public override double[] choose(option RC, candidate cand)
{
return(null);
}
/* Here we outline what an inherited class must contain. Basically it should have
* methods for the 2 types of decisions that are made - decisions on what option
* to invoke and decisions for the variables required for the process. */
/* Given the list of options and the candidate, determine what option to invoke.
* Return the integer index of this option from the list. */
public abstract int choose(List <option> options, candidate cand);
private void openToolStripMenuItem_Click(object sender, EventArgs e)
{
string filename;
try
{
filename = getOpenFilename(Program.settings.workingDirectory);
}
catch { filename = ""; }
if (filename != "" && filename != null)
{
/* Load the file. */
XmlDocument doc = new XmlDocument();
doc.Load(filename);
/* create prefix<->namespace mappings (if any) */
XmlNamespaceManager nsMgr = new XmlNamespaceManager(doc.NameTable);
/* Query the document */
if (doc.SelectNodes("/grammarRule", nsMgr).Count > 0)
{
grammarRule rule = grammarRule.openRuleFromXml(filename);
grammarDisplays.Add(new grammarRuleDisplay(rule, Path.GetFileName(filename)));
grammarDisplays[grammarChildCount].Show();
}
else if (doc.SelectNodes("/ruleSet", nsMgr).Count > 0)
{
ruleSet rs = ruleSet.openRuleSetFromXml(filename, Program.settings.defaultGenSteps);
ruleSetDisplays.Add(new ruleSetDisplay(rs, Path.GetFileName(filename)));
ruleSetDisplays[ruleSetChildCount].Show();
}
else if (doc.SelectNodes("/designGraph", nsMgr).Count > 0)
{
designGraph graph = designGraph.openGraphFromXml(filename);
addAndShowGraphDisplay(graph, Path.GetFileName(filename));
}
else if (doc.SelectNodes("/candidate", nsMgr).Count > 0)
{
string tempString = "";
candidate c = candidate.openCandidateFromXml(filename);
SearchIO.output("The candidate found in " + filename, 0);
if (c.performanceParams.Count > 0)
{
tempString = "has the following performance parameters";
foreach (double a in c.performanceParams)
{
tempString += ": " + a.ToString();
}
SearchIO.output(tempString, 0);
}
if (c.age > 0)
{
SearchIO.output("The candidate has existed for " + c.age.ToString() + " iterations.", 0);
}
SearchIO.output("It's generation ended in RuleSet #" + c.activeRuleSetIndex.ToString(), 0);
tempString = "Generation terminated with";
foreach (GenerationStatuses a in c.GenerationStatus)
{
tempString += ": " + a.ToString();
}
SearchIO.output(tempString, 0);
addAndShowGraphDisplay(c.graph, "Candidate in " + Path.GetFileName(filename));
}
else
{
MessageBox.Show("The XML files that you have attempted to open contains an unknown" +
"type (not designGraph, grammarRule, ruleSet, or candidate).", "XML Serialization Error",
MessageBoxButtons.OK, MessageBoxIcon.Information);
}
}
}
public override int choose(List <option> options, candidate cand)
{
return(rnd.Next(options.Count));
}
/// <summary>
/// Recognizes the choose apply cycle. Here is the main Recognize, Choose, and
/// Apply Generation Cycle. It accepts the host candidate (not graph), the index
/// of what ruleSet to invoke, and an array of size equal to the number of ruleSets.
/// At the end of the process, it returns the updated candidate. The three step
/// process may, however exit at any of five places in the loop, these are described below.
/// 1. the ruleSet invoked may not have any calls left. This will cause the GenerationStatus
/// to be CycleLimit, and the process will execute what is stored in the 3rd position of
/// generationSteps, ruleSet->nextGenerationStep[2], either Stop, Loop, GoToPrevious(ruleSet),
/// GoToNext(ruleSet), or GoToRuleSet#
/// 2. the choice operation has sent a STOP message, or more precisely a negative # or
/// a number greater than the list of option. This results in a GenerationStatus of Choice
/// and the execution of ruleSet->nextGenerationStep[1] (any of the options stated above).
/// 3. there are no rules recognized for the graph. This results in a GenerationStatus of
/// NoRules and the execution of ruleSet->nextGenerationStep[3] (any of the options above).
/// 4. A trigger rule has been applied. This results in a GenerationStatus of TriggerRule
/// and the execution of ruleSet->nextGenerationStep[4] (any of the options stated above).
/// 5. the recognize, choose, and apply cycle performed as intended - no abnormal activites.
/// This results in a GenerationStatus of Normal and the execution of
/// ruleSet->nextGenerationStep[0] (any of the options stated above).*/
/// </summary>
/// <param name = "host">The host.</param>
/// <param name = "ruleSetIndex">Index of the rule set.</param>
/// <param name = "numOfCallsLeft">The num of calls left.</param>
protected void RecognizeChooseApplyCycle(candidate host, int ruleSetIndex, int[] numOfCallsLeft)
{
while ((ruleSetIndex >= 0) && (ruleSetIndex < NumOfRuleSets))
{
host.activeRuleSetIndex = ruleSetIndex;
SearchIO.output("Active Rule Set = " + ruleSetIndex, 4);
#region terminate immediately if there are no cycles left
if (numOfCallsLeft[ruleSetIndex] == 0)
{
/* it is possible that another ruleset intends to invoke this one, but your
* number of calls for this set has hit its limit. */
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.CycleLimit;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.CycleLimit);
SearchIO.output("cycle limit reached", 4);
continue;
}
#endregion
#region ***** RECOGNIZE *****
SearchIO.output("begin RCA loop for RuleSet #" + ruleSetIndex, 4);
var options = Rulesets[ruleSetIndex].recognize(host.graph, InParallel);
SearchIO.output("There are " + options.Count + " rule choices.", 4);
if (options.Count == 0)
{
/* There are no rules to recognize, exit here. */
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.NoRules;
var newRSIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.NoRules);
if (newRSIndex == ruleSetIndex)
{
throw new Exception("Same ruleset chosen when no rules are recognized.");
}
ruleSetIndex = newRSIndex;
continue;
}
if (SearchIO.GetTerminateRequest(Thread.CurrentThread.ManagedThreadId))
{
return;
}
#endregion
#region ***** CHOOSE *****
if (Rulesets[ruleSetIndex].choiceMethod == choiceMethods.Automatic)
{
choice = new[] { 0 }
}
;
else
{
choice = choose(options, host);
}
if (choice[0] == -1)
{
host.undoLastRule();
if (Display)
{
SearchIO.addAndShowGraphWindow(host.graph.copy(),
"Revert to after calling " + host.numRulesCalled + " rules");
}
continue;
}
if ((choice == null) || (choice[0] < 0) || (choice[0] >= options.Count))
{
SearchIO.output("Choice = #" + IntCollectionConverter.Convert(choice), 4);
/* the overloaded choice function may want to communicate to the loop that it
* should finish the process. */
SearchIO.output("Choice received a STOP request", 4);
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.Choice;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.Choice);
continue;
}
if (SearchIO.GetTerminateRequest(Thread.CurrentThread.ManagedThreadId))
{
return;
}
#endregion
#region ***** APPLY *****
host.saveCurrent();
foreach (var c in choice)
{
options[c].apply(host.graph, choose(options[c], host));
host.addToRecipe(options[c]);
SearchIO.output("Rule sucessfully applied", 4);
}
if (Display && Rulesets[ruleSetIndex].choiceMethod == choiceMethods.Design)
{
SearchIO.addAndShowGraphWindow(host.graph.copy(),
"After calling " + host.numRulesCalled + " rules");
}
if (SearchIO.GetTerminateRequest(Thread.CurrentThread.ManagedThreadId))
{
return;
}
#endregion
#region Check to see if loop is done
/* First thing we do is reduce the number of calls left. Note that if you start with
* a negative number, the process will continue to make it more negative - mimicking
* no cycle limit. It is safer to use the globalvar, maxRulesToApply though.*/
if (this is LindenmayerChooseRCA)
{
numOfCallsLeft[ruleSetIndex]--;
}
else
{
numOfCallsLeft[ruleSetIndex] = numOfCallsLeft[ruleSetIndex] - choice.GetLength(0);
}
if (choice.Any(c => (options[c].ruleNumber == Rulesets[ruleSetIndex].TriggerRuleNum)))
{
/* your ruleset loops until a trigger rule and the trigger rule was just called. */
SearchIO.output("The trigger rule has been chosen.", 4);
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.TriggerRule;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.TriggerRule);
}
else
{
/* Normal operation */
SearchIO.output("RCA loop executed normally.", 4);
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.Normal;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.Normal);
}
#endregion
}
}
//+ виводить кількість однакових елементів серед 2 кандидатів
public int findr(candidate A)
{
int r = 0; // кількість однакових ел. в кандидатах.
for (int i = 0; i < this.Itemset.Count; i++)
{
for (int j = 0; j < A.Itemset.Count; j++)
{
if (this.Itemset[i] == A.Itemset[j])
{
r++;
}
}
}
return r;
}
public async Task <HttpResponseMessage> Edit()
{
if (!Request.Content.IsMimeMultipartContent())
{
throw new HttpResponseException(HttpStatusCode.UnsupportedMediaType);
}
var root = HttpContext.Current.Server.MapPath("~/Assets/Uploads");
Directory.CreateDirectory(root);
var provider = new CustomMultipartFormDataStreamProvider(root);
var result = await Request.Content.ReadAsMultipartAsync(provider);
if (result.FormData["model"] == null)
{
throw new HttpResponseException(HttpStatusCode.BadRequest);
}
var model = result.FormData["model"];
var serializer = new JavaScriptSerializer();
CandidateModel modelToAdd = serializer.Deserialize <CandidateModel>(model);
string pattern = @"^[A-Za-z ]+$";
Regex regex = new Regex(pattern);
if (!regex.IsMatch(modelToAdd.name))
{
HttpResponseMessage response = this.Request.CreateErrorResponse(HttpStatusCode.BadRequest, "Name must only contains letters and white space");
throw new HttpResponseException(response);
}
if (!regex.IsMatch(modelToAdd.sur_name))
{
HttpResponseMessage response = this.Request.CreateErrorResponse(HttpStatusCode.BadRequest, "Surname must only contains letters and white space");
throw new HttpResponseException(response);
}
candidate obj = candidate.GetById(modelToAdd.id);
if (obj != null)
{
obj.name = modelToAdd.name;
obj.sur_name = modelToAdd.sur_name;
obj.position = modelToAdd.position;
}
//get the files
if (result.FileData.Count > 0)
{
//TODO: Do something with each uploaded file
if (obj != null)
{
foreach (var file in result.FileData)
{
obj.curriculum = Path.GetFileName(file.LocalFileName);
}
obj.Update();
}
}
else
{
obj.Update();
}
IEnumerable <candidate> all = candidate.GetAll();
return(Request.CreateResponse(HttpStatusCode.OK, all));
}
public ComparePathWithDesired(candidate c, double[,] desiredPath, MechSimulation sim)
{
this.c = c;
this.desiredPath = desiredPath;
this.sim = sim;
}
// Get Candidates
public ActionResult Candidates()
{
int all = 0;
HttpClient client = new HttpClient();
client.BaseAddress = new Uri("http://*****:*****@"""" + s + @"""";
prog.dateNaissance = programmeViewModel.dateNaissance;
prog.userId = programmeViewModel.userId;
prog.codePostal = programmeViewModel.codePostal;
prog.email = programmeViewModel.email;
prog.role = programmeViewModel.role;
prog.etat = programmeViewModel.etat;
candidates.Add(prog);
}
ViewBag.accepted = candidates;
ViewBag.naccepted = candidates.Count;
all = all + candidates.Count;
}
else
{
ViewBag.result = "erreur";
}
//entrevue
response = client.GetAsync("/MapLevio-web/rest/candidate/CandidateEntrevu").Result;
if (response.IsSuccessStatusCode)
{
ICollection <candidate> listcandidate = new List <candidate>();
IEnumerable <CandidateModel> listcandidateViewModel = response.Content.ReadAsAsync <IEnumerable <CandidateModel> >().Result;
foreach (CandidateModel programmeViewModel in listcandidateViewModel)
{
candidate prog = new candidate();
string s = "/Date(" + 1485298353000 + ")/";
string sa = @"""" + s + @"""";
prog.dateNaissance = programmeViewModel.dateNaissance;
prog.userId = programmeViewModel.userId;
prog.codePostal = programmeViewModel.codePostal;
prog.email = programmeViewModel.email;
prog.role = programmeViewModel.role;
prog.etat = programmeViewModel.etat;
listcandidate.Add(prog);
}
ViewBag.entrevue = listcandidate;
ViewBag.nentrevue = listcandidate.Count;
all = all + listcandidate.Count;
}
else
{
ViewBag.result = "erreur";
}
//entretien
response = client.GetAsync("/MapLevio-web/rest/candidate/CandidateEntretien").Result;
if (response.IsSuccessStatusCode)
{
ICollection <candidate> listcandidate = new List <candidate>();
IEnumerable <CandidateModel> listcandidateViewModel = response.Content.ReadAsAsync <IEnumerable <CandidateModel> >().Result;
foreach (CandidateModel programmeViewModel in listcandidateViewModel)
{
candidate prog = new candidate();
string s = "/Date(" + 1485298353000 + ")/";
string sa = @"""" + s + @"""";
prog.dateNaissance = programmeViewModel.dateNaissance;
prog.userId = programmeViewModel.userId;
prog.codePostal = programmeViewModel.codePostal;
prog.email = programmeViewModel.email;
prog.role = programmeViewModel.role;
prog.etat = programmeViewModel.etat;
listcandidate.Add(prog);
}
ViewBag.entretien = listcandidate;
ViewBag.nentretien = listcandidate.Count;
all = all + listcandidate.Count;
}
else
{
ViewBag.result = "erreur";
}
//fr
response = client.GetAsync("/MapLevio-web/rest/candidate/CandidateFr").Result;
if (response.IsSuccessStatusCode)
{
ICollection <candidate> listcandidate = new List <candidate>();
IEnumerable <CandidateModel> listcandidateViewModel = response.Content.ReadAsAsync <IEnumerable <CandidateModel> >().Result;
foreach (CandidateModel programmeViewModel in listcandidateViewModel)
{
candidate prog = new candidate();
string s = "/Date(" + 1485298353000 + ")/";
string sa = @"""" + s + @"""";
prog.dateNaissance = programmeViewModel.dateNaissance;
prog.userId = programmeViewModel.userId;
prog.codePostal = programmeViewModel.codePostal;
prog.email = programmeViewModel.email;
prog.role = programmeViewModel.role;
prog.etat = programmeViewModel.etat;
listcandidate.Add(prog);
}
ViewBag.fr = listcandidate;
ViewBag.nfr = listcandidate.Count;
all = all + listcandidate.Count;
}
else
{
ViewBag.result = "erreur";
}
ViewBag.all = all;
return(View());
}
/// <summary>
/// Adds the child to sorted candidate list based on the value of f0 (performanceParams[0]).
/// The OptimizeDirection is not used as the list is always sorted from lowest to highest.
/// </summary>
/// <param name = "candidates">The candidates.</param>
/// <param name = "child">The child.</param>
protected void addChildToSortedCandList(List <candidate> candidates, candidate child)
{
throw new NotSupportedException("This method is no longer supported. It is more efficient"
+
"to use a SortedList or Dictionary, as this insertion into the list is inefficient.");
}
public void evalGT(candidate c)
{
current = c;
reorderNodes(c);
Boolean found = false;
/* recall that gearcount is found in reorderNodes, Albert! */
stateVars = new double[gearcount + 1, 10];
#region Set up optMethod
//NelderMead optMethod =
// new NelderMead(.001, 10, true);
//GradientBasedUnconstrained optMethod =
// new GradientBasedUnconstrained(10);
GradientBasedOptimization optMethod = new GradientBasedOptimization(10);
//SequentialQuadraticProgramming optMethod = new SequentialQuadraticProgramming(true);
//GeneralizedReducedGradientActiveSet optMethod =
// new GeneralizedReducedGradientActiveSet(true);
optMethod.Add(new ArithmeticMean(optMethod, 0.001, 2, 200));
//optMethod.Add(new GoldenSection(optMethod, 0.001,200, int.MaxValue));
//optMethod.Add(new BFGSDirection());
optMethod.Add(new FletcherReevesDirection());
optMethod.Add(new convergenceBasic(BasicConvergenceTypes.OrBetweenSetConditions, 200, 0.0001, double.NaN, double.NaN, int.MaxValue));
//optMethod.Add(new convergenceBasic(BasicConvergenceTypes.AndBetweenSetConditions, 20, 0.01, double.NaN, double.NaN, int.MaxValue));
optMethod.Add(new squaredExteriorPenalty(optMethod, 10.0));
//optMethod.Add(new linearExteriorPenaltySum(optMethod, 10.0));
DiscreteSpaceDescriptor dsd = new DiscreteSpaceDescriptor(optMethod, 4 * gearcount);
optMethod.Add(dsd);
#endregion
for (int i = 0; i < gearcount; i++)
{
foreach (GearFamily gf in gearFamilies)
{
if (c.graph.nodes[i].localLabels.Contains(gf.label))
{
for (int j = 0; j < 3; j++)
{
if (c.graph.nodes[i].localVariables.Count < j + 1)
{
c.graph.nodes[i].localVariables.Add(double.NaN);
}
}
c.graph.nodes[i].localVariables[0] = gf.Sfb;
c.graph.nodes[i].localVariables[1] = gf.Sfc;
c.graph.nodes[i].localVariables[2] = gf.density;
dsd.addLinkedVariableValues(new int[] { 4 * i, 4 * i + 1, 4 * i + 2 }, gf.gears);
}
}
}
SearchIO.output("The parametric space is " + dsd.SizeOfSpace.ToString(), 3);
//setup constraints for optimization
//slot 1 - number of teeth
//slot 2 - pitch
//slot 3 - face Width
//slot 4 - location variable
#region Constraint Building Region
double[] x = new double[4 * gearcount];
double[] xStar = new double[4 * gearcount];
//double fStar = double.PositiveInfinity;
double fStar = 0.0;
double ftemp = 1000;
double weightstar = 100;
double lowestmass = 100;
double massstar = 100;
double mass = 100;
double[,] stateVarsStar = new double[gearcount + 1, 10];
outputSpeedConstraint oSC =
new outputSpeedConstraint(stateVars, this);
optMethod.Add(oSC);
stressConstraint sc =
new stressConstraint(stateVars, c, this);
optMethod.Add(sc);
boundingboxConstraint bbc =
new boundingboxConstraint(stateVars, c, this);
optMethod.Add(bbc);
outputLocationConstraint olc =
new outputLocationConstraint(stateVars, c, this);
optMethod.Add(olc);
List <samePitch> samePitches = new List <samePitch>();
for (int i = 0; i < gearcount; i++)
{
if ((c.graph.nodes[i].localLabels.Contains("contact")) || (c.graph.nodes[i].localLabels.Contains("bevelcontact")) || (c.graph.nodes[i].localLabels.Contains("wormcontact")))
{
samePitches.Add(new samePitch(((i - 1) * 4) + 1, ((i * 4) + 1)));
}
}
f = new totalObjFunction(this, current, stateVars);
optMethod.Add(f);
#endregion
int numVars = dsd.linkedSpace.Count;
int[] VarIndices = new int[numVars];
for (int i = 0; i < numVars; i++)
{
VarIndices[i] = 0;
}
int[] VarMaxes = new int[numVars];
for (int i = 0; i < numVars; i++)
{
VarMaxes[i] = dsd.linkedSpace[i].GetLength(0);
}
int currentI = 0;
VarIndices[currentI]--; //this is an unavoidable hack to start at all zeroes.
do
{
VarIndices[currentI]++;
if (VarIndices[currentI] == VarMaxes[currentI])
{
VarIndices[currentI] = 0;
currentI++;
if ((currentI > numVars - 3) && (currentI < numVars))
{
SearchIO.output("Index " + currentI.ToString() + " changed to " + VarIndices[currentI].ToString(), 4);
}
}
else
{
currentI = 0;
x = dsd.GetDesignVector(null, VarIndices);
//fStar = f.calculate(x);
Boolean Feasible = true;
foreach (samePitch sp in samePitches)
{
if (!sp.feasible(x))
{
Feasible = false;
}
}
if (Feasible && oSC.feasible(x))
{
if (sc.feasible(x))
{
//run optMethod here
double[] xTuned;
mass = 0;
double fTuned = optMethod.run(x, out xTuned);
for (int k = 0; k < gearcount; k++)
{
mass += stateVars[k, 2];
}
found = false;
if (fTuned < ftemp)
{
ftemp = fTuned;
}
found = isCurrentTheGoal(stateVars, udg);
if (found == true)
{
if (mass < massstar)
{
fStar = fTuned;
xStar = (double[])xTuned.Clone();
massstar = mass;
stateVarsStar = (double[, ])stateVars.Clone();
}
}
}
}
}
} while (currentI < numVars);
SearchIO.output("final report f = " + c.f0, 3);
if (gearcount > 2)
{
if (massstar == 100)
{
fStar = ftemp + 50;
}
}
c.f0 = fStar;
c.f2 = massstar;
c.f1 = calcInefficiency(stateVarsStar);
string outputString = "";
string outputString2 = "";
double p = 0;
for (int i = 0; i < gearcount; i++)
//var order
//x, y, z, vx, vy, vz, face width, diameter, number of teeth, type ID number
{//output for gear visualizer!
outputString += "gear," + (i + 1) + "," + stateVarsStar[i, 3] + "," + stateVarsStar[i, 4] +
"," + stateVarsStar[i, 5] + "," + stateVarsStar[i, 6] + "," + stateVarsStar[i, 7] + "," +
stateVarsStar[i, 8] + "," + xStar[i * 4 + 2] + "," + (xStar[i * 4] / xStar[i * 4 + 1]) +
"," + xStar[i * 4] + "," + stateVarsStar[i, 9] + "\n";
if (i != 0)
{
if ((stateVarsStar[i, 3] - stateVarsStar[(i - 1), 3] == 0) && (stateVarsStar[i, 4] - stateVarsStar[(i - 1), 4] == 0))
{
outputString2 += "rod," + (p) + "," + i + "," + (i - 1);//(stateVarsStar[i, 5] - stateVarsStar[(i - 1), 5]);
p += 1;
}
}
for (int j = 1; j < 9; j++)
{
if (c.graph.nodes[i].localVariables.Count <= j)
{
c.graph.nodes[i].localVariables.Add(double.NaN);
}
}
c.graph.nodes[i].localVariables[3] = stateVarsStar[i, 3];
c.graph.nodes[i].localVariables[4] = stateVarsStar[i, 4];
c.graph.nodes[i].localVariables[5] = stateVarsStar[i, 5];
c.graph.nodes[i].localVariables[6] = stateVarsStar[i, 6];
c.graph.nodes[i].localVariables[7] = stateVarsStar[i, 7];
c.graph.nodes[i].localVariables[8] = stateVarsStar[i, 8];
}
for (int j = 1; j < 9; j++)
{
if (c.graph.nodes[gearcount].localVariables.Count <= j)
{
c.graph.nodes[gearcount].localVariables.Add(double.NaN);
}
}
c.graph.nodes[gearcount].localVariables[3] = stateVarsStar[gearcount, 3];
c.graph.nodes[gearcount].localVariables[4] = stateVarsStar[gearcount, 4];
c.graph.nodes[gearcount].localVariables[5] = stateVarsStar[gearcount, 5];
c.graph.nodes[gearcount].localVariables[6] = stateVarsStar[gearcount, 6];
c.graph.nodes[gearcount].localVariables[7] = stateVarsStar[gearcount, 7];
c.graph.nodes[gearcount].localVariables[8] = stateVarsStar[gearcount, 8];
string filename = Program.settings.outputDirectory + "visualizerOutput1.txt";
FileStream fs = new FileStream(filename, FileMode.Create);
StreamWriter outputWriter = new StreamWriter(fs);
outputWriter.WriteLine(outputString);
outputWriter.WriteLine(outputString2);
outputWriter.Close();
fs.Close();
}
private void reorderNodes(candidate c)
{
node inputShaft = null;
node temp;
/* find first gear (connected to input shaft) */
foreach (node n in c.graph.nodes)
{
if (n.localLabels.Contains("seed") && n.localLabels.Contains("shaft"))
{
inputShaft = n;
break;
}
}
foreach (arc a in inputShaft.arcs)
{
if (a.otherNode(inputShaft).localLabels.Contains("gear"))
{
temp = a.otherNode(inputShaft);
c.graph.nodes.Remove(temp);
c.graph.nodes.Insert(0, temp);
break;
}
}
gearcount = 1;
Boolean foundNextGear;
do
{
foundNextGear = false;
node gear = c.graph.nodes[gearcount - 1];
foreach (arc a in gear.arcsFrom)
{
if ((a.otherNode(gear).localLabels.Contains("gear")) || (a.otherNode(gear).localLabels.Contains("gear1")))
{
temp = a.otherNode(gear);
c.graph.nodes.Remove(temp);
c.graph.nodes.Insert(gearcount++, temp);
foundNextGear = true;
break;
}
}
if (!foundNextGear)
{
// this means that either that was the last gear or we need to traverse thru an idler shaft
foreach (arc a in gear.arcsFrom)
{
if (a.otherNode(gear).localLabels.Contains("shaft"))
{
node shaft = a.otherNode(gear);
foreach (arc aa in shaft.arcsFrom)
{
if ((aa.otherNode(shaft).localLabels.Contains("gear")) || (aa.otherNode(shaft).localLabels.Contains("gear1")))
{
temp = aa.otherNode(shaft);
c.graph.nodes.Remove(temp);
c.graph.nodes.Insert(gearcount++, temp);
foundNextGear = true;
break;
}
}
}
}
}
} while (foundNextGear);
}
public candidate ChooseAndApplyCarboxOption(candidate cand)
{
var options = GetCarboxylOptions(cand);
return(options.Count > 0 ? CopyAndApplyOption(options[Rand.Next(options.Count)], cand, true) : null);
}
/* Given that the rule has now been chosen, determine the values needed by the * rule to properly apply it to the candidate, cand. The array of double is to * be determined by parametric apply rules written in complement C# files for * the ruleSet being used. */ public abstract double[] choose(option RC, candidate cand);
/// <summary>
/// Evaluate the fitness of the molecule.
/// </summary>
/// <param name="cand"></param>
/// <returns></returns>
public static double Evaluate(candidate cand)
{
return(0);
}
/* Here is the main Recognize, Choose, and Apply Generation Cycle. It accepts the host candidate
* (not graph), the index of what ruleSet to invoke, and an array of size equal to the number
* of ruleSets. At the end of the process, it returns the updated candidate. The three step
* process may, however exit at any of five places in the loop, these are described below.
* 1. the ruleSet invoked may not have any calls left. This will cause the GenerationStatus
* to be CycleLimit, and the process will execute what is stored in the 3rd position of
* generationSteps, ruleSet->nextGenerationStep[2], either Stop, Loop, GoToPrevious(ruleSet),
* GoToNext(ruleSet), or GoToRuleSet#
* 2. the choice operation has sent a STOP message, or more precisely a negative # or
* a number greater than the list of option. This results in a GenerationStatus of Choice
* and the execution of ruleSet->nextGenerationStep[1] (any of the options stated above).
* 3. there are no rules recognized for the graph. This results in a GenerationStatus of
* NoRules and the execution of ruleSet->nextGenerationStep[3] (any of the options above).
* 4. A trigger rule has been applied. This results in a GenerationStatus of TriggerRule
* and the execution of ruleSet->nextGenerationStep[4] (any of the options stated above).
* 5. the recognize, choose, and apply cycle performed as intended - no abnormal activites.
* This results in a GenerationStatus of Normal and the execution of
* ruleSet->nextGenerationStep[0] (any of the options stated above).*/
public void RecognizeChooseApplyCycle(candidate host, int ruleSetIndex, int[] numOfCallsLeft)
{
while ((ruleSetIndex >= 0) && (ruleSetIndex < numOfRuleSets))
{
host.activeRuleSetIndex = ruleSetIndex;
SearchIO.output("Active Rule Set = " + ruleSetIndex.ToString(), 4);
#region terminate immediately if there are no cycles left
if (numOfCallsLeft[ruleSetIndex] == 0)
{
/* it is possible that another ruleset intends to invoke this one, but your
* number of calls for this set has hit its limit. */
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.CycleLimit;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.CycleLimit);
SearchIO.output("cycle limit reached", 4);
continue;
}
#endregion
#region ***** RECOGNIZE *****
SearchIO.output("begin RCA loop for RuleSet #" + ruleSetIndex.ToString(), 4);
List <option> options = rulesets[ruleSetIndex].recognize(host.graph);
SearchIO.output("There are " + options.Count.ToString() + " rule choices.", 4);
if (options.Count == 0)
{
/* There are no rules to recognize, exit here. */
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.NoRules;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.NoRules);
continue;
}
if (SearchIO.terminateRequest)
{
return;
}
#endregion
#region ***** CHOOSE *****
if (rulesets[ruleSetIndex].choiceMethod == choiceMethods.Automatic)
{
choice = 0;
}
else
{
choice = choose(options, host);
}
SearchIO.output("Choice = #" + choice.ToString(), 4);
if (choice == -1)
{
host.undoLastRule();
if (display)
{
SearchIO.addAndShowGraphDisplay(host.graph.copy(),
"Revert to after calling " + host.numRulesCalled + " rules");
}
continue;
}
if ((choice < 0) || (choice >= options.Count))
{
/* the overloaded choice function may want to communicate to the loop that it
* should finish the process. */
SearchIO.output("Choice received a STOP request", 4);
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.Choice;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.Choice);
continue;
}
if (SearchIO.terminateRequest)
{
return;
}
#endregion
#region ***** APPLY *****
host.saveCurrent();
options[choice].apply(host.graph, choose(options[choice], host));
host.addToRecipe(options[choice]);
SearchIO.output("Rule sucessfully applied", 4);
/* display state? */
if (display)
{
SearchIO.addAndShowGraphDisplay(host.graph.copy(),
"After calling " + host.numRulesCalled + " rules");
}
if (SearchIO.terminateRequest)
{
return;
}
#endregion
#region Check to see if loop is done
/* First thing we do is reduce the number of calls left. Note that if you start with
* a negative number, the process will continue to make it more negative - mimicking
* no cycle limit. It is safer to use the globalvar, maxRulesToApply though.*/
numOfCallsLeft[ruleSetIndex]--;
/* a significant change is made here in Version 1.1.2.0, it is actually the removal of
* code. We were checking the numOfCallsLeft here as well as the top, but it has been decided
* that it is ambiguous to check in both locations. Later, it may be determined that two
* independent cycle limits need to be imposed, but in the mean time, the following code will be
* commented out.
* if (numOfCallsLeft[ruleSetIndex] == 0)
* { /* there of no more calls on this ruleset allowed, the limit has been reached.
* SearchIO.output("The maximum num of calls has been reached", 4);
* host.GenerationStatus[ruleSetIndex] = GenerationStatuses.CycleLimit;
* ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.CycleLimit);
* }
* else */
if (options[choice].ruleNumber == rulesets[ruleSetIndex].triggerRuleNum)
{ /* your ruleset loops until a trigger rule and the trigger rule was just called. */
SearchIO.output("The trigger rule has been chosen.", 4);
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.TriggerRule;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.TriggerRule);
}
else
{ /* Normal operation */
SearchIO.output("RCA loop executed normally.", 4);
host.GenerationStatus[ruleSetIndex] = GenerationStatuses.Normal;
ruleSetIndex = nextRuleSet(ruleSetIndex, GenerationStatuses.Normal);
}
#endregion
}
}
public static string GetLinkerName(candidate cand)
{
var arr = cand.recipe.Select(x => Convert.ToString(x.optionNumber));
return(String.Join("-", arr));
}
: NonFuzzyMatchPatternChunk(candidate, patternChunk, punctuationStripped));
public static void runSearchProcess()
{
userDefinedGoals udg = new userDefinedGoals();
Form2 inputBox = new Form2(udg);
//inputBox.ShowDialog();
ruleSet.loadAndCompileSourceFiles(rulesets, Program.settings.recompileRules, Program.settings.compiledparamRules, Program.settings.execDir);
List <candidate> candidates = new List <candidate>();
candidate current = null;
candidate seedCandidate = new candidate(seed, Program.settings.numOfRuleSets);
Boolean found = false;
candidates.Add(seedCandidate);
GearEvaluator ge = new GearEvaluator(udg);
Guidance.PNormProportionalPopulationSelection GuidanceApproach
= new Guidance.PNormProportionalPopulationSelection(0, Guidance.optimize.minimize, true, true, 1);
int maxPop = 10000;
while (!found && (candidates.Count != 0))
{
current = candidates[0];
candidates.RemoveAt(0);
SearchIO.iteration = current.recipe.Count;
//RECOGNIZE
List <option> ruleChoices = rulesets[0].recognize(current.graph);
SearchIO.miscObject = candidates.Count;
//
if (current.recipe.Count >= 2)
{
found = isCurrentTheGoal(current, udg);
if (found == true)
{
ge.evalGT(current);
break;
}
}
//else current.f0 = double.PositiveInfinity;
for (int i = 0; i != ruleChoices.Count; i++)
{
candidate child = current.copy();
ruleChoices = rulesets[0].recognize(child.graph);
ruleChoices[i].apply(child.graph, null);
child.addToRecipe(ruleChoices[i]);
child.f0 = double.NaN;
child.f1 = double.NaN;
child.f2 = double.NaN;
ge.evalGT(child);
child.f3 = (child.f0) / 100 + child.f1;
//1 efficiency
//2 mass
//3 combination
addChildToSortedCandList(candidates, child, 3);
//candidates.Add(child);
}
}
Program.addAndShowGraphDisplay(current.graph, "Here is your gear train!!!");
candidate.saveToXml(current, "testTuned", settings.outputDirectory);
}
//+
public bool del2(candidate A)
{
bool D = false;
int r = 0; // кількість однакових ел. в кандидатах.
for (int i = 0; i < this.Itemset.Count; i++)
{
for (int j = 0; j < A.Itemset.Count; j++)
{
if (this.Itemset[i] == A.Itemset[j])
{
r++;
}
}
}
if (r == this.Itemset.Count) { D = true; }
return D;
}
/// <summary>
/// Chooses the specified options. Given the list of options and the candidate,
/// determine what option to invoke. Return the integer index of this option from the list.
/// </summary>
/// <param name="options">The options.</param>
/// <param name="cand">The cand.</param>
/// <returns></returns>
public override int[] choose(List <option> options, candidate cand)
{
return(new[] { rnd.Next(-1, options.Count) });
}
//СТВОРЕННЯ НОВОГО КАНДИДАТА+
public candidate newcandidat(candidate A, int[,] B)
{
candidate newc = new candidate(new List<int>(), 0);
for (int i = 0; i < this.Itemset.Count; i++)
{
newc.Itemset.Add(this.Itemset[i]);
}
for (int i = 0; i < A.Itemset.Count; i++)
{
newc.Itemset.Add(A.Itemset[i]);
}
newc.cleanItemset();
newc.support(B);
return newc;
}
public bool IsTerminalCandidate(candidate cand)
{
var numRule = cand.recipe.Count;
return(cand.recipe[numRule - 1].ruleSetIndex == 1);
}
