private void Generate(uint stepCount, double probability, uint edges)
{
GenerateInitialGraph(probability);
container = initialcontainer;
while (stepCount > 0)
{
double[] probabilyArray = container.CountProbabilities();
container.AddVertex();
container.RefreshNeighbourships(MakeGenerationStep(probabilyArray, edges));
--stepCount;
}
}
public override Object Clone()
{
NonHierarchicContainer clone = (NonHierarchicContainer)this.MemberwiseClone();
clone.data = (BitArray[])this.data.Clone();
clone.degrees = new List <int>(this.degrees);
if (evolutionInformation != null)
{
clone.evolutionInformation = (EdgesInformation)this.evolutionInformation.Clone();
}
if (activeNodes != null)
{
clone.activeNodes = (BitArray)this.activeNodes.Clone();
}
return(clone);
}
/// <summary>
/// Clones the container.
/// </summary>
/// <returns>Cloned container.</returns>
public NonHierarchicContainer Clone()
{
NonHierarchicContainer clone = (NonHierarchicContainer)this.MemberwiseClone();
clone.neighbourship = new SortedDictionary <int, List <int> >(this.neighbourship);
foreach (var item in this.neighbourship)
{
clone.neighbourship[item.Key] = new List <int>(item.Value);
}
clone.degrees = new List <int>(this.degrees);
clone.existingEdges = new List <KeyValuePair <int, int> >(existingEdges);
clone.nonExistingEdges = new List <KeyValuePair <int, int> >(nonExistingEdges);
if (activeNodes != null)
{
clone.activeNodes = new BitArray(activeNodes);
}
return(clone);
}
public NonHierarchicContainer Clone()
{
NonHierarchicContainer clone = (NonHierarchicContainer)this.MemberwiseClone();
clone.data = new BitArray[maxSize];
for (int i = 1; i < maxSize; i++)
{
clone.data[i] = new BitArray(i, false);
}
for (int i = 0; i < size; i++)
{
for (int j = i + 1; j < size; j++)
{
if (AreConnected(i, j))
{
clone.AddConnection(i, j);
}
}
}
clone.degrees = new List <int>(this.degrees);
return(clone);
}
public BANetworkGenerator()
{
container = new NonHierarchicContainer();
initialcontainer = new NonHierarchicContainer();
}
public ConnectedComponents(NonHierarchicContainer component, Dictionary<int, int> vertexmatching, int count)
{
this.component = component;
this.vertexmatching = vertexmatching;
this.count = count;
}
public ERNetworkGenerator()
{
container = new NonHierarchicContainer();
}
protected override SortedDictionary<UInt32, Double> CalculateCycles3Trajectory()
{
// Retrieving research parameters from network. //
// TODO without parce
if(network.ResearchParameterValues == null)
throw new CoreException("Research parameters are not set.");
UInt32 stepCount = UInt32.Parse(network.ResearchParameterValues[ResearchParameter.EvolutionStepCount].ToString());
Single nu = Single.Parse(network.ResearchParameterValues[ResearchParameter.Nu].ToString());
bool permanentDistribution = Boolean.Parse(network.ResearchParameterValues[ResearchParameter.PermanentDistribution].ToString());
object v = network.ResearchParameterValues[ResearchParameter.TracingStepIncrement];
UInt16 tracingStepIncrement = ((v != null) ? UInt16.Parse(v.ToString()) : (ushort)0);
// keep initial container
NonHierarchicContainer initialContainer = container.Clone();
SortedDictionary<UInt32, double> trajectory = new SortedDictionary<UInt32, double>();
uint currentStep = 0;
uint currentTracingStep = tracingStepIncrement;
double currentCycle3Count = CalculateCycles3();
trajectory.Add(currentStep, currentCycle3Count);
NonHierarchicContainer previousContainer = new NonHierarchicContainer();
RNGCrypto rand = new RNGCrypto();
while (currentStep != stepCount)
{
previousContainer = container.Clone();
try
{
++currentStep;
long deltaCount = permanentDistribution ?
container.PermanentRandomization() :
container.NonPermanentRandomization();
double newCycle3Count = currentCycle3Count + deltaCount;
int delta = (int)(newCycle3Count - currentCycle3Count);
if (delta > 0)
{
// accept
trajectory.Add(currentStep, newCycle3Count);
currentCycle3Count = newCycle3Count;
}
else
{
double probability = Math.Exp((-nu * Math.Abs(delta)));
if (rand.NextDouble() < probability)
{
// accept
trajectory.Add(currentStep, newCycle3Count);
currentCycle3Count = newCycle3Count;
}
else
{
// reject
trajectory.Add(currentStep, currentCycle3Count);
container = previousContainer;
}
}
if (currentTracingStep == currentStep - 1)
{
container.Trace(network.ResearchName,
"Realization_" + network.NetworkID.ToString(),
"Matrix_" + currentTracingStep.ToString());
currentTracingStep += tracingStepIncrement;
}
}
catch (Exception ex)
{
container = initialContainer;
//log.Error(String.Format("Error occurred in step {0} ,Error message {1} ", currentStep, ex.InnerException));
}
}
container = initialContainer;
return trajectory;
}
public ConnectedComponents(NonHierarchicContainer component, Dictionary <int, int> vertexmatching, int count)
{
this.component = component;
this.vertexmatching = vertexmatching;
this.count = count;
}
protected override SortedDictionary<Double, Double> CalculateCycles3Trajectory()
{
// Retrieving research parameters from network. Research MUST be Evolution. //
Debug.Assert(network.ResearchParameterValues != null);
Debug.Assert(network.ResearchParameterValues.ContainsKey(ResearchParameter.EvolutionStepCount));
Debug.Assert(network.ResearchParameterValues.ContainsKey(ResearchParameter.TracingStepIncrement));
Debug.Assert(network.ResearchParameterValues.ContainsKey(ResearchParameter.Nu));
Debug.Assert(network.ResearchParameterValues.ContainsKey(ResearchParameter.PermanentDistribution));
UInt32 stepCount = Convert.ToUInt32(network.ResearchParameterValues[ResearchParameter.EvolutionStepCount]);
Double nu = Convert.ToDouble(network.ResearchParameterValues[ResearchParameter.Nu]);
bool permanentDistribution = Convert.ToBoolean(network.ResearchParameterValues[ResearchParameter.PermanentDistribution]);
object v = network.ResearchParameterValues[ResearchParameter.TracingStepIncrement];
UInt32 tracingStepIncrement = ((v != null) ? Convert.ToUInt32(v) : 0);
// keep initial container
NonHierarchicContainer initialContainer = container.Clone();
SortedDictionary<Double, Double> trajectory = new SortedDictionary<Double, Double>();
uint currentStep = 0;
uint currentTracingStep = tracingStepIncrement;
double currentCycle3Count = CalculateCycles3();
trajectory.Add(currentStep, currentCycle3Count);
NonHierarchicContainer previousContainer = new NonHierarchicContainer();
RNGCrypto rand = new RNGCrypto();
while (currentStep != stepCount)
{
previousContainer = container.Clone();
try
{
++currentStep;
long deltaCount = permanentDistribution ?
container.PermanentRandomization() :
container.NonPermanentRandomization();
double newCycle3Count = currentCycle3Count + deltaCount;
int delta = (int)(newCycle3Count - currentCycle3Count);
if (delta > 0)
{
// accept
trajectory.Add(currentStep, newCycle3Count);
currentCycle3Count = newCycle3Count;
}
else
{
double probability = Math.Exp((-nu * Math.Abs(delta)));
if (rand.NextDouble() < probability)
{
// accept
trajectory.Add(currentStep, newCycle3Count);
currentCycle3Count = newCycle3Count;
}
else
{
// reject
trajectory.Add(currentStep, currentCycle3Count);
container = previousContainer;
}
}
network.UpdateStatus(NetworkStatus.StepCompleted);
if (currentTracingStep == currentStep)
{
container.Trace(network.ResearchName,
"Realization_" + network.NetworkID.ToString(),
"Matrix_" + currentTracingStep.ToString());
currentTracingStep += tracingStepIncrement;
network.UpdateStatus(NetworkStatus.StepCompleted);
}
}
catch (SystemException)
{
container = initialContainer;
}
}
container = initialContainer;
return trajectory;
}
public ConnectedComponents GetSubGraph(IEnumerable<int> nodes)
{
ConnectedComponents comp;
NonHierarchicContainer g = new NonHierarchicContainer();
Dictionary<int, int> tempnodes = new Dictionary<int, int>();
for (int i = 0; i < nodes.Count(); i++)
{
tempnodes = AddNodesToList(nodes);
g.AddVertex();
}
foreach (var n in g.Neighbourship.Keys.ToList())
{
foreach (var neigh in container.Neighbourship[tempnodes[n]])
{
for (int i = 0; i < tempnodes.Count; i++)
{
if (tempnodes[i] == neigh)
{
g.AddConnection(n, i);
}
}
}
}
comp = new ConnectedComponents(g, tempnodes, tempnodes.Count);
return comp;
}
