private int GetImitationLearningFile(out LinearModel model, string distribution, string dimension,
TrainingSet.Trajectory track, bool extended, int numFeatures, int modelID, string directoryName, string stepwiseBias,
bool timedependent = false, int iter = -1)
{
DirectoryInfo dir = new DirectoryInfo(String.Format(@"{0}\PREF\weights", directoryName));
string pat = String.Format("\\b(exhaust|full)\\.{0}.{1}.{2}.(OPT|IL([0-9]+){3}{4}{5}).{6}.weights.{7}",
distribution, dimension,
(char) PreferenceSet.Ranking.PartialPareto, track.ToString().Substring(2),
numFeatures < Features.LocalCount ? String.Format("_F{0}M{1}", numFeatures, modelID) : "",
extended ? "EXT" : "",
stepwiseBias,
timedependent ? "timedependent" : "timeindependent");
Regex reg = new Regex(pat);
var files = dir.GetFiles("*.csv").Where(path => reg.IsMatch(path.ToString())).ToList();
if (files.Count <= (iter >= 0 ? iter : 0))
throw new Exception(String.Format("Cannot find any weights belonging to {0}. Start with optimal!", track));
int[] iters = new int[files.Count];
for (int i = 0; i < iters.Length; i++)
{
Match m = reg.Match(files[i].Name);
if (m.Groups[2].Value == "OPT")
iters[i] = 0;
else
iters[i] = Convert.ToInt32(m.Groups[3].Value);
}
if (iter < 0) iter = iters.Max(); // then take the latest version
FileInfo weightFile = files[Array.FindIndex(iters, x => x == iter)];
model = new LinearModel(weightFile, numFeatures, modelID, Distribution, Dimension);
return iters.Max();
}
private static string Trajectory2String(TrainingSet.Trajectory track, int iter, bool extended)
{
string str;
switch (track)
{
case TrainingSet.Trajectory.ILSUP:
case TrainingSet.Trajectory.ILUNSUP:
case TrainingSet.Trajectory.ILFIXSUP:
str = iter > 0
? String.Format("IL{0}{1}", iter, track.ToString().Substring(2))
: String.Format("{0}", TrainingSet.Trajectory.OPT);
break;
default:
str = String.Format("{0}", track);
break;
}
if (extended)
str += "EXT";
return str;
}