/// <summary>
/// Initializes a new instance of the Optimizer class.
/// </summary>
public Optimizer()
{
maxBoundsArray = new List <double>();
minBoundsArray = new List <double>();
pValues = new List <double>();
pNames = new List <string>();
rr = new RoadRunner();
}
public OptimizationResult optimizeWithGeneticAlgo(string sbmlInput, AlgorithmArguments algoArgs
, Action <ArrayList> updateStatus = null)
{
hasUpdateStatusMethod = updateStatus != null;
evalOption = algoArgs.EvalOption;
try
{
isProcessActive = true;
if (rr == null)
{
rr = new RoadRunner();
}
rr.loadSBML(sbmlInput);
rr.setNumPoints(numPoints);
#region OSCillator
if (algoArgs.EvalOption == OSCILLATOR || algoArgs.EvalOption == TURNINGPOINT)
{
//Read num of Generations
numOfGenerations = algoArgs.Args.NumOfGenerations;
//Read num of Members in Population
numOfMembers = algoArgs.Args.NumOfMembers;
//Read num of Parameters in Member
numOfParameters = 0;
pNames.Clear();
pValues.Clear();
minBoundsArray.Clear();
maxBoundsArray.Clear();
foreach (var nameValueBound in algoArgs.NameValueBounds)
{
pNames.Add(nameValueBound.Item1);
pValues.Add(nameValueBound.Item2);
minBoundsArray.Add(nameValueBound.Item3);
maxBoundsArray.Add(nameValueBound.Item4);
++numOfParameters;
}
//Read num of Members to Select from Population
numOfMemSelect = algoArgs.Args.NumOfMemSelect;
//Read the value of Pc - Crossover probability
Pc = algoArgs.Args.Pc;
//Read the value of Pm - Mutation probability
Pm = algoArgs.Args.Pm;
//Read the value of TOLERANCE Level
Tolerance = algoArgs.Args.Tolerance;
//Read the value of Random_Number Seed
randomSeed = algoArgs.Args.RandomSeed;
simulationTime = algoArgs.Args.SimulationTime;
//Read the number of iterations after which you want to receive the update
nUpdate = algoArgs.Args.NUpdate;
//Try to compute the steady state using the initial parameter values.
//If steady state cannot be reached throw an exception to user asking him
//to reset the initial parameter values.
initMember = new Member {
numOfParameters = pValues.Count, pValuesArray = new List <double>(pValues)
};
try
{
compObjFnFitnessForInitMem(ref initMember);
}
catch (SBWException)
{
var ex =
new BifException(
"Unable to calculate steadystate value with the current initial parameter values.",
"Please start with different set of parameter values!!! ");
throw ex;
}
var generationArray = new[] { new Generation(), new Generation() };
//using only two generation objects
generationArray[0].allocMemberArray(algoArgs.Args.NumOfMembers);
var m = new Member();
rnd = algoArgs.Args.RandomSeed == 0
? new MT19937Generator()
: new MT19937Generator(algoArgs.Args.RandomSeed);
for (int i = 0; i < algoArgs.Args.NumOfMembers; i++)
{
m.initParamArray(pValues, minBoundsArray, maxBoundsArray);
evalObjFnFitness(ref m);
generationArray[0].setMember(i, m);
//cout << "Member: " << i << endl;
if (!isProcessActive)
{
return(new OptimizationResult
{
Iterations = 0,
Score = generationArray[0].genFitScore,
Values = generationArray[0].memArray[0].pValuesArray,
RealEigenValues = generationArray[0].memArray[0].realEigenValueArray,
ImagEigenValues = generationArray[0].memArray[0].complexEigenValueArray
});
}
//Send status update after every nUpdate simulations.
NotifyAboutNumberOfSImulations(updateStatus, i);
}
generationArray[0].sortMemberArray();
generationArray[0].setFitScore();
//update status after first generation
var updateParamValuesArray = new List <double>();
Member best = generationArray[0].getMember(0);
for (int i = 0; i < numOfParameters; i++)
{
updateParamValuesArray.Add(best.pValuesArray[i]);
}
int minRealEigenIndex;
if (hasUpdateStatusMethod)
{
try
{
var update = new ArrayList {
1, 0, generationArray[0].genFitScore, updateParamValuesArray
};
if (best.realEigenValueArray.Count > 0)
{
minRealEigenIndex = findMinRealEigenValueIndex(
best.realEigenValueArray);
update.Add(best.realEigenValueArray[minRealEigenIndex]);
update.Add(best.complexEigenValueArray[minRealEigenIndex]);
}
else
{
update.Add(0.0);
update.Add(0.0);
}
update.Add(best.realEigenValueArray);
update.Add(best.complexEigenValueArray);
updateStatus(update);
}
catch (SBWException ae)
{
ae = new BifException("Error in sbwBifGAOptimize",
"Unable to call update status!!!");
throw ae;
}
}
generationArray[1].allocMemberArray(algoArgs.Args.NumOfMembers);
if (algoArgs.Args.NumOfGenerations == 1)
{
generationArray[1] = generationArray[0];
}
int Iterations = 1;
for (int j = 1; j < algoArgs.Args.NumOfGenerations; j++)
{
for (int k = 0; k < (algoArgs.Args.NumOfMembers); k = k + 2)
{
var parent1 = selectMember(generationArray[0]);
var parent2 = selectMember(generationArray[0]);
var child1 = new Member();
var child2 = new Member();
crossOver(parent1, parent2, ref child1, ref child2);
mutate(ref child1);
evalObjFnFitness(ref child1);
mutate(ref child2);
evalObjFnFitness(ref child2);
Member bestMem1;
Member bestMem2;
selectBestTwo(parent1, parent2, child1, child2, out bestMem1, out bestMem2);
generationArray[1].setMember(k, bestMem1);
generationArray[1].setMember(k + 1, bestMem2);
if (!isProcessActive)
{
return(new OptimizationResult
{
Iterations = 0,
Score = generationArray[0].genFitScore,
Values = generationArray[0].memArray[0].pValuesArray,
RealEigenValues = generationArray[0].memArray[0].realEigenValueArray,
ImagEigenValues = generationArray[0].memArray[0].complexEigenValueArray
});
}
//Send status update after every predefined number of simulations.
NotifyAboutNumberOfSImulations(updateStatus, k);
}
generationArray[1].sortMemberArray();
generationArray[1].setFitScore();
if (generationArray[1].genFitScore > generationArray[0].genFitScore)
{
generationArray[1].setMember(0, best);
generationArray[1].genFitScore = generationArray[0].genFitScore;
}
//os << j << "\t" << generationArray[1].getGenFitScore() << "\t";
updateParamValuesArray.Clear();
best = generationArray[1].getMember(0);
for (int i = 0; i < numOfParameters; i++)
{
updateParamValuesArray.Add(best.pValuesArray[i]);
//os << generationArray[1].getMember(0).pValuesArray[i] << "\t";
}
//os << endl;
//Call update status once you finish calculating the fitness score.
if (hasUpdateStatusMethod)
{
try
{
var update = new ArrayList
{
1,
j,
generationArray[1].genFitScore,
updateParamValuesArray
};
if (best.realEigenValueArray.Count > 0)
{
minRealEigenIndex = findMinRealEigenValueIndex(best.realEigenValueArray);
update.Add(best.realEigenValueArray[minRealEigenIndex]);
update.Add(best.complexEigenValueArray[minRealEigenIndex]);
}
else
{
update.Add(0.0);
update.Add(0.0);
}
update.Add(best.realEigenValueArray);
update.Add(best.complexEigenValueArray);
updateStatus(update);
}
catch (SBWException ae)
{
ae = new BifException("Error in sbwBifGAOptimize",
"Unable to call update status!!!");
throw ae;
}
}
//Code to check if the Tolerance level has reached
var bufferArray = new List <double>();
bufferArray.Clear();
double realPart;
double complexPart;
int eigenArraySize = best.eigenValueArrayDim;
if (algoArgs.EvalOption == OSCILLATOR)
{
for (int i = 0; i < eigenArraySize; i++)
{
realPart = best.realEigenValueArray[i];
complexPart = best.complexEigenValueArray[i];
if (Math.Abs(complexPart) == 0.0) // Check if the imaginary part is eqaul to zero.
{
// If it is so then assign 10^6 to bufferArray Element.
bufferArray.Add(10E6);
}
else // Else store the absolute value of real eigen value.
{
bufferArray.Add(Math.Abs(realPart));
}
}
}
if (algoArgs.EvalOption == TURNINGPOINT)
{
for (int i = 0; i < eigenArraySize; i++)
{
realPart = best.realEigenValueArray[i];
complexPart = best.complexEigenValueArray[i];
if (Math.Abs(complexPart) > 0.0) // Check if the imaginary part is greater than zero.
{
// If it is so then assign 10^6 to bufferArray Element.
bufferArray.Add(10E6);
}
else // Else store the absolute value of real eigen value.
{
bufferArray.Add(Math.Abs(realPart));
}
}
}
if (bufferArray.Count > 0)
{
minRealEigenIndex = findMinRealEigenValueIndex(bufferArray);
if (Math.Abs(bufferArray[minRealEigenIndex]) < algoArgs.Args.Tolerance)
{
break;
}
}
generationArray[0] = generationArray[1];
++Iterations;
//Check the isProcessActive Flag here and gracefully exit the optimizer if it is false.
if (!isProcessActive)
{
break;
}
}
return(new OptimizationResult
{
Iterations = Iterations,
Score = generationArray[0].genFitScore,
Values = generationArray[0].memArray[0].pValuesArray,
RealEigenValues = generationArray[0].memArray[0].realEigenValueArray,
ImagEigenValues = generationArray[0].memArray[0].complexEigenValueArray
});
}
#endregion
}
catch (SBWException)
{
throw;
}
catch
{
throw new BifException("Problem occured in main method", " ");
}
return(null);
}
static MainForm()
{
Simulator = new RoadRunner();
SelectForm = null;
}
static MainForm()
{
Simulator = new RoadRunner();
SelectForm = null;
}
private void cmdSteadyState_Click(object sender, EventArgs e)
{
thread.QueueItem(() =>
{
if (m_sSBML != null)
{
if (m_sSBML.Length > 0)
{
try
{
if (sim == null)
{
sim = new RoadRunner();
}
sim.loadSBML(m_sSBML);
sim.simulate();
double steadyState = sim.steadyState();
if (
MessageBox.Show(
"Found steady state with sums of squares: " + steadyState +
". Do you want to use this state to be used?", "Steady state calculated",
MessageBoxButtons.YesNo, MessageBoxIcon.Question) != DialogResult.Yes)
{
return;
}
sim.simulate();
ArrayList floatingSpeciesNames = sim.getFloatingSpeciesNames();
double[] speciesConcentrations1 = sim.getFloatingSpeciesConcentrations();
ArrayList boundarySpeciesNames = sim.getBoundarySpeciesNames();
double[] speciesConcentrations2 = sim.getBoundarySpeciesConcentrations();
ArrayList parameterTupleList = sim.getAllGlobalParameterTupleList();
NOM.loadSBML(m_sSBML);
for (int index = 0; index < floatingSpeciesNames.Count; ++index)
{
NOM.setValue((string)floatingSpeciesNames[index], speciesConcentrations1[index]);
}
for (int index = 0; index < boundarySpeciesNames.Count; ++index)
{
NOM.setValue((string)boundarySpeciesNames[index], speciesConcentrations2[index]);
}
foreach (ArrayList arrayList in parameterTupleList)
{
NOM.setValue((string)arrayList[0], (double)arrayList[1]);
}
m_sSBML = NOM.getSBML();
loadSBML(m_sSBML);
return;
}
catch (Exception ex)
{
MessageBox.Show("Could not compute steady state due to: " + ex.Message,
"Steady state could not be computed ...", MessageBoxButtons.OK, MessageBoxIcon.Hand);
return;
}
}
}
MessageBox.Show("There is no model to analyze. Load a model first.", "No Model loaded",
MessageBoxButtons.OK, MessageBoxIcon.Hand);
});
}
