public RandomBaseline(GlobalSettings settings) : base(settings)
{
RequireSeed = true;
RequiredNumRuleSets = 1;
AutoPlay = true;
Seed = new candidate(OBFunctions.tagconvexhullpoints(settings.seed), settings.numOfRuleSets);
_runDirectory = Path.Combine(settings.OutputDirAbs, "RandomBaseline");
if (Directory.Exists(_runDirectory))
{
Directory.Delete(_runDirectory, true);
}
Directory.CreateDirectory(_runDirectory);
var learnDirectory = Path.Combine(settings.OutputDirAbs, "morfLearn");
computation = new Computation(_runDirectory, learnDirectory, "point", "stiff");
writer = new StreamWriter(Path.Combine(_runDirectory, "RandomBaseline.txt"));
jobBuffer = new JobBuffer(_runDirectory);
agent = new Algorithms.Random(settings);
}
private void GenerateFixed()
{
var agent = new Algorithms.Random(settings);
var linkerBeforeCarboxDict = new Dictionary <string, candidate>();
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;
candidate finalCand = null;
while (cand == null || finalCand == 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;
}
finalCand = agent.ChooseAndApplyCarboxOption(cand);
if (finalCand == null)
{
Console.WriteLine("Fail on finding final carbox");
}
}
var linkerName = AbstractAlgorithm.GetLinkerName(cand);
Console.WriteLine(linkerName);
if (linkerBeforeCarboxDict.ContainsKey(linkerName))
{
total_rule--;
continue;
}
linkerBeforeCarboxDict[linkerName] = cand;
}
}
Console.WriteLine(linkerBeforeCarboxDict.Count);
for (var e = 0; e < NUM_EPOCH; e++)
{
Console.WriteLine("Epoch: {0}", e);
foreach (var item in linkerBeforeCarboxDict)
{
var submitCand = agent.ChooseAndApplyCarboxOptionUsingEstimator(item.Value, computation, client, _runDirectory, e);
//cand = agent.ChooseAndApplyCarboxOptionBestAngle(item.Value());
//cand = agent.ChooseAndApplyCarboxOptionUsingEstimator(item.Value(), computation, client, _runDirectory);
if (submitCand == null)
{
Console.WriteLine("Fail on finding final carbox, should never happen");
Environment.Exit(0);
}
var linkerName = AbstractAlgorithm.GetLinkerName(submitCand) + "-E" + e.ToString();
var coeff = Path.Combine(_runDirectory, "data", "linker" + linkerName + ".coeff");
var lmpdat = Path.Combine(_runDirectory, "data", "linker" + linkerName + ".lmpdat");
agent.Converter.moltoUFF(OBFunctions.designgraphtomol(submitCand.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;
}
