the GetValue method.
root = (Node)info.GetValue("root", typeof(Node));
isEmpty = (bool)info.GetValue("isEmpty", typeof(bool));
}
public Tree()
{
root = new Node(Params.deltaX, Params.deltaY, Params.blx, Params.bly, Params.savePath);
isEmpty = true;
}
// The special constructor is used to deserialize values.
public ClusterPair(SerializationInfo info, StreamingContext context)
{
clusterX = (ClusterX)info.GetValue("clusterX", typeof(ClusterX));
clusterY = (ClusterY)info.GetValue("clusterY", typeof(ClusterY));
correspondChild = (Node)info.GetValue("correspondChild", typeof(Node));
}
public void SetParentsToXCluster(Node parent)
{
this.clusterX.Parent = parent;
}
public Cluster(Sample sample, Node parent)
{
this.items = new List<Vector>();
this.parent = parent;
}
private void Swap_Modified()
{
if (!Params.StoreSamples)
{
throw new InvalidOperationException("Unable to swap modified because samples are not stored in memory.");
}
this.IsLeafNode = false;
for (int i = 0; i < Params.q; i++)
{
// create node + set save path
Node node = new Node(this, this.path + "node" + (children.Count + 1).ToString() + @"\", this.deltaX * Params.deltaMultiplyReduction, this.deltaY * Params.deltaMultiplyReduction, this.blx * Params.blxMultiplyIncrease, this.bly * Params.blyMultiplyIncrease);
List<ClusterPair> clPairs = this.clusterPairs.Where(cp => cp.CurrentCenter == i).ToList();
// set id of node
node.Id = this.children.Count + 1;
List<Sample> sampleForNewNode = new List<Sample>();
foreach (var item in clPairs)
{
// set reference to ClusterPair, which node correspond to this ClusterPair
item.CorrespondChild = node;
sampleForNewNode.AddRange(item.Samples);
}
foreach (var item in sampleForNewNode)
{
node.UpdateNode_ForSwapping(item);
}
// set parent to clusters X
node.SetParentToXClusters();
// count covariance patrix mean for probability counting
// node.CountCovarianceMatrixMean();
// set plastic/non plastic
this.UpdatePlasticityOfParents(node);
// add children
this.children.Add(node);
}
}
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);
}
public void GetNearestClusterPairX_GetCorrectClusterPair()
{
Node node = new Node(0.0, 0.0, 0.0, 0.0, "");
Sample s1 = new Sample(new double[] { 1, 2, 3 }, 1, 0);
Sample s2 = new Sample(new double[] { 1, 2, 3 }, 1, 0);
Sample s3 = new Sample(new double[] { 1, 2, 3 }, 1, 0);
node.ClustersX.Add(new ClusterX(s1, null));
}
public ClusterY(Node parent)
: base(parent)
{
this.dimension = Params.outputDataDimension;
}
public Node(Node parent, string path, double deltaX, double deltaY, double blx, double bly)
{
this.parent = parent;
clustersX = new List<ClusterX>();
clustersY = new List<ClusterY>();
this.samples = new List<Sample>();
clusterPairs = new List<ClusterPair>();
isLeafNode = true;
countOfSamples = 0;
this.children = new List<Node>(Params.q);
this.path = path;
this.deltaX = Math.Max(deltaX, Params.deltaXMin);
this.deltaY = Math.Max(deltaY, Params.deltaYMin);
this.blx = Math.Min(Math.Round(blx), Params.blxMax);
this.bly = Math.Min(Math.Round(bly), Params.blyMax);
this.IsPlastic = true;
}
private void UpdatePlasticityOfParents(Node node)
{
//Node n = node;
//for (int i = 0; i <= Params.l; i++)
//{
// if (n.parent != null)
// {
// n = n.parent;
// if (i == Params.l)
// {
// n.IsPlastic = false;
// }
// }
// else
// {
// return;
// }
//}
Node n = node;
int distanceFromLeaf = 0;
while (node != null)
{
if (node.Parent != null)
{
node = node.Parent;
}
else
{
node = null;
}
distanceFromLeaf++;
if (node != null && distanceFromLeaf > Params.l)
{
node.IsPlastic = false;
}
}
}
public ClusterX(Node parent)
: base(parent)
{
this.child = null;
}
/// <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);
}
private void Swap()
{
this.IsLeafNode = false;
for (int i = 0; i < Params.q; i++)
{
// create node + set save path
Node node = new Node(this, this.path + "node" + (children.Count + 1).ToString() + @"\", this.deltaX * Params.deltaMultiplyReduction, this.deltaY * Params.deltaMultiplyReduction, this.blx * Params.blxMultiplyIncrease, this.bly * Params.blyMultiplyIncrease);
List<ClusterPair> clPairs = this.clusterPairs.Where(cp => cp.CurrentCenter == i).ToList();
// set id of node
node.Id = this.children.Count + 1;
foreach (var item in clPairs)
{
ClusterPair clusterPair = item.GetClone();
node.AddClusterPair(clusterPair);
// set reference to ClusterPair, which node correspond to this ClusterPair
item.CorrespondChild = node;
if (node == null)
{
throw new InvalidOperationException("Node cant be null");
}
}
// set parent to clusters X
node.SetParentToXClusters();
// count covariance patrix mean for probability counting
// node.CountCovarianceMatrixMean();
// set plastic/non plastic
this.UpdatePlasticityOfParents(node);
// add children
this.children.Add(node);
}
}
public void GetVarianceOfVectors()
{
Node node = new Node(0.0, 0.0, 0.0, 0.0, string.Empty);
List<Vector> vectors = new List<Vector>();
vectors.Add(new Vector(new double[] { 9, 2, 7 }, new double[] { 9, 2, 7 }));
vectors.Add(new Vector(new double[] { 3, 3, 10 }, new double[] { 3, 3, 10 }));
vectors.Add(new Vector(new double[] { 6, 4, 25 }, new double[] { 6, 4, 25 }));
node.CountMeanMDF(vectors);
node.CountVarianceMDF(vectors);
Assert.AreEqual(node.VarianceMDF.ToArray()[0], 9);
Assert.AreEqual(node.VarianceMDF.ToArray()[1], 1);
Assert.AreEqual(node.VarianceMDF.ToArray()[2], 93);
}
public void GetCFromClustersX_GetCorrectC()
{
Params.inputDataDimension = 3;
Node node = new Node(0.0, 0.0, 0.0, 0.0, "");
Sample s1 = new Sample(new double[] { 1, 2, 3 }, 1, 0);
Sample s2 = new Sample(new double[] { 2, 3, 4 }, 1, 0);
Sample s3 = new Sample(new double[] { 3, 4, 5 }, 1, 0);
node.ClustersX.Add(new ClusterX(s1, null));
node.ClustersX.Add(new ClusterX(s2, null));
node.ClustersX.Add(new ClusterX(s3, null));
Vector C = node.GetCFromClustersX();
Assert.AreEqual(C.Values[0], 2.0);
Assert.AreEqual(C.Values[1], 3.0);
Assert.AreEqual(C.Values[2], 4.0);
}
