/* some functions for invoking the RecognizeChooseApplyCycle above. That function is
* protected so we invoke it through one of these four functions. This functions will
* not work, however, if one has not yet initiated the generation process to establish
* the fields of the Generation object. */
public candidate generateOneCandidate()
{
int[] numOfCalls = new int[numOfRuleSets];
/* this copy set is needed because array are reference types and the RCA cycle
* will modify the numOfCalls inside of it. */
maxNumOfCalls.CopyTo(numOfCalls, 0);
candidate newCand = seed.copy();
RecognizeChooseApplyCycle(newCand, seed.activeRuleSetIndex, numOfCalls);
return(newCand);
}
/// <summary>
/// Calls one rule on candidate - the rule is determined by the choose function.
/// </summary>
/// <param name = "cand">The cand.</param>
/// <param name = "startingRuleSet">The starting rule set.</param>
/// <returns></returns>
public virtual candidate CallOneRuleOnCandidate(candidate cand = null, int startingRuleSet = -1)
{
if (cand == null)
{
cand = Host;
}
if (startingRuleSet == -1)
{
startingRuleSet = cand.activeRuleSetIndex;
}
/* the RecognizeChooseApplyCycle requires an array of ruleSet limits,
* since we only intend to make one call on the activeRuleSet we make
* an array (it should initialize to all zeros) of the proper length
* and set its one value at the activeRuleSetIndex to 1. */
var numOfCalls = new int[NumOfRuleSets];
numOfCalls[cand.activeRuleSetIndex] = 1;
var newCand = cand.copy();
/* here the main cycle is invoked. First, we must pass a copy of the candidate
* to the RCA cycle since the apply set will modify it, and then move the prevoius
* state onto the prevStates under the candidate. It is not incorrect to state
* merely the candidate here, but the prevStates will not be stored correctly.*/
RecognizeChooseApplyCycle(newCand, startingRuleSet, numOfCalls);
return(newCand);
}
private candidate GenerateCand()
{
candidate cand = null;
while (cand == null)
{
cand = Seed.copy();
while (true)
{
cand = agent.ChooseAndApplyAnyOption(cand);
if (cand == null)
{
Console.WriteLine("Fail, Rebuild");
break;
}
if (agent.IsTerminalCandidate(cand))
{
//Console.WriteLine("Choose terminal rule.");
break;
}
//Console.WriteLine("Choose non-terminal rule.");
}
}
return(cand);
}
/// <summary>
/// Generates all neighbors of the current.
/// </summary>
/// <param name = "current">The current.</param>
/// <param name = "IncludingParent">if set to <c>true</c> [including parent].</param>
/// <param name = "MaxNumber">The max number.</param>
/// <returns></returns>
public virtual List <candidate> GenerateAllNeighbors(candidate current = null, Boolean IncludingParent = false,
int MaxNumber = int.MaxValue)
{
if (current == null)
{
current = Host;
}
var rand = new Random();
var neighbors = new List <candidate>();
var options = Rulesets[current.activeRuleSetIndex].recognize(current.graph, InParallel);
while (MaxNumber < options.Count)
{
var i = rand.Next(options.Count);
options.RemoveAt(i);
}
if (IncludingParent)
{
var parent = current.copy();
parent.undoLastRule();
neighbors.Add(parent);
}
if (InParallel)
{
Parallel.ForEach(options, opt =>
{
var child = current.copy();
SearchProcess.transferLmappingToChild(child.graph, current.graph, opt);
opt.apply(child.graph, null);
child.addToRecipe(opt);
neighbors.Add(child);
});
}
else
{
foreach (var opt in options)
{
var child = current.copy();
SearchProcess.transferLmappingToChild(child.graph, current.graph, opt);
opt.apply(child.graph, null);
child.addToRecipe(opt);
neighbors.Add(child);
}
}
return(neighbors);
}
/// <summary>
/// Copies the candidate graph, transfers the L-mapping, and returns the resultant candidate.
/// </summary>
public candidate CopyAndApplyOption(option opt, candidate cand, bool doMinimize)
{
var newCand = cand.copy();
var newOpt = opt.copy();
SearchProcess.transferLmappingToChild(newCand.graph, cand.graph, newOpt);
ApplyOption(newOpt, newCand, doMinimize);
return(newCand);
}
/// <summary>
/// Generates one candidate. A simple function for invoking the RecognizeChooseApplyCycle.
/// That function is protected so we invoke it through a function like this.
/// </summary>
/// <param name = "cand">The cand to build upon (if null, then the seed will be used).</param>
/// <param name = "startingRuleSet">The starting rule set (if unspecified then the candidate's active ruleset will be used.</param>
/// <returns></returns>
public virtual candidate GenerateOneCandidate(candidate cand = null, int startingRuleSet = -1)
{
if (cand == null)
{
cand = Host;
}
if (startingRuleSet == -1)
{
startingRuleSet = cand.activeRuleSetIndex;
}
var numOfCalls = (int[])MaxNumOfCalls.Clone();
/* this copy set is needed because array are reference types and the RCA cycle
* will modify the numOfCalls inside of it. */
var newCand = cand.copy();
RecognizeChooseApplyCycle(newCand, startingRuleSet, numOfCalls);
return(newCand);
}
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>
/// Returns whether the graph violates angle or carboxyl-blocking constraints.
/// </summary>
private static bool IsValidChild(candidate cand, option opt)
{
var newCand = cand.copy();
var newOpt = opt.copy();
SearchProcess.transferLmappingToChild(newCand.graph, cand.graph, newOpt);
newOpt.apply(newCand.graph, null);
var mol = OBFunctions.designgraphtomol(newCand.graph);
var mapping = OBFunctions.findcarboxylates(mol);
if (mapping.Count < 2)
{
return(true);
}
var carbA = mol.GetAtom(mapping[0][1]); // carbon in carboxylate
var aA = mol.GetAtom(mapping[0][3]); // atom that the carbon connects to
var carbB = mol.GetAtom(mapping[1][1]);
var aB = mol.GetAtom(mapping[1][3]);
var pAngle = OBFunctions.pairwiseangle(carbA, aA, carbB, aB); // angle between carboxylates
return(!OBFunctions.carboxylatesBlocked(mol, aA, carbA, aB, carbB) && pAngle >= AngleFloor);
}
private void Generate()
{
Console.WriteLine("ddddd!");
var agent = new Algorithms.Random(settings);
var linkerSet = new HashSet <string>();
for (var e = 0; e < NUM_EPOCH; e++)
{
Console.WriteLine("Epoch: {0}", e);
for (var t = 0; t < NUM_TRAIL; t++)
{
Console.WriteLine("Trail: {0}", t);
for (var total_rule = TOTAL_RULE_MIN; total_rule < TOTAL_RULE_MAX + 1; total_rule++)
{
candidate cand = null;
while (cand == null)
{
cand = Seed.copy();
Console.WriteLine("Total Intermediate Rules: {0}", total_rule);
for (var step = 0; step < total_rule; step++)
{
cand = agent.ChooseAndApplyOption(cand);
if (cand == null)
{
Console.WriteLine("Fail on step {0}", step + 1);
break;
}
}
if (cand == null)
{
continue;
}
//var carboxOpt = agent.ChooseAndApplyOption(cand);
cand = agent.ChooseAndApplyCarboxOptionBestAngle(cand);
//cand = agent.ChooseAndApplyCarboxOptionUsingEstimator(cand, computation, client, _runDirectory);
if (cand == null)
{
Console.WriteLine("Fail on finding final carbox");
}
}
var candSmile = OBFunctions.moltoSMILES(OBFunctions.designgraphtomol(cand.graph));
var linkerName = AbstractAlgorithm.GetLinkerName(cand);
//Console.WriteLine(candSmile);
Console.WriteLine(linkerName);
if (linkerSet.Contains(linkerName))
{
total_rule--;
continue;
}
linkerSet.Add(linkerName);
var coeff = Path.Combine(_runDirectory, "data", "linker" + linkerName + ".coeff");
var lmpdat = Path.Combine(_runDirectory, "data", "linker" + linkerName + ".lmpdat");
agent.Converter.moltoUFF(OBFunctions.designgraphtomol(cand.graph), coeff, lmpdat, false, 100);
double piority = 0;
if (CARBOXTYPE == "estimator")
{
piority = -Convert.ToDouble(client.SendMessage("[Predict]" + " " + linkerName));
}
else
{
piority = AbstractAlgorithm.Rand.NextDouble();
computation.CalculateFeature(linkerName);
}
//mutex.WaitOne();
jobBuffer.Add(linkerName, piority, e);
//mutex.ReleaseMutex();
}
}
if (CARBOXTYPE == "estimator")
{
while (true)
{
var on_simulation = jobBuffer.Num_simulating();
if (on_simulation == 0)
{
break;
}
Console.WriteLine("Wait for current {0} linkers to finish simulation....", on_simulation);
Thread.Sleep(10000);
}
client.SendMessage("[FitModel]");
}
}
allSubmitFlag = true;
}
/// <summary>
/// Generates all neighbors of the current.
/// </summary>
/// <param name = "current">The current.</param>
/// <param name = "IncludingParent">if set to <c>true</c> [including parent].</param>
/// <param name = "MaxNumber">The max number.</param>
/// <returns></returns>
public static List <candidate> GenerateAllNeighbors(candidate current, IList <ruleSet> Rulesets, Boolean IncludingParent = false,
bool InParallel = false, bool includeStopCondition = false, int MaxNumber = int.MaxValue)
{
var rand = new Random();
var neighbors = new List <candidate>();
var ruleset = Rulesets[current.activeRuleSetIndex];
var options = ruleset.recognize(current.graph, InParallel);
if (options.Count == 0)
{
var noRuleCandidate = current.copy();
noRuleCandidate.activeRuleSetIndex = ruleset.nextRuleSet(GenerationStatuses.NoRules);
neighbors.Add(noRuleCandidate);
return(neighbors);
}
while (MaxNumber < options.Count)
{
var i = rand.Next(options.Count);
options.RemoveAt(i);
}
if (IncludingParent)
{
var parent = current.copy();
parent.undoLastRule();
neighbors.Add(parent);
}
if (InParallel)
{
Parallel.ForEach(options, opt =>
{
var child = current.copy();
SearchProcess.transferLmappingToChild(child.graph, current.graph, opt);
opt.apply(child.graph, null);
child.addToRecipe(opt);
neighbors.Add(child);
});
}
else
{
foreach (var opt in options)
{
var child = current.copy();
SearchProcess.transferLmappingToChild(child.graph, current.graph, opt);
opt.apply(child.graph, null);
child.addToRecipe(opt);
neighbors.Add(child);
if (opt.ruleNumber == ruleset.TriggerRuleNum)
{
child.activeRuleSetIndex = ruleset.nextRuleSet(GenerationStatuses.TriggerRule);
}
else
{
child.activeRuleSetIndex = ruleset.nextRuleSet(GenerationStatuses.Normal);
}
}
}
var stopCandidate = current.copy();
stopCandidate.activeRuleSetIndex = ruleset.nextRuleSet(GenerationStatuses.Choice);
neighbors.Add(stopCandidate);
return(neighbors);
}
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);
}
