/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object can be used to retrieve data from input parameters and
/// to store data in output parameters.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
Mesh mesh = new Mesh();
List <double> initialValues = new List <double>();
double desiredWidth = 0.0;
double lambda = 0.0;
double nu = 0.0;
int maxCalls = 0;
int optim = 0;
double objective = 10;
if (!DA.GetData(0, ref mesh))
{
return;
}
if (!DA.GetDataList(1, initialValues))
{
return;
}
if (!DA.GetData(2, ref desiredWidth))
{
return;
}
if (!DA.GetData(3, ref lambda))
{
return;
}
if (!DA.GetData(4, ref nu))
{
return;
}
if (!DA.GetData(5, ref maxCalls))
{
return;
}
if (!DA.GetData(6, ref optim))
{
return;
}
if (!DA.GetData(7, ref objective))
{
return;
}
mesh.FaceNormals.ComputeFaceNormals();
GeodesicsFromLevelSets levelSets = new GeodesicsFromLevelSets(mesh, initialValues, desiredWidth, lambda, nu);
var optimizer = new Bobyqa(mesh.Vertices.Count, levelSets.Compute);
optimizer.MaximumFunctionCalls = maxCalls;
double[] x = initialValues.ToArray();
var func = new LevelSetOpt(levelSets);
var opt = new clsOptNelderMead(func);
//if (optim == 1) opt = new clsOptNelderMead(func);
//if (optim == 2) opt = new clsOptSimulatedAnnealing(func);
//if (optim == 3) opt = new clsFireFly(func);
opt.IsUseCriterion = false;
opt.InitialPosition = initialValues.ToArray();
opt.Iteration = maxCalls;
double finalError = 0;
bool retry = false;
do
{
double[] resultX = { };
if (optim == 0)
{
var result = optimizer.FindMinimum(x);
finalError = result.F;
resultX = result.X;
}
else if (optim > 0)
{ // Nelder mead & simulated annealing
opt.Init();
while (opt.DoIteration(100) == false)
{
var eval = opt.Result.Eval;
//my criterion
if (eval < 10)
{
break;
}
Console.WriteLine("Iter: {0} Eval: {1}", opt.IterationCount, opt.Result.Eval);
}
finalError = opt.Result.Eval;
clsUtil.DebugValue(opt);
}
//if (optim == 0) {
// String str = "Optimization ended with an error of" + finalError + "\nDo you wish to run again using this results as input?";
// if (MessageBox.Show(str, "Optimization Ended", MessageBoxButtons.YesNo, MessageBoxIcon.Stop, MessageBoxDefaultButton.Button1) == DialogResult.Yes)
// {
// x = resultX;
// retry = true;
// }
// else
// {
// retry = false;
// }
//}
} while (retry == true);
//levelSets = func.levelSets;
List <double> desiredLevels = new List <double>();
double max = 0.0;
double min = 0.0;
for (int i = 0; i < levelSets.VertexValues.Count; i++)
{
double value = levelSets.VertexValues[i];
if (i == 0)
{
max = value; min = value;
}
if (value < min)
{
min = value;
}
if (value > max)
{
max = value;
}
}
double diff = max - min;
int levelCount = (int)(diff / desiredWidth);
for (int j = 0; j <= levelCount; j++)
{
desiredLevels.Add(min + (j * desiredWidth));
}
DataTree <Line> pattern = levelSets.DrawLevelSetCurves(desiredLevels);
List <double> divergence = levelSets.ComputeDivergence(false);
List <double> divergenceNorm = levelSets.ComputeDivergence(true);
DA.SetDataTree(0, pattern);
DA.SetDataList(1, levelSets.Gradient);
DA.SetDataList(2, divergence);
DA.SetDataList(3, levelSets.VertexVoronoiArea);
DA.SetData(4, finalError);
DA.SetDataList(5, levelSets.VertexValues);
DA.SetDataList(6, divergenceNorm);
}
static void Main(string[] args)
{
//Typical use
{
//Target Function
var func = new RosenBlock();
//Set Function
var opt = new clsOptNelderMead(func);
opt.Init();
//Optimization
opt.DoIteration();
//Check Error
if (opt.IsRecentError() == true)
{
return;
}
else
{
//Get Result
clsUtil.DebugValue(opt);
}
}
//Unconstrained or Constrained
{
//Target Function
var func = new UnconstrainedOptimization();
//Set Function
var opt = new clsOptRealGASPX(func);
opt.Init();
//Optimization
opt.DoIteration();
//Check Error
if (opt.IsRecentError() == true)
{
return;
}
else
{
//Get Result
clsUtil.DebugValue(opt);
}
}
{
//Target Function
var func = new ConstrainedOptimization();
//Set Function
var opt = new clsOptRealGASPX(func);
opt.Init();
//Optimization
opt.DoIteration();
//Check Error
if (opt.IsRecentError() == true)
{
return;
}
else
{
//Get Result
clsUtil.DebugValue(opt);
}
}
//LowerBounds and UpperBounds
{
//Target Function
var func = new LibOptimization.BenchmarkFunction.clsBenchSphere(2);
//Set Function
var opt = new clsOptRealGASPX(func);
//Set opt optparameter
opt.LowerBounds = new double[] { 0.0, 0.0 };
opt.UpperBounds = new double[] { 1.0, 1.0 };
opt.Init();
//Optimization
opt.DoIteration();
//Check Error
if (opt.IsRecentError() == true)
{
return;
}
else
{
//Get Result
clsUtil.DebugValue(opt);
}
}
}