// *** IOptimizer interface implementation ***
public ArrayList <double> Optimize(double[] initParamVec, IEval eval)
{
Utils.ThrowException(initParamVec == null ? new ArgumentNullException("initParamVec") : null);
Utils.ThrowException(eval == null ? new ArgumentNullException("eval") : null);
ArrayList <double> paramVec = new ArrayList <double>(initParamVec);
Utils.ThrowException(paramVec.Count == 0 ? new ArgumentValueException("initParamVec") : null);
SetInitPopul(paramVec.Count, paramVec.Count * 10); // *** make this multiplier configurable
mPopul[0] = new Pair <double, ArrayList <double> >(0, paramVec);
double bestGlobalVal = double.MinValue;
ArrayList <double> bestParamVec = null;
// evaluate initial population
for (int i = 0; i < mPopul.Count; i++)
{
Pair <double, ArrayList <double> > indiv = mPopul[i];
double localVal = eval.Eval(indiv.Second);
mPopul[i] = new Pair <double, ArrayList <double> >(localVal, indiv.Second);
if (localVal > bestGlobalVal)
{
bestGlobalVal = localVal; bestParamVec = indiv.Second;
}
}
// optimize
int numNoChangeIter = 0;
double bestVal = bestGlobalVal;
while (numNoChangeIter < mMinNoChangeIter)
{
SetNextPopul(eval, mWgtFactor, mCrossover, ref bestVal, ref bestParamVec);
if (bestVal > bestGlobalVal)
{
numNoChangeIter = 0; bestGlobalVal = bestVal;
}
else
{
numNoChangeIter++;
}
mLogger.Info("Optimize", "Iteration status:\r\n" +
"No-change iterations: {0} / {1}\r\n" +
"Current best solution vector: {2}\r\n" +
"Current best solution score: {3}", numNoChangeIter, mMinNoChangeIter, bestParamVec, bestGlobalVal);
}
return(bestParamVec);
}
private void SetNextPopul(IEval eval, double wgtFactor, double crossover, ref double bestVal, ref ArrayList <double> bestParamVec)
{
ArrayList <Pair <double, ArrayList <double> > > nextPopul = new ArrayList <Pair <double, ArrayList <double> > >(mPopul.Count);
// for each individual in the population ...
foreach (Pair <double, ArrayList <double> > indivInfo in mPopul)
{
ArrayList <double> indiv = indivInfo.Second;
ArrayList <double> rndIndiv1 = null, rndIndiv2 = null, rndIndiv3 = null;
// randomly select parents
GetRandomIndiv(indiv, ref rndIndiv1, ref rndIndiv2, ref rndIndiv3);
// create initial candidate v
ArrayList <double> v = ComputeInitialCandidate(rndIndiv1, rndIndiv2, rndIndiv3, wgtFactor);
// create final candidate u
ArrayList <double> u = indiv.Clone();
for (int i = 0; i < u.Count; i++)
{
if (mRandom.NextDouble() < crossover)
{
u[i] = v[i];
}
}
// accept or reject candidate u
double newVal = eval.Eval(u);
if (newVal > indivInfo.First)
{
nextPopul.Add(new Pair <double, ArrayList <double> >(newVal, u));
if (newVal > bestVal)
{
bestVal = newVal; bestParamVec = u;
}
}
else
{
nextPopul.Add(indivInfo);
if (indivInfo.First > bestVal)
{
bestVal = indivInfo.First; bestParamVec = indivInfo.Second;
}
}
}
mPopul = nextPopul;
}
static void OutputFormula(RuntimeData runtime, IFormula f, int level)
{
for (int i = 0; i < f.Count; i++)
{
if (f.Items[i] is IFormula)
{
Console.WriteLine("{0}{1} [{2}]{3}",
"".PadLeft(level * 2, ' '),
i + 1,
f.Items[i].GetType().Name,
f.Items[i]);
OutputFormula(runtime, f.Items[i] as IFormula, level + 1);
}
else if (f.Items[i] is Operator)
{
Operator e = (f.Items[i] as Operator);
Console.WriteLine("{0}{1}. {2}[{3}] => {4}",
"".PadLeft(level * 2, ' '),
i + 1,
f.Items[i],
f.Items[i].GetType().Name,
e.Evaluator.Name);
}
else
{
IEval e = (f.Items[i] as IEval);
var ee = e != null?e.Eval(runtime) : null;
Console.WriteLine("{0}{1}. {2}[{3}] => {4}",
"".PadLeft(level * 2, ' '),
i + 1,
f.Items[i],
f.Items[i].GetType().Name,
e != null ? ee.ToString() + "[" + ee.GetType().Name + "]" : "不明");
}
}
}
public void LtlDisjunctionTest(string input, string ltl)
{
IEval evaluation = Program.BuildAstLTL(_ltlEval, ltl);
Assert.True(evaluation.Eval(input), $"{input} should be true");
}
public void ConstEvalTest1() => Assert.Equal(7, _constExpr1.Eval());
// *** IOptimizer interface implementation ***
public ArrayList<double> Optimize(double[] initParamVec, IEval eval)
{
Utils.ThrowException(initParamVec == null ? new ArgumentNullException("initParamVec") : null);
Utils.ThrowException(eval == null ? new ArgumentNullException("eval") : null);
ArrayList<double> paramVec = new ArrayList<double>(initParamVec);
Utils.ThrowException(paramVec.Count == 0 ? new ArgumentValueException("initParamVec") : null);
SetInitPopul(paramVec.Count, paramVec.Count * 10); // *** make this multiplier configurable
mPopul[0] = new Pair<double, ArrayList<double>>(0, paramVec);
double bestGlobalVal = double.MinValue;
ArrayList<double> bestParamVec = null;
// evaluate initial population
for (int i = 0; i < mPopul.Count; i++)
{
Pair<double, ArrayList<double>> indiv = mPopul[i];
double localVal = eval.Eval(indiv.Second);
mPopul[i] = new Pair<double, ArrayList<double>>(localVal, indiv.Second);
if (localVal > bestGlobalVal) { bestGlobalVal = localVal; bestParamVec = indiv.Second; }
}
// optimize
int numNoChangeIter = 0;
double bestVal = bestGlobalVal;
while (numNoChangeIter < mMinNoChangeIter)
{
SetNextPopul(eval, mWgtFactor, mCrossover, ref bestVal, ref bestParamVec);
if (bestVal > bestGlobalVal) { numNoChangeIter = 0; bestGlobalVal = bestVal; } else { numNoChangeIter++; }
mLogger.Info("Optimize", "Iteration status:\r\n" +
"No-change iterations: {0} / {1}\r\n" +
"Current best solution vector: {2}\r\n" +
"Current best solution score: {3}", numNoChangeIter, mMinNoChangeIter, bestParamVec, bestGlobalVal);
}
return bestParamVec;
}
private void SetNextPopul(IEval eval, double wgtFactor, double crossover, ref double bestVal, ref ArrayList<double> bestParamVec)
{
ArrayList<Pair<double, ArrayList<double>>> nextPopul = new ArrayList<Pair<double, ArrayList<double>>>(mPopul.Count);
// for each individual in the population ...
foreach (Pair<double, ArrayList<double>> indivInfo in mPopul)
{
ArrayList<double> indiv = indivInfo.Second;
ArrayList<double> rndIndiv1 = null, rndIndiv2 = null, rndIndiv3 = null;
// randomly select parents
GetRandomIndiv(indiv, ref rndIndiv1, ref rndIndiv2, ref rndIndiv3);
// create initial candidate v
ArrayList<double> v = ComputeInitialCandidate(rndIndiv1, rndIndiv2, rndIndiv3, wgtFactor);
// create final candidate u
ArrayList<double> u = indiv.Clone();
for (int i = 0; i < u.Count; i++)
{
if (mRandom.NextDouble() < crossover)
{
u[i] = v[i];
}
}
// accept or reject candidate u
double newVal = eval.Eval(u);
if (newVal > indivInfo.First)
{
nextPopul.Add(new Pair<double, ArrayList<double>>(newVal, u));
if (newVal > bestVal) { bestVal = newVal; bestParamVec = u; }
}
else
{
nextPopul.Add(indivInfo);
if (indivInfo.First > bestVal) { bestVal = indivInfo.First; bestParamVec = indivInfo.Second; }
}
}
mPopul = nextPopul;
}