public async void CACO_FitnessEvaluation(int[] token)
{
List <Task> lTasks = new List <Task>();
int remainTasks = poolSize;
for (int j = 0; j < disQualifyCEAry.Length; j++)
{
Console.Write("{0} ", j);
if (disQualifyCEAry[j] == 1)
{
// Test inputs Generation
// Fitness Evaluation
for (int k = 0; k < poolSize; k++)
{
//Console.WriteLine("{0}'th genotype fitness assement", k);
CACO3 caco = cePool[j].ElementAt(k).Key;
//caco.GetEstSizeAndTestSizeOfCEx(testSizeOfCEx);
//cePool[j][caco][0] = caco.CalEstFitness(enVar);
//cePool[j][caco][1] = caco.CalTrueFitness(enVar);
lTasks.Add(Task.Run(() =>
{
caco.GetEstSizeAndTestSizeOfCEx(testSizeOfCEx[j]);
cePool[j][caco][0] = caco.CalEstFitness(enVar);
//cePool[j][caco][1] = caco.CalTrueFitness(enVar);
// Console.WriteLine("{0}'th finished assement", k);
}));
if (lTasks.Count == 6 || (poolSize - k - 1 < 6))
{
for (int i = 0; i < lTasks.Count; i++)
{
await lTasks[i];
}
lTasks.Clear();
}
}
}
}
token[0] = 1;
//Console.WriteLine("Fitness Evaluation Done");
}
public void CACO_Initialization()
{
maxGen = (int)enVar.pmProblem["MaxGen"];
initialtestsize = (int)enVar.pmProblem["TestSize"];
testSizeOfCEx = new int[(int)enVar.pmProblem["NumOfCE"]];
for (int i = 0; i < (int)enVar.pmProblem["NumOfCE"]; i++)
{
testSizeOfCEx[i] = initialtestsize;
}
disQualifyCEAry = new int[(int)enVar.pmProblem["NumOfCE"]];
usedgranttestinputs = new int[(int)enVar.pmProblem["NumOfCE"]];
estCExSizeAry = new double[(int)enVar.pmProblem["NumOfCE"]];
ceFitHistoryAry = new Queue <double> [(int)enVar.pmProblem["NumOfCE"]];
cePool = new CEPool[(int)enVar.pmProblem["NumOfCE"]];
for (int i = 0; i < ceFitHistoryAry.Length; i++)
{
ceFitHistoryAry[i] = new Queue <double>();
}
for (int k = 0; k < cePool.Length; k++)
{
cePool[k] = new CEPool();
for (int i = 0; i < poolSize; i++)
{
CACO3 tmpSol = new CACO3(numOfTheta, dimension, k);
tmpSol.Randomize(enVar);
double[] tmpFit = new double[] { -10, -10 };
cePool[k].Add(tmpSol, tmpFit);
}
}
for (int i = 0; i < disQualifyCEAry.Length; i++)
{
disQualifyCEAry[i] = 1;
}
}
public List <string> Print_Solution(CACO3 solution)
{
string theta = null;
string weight = null;
for (int i = 0; i < solution.thetas.Length; i++)
{
string tmp = "(";
for (int j = 0; j < dimension; j++)
{
tmp = tmp + Math.Round(solution.thetas[i][j], 2) + " ";
}
tmp.Remove(tmp.Length - 1, 1);
tmp = tmp + ")";
theta = theta + tmp + " ";
}
theta.Remove(theta.Length - 1, 1);
for (int i = 0; i < solution.weights.Length; i++)
{
string tmp = "[";
for (int j = 0; j < dimension; j++)
{
tmp = tmp + Math.Round(solution.weights[i][j], 2) + " ";
}
tmp.Remove(tmp.Length - 1, 1);
tmp = tmp + "]";
weight = weight + tmp + " ";
}
weight.Remove(weight.Length - 1, 1);
return(new List <string>()
{
theta, weight
});
}
public void CACO_Start()
{
LocalFileAccess lfa = new LocalFileAccess();
_record.Watch[_runIndex].Start();
CACO_Initialization();
int[] token = new int[1];
Console.WriteLine("In Fitness Evaluation...");
CACO_FitnessEvaluation(token); //Find unqualied ce, find out test size, evaluate those
while (token[0] == 0)
{
;
}
token[0] = 0;
Console.WriteLine("In Update Ranks, Parameters Update...");
CACO_UpdateRanks(); // Update ranks for unqualified ce
CACO_ParametersUpdate(); // Update the parameters for unqualified ce
// Update Records
List <string> generation = new List <string>()
{
gen.ToString()
};
List <string> currentCElist = new List <string>();
List <string> currentBestSolution = new List <string>();
List <double> currentFitnessList = new List <double>();
Console.WriteLine("Generation: {0}", gen);
for (int i = 0; i < cePool.Length; i++)
{
string dataOut = "CE: " + i.ToString() + ", " + "Fit: " + ceFitHistoryAry[i].Last().ToString();
currentCElist.Add(dataOut);
currentFitnessList.Add(ceFitHistoryAry[i].Last());
Console.WriteLine(dataOut);
}
// Output CurrentBest Result;
for (int i = 0; i < (int)enVar.pmProblem["NumOfCE"]; i++)
{
CACO3 currentBest = cePool[i].Keys.Where(x => x.rank == 1).First();
_record.currentBestSolution[_runIndex][i] = Print_Solution(currentBest);
}
_record.currentGen[_runIndex] = generation;
_record.currentCElist[_runIndex] = currentCElist;
_record.currentFitnessList[_runIndex] = currentFitnessList;
_record.updateIndicate[_runIndex] = true;
while (_record.updateIndicate[_runIndex] == true)
{
;
}
// Start C-ACOs
while (gen < maxGen)
{
gen = gen + 1;
if (disQualifyCEAry.Sum() == 0)
{
var a = cePool[0].First(x => x.Key.rank == 1);
break;
}
Console.WriteLine("In Solution Construction...");
CACO_SolutionConstruction();
Console.WriteLine("In Fitness Evaluations...");
CACO_FitnessEvaluation(token);
while (token[0] == 0)
{
;
}
token[0] = 0;
Console.WriteLine("In Rank update and parameter update...");
CACO_UpdateRanks();
CACO_ParametersUpdate();
Array.Clear(disQualifyCEAry, 0, disQualifyCEAry.Length);
for (int i = 0; i < (int)enVar.pmProblem["NumOfCE"]; i++)
{
disQualifyCEAry[2] = 0; //IMPORTANT: THIS IS ONLY FOR calday
disQualifyCEAry[6] = 0; //IMPORTANT: THIS IS ONLY FOR calday
if (ceFitHistoryAry[i].Last() < fitThreshold[0])
{
disQualifyCEAry[i] = 1;
}
else
{
if (usedgranttestinputs[i] == 0)
{
int worstPerformanceCEIndex = cePool.OrderBy(x => x.Values.Average(y => y[(int)enVar.evaToken]))
.Where(x => x.Keys.First().ceIndex != 2) //IMPORTANT This is only for CALDAY SUT
.Where(x => x.Keys.First().ceIndex != 6) //IMPORTANT This is only for CALDAY SUT
.First()
.Keys.First().ceIndex;
testSizeOfCEx[i] = 0;
testSizeOfCEx[worstPerformanceCEIndex] = testSizeOfCEx[worstPerformanceCEIndex] + initialtestsize;
usedgranttestinputs[i] = 1;
}
}
if (ceFitHistoryAry[i].Count > numOfRecordHistory)
{
ceFitHistoryAry[i].Dequeue();
}
}
// Update Records
generation = new List <string>()
{
gen.ToString()
};
currentCElist = new List <string>();
currentBestSolution = new List <string>();
currentFitnessList = new List <double>();
Console.WriteLine("Generation: {0}", gen);
for (int i = 0; i < cePool.Length; i++)
{
string dataOut = "CE: " + i.ToString() + ", " + "Fit: " + ceFitHistoryAry[i].Last().ToString();
currentCElist.Add(dataOut);
currentFitnessList.Add(ceFitHistoryAry[i].Last());
Console.WriteLine(dataOut);
}
// Output CurrentBest Result;
for (int i = 0; i < (int)enVar.pmProblem["NumOfCE"]; i++)
{
CACO3 currentBest = cePool[i].Keys.Where(x => x.rank == 1).First();
_record.currentBestSolution[_runIndex][i] = Print_Solution(currentBest);
}
_record.currentGen[_runIndex] = generation;
_record.currentCElist[_runIndex] = currentCElist;
_record.currentFitnessList[_runIndex] = currentFitnessList;
_record.updateIndicate[_runIndex] = true;
while (_record.updateIndicate[_runIndex] == true)
{
;
}
}
_record.Watch[_runIndex].Stop();
enVar.finishIndicate = true;
}
public void CACO_SolutionConstruction()
{
List <double[]> gaussians = new List <double[]>();
CEPool[] tmpPool = new CEPool[(int)enVar.pmProblem["NumOfCE"]];
for (int i = 0; i < tmpPool.Length; i++)
{
tmpPool[i] = new CEPool();
}
double[] lowbounds = new double[dimension];
double[] highbounds = new double[dimension];
double[] weightlowbound = new double[dimension];
double[] weighthighbound = new double[dimension];
for (int o = 0; o < dimension; o++)
{
lowbounds[o] = ((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[o].Item1;
highbounds[o] = ((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[o].Item2;
weightlowbound[o] = 0;
weighthighbound[o] = numOfTheta + 1;
}
for (int m = 0; m < cePool.Length; m++)
{
if (disQualifyCEAry[m] != 1)
{
tmpPool[m] = cePool[m];
continue;
}
var tmp = Copy.DeepCopy(cePool[m].OrderByDescending(x => x.Value[enVar.evaToken]).First());
for (int i = 0; i < poolSize; i++) // Generate a pool of individuals
{
tmpPool[m].Add(new CACO3(numOfTheta, dimension, m), new double[] { -10, -10 });
int selectedGaussIndex = -1;
// First, Select a Gaussian Component
double lowIndex = 0.0;
double sumOfWeight = cePool[m].Sum(x => x.Key.omega);
double rndNum = enVar.rnd.NextDouble() * sumOfWeight;
for (int j = 0; j < poolSize; j++)
{
if (rndNum >= lowIndex && rndNum <= lowIndex + cePool[m].ElementAt(j).Key.omega)
{
selectedGaussIndex = j;
break;
}
lowIndex = lowIndex + cePool[m].ElementAt(j).Key.omega;
}
CACO3 selectedSol = cePool[m].ElementAt(selectedGaussIndex).Key;
Vector <double> constTheta = Vector.Build.Dense(lowbounds);
Vector <double> constWeight = Vector.Build.Dense(weightlowbound);
Vector <double> remain = Vector.Build.Dense(weighthighbound);
for (int k = 0; k < numOfTheta; k++)
{
// Second, Generate new Theta at k'th Column
Vector <double> meanOfTheta = selectedSol.thetas[k];
Vector <double> varOfTheta = selectedSol.varThetas[k];
Vector <double> sampleTheta = Vector.Build.Dense(dimension);
int timeoutCounter = 0;
do
{
sampleTheta = TruncatedGaussianSampling(
meanOfTheta, varOfTheta, constTheta,
Vector.Build.Dense(lowbounds),
Vector.Build.Dense(highbounds)
);
if (timeoutCounter >= timeoutT)
{
sampleTheta = Vector.Build.Dense(highbounds);
// Console.WriteLine("TimeoutCount at ~309");
}
timeoutCounter = timeoutCounter + 1;
}while ((sampleTheta - Vector.Build.Dense(highbounds)).Any(x => x > 0));
constTheta = sampleTheta;
tmpPool[m].ElementAt(i).Key.thetas[k] = sampleTheta;
// Third, Generate new Weight at k'th Column
Vector <double> meanOfWeight = selectedSol.weights[k];
Vector <double> varOfWeight = selectedSol.varWeights[k];
Vector <double> sampleWeight = Vector.Build.Dense(dimension);
constWeight = 2 * meanOfWeight - remain;
Vector <double> realSampleWeight = null;
timeoutCounter = 0;
do
{
sampleWeight = TruncatedGaussianSampling(
meanOfWeight, varOfWeight, constWeight,
Vector.Build.Dense(weightlowbound),
Vector.Build.Dense(weighthighbound)
);
realSampleWeight = 2 * meanOfWeight - sampleWeight;
if (timeoutCounter >= timeoutT)
{
//Console.WriteLine("TimeoutCount at ~231");
realSampleWeight.Clear();
}
timeoutCounter = timeoutCounter + 1;
} while (realSampleWeight.Any(x => x < 0));
tmpPool[m].ElementAt(i).Key.weights[k] = realSampleWeight;
remain = remain - realSampleWeight;
}
// The last block, its weight is: Sum - used weights
Vector <double> sumVect = Vector.Build.Dense(dimension);
foreach (Vector <double> t in tmpPool[m].ElementAt(i).Key.weights)
{
sumVect = sumVect + t;
}
if (sumVect.Any(x => x > numOfTheta + 1))
{
int DEBUG = 1;
}
tmpPool[m].ElementAt(i).Key.weights[numOfTheta] = sumVect.SubtractFrom(numOfTheta + 1);
}
//tmpPool[m].Add(tmp.Key, tmp.Value);
}
cePool = tmpPool;
}
