private bool runSim6()
// calculate n/N where n - the number of leading eigen vector components exceeding 10% of the average component size.
{
bool symRunStop = true;
int runs = 0;
parameters.Connectivity = ExpPars.K;
parameters.K = ExpPars.K;
adjMat = null;
Matrix M;
int maxLambdaIndex = 0;
if (parameters.NetType == "BA")
{
adjMat = ResearchNetwork.barbasiAlbert(parameters.N, (int)parameters.K);
M = new Matrix(adjMat);
M.calcEigen();
}
else
{
genAdjMat(); // gen new network (this updates only the adjMat)
M = new Matrix(adjMat);
// get eigen vectors.
M.CalcEigenGeneral();
// find largest eigen vector.
for (int i = 0; i < M.LambdaR.Length; i++)
{
if (M.LambdaR[i] > M.LambdaR[maxLambdaIndex])
{
maxLambdaIndex = i;
}
}
}
// get the avg componet size.
double norm = 0;
double max = 0;
List <double> v = new List <double>(parameters.N);
for (int i = 0; i < parameters.N; i++)
{
double e;
if (parameters.NetType == "BA")
{
e = Math.Abs(M.EigenV[i][0]);
}
else
{
e = Math.Abs(M.EigenVectors[i, maxLambdaIndex]);
}
norm += Math.Pow(e, 2);
if (e > max)
{
max = e;
}
v.Add(e);
}
norm = Math.Sqrt(norm);
// get the number of components exceeding the threshold.
int numberAboveThreshold = 0;
double threshold = ExpPars.StopCodition * max / 100;
for (int i = 0; i < parameters.N; i++)
{
if (v[i] > threshold)
{
numberAboveThreshold++;
}
}
M = null;
// record result.
Results.MeanActivity = numberAboveThreshold; // use this container to store the output.
Results.Success = true;
return(true);
//throw new NotImplementedException();
}
protected void genAdjMat()
// generates the network adjMatrix.
{
adjMat = new List <double[]>();
bool useWeightedAlgorithmForDynamics = false;
if (parameters.NetType.Equals("BA")) //powerLaw - Barabási–Albert
{
ResearchNetwork barbasi = new ResearchNetwork(parameters.N, (int)parameters.K, parameters.nClustes);
adjMat = barbasi.adjMatrix;
}
else if (parameters.NetType.Equals(netTypes[3])) // Chain network.
{
bool closeLoop = parameters.K > 1; // connect tail to head if K > 1
useWeightedAlgorithmForDynamics = ResearchNetwork.ChainNet(adjMat, parameters.N, closeLoop, parameters.DirectedNetwork, parameters.SpecialPars);
}
else
{
double L_SQUARED = Math.Pow((parameters.K * parameters.bc.x) / (2 * Math.Sqrt(parameters.N)), 2.0);
double conn = parameters.Connectivity / 100d;
for (int i = 0; i < sysSize; i++)
{
double[] dist = new double[sysSize];
adjMat.Add(new double[sysSize]);
}
for (int i = 0; i < sysSize; i++)
{
//double[] dist = new double[sysSize];
//adjMat.Add(new double[sysSize]);
for (int j = i; j < sysSize; j++)
{
if (parameters.NetType.Equals("DF")) // Diffusive
{
if (i == j)
{
adjMat[i][j] = 0; continue;
}
adjMat[i][j] = Math.Pow((systemState[i][0] - systemState[j][0]), 2) +
Math.Pow((systemState[i][1] - systemState[j][1]), 2);
//dist[j] = PARAM_R / Math.Exp(dist[j] / L_SQUARED);
adjMat[i][j] = 1 / Math.Exp(adjMat[i][j] / L_SQUARED);
adjMat[j][i] = adjMat[i][j]; //symertry
}
else if (parameters.NetType.Equals("ER")) //uniform - Erdős–Rényi
{
if (i == j)
{
adjMat[i][j] = 0; continue;
}
if (rdn.NextDouble() < conn)
{
adjMat[i][j] = 1;
}
else
{
adjMat[i][j] = 0;
}
adjMat[j][i] = adjMat[i][j]; //symertry
}
}
//adjMat[i] = dist;
}
}
if (parameters.DirectedNetwork && !parameters.NetType.Equals(netTypes[3])) // randomize connection directions if not a circle net.
{
GetDirectedNetwork();
}
if (parameters.Weights && !parameters.NetType.Equals(netTypes[0])) // randomize connection directions if not a circle net.
{
ApplyRandomWeights();
}
if (!parameters.Weights && useWeightedAlgorithmForDynamics) // if special weights are used bypassing the ApplyRandomWeights() this statement makes sure the right algo is used for the dynamics
{
parameters.Weights = true;
}
if (adjMat != null)
{
ready = true;
}
}
