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;
}
public static LinearModel[] GetAllExhaustiveModels(string distribution, string dimension,
TrainingSet.Trajectory track, int iter, bool extended, PreferenceSet.Ranking rank, bool timedependent,
DirectoryInfo dataDir, string stepwiseBias)
{
string pat = String.Format("exhaust.{0}.{1}.{2}.{3}.{4}.weights.{5}.csv",
distribution, dimension, (char) rank, Trajectory2String(track, iter, extended),
stepwiseBias, timedependent ? "timedependent" : "timeindependent");
DirectoryInfo dir = new DirectoryInfo(String.Format(@"{0}\PREF\weights", dataDir.FullName));
Regex reg = new Regex(pat);
var files = dir.GetFiles("*.csv").Where(path => reg.IsMatch(path.ToString())).ToList();
return files.Count < 1 ? null : ReadLoggedLinearWeights(files[0], distribution, dimension, Model.PREF);
}
public static LinearModel[] GetAllVaryLmaxModels(string distribution, string dimension,
TrainingSet.Trajectory track, int iter, bool extended, PreferenceSet.Ranking rank, bool timedependent,
DirectoryInfo dataDir, string stepwiseBias)
{
string pat = String.Format("full.{0}.{1}.{2}.{3}.{4}.weights.{5}_lmax[0-9]+.csv",
distribution, dimension, (char) rank, Trajectory2String(track, iter, extended),
stepwiseBias, timedependent ? "timedependent" : "timeindependent");
DirectoryInfo dir = new DirectoryInfo(String.Format(@"{0}\PREF\weights", dataDir.FullName));
Regex reg = new Regex(pat);
var files = dir.GetFiles("*.csv").Where(path => reg.IsMatch(path.ToString())).ToList();
if (files.Count < 1) return null;
LinearModel[] models = new LinearModel[0];
foreach (
var model in
files.Select(file => ReadLoggedLinearWeights(file, distribution, dimension, Model.PREF))
.Where(model => model != null))
{
Array.Resize(ref models, models.Length + model.Length);
Array.Copy(model, 0, models, models.Length - model.Length, model.Length);
}
return models;
}
public LinearModel(string distribution, string dimension, TrainingSet.Trajectory track, bool extended,
PreferenceSet.Ranking rank, bool timedependent, DirectoryInfo dataDir,
int numFeatures, int modelID, string stepwiseBias, int iter = -1, Features.Mode featMode = Features.Mode.Local)
: this(null, featMode, numFeatures, modelID, !timedependent, distribution, dimension, Model.PREF)
{
switch (track)
{
case TrainingSet.Trajectory.ILFIXSUP:
case TrainingSet.Trajectory.ILUNSUP:
case TrainingSet.Trajectory.ILSUP:
LinearModel model;
Iteration = GetImitationLearningFile(out model, distribution, dimension, track, extended,
numFeatures, modelID, dataDir.FullName, stepwiseBias, timedependent, iter);
FileInfo = model.FileInfo;
LocalWeights = model.LocalWeights;
return;
default:
string pat = String.Format("\\b(exhaust|full)\\.{0}.{1}.{2}.{3}{4}.{5}.{6}weights.{7}.csv",
distribution, dimension, (char) rank,
track, extended ? "EXT" : "", stepwiseBias,
FeatureMode == Features.Mode.Global ? "(Global|SDR)" : "",
timedependent ? "timedependent" : "timeindependent");
DirectoryInfo dir = new DirectoryInfo(String.Format(@"{0}\PREF\weights", dataDir.FullName));
Regex reg = new Regex(pat);
var files = dir.GetFiles("*.csv").Where(path => reg.IsMatch(path.ToString())).ToList();
if (files.Count <= 0)
throw new Exception(String.Format("Cannot find any weights belonging to {0}!", pat));
foreach (var file in files)
{
LinearModel[] logWeights = ReadLoggedLinearWeights(file, distribution, dimension, Model.PREF);
FileInfo = file;
foreach (
var w in logWeights.Where(w => w._numFeatures == _numFeatures && w._modelID == _modelID))
{
LocalWeights = w.LocalWeights;
GlobalWeights = w.GlobalWeights;
return;
}
}
throw new Exception(String.Format("Cannot find weights {0} to user requirements from {1}!", Name,
files[0].Name));
}
}