private void Save(List <GameObjectRefs> refs)
{
BinarySerializer writer = new BinarySerializer(GetPath(backupName), FileAccess.Write);
Exception ex = writer.Serialize(refs);
if (ex == null)
{
ex = writer.Close();
}
else
{
writer.Close();
}
}
public static void Save(List <ModelImport> settings)
{
CustomAssetPostprocessor.modelSettings = null;
BinarySerializer serializer = new BinarySerializer(GetStorePath(), FileAccess.Write);
serializer.Serialize(settings);
serializer.Close();
}
static CategorizerDemo()
{
string fileName = HttpContext.Current.Server.MapPath("App_Data\\model.bin");
BinarySerializer binReader = new BinarySerializer(fileName, FileMode.Open);
mBowSpace = new BowSpace(binReader);
mBowSpace.CutLowWeightsPerc = 0.2;
mCategorizer = Utils.LoadDictionary<string, IModel<string>>(binReader);
binReader.Close();
mReady = true;
}
static TwitterSentimentDemo()
{
string modelFileName = HttpContext.Current.Server.MapPath("App_Data\\AdCfy.bin");
string bowSpcFileName = HttpContext.Current.Server.MapPath("App_Data\\AdCfyBowSpc.bin");
mClassifier = new SvmBinaryClassifier<int>();
mClassifier.LoadModel(modelFileName);
BinarySerializer bs = new BinarySerializer(bowSpcFileName, FileMode.Open);
mBowSpace = new BowSpace(bs);
bs.Close();
mReady = true;
}
static RssFeedComponent()
{
Assembly assembly = Assembly.GetExecutingAssembly();
foreach (string resName in assembly.GetManifestResourceNames())
{
if (resName.EndsWith(".ldp"))
{
BinarySerializer ser = new BinarySerializer(assembly.GetManifestResourceStream(resName));
LanguageProfile langProfile = new LanguageProfile(ser);
ser.Close();
mCodePageDetector.AddLanguageProfile(langProfile);
}
}
}
static DocumentCategorizerComponent()
{
Logger.GetLogger(typeof(DocumentCategorizerComponent)).Info("CategorizerComponent", "Loading model ...");
string fileName = Utils.GetConfigValue("CategorizationModel", ".\\CategorizationModel.bin");
BinarySerializer binReader = new BinarySerializer(fileName, FileMode.Open);
mBowSpace = new BowSpace(binReader);
mBowSpace.CutLowWeightsPerc = 0.2;
mCategorizer = Utils.LoadDictionary<string, IModel<string>>(binReader);
binReader.Close();
//using (BinarySerializer writer = new BinarySerializer(@"C:\Work\AchimPipe\DocumentCategorizerModel.bin", FileMode.Create))
//{
// mBowSpace.Save(writer);
// Utils.SaveDictionary<string, IModel<string>>(mCategorizer, writer);
//}
Logger.GetLogger(typeof(DocumentCategorizerComponent)).Info("CategorizerComponent", "Done.");
}
static DocumentCategorizerComponent()
{
Logger.GetLogger(typeof(DocumentCategorizerComponent)).Info("CategorizerComponent", "Loading model ...");
string fileName = Utils.GetConfigValue("CategorizationModel", ".\\CategorizationModel.bin");
BinarySerializer binReader = new BinarySerializer(fileName, FileMode.Open);
mBowSpace = new BowSpace(binReader);
mBowSpace.CutLowWeightsPerc = 0.2;
mCategorizer = Utils.LoadDictionary <string, IModel <string> >(binReader);
binReader.Close();
//using (BinarySerializer writer = new BinarySerializer(@"C:\Work\AchimPipe\DocumentCategorizerModel.bin", FileMode.Create))
//{
// mBowSpace.Save(writer);
// Utils.SaveDictionary<string, IModel<string>>(mCategorizer, writer);
//}
Logger.GetLogger(typeof(DocumentCategorizerComponent)).Info("CategorizerComponent", "Done.");
}
public static LanguageDetector GetLanguageDetectorPrebuilt()
{
LanguageDetector ld = new LanguageDetector();
Assembly assembly = Assembly.GetExecutingAssembly();
foreach (string resName in assembly.GetManifestResourceNames())
{
if (resName.EndsWith(".ldp"))
{
// load language detector profile
BinarySerializer ser = new BinarySerializer(assembly.GetManifestResourceStream(resName));
LanguageProfile langProfile = new LanguageProfile(ser);
ser.Close();
ld.AddLanguageProfile(langProfile);
mLogger.Debug("GetLanguageDetectorPrebuilt", "Loaded resource {0}.", resName);
}
}
return(ld);
}
private void SaveSettings(object sender, EventArgs e)
{
var sfd = new SaveFileDialog {
DefaultExt = ".tgc", Filter = "Файлы настроек (*.tgc)|*.tgc|Все файлы (*.*)|*.*"
};
if (sfd.ShowDialog() != DialogResult.OK)
{
return;
}
var s = new BinarySerializer(sfd.FileName);
if (!s.Open())
{
MessageBox.Show("Не удалось открыть файл"); return;
}
if (!_scene.Settings.Serialize(s))
{
MessageBox.Show("Не удалось сохранить настройки");
}
s.Close();
}
private void Load()
{
ClearResults();
refDiffs.Clear();
BinarySerializer reader = new BinarySerializer(fileToLoad.FullName, FileAccess.Read);
List <GameObjectRefs> backup = reader.Deserialize <List <GameObjectRefs> >();
foreach (GameObjectRefs refs in backup)
{
Transform t = TransformUtil.Search(refs.path);
if (t != null)
{
Component[] comps = t.GetComponents(typeof(Component));
for (int i = 0; i < comps.Length && i < refs.refs.Count; ++i)
{
Component c = comps[i];
if (c == null)
{
continue;
}
CompRefs compRefs = refs.refs[i];
if (compRefs.compType == c.GetType().FullName)
{
foreach (FieldInfo f in registry.GetFields(c.GetType()))
{
Object backupVal = compRefs.GetValue(f.Name);
if (backupVal != null)
{
refDiffs.Add(new RefDiff(c, f, backupVal));
}
}
}
}
}
}
reader.Close();
}
static void Main(string[] args)
{
LanguageDetector langDet = LanguageDetector.GetLanguageDetectorPrebuilt();
//LanguageDetector langDet = new LanguageDetector();
//langDet.ReadCorpus(@"C:\Users\mIHA\Desktop\langdet");
LanguageProfile p = langDet.FindMatchingLanguage("To je slovenski stavek. Čeprav ga naš detektor ne zazna pravilno. Mogoče šumniki pomagajo...");
Console.WriteLine(p.Language);
p = langDet.FindMatchingLanguage("I love you.");
Console.WriteLine(p.Language);
p = langDet.FindMatchingLanguage("Baš te volim.");
Console.WriteLine(p.Language);
p = langDet.FindMatchingLanguage("Je t'aime.");
Console.WriteLine(p.Language);
foreach (LanguageProfile pr in langDet.LanguageProfiles)
{
BinarySerializer ser = new BinarySerializer(string.Format(@"C:\Users\mIHA\Desktop\langdet\{0}.ldp", pr.Language), FileMode.Create);
pr.Save(ser);
ser.Close();
}
//Console.WriteLine(langDet.GetLanguageProfile("et"));
//StreamWriter w = new StreamWriter("c:\\krneki\\langSim.txt");
//foreach (LanguageProfile p in langDet.LanguageProfiles)
//{
// w.Write("{0}\t", p.Code);
//}
//w.WriteLine();
//foreach (LanguageProfile p in langDet.LanguageProfiles)
//{
// foreach (LanguageProfile p2 in langDet.LanguageProfiles)
// {
// //w.Write("{0}\t", Math.Max(p.CalcSpearman(p2), p2.CalcSpearman(p)));
// }
// w.WriteLine();
//}
//w.Close();
}
static void Main(string[] args)
{
LanguageDetector langDet = LanguageDetector.GetLanguageDetectorPrebuilt();
//LanguageDetector langDet = new LanguageDetector();
//langDet.ReadCorpus(@"C:\Users\mIHA\Desktop\langdet");
LanguageProfile p = langDet.FindMatchingLanguage("To je slovenski stavek. Čeprav ga naš detektor ne zazna pravilno. Mogoče šumniki pomagajo...");
Console.WriteLine(p.Language);
p = langDet.FindMatchingLanguage("I love you.");
Console.WriteLine(p.Language);
p = langDet.FindMatchingLanguage("Baš te volim.");
Console.WriteLine(p.Language);
p = langDet.FindMatchingLanguage("Je t'aime.");
Console.WriteLine(p.Language);
foreach (LanguageProfile pr in langDet.LanguageProfiles)
{
BinarySerializer ser = new BinarySerializer(string.Format(@"C:\Users\mIHA\Desktop\langdet\{0}.ldp", pr.Language), FileMode.Create);
pr.Save(ser);
ser.Close();
}
//Console.WriteLine(langDet.GetLanguageProfile("et"));
//StreamWriter w = new StreamWriter("c:\\krneki\\langSim.txt");
//foreach (LanguageProfile p in langDet.LanguageProfiles)
//{
// w.Write("{0}\t", p.Code);
//}
//w.WriteLine();
//foreach (LanguageProfile p in langDet.LanguageProfiles)
//{
// foreach (LanguageProfile p2 in langDet.LanguageProfiles)
// {
// //w.Write("{0}\t", Math.Max(p.CalcSpearman(p2), p2.CalcSpearman(p)));
// }
// w.WriteLine();
//}
//w.Close();
}
public static SparseMatrix <double> Gis <LblT>(ILabeledExampleCollection <LblT, BinaryVector> dataset, int cutOff, int numIter, bool clearDataset, string mtxFileName, ref LblT[] idxToLbl, int numThreads, double allowedDiff, IEqualityComparer <LblT> lblCmp, Logger logger)
{
logger.Info("Gis", "Creating observation matrix ...");
SparseMatrix <double> observations = null;
if (Utils.VerifyFileNameOpen(mtxFileName))
{
BinarySerializer reader = new BinarySerializer(mtxFileName, FileMode.Open);
idxToLbl = new ArrayList <LblT>(reader).ToArray();
observations = new SparseMatrix <double>(reader);
reader.Close();
}
else
{
observations = CreateObservationMatrix(dataset, ref idxToLbl, lblCmp, logger);
if (Utils.VerifyFileNameCreate(mtxFileName))
{
BinarySerializer writer = new BinarySerializer(mtxFileName, FileMode.Create);
new ArrayList <LblT>(idxToLbl).Save(writer);
observations.Save(writer);
writer.Close();
}
}
int numClasses = observations.GetLastNonEmptyRowIdx() + 1;
int numExamples = dataset.Count;
if (cutOff > 0)
{
logger.Info("Gis", "Performing cut-off ...");
observations = CutOff(observations, cutOff);
}
logger.Info("Gis", "Preparing structures ...");
SparseMatrix <double> lambda = CopyStructure(observations);
SparseMatrix <double> expectations = CopyStructure(observations);
double f = GisFindMaxF(dataset);
SparseMatrix <double> trainMtxTr = TransposeDataset(dataset, clearDataset);
logger.Info("Gis", "Entering main loop ...");
double[] oldLambda = null;
if (allowedDiff > 0)
{
oldLambda = new double[lambda.CountValues()];
}
for (int i = 0; i < numIter; i++)
{
logger.Info("Gis", "Iteration {0} / {1} ...", i + 1, numIter);
logger.Info("Gis", "Updating expectations ...");
if (numThreads > 1)
{
UpdateExpectationMatrix(numClasses, numExamples, trainMtxTr, lambda, expectations, numThreads, logger);
}
else
{
UpdateExpectationMatrix(numClasses, numExamples, trainMtxTr, lambda, expectations, logger);
}
logger.Info("Gis", "Updating lambdas ...");
GisUpdate(lambda, expectations, observations, f);
Reset(expectations);
// check lambda change
if (allowedDiff > 0)
{
int j = 0;
double maxDiff = 0;
foreach (IdxDat <SparseVector <double> > row in lambda)
{
foreach (IdxDat <double> item in row.Dat)
{
double diff = Math.Abs(item.Dat - oldLambda[j]);
if (diff > maxDiff)
{
maxDiff = diff;
}
oldLambda[j] = item.Dat;
j++;
}
}
logger.Info("Gis", "Max lambda diff: {0:0.0000}", maxDiff);
if (maxDiff <= allowedDiff)
{
logger.Info("Gis", "Max lambda diff is small enough. Exiting optimization loop.");
break;
}
}
}
return(lambda);
}
public static SparseMatrix <double> Gis <LblT>(IExampleCollection <LblT, BinaryVector <int> .ReadOnly> dataset, int cut_off, int num_iter, bool clear_dataset, string mtx_file_name, ref LblT[] idx_to_lbl, int num_threads)
{
Utils.VerboseLine("Creating observation matrix ...");
SparseMatrix <double> observations = null;
if (Utils.VerifyFileNameOpen(mtx_file_name))
{
BinarySerializer reader = new BinarySerializer(mtx_file_name, FileMode.Open);
idx_to_lbl = new ArrayList <LblT>(reader).ToArray();
observations = new SparseMatrix <double>(reader);
reader.Close();
}
else
{
observations = CreateObservationMatrix2(dataset, ref idx_to_lbl);
//SparseMatrix<double> test = CreateObservationMatrix(dataset, ref idx_to_lbl);
//Console.WriteLine(test.ContentEquals(observations));
if (Utils.VerifyFileNameCreate(mtx_file_name))
{
BinarySerializer writer = new BinarySerializer(mtx_file_name, FileMode.Create);
new ArrayList <LblT>(idx_to_lbl).Save(writer);
observations.Save(writer);
writer.Close();
}
}
int num_classes = observations.GetLastNonEmptyRowIdx() + 1;
int num_examples = dataset.Count;
if (cut_off > 0)
{
Utils.VerboseLine("Performing cut-off ...");
observations = CutOff(observations, cut_off);
}
Utils.VerboseLine("Preparing structures ...");
SparseMatrix <double> lambda = CopyStructure(observations);
SparseMatrix <double> expectations = CopyStructure(observations);
double f = GisFindMaxF(dataset);
SparseMatrix <double> train_mtx_tr = TransposeDataset(dataset, clear_dataset);
Utils.VerboseLine("Entering main loop ...");
for (int i = 0; i < num_iter; i++)
{
Utils.VerboseLine("Iteration {0} / {1} ...", i + 1, num_iter);
Utils.VerboseLine("Updating expectations ...");
if (num_threads > 1)
{
UpdateExpectationMatrix(num_classes, num_examples, train_mtx_tr, lambda, expectations, num_threads);
}
else
{
UpdateExpectationMatrix(num_classes, num_examples, train_mtx_tr, lambda, expectations);
}
Utils.VerboseLine("Updating lambdas ...");
GisUpdate(lambda, expectations, observations, f);
//SaveForMatlab(expectations, "c:\\mec\\old\\expem.txt");
Reset(expectations);
}
//SaveForMatlab(lambda, "c:\\mec\\old\\lamem.txt");
//SaveForMatlab(observations, "c:\\mec\\old\\obsem.txt");
return(lambda);
}
static void Main(string[] args)
{
try
{
if (args.Length < 2)
{
OutputHelp();
}
else
{
string corpusFileName = null, modelFileName = null, lexiconFileName = null;
bool considerTag = false;
bool treeOpt = false;
bool verbose = false;
if (ParseParams(args, ref verbose, ref considerTag, ref treeOpt, ref corpusFileName, ref modelFileName, ref lexiconFileName))
{
Logger logger = Logger.GetRootLogger();
if (!verbose)
{
logger.LocalLevel = Logger.Level.Off;
logger.LocalProgressOutputType = Logger.ProgressOutputType.Off;
}
else
{
logger.LocalOutputType = Logger.OutputType.Custom;
Logger.CustomOutput = new Logger.CustomOutputDelegate(delegate(string loggerName, Logger.Level level, string funcName, Exception e,
string message, object[] msgArgs) { Console.WriteLine(message, msgArgs); });
}
Corpus corpus = new Corpus();
logger.Info(/*funcName=*/ null, "Nalagam učni korpus ...");
corpus.LoadFromXmlFile(corpusFileName, /*tagLen=*/ int.MaxValue);
LemmatizerSettings lemmatizerSettings = new LemmatizerSettings();
lemmatizerSettings.eMsdConsider = considerTag ? LemmatizerSettings.MsdConsideration.Distinct : LemmatizerSettings.MsdConsideration.Ignore;
lemmatizerSettings.bUseFromInRules = true;
lemmatizerSettings.iMaxRulesPerNode = 0;
lemmatizerSettings.bBuildFrontLemmatizer = false;
lemmatizerSettings.bStoreAllFullKnownWords = false;
lemmatizerSettings.bUseMsdSplitTreeOptimization = treeOpt;
Lemmatizer lemmatizer = new Lemmatizer(lemmatizerSettings);
for (int i = 0; i < corpus.TaggedWords.Count; i++)
{
logger.ProgressFast(Logger.Level.Info, /*funcName=*/ null, "{0} / {1}", i + 1, corpus.TaggedWords.Count);
TaggedWord word = corpus.TaggedWords[i];
if (!word.MoreInfo.Punctuation)
{
lemmatizer.AddExample(word.WordLower, word.Lemma.ToLower(), 1, word.Tag);
}
}
if (lexiconFileName != null)
{
logger.Info(/*funcName=*/ null, "Nalagam leksikon ...");
StreamReader lexReader = new StreamReader(lexiconFileName);
string lexLine;
int i = 0;
while ((lexLine = lexReader.ReadLine()) != null)
{
// lexicon format: word \t lemma \t tag \t freq
logger.ProgressFast(Logger.Level.Info, /*funcName=*/ null, "{0}", ++i, /*numSteps=*/ 0);
string[] lexData = lexLine.Split('\t');
string word = lexData[0];
string lemma = lexData[1];
string tag = lexData[2];
double freq = Math.Max(0.1, Convert.ToDouble(lexData[3]));
lemmatizer.AddExample(word.ToLower(), lemma.ToLower(), freq, tag);
}
logger.ProgressFast(Logger.Level.Info, /*funcName=*/ null, "{0}", i, i);
lexReader.Close();
}
logger.Info(/*funcName=*/ null, "Gradim model za lematizacijo ...");
if (treeOpt)
{
string msdSpec = Utils.GetManifestResourceString(typeof(Program), "MsdSpecsSloSloCodes.txt");
MsdSplitTree.BeamSearchParams beamSearchParams = new MsdSplitTree.BeamSearchParams();
beamSearchParams.beamsPerLevel[0] = 2;
lemmatizer.BuildModel(msdSpec, beamSearchParams);
}
else
{
lemmatizer.BuildModel();
}
logger.Info(/*funcName=*/ null, "Optimiram lematizacijsko drevo ...");
lemmatizer.OptimizeMemorySize();
logger.Info(/*funcName=*/ null, "Zapisujem model ...");
BinarySerializer writer = new BinarySerializer(modelFileName, FileMode.Create);
writer.WriteBool(considerTag);
lemmatizer.Save(writer);
writer.Close();
logger.Info(/*funcName=*/ null, "Končano.");
}
}
}
catch (Exception exception)
{
Console.WriteLine();
Console.WriteLine("*** Nepričakovana napaka. Podrobnosti: {0}\r\n{1}", exception, exception.StackTrace);
}
}
public override void Run(object[] args)
{
// Create an ArrayList of sets of numbers and populate it.
ArrayList <Set <int> > array = new ArrayList <Set <int> >(
new Set <int>[] {
new Set <int>(new int[] { 1, 3, 5 }),
new Set <int>(new int[] { 2, 4, 6 }),
new Set <int>(new int[] { 1, 2, 3 })
});
Output.WriteLine(array);
// Output: ( { 1 3 5 } { 2 4 6 } { 1 2 3 } )
// Serialize the instance into memory.
BinarySerializer memSer = new BinarySerializer();
array.Save(memSer);
array = null; // Loose the instance.
// Deserialize the instance.
memSer.Stream.Position = 0;
array = new ArrayList <Set <int> >(memSer);
Output.WriteLine(array);
// Output: ( { 1 3 5 } { 2 4 6 } { 1 2 3 } )
// Serialize the instance into a file.
BinarySerializer fileSer = new BinarySerializer("array.bin",
FileMode.Create);
array.Save(fileSer);
fileSer.Close();
array = null; // Loose the instance.
// Deserialize the instance from the file.
fileSer = new BinarySerializer("array.bin", FileMode.Open);
array = new ArrayList <Set <int> >(fileSer);
fileSer.Close();
Output.WriteLine(array);
// Output: ( { 1 3 5 } { 2 4 6 } { 1 2 3 } )
// Create an instance of SerializableObject and assign the
// Tag member variable.
SerializableObject serObj = new SerializableObject();
serObj.Tag = array;
// Serialize serObj into memory and deserialize it again.
Output.WriteLine(serObj.Tag);
// Output: ( { 1 3 5 } { 2 4 6 } { 1 2 3 } )
memSer = new BinarySerializer();
serObj.Save(memSer);
serObj = null; // Loose the instance.
memSer.Stream.Position = 0;
serObj = new SerializableObject(memSer);
Output.WriteLine(serObj.Tag);
// Output: ( { 1 3 5 } { 2 4 6 } { 1 2 3 } )
}
public static LanguageDetector GetLanguageDetectorPrebuilt()
{
LanguageDetector ld = new LanguageDetector();
Assembly assembly = Assembly.GetExecutingAssembly();
foreach (string resName in assembly.GetManifestResourceNames())
{
if (resName.EndsWith(".ldp"))
{
// load language detector profile
BinarySerializer ser = new BinarySerializer(assembly.GetManifestResourceStream(resName));
LanguageProfile langProfile = new LanguageProfile(ser);
ser.Close();
ld.AddLanguageProfile(langProfile);
mLogger.Debug("GetLanguageDetectorPrebuilt", "Loaded resource {0}.", resName);
}
}
return ld;
}
static void Main(string[] args)
{
try
{
if (args.Length < 2)
{
OutputHelp();
}
else
{
int cutOff = 2;
int numIter = 50;
int numThreads = 1;
string corpusFileName = null, modelFileName = null, lexiconFileName = null;
bool verbose = false;
if (ParseParams(args, ref verbose, ref cutOff, ref numIter, ref numThreads, ref corpusFileName, ref modelFileName, ref lexiconFileName))
{
Logger logger = Logger.GetRootLogger();
if (!verbose)
{
logger.LocalLevel = Logger.Level.Off;
logger.LocalProgressOutputType = Logger.ProgressOutputType.Off;
}
else
{
logger.LocalOutputType = Logger.OutputType.Custom;
Logger.CustomOutput = new Logger.CustomOutputDelegate(delegate(string loggerName, Logger.Level level, string funcName, Exception e,
string message, object[] msgArgs) { Console.WriteLine(message, msgArgs); });
}
Corpus corpus = new Corpus();
logger.Info(/*funcName=*/ null, "Nalagam učni korpus ...");
corpus.LoadFromXmlFile(corpusFileName, /*tagLen=*/ -1);
GC.Collect();
long oldMemUse = Process.GetCurrentProcess().PrivateMemorySize64;
PatriciaTree suffixTree = new PatriciaTree();
foreach (TaggedWord word in corpus.TaggedWords)
{
suffixTree.AddWordTagPair(word.WordLower, word.Tag);
}
if (lexiconFileName != null)
{
logger.Info(/*funcName=*/ null, "Nalagam leksikon ...");
StreamReader lexReader = new StreamReader(lexiconFileName);
string lexLine;
while ((lexLine = lexReader.ReadLine()) != null)
{
string[] lexData = lexLine.Split('\t');
suffixTree.AddWordTagPair(lexData[0].ToLower(), lexData[2]);
}
lexReader.Close();
}
GC.Collect();
long memUse = Process.GetCurrentProcess().PrivateMemorySize64;
Console.WriteLine("Poraba pomnilnika (drevo končnic): {0:0.00} MB", (double)(memUse - oldMemUse) / 1048576.0);
oldMemUse = memUse;
suffixTree.PropagateTags();
GC.Collect();
memUse = Process.GetCurrentProcess().PrivateMemorySize64;
Console.WriteLine("Poraba pomnilnika (propagirane oznake): {0:0.00} MB", (double)(memUse - oldMemUse) / 1048576.0);
MaximumEntropyClassifierFast <string> model = new MaximumEntropyClassifierFast <string>();
LabeledDataset <string, BinaryVector> dataset = new LabeledDataset <string, BinaryVector>();
Dictionary <string, int> featureSpace = new Dictionary <string, int>();
logger.Info(/*funcName=*/ null, "Pripravljam vektorje značilk ...");
for (int i = 0; i < corpus.TaggedWords.Count; i++)
{
logger.ProgressFast(Logger.Level.Info, /*funcName=*/ null, "{0} / {1}", i + 1, corpus.TaggedWords.Count);
BinaryVector featureVector = corpus.GenerateFeatureVector(i, featureSpace, /*extendFeatureSpace=*/ true, suffixTree);
dataset.Add(corpus.TaggedWords[i].Tag, featureVector);
}
logger.Info(/*funcName=*/ null, "Gradim model ...");
DateTime startTime = DateTime.Now;
model.CutOff = cutOff;
model.NumThreads = numThreads;
model.NumIter = numIter;
model.Train(dataset);
TimeSpan span = DateTime.Now - startTime;
logger.Info(/*funcName=*/ null, "Trajanje gradnje modela: {0:00}:{1:00}:{2:00}.{3:000}.", span.Hours, span.Minutes, span.Seconds, span.Milliseconds);
logger.Info(/*funcName=*/ null, "Zapisujem model ...");
BinarySerializer writer = new BinarySerializer(modelFileName, FileMode.Create);
suffixTree.Save(writer);
Utils.SaveDictionary(featureSpace, writer);
model.Save(writer);
writer.Close();
logger.Info(/*funcName=*/ null, "Končano.");
}
}
}
catch (Exception exception)
{
Console.WriteLine();
Console.WriteLine("*** Nepričakovana napaka. Podrobnosti: {0}\r\n{1}", exception, exception.StackTrace);
}
}
