public candidate ChooseAndApplyCarboxOptionUsingEstimator(candidate cand, Computation cpt, MessageClient clt, string runDir, int epoch)
{
option bestOpt = null;
var bestProperty = Double.NegativeInfinity;
var options = GetCarboxylOptions(cand);
foreach (var opt in options)
{
var evalcand = CopyAndApplyOption(opt, cand, true);
var mol = OBFunctions.designgraphtomol(evalcand.graph);
var linkerName = AbstractAlgorithm.GetLinkerName(evalcand) + "-E" + epoch.ToString();
var coeff = Path.Combine(runDir, "data", "linker" + linkerName + ".coeff");
var lmpdat = Path.Combine(runDir, "data", "linker" + linkerName + ".lmpdat");
Converter.moltoUFF(OBFunctions.designgraphtomol(evalcand.graph), coeff, lmpdat, false, 100);
cpt.CalculateFeature(linkerName);
var properpty = Convert.ToDouble(clt.SendMessage("[Predict]" + " " + linkerName));
if (properpty > bestProperty)
{
bestProperty = properpty;
bestOpt = opt;
}
}
Console.WriteLine("Best {0}", bestProperty);
return(bestOpt == null ? null : CopyAndApplyOption(bestOpt, cand, true));
}
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;
}
