protected override void Run()
{
/* When the bolded "Run Search Process" is clicked from the
* pulldown window, this function is initiated. It can be removed
* so that any of the included functions can be called directly, or
* other code can fill this entire file. --> YOUR RESEARCH GOES HERE.
*/
// designGraph testGraph = designGraph.openGraphFromXml(settings.inputDirectory + "c1.gxml");]
designGraph testGraph = this.seedGraph;
/** reciprocating follower arc ***/
//double[,] desiredPath ={{1.87,8},{2.93,8.46},{2.80,8.41},
// {1.99,8.06},{0.96,7.46},{0,6.71},{-0.77,5.93},{-1.3,5.26},{-1.60,4.81},{-1.65,4.75},{-1.25,5.33},{0,6.71}};
/** bean shape **/
double[,] desiredPath = { { 125, 225 }, { 165.44, 217.76 }, { 189.57, 200.42 }, { 185.89, 178.49 }, { 158.65, 161.92 }, { 109.38, 135.30 }, { 57.997, 101.69 }, { 24.59, 82.07 }, { 0.33, 76.90 }, { -17.03, 91.46 },
{ -13.92, 129.10 }, { -0.74, 155.01 }, { 20.73, 180.91 }, { 53.78, 205.65 }, { 88.17, 219.90 } };
double startAngle = 0;
double endAngle = 2 * Math.PI;
double iOmega = 2;
MechSimulation ev = new MechSimulation();
ev.Graph = testGraph;
ev.calculate(); //new[] { 0.0, 0.0, 6.0, 0.0, 1.874099, 7.998559, 1.73, 1 });
var CPWD = new ComparePathWithDesired(seedCandidate, desiredPath, ev);
var rms = CPWD.calculate(new double[1]);
//ev.saveParameterData(settings.outputDirectory + "outputPathC1");
SearchIO.output("***Completed!*** RMS = " + rms, 0);
//
// c now needs values for the positions of the pivots unless of course it's set in the xml
// otherwise you could set by calling calc(x).
//
//double[,] path = ev.returnPath();
//
// save double as csv or something (see Albert and Dagu).
//
//
//runSearchProcessBFS();
//runSearchProcessDFS();
//runSearchProcessBESTFIRST();
}
protected override void Run()
{
Random r = new Random(); //1);
// double[,] desiredPath ={{1.87,8},{2.93,8.46},{2.80,8.41},
// {1.99,8.06},{0.96,7.46},{0,6.71},{-0.77,5.93},{-1.3,5.26},{-1.60,4.81},{-1.65,4.75},{-1.25,5.33},{0,6.71}};
double[,] desiredPath = { { 125, 225 }, { 165.44, 217.76 }, { 189.57, 200.42 }, { 185.89, 178.49 }, { 158.65, 161.92 }, { 109.38, 135.30 }, { 57.997, 101.69 }, { 24.59, 82.07 }, { 0.33, 76.90 }, { -17.03, 91.46 }, { -13.92, 129.10 }, { -0.74, 155.01 }, { 20.73, 180.91 }, { 53.78, 205.65 }, { 88.17, 219.90 } };
double startAngle = 0;
double endAngle = 2 * Math.PI;
double iOmega = 2;
double iAlpha = 0;
MechSimulation sim = new MechSimulation();
//Below is a relation for bounding box and also the first point
double bb_min, bb_max;
// bb_min = StarMath.Min(desiredPath);
// bb_max = StarMath.Max(desiredPath);
//now that min and max are obtained - we will form a bounding box using these max and min values
bb_max = 250;
bb_min = 250;
sim.Graph = seedGraph;
// designGraph testGraph = this.seedGraph;
// ev.c = new candidate(testGraph, 0);
// ev.c = this.seedGraph;
//bounding box - trying to contain the solutions within a particular box
// BoundingBox bb = new BoundingBox(sim, bb_max,bb_min);
// GrashofCriteria cc = new GrashofCriteria(sim, 0);
//adding a new objective function which can be taken by the optimization program
var pathObjFun = new ComparePathWithDesired(seedCandidate, desiredPath, sim);
//initializing the optimization program
var optMethod = new NelderMead();
//var optMethod = new GradientBasedOptimization();
optMethod.Add(new PowellMethod());
optMethod.Add(new DSCPowell(0.00001, .5, 1000));
// optMethod.Add(new GoldenSection(0.001,300));
optMethod.Add(new ArithmeticMean(0.001, 0.1, 300));
//adding simulation
optMethod.Add(sim);
//adding objective function to this optimization routine
optMethod.Add(pathObjFun);
//we are removing this since we do not have a merit function defined
optMethod.Add(new squaredExteriorPenalty(optMethod, 1.0));
// optMethod.Add(bb);
// optMethod.Add(cc);
// convergence
optMethod.Add(new MaxIterationsConvergence(100));
// optMethod.Add(new DeltaXConvergence(0.01));
optMethod.Add(new ToKnownBestFConvergence(0.0, 0.1));
optMethod.Add(new MaxSpanInPopulationConvergence(0.01));
var n = 6;
var dsd = new DesignSpaceDescription();
var minX = StarMath.Min(StarMath.GetColumn(0, desiredPath));
var maxX = StarMath.Max(StarMath.GetColumn(0, desiredPath));
var minY = StarMath.Min(StarMath.GetColumn(1, desiredPath));
var maxY = StarMath.Max(StarMath.GetColumn(1, desiredPath));
var delta = maxX - minX;
minX -= delta;
maxX += delta;
delta = maxY - minY;
minY -= delta;
maxY += delta;
for (int i = 0; i < n; i++)
{
if (i % 2 == 0)
{
dsd.Add(new VariableDescriptor(minX, maxX));
}
else
{
dsd.Add(new VariableDescriptor(minY, maxY));
}
}
// dsd.Add(new VariableDescriptor(0,300));
var LHC = new LatinHyperCube(dsd, VariablesInScope.BothDiscreteAndReal);
var initPoints = LHC.GenerateCandidates(null, 100);
//for each initPoints - generate the fstar value
//generating random x,y values
//double[] x0 = new double[8];
//for (int i = 0; i < x0.GetLength(0); i++) //since I am going to assign ground pivots as they are
// x0[i] = 100*r.NextDouble();
//sim.calculate(x0);
// double[] xStar;
// double fStar = optMethod.Run(out xStar, x0);
//// double fStar = optMethod.Run(out xStar, 8);
double[] fStar1 = new double[initPoints.Count];
List <double[]> xStar1 = new List <double[]>();
for (int i = 0; i < fStar1.GetLength(0); i++)
{
double[] x0 = new double[n];
x0 = initPoints[i];
double[] xStar;
double fStar = optMethod.Run(out xStar, x0);
fStar1[i] = fStar;
xStar1.Add(xStar);
SearchIO.output("LHC i: " + i);
}
int xstarindex;
SearchIO.output("fStar Min=" + StarMath.Min(fStar1, out xstarindex), 0);
SearchIO.output("Xstar Values:" + xStar1[xstarindex]);
SearchIO.output("***Converged by" + optMethod.ConvergenceDeclaredByTypeString, 0);
SearchIO.output("Rerunning with new x values", 0);
// var optMethod1 = new GradientBasedOptimization();
// optMethod1.Add(new FletcherReevesDirection());
// // optMethod1.Add(new ArithmeticMean(0.001, 0.1, 300));
// optMethod1.Add(new GoldenSection(0.001, 300));
// optMethod1.Add(sim);
// optMethod1.Add(pathObjFun);
// optMethod1.Add(new squaredExteriorPenalty(optMethod, 1.0));
//// optMethod1.Add(new MaxIterationsConvergence(100));
// optMethod1.Add(new ToKnownBestFConvergence(0.0, 0.1));
// // optMethod.Add(new MaxSpanInPopulationConvergence(0.01))
// double[] xStar2;
// double fStar2 = optMethod1.Run(out xStar2, xStar1[xstarindex]);
// SearchIO.output("New Fstar = " + fStar2, 0);
//double xstarmin, xstarmax;
//xstarmax = StarMath.Max(xStar1[xstarindex]);
//xstarmin = StarMath.Min(xStar1[xstarindex]);
//var dsd1 = new DesignSpaceDescription();
//dsd1.Add(new VariableDescriptor(xstarmin, xstarmax));
//var LHC1 = new LatinHyperCube(dsd1, VariablesInScope.BothDiscreteAndReal);
//var initPoints1 = LHC.GenerateCandidates(null, 100);
//double[] fstar1 = new double[initPoints1.Count];
//List<double[]> xstar_second = new List<double[]>();
//for (int i = 0; i < fstar1.GetLength(0); i++)
//{
// double[] x0 = new double[n];
// x0 = initPoints[i];
// double[] xStar;
// double fStar = optMethod.Run(out xStar, x0);
// fstar1[i] = fStar;
// xstar_second.Add(xStar);
// SearchIO.output("LHC i: " + i);
//}
//SearchIO.output("New fStar = " + StarMath.Min(fstar1), 0);
//SearchIO.output("***Converged by" + optMethod.ConvergenceDeclaredByTypeString, 0);
}