/// <summary>
/// Apply the option to the candidate and store the agent's evaluation.
/// </summary>
public void ApplyOption(option opt, candidate cand, bool doMinimize)
{
cand.graph.globalVariables.Add(cand.f0); // track fitness values of previous states
opt.apply(cand.graph, null);
cand.addToRecipe(opt);
if (doMinimize)
{
cand.graph = Minimize(cand.graph);
}
cand.graph = OBFunctions.tagconvexhullpoints(cand.graph);
}
/// <summary>
/// Apply the option to the candidate and store the agent's evaluation.
/// </summary>
public void ApplyOption(option opt, candidate cand, bool doMinimize, bool doEvaluate = false)
{
cand.graph.globalVariables.Add(cand.f0); // track fitness values of previous states
// Minimize and evaluate
opt.apply(cand.graph, null);
cand.addToRecipe(opt);
if (doMinimize)
{
cand.graph = Minimize(cand.graph);
}
if (doEvaluate)
{
cand.f0 = Evaluate(cand);
}
// MC Is this like line 64? by the way, tracking fitness here is better than in cand.graph
}
protected override void Run()
{
Stack <candidate> candidates = new Stack <candidate>();
candidate current = null;
Boolean found = false;
candidates.Push(seedCandidate);
while (!found && (candidates.Count != 0) && !SearchIO.terminateRequest)
{
current = candidates.Pop();
SearchIO.iteration = current.recipe.Count;
SearchIO.miscObject = candidates.Count;
if (isCurrentTheGoal(current))
{
found = true;
}
else
{
int rsIndex = current.activeRuleSetIndex;
List <option> ruleChoices = rulesets[rsIndex].recognize(current.graph);
foreach (option opt in ruleChoices)
{
candidate child = current.copy();
transferLmappingToChild(child.graph, current.graph, opt.location);
opt.apply(child.graph, null);
child.addToRecipe(opt);
candidates.Push(child);
}
}
}
SearchIO.addAndShowGraphWindow(current.graph, "cand1");
Save(settings.outputDirectory + "\\DFScandidate.xml", current);
}
/// <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
}
}
/* 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 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);
}
