private static int CompareFuzzySets(FuzzySet fSet1, FuzzySet fSet2)
{
if (fSet1.FX < fSet2.FX)
{
return(1);
}
else if (fSet1.FX == fSet2.FX)
{
return(0);
}
else
{
return(-1);
}
}
private List <FuzzySet> GetDescendants(FuzzySet parent)
{
List <FuzzySet> descendants = new List <FuzzySet>();
if (parent.LeftChild != null)
{
descendants.Add(parent.LeftChild);
descendants.AddRange(GetDescendants(parent.LeftChild));
}
if (parent.RightChild != null)
{
descendants.Add(parent.RightChild);
descendants.AddRange(GetDescendants(parent.RightChild));
}
return(descendants);
}
public FuzzySet(List <WaveletPacket> packetList, double r, FuzzySet parent = null)
{
List <int> dataLength = packetList.Select(packet => packet.Coefficients.Length).Distinct().ToList();
if (dataLength.Count != 1)
{
throw new ArgumentException("Training packet lengths must match to perform fuzzy c-means");
}
Parent = parent;
Sibling = null;
R = r;
DecompositionLevel = packetList[0].DecompositionLevel;
SubspaceIndex = packetList[0].SubspaceIndex;
// if there are any children then build the child nodes
if (packetList[0].LeftChild != null)
{
List <WaveletPacket> leftChildren = packetList.Select(packet => packet.LeftChild).ToList();
List <WaveletPacket> rightChildren = packetList.Select(packet => packet.RightChild).ToList();
LeftChild = new FuzzySet(leftChildren, r, this);
RightChild = new FuzzySet(rightChildren, r, this);
LeftChild.Sibling = RightChild;
RightChild.Sibling = LeftChild;
}
// calculate standard deviations for each feature in this subspace
List <double> standardDeviations = GetStandardDeviations(packetList);
// calculate the mean for each user (class)
Dictionary <String, List <double> > means = GetMeans(packetList);
// determine features to exclude (store index list of excluded features)
List <int> exclusionCriterion = ExcludionCriterion(packetList, means, standardDeviations);
// using list of excluded feature calculate the fuzzy c-means clustering membership allocation
FX = CalculateFuzzyMembershipCriterion(packetList, means, standardDeviations, exclusionCriterion);
}
private List <FuzzySet> GetFuzzySetList(FuzzySet parent = null)
{
if (parent == null)
{
parent = this;
}
List <FuzzySet> fsList = new List <FuzzySet>
{
parent
};
if (parent.LeftChild != null)
{
fsList.AddRange(GetFuzzySetList(parent.LeftChild));
}
if (parent.RightChild != null)
{
fsList.AddRange(GetFuzzySetList(parent.RightChild));
}
return(fsList);
}
public List <FuzzySet> GetOptimalFuzzySetList()
{
List <FuzzySet> fsList = GetFuzzySetList();
// sort fsList in descending order according to fuzzy membership criterion
fsList.Sort(CompareFuzzySets);
List <FuzzySet> optimalDecomposition = new List <FuzzySet>();
while (fsList.Count > 0)
{
// move the first subspace into the optimal decomposition
FuzzySet next = fsList.First();
optimalDecomposition.Add(next);
fsList.RemoveAt(0);
// remove all remaining ancestors or decendents of this subspace
List <FuzzySet> ancestors = next.GetAncestors(next.Parent);
List <FuzzySet> descendants = next.GetDescendants(next);
fsList.RemoveAll(fs => ancestors.Contains(fs));
fsList.RemoveAll(fs => descendants.Contains(fs));
}
return(optimalDecomposition);
}