public JsonResult GetCategorySingleContent(InformationContentPostModel model)
{
try
{
InfrastructureModel <InformationContentSingleDataModel> response = new InfrastructureModel <InformationContentSingleDataModel>();
InformationContentSingleDataModel resultModel = new InformationContentSingleDataModel();
try
{
InformationContent informationContext = informationContentOperation.GetCategorySingleInformationContent(model.AppUserId, model.CategoryId);
resultModel.AuthorFullName = informationContext.Author.Name + " " + informationContext.Author.Surname;
resultModel.Explanation = informationContext.Explanation;
resultModel.ImagePath = informationContext.PostImagePath;
resultModel.LikeCount = informationContext.LikeCount.ToString();
resultModel.Title = informationContext.Title;
response.ResultModel = resultModel;
response.ResultStatus = true;
}
catch (Exception ex)
{
response.ResultStatus = false;
throw ex;
}
return(new JsonResult(response));
}
catch (Exception ex)
{
throw ex;
}
}
/// <summary>
/// Method for collecting candidates for replacing target words (at the sentence level). Candidates are retrieved as most similar words obtained via word embeddings (cosine similarity between embedding vectors)
/// </summary>
/// <param name="sentence">Sentence to be simplified</param>
/// <param name="substCandidates">List of all substitution candidates</param>
/// <param name="icTreshold">The information content treshold</param>
/// <param name="word">Target word to be replaced (if not provided, all content words are considered for replacing)</param>
public void CollectCandidates(SentenceAnnotation sentence, List <Tuple <TokenAnnotation, List <Tuple <string, double> > > > substCandidates, double icTreshold, string word = null)
{
List <string> sides = new List <string> {
"north", "west", "south", "east"
};
var contentTokens = sentence.Tokens.Where(t => t.IsContent() && !t.POSTag.StartsWith("C") && string.IsNullOrEmpty(t.NamedEntity) && !sides.Any(si => t.Text.ToLower().Contains(si))).ToList();
contentTokens.ForEach(ct => {
var ic = InformationContent.GetRelativeInformationContent(string.IsNullOrEmpty(ct.Lemma) ? ct.Text.ToLower() : ct.Lemma.ToLower());
if (ic > icTreshold && (!char.IsUpper(ct.Text[0]) || sentence.Tokens.IndexOf(ct) == 0))
{
if (word == null || ct.Text.ToLower() == word.ToLower())
{
var candidates = new List <Tuple <string, double> >();
var cands1 = VectorSpace.GetMostSimilar(ct.Text.ToLower(), 30);
if (cands1 != null)
{
candidates.AddRange(cands1);
}
if (!string.IsNullOrEmpty(ct.Lemma))
{
var cands2 = VectorSpace.GetMostSimilar(ct.Lemma.ToLower(), 30);
if (cands2 != null)
{
candidates.AddRange(cands2);
}
}
candidates = candidates.OrderByDescending(x => x.Item2).ToList();
substCandidates.Add(new Tuple <TokenAnnotation, List <Tuple <string, double> > >(ct, candidates));
}
}
});
}
public InformationContent GetSingleInformationContent(int appUserId = 0)
{
List <int> readList = informationReadLogOperation.GetReadedInformationContentByAppUserId(appUserId);
InformationContent informationContent = new InformationContent();
if (appUserId != 0)
{
informationContent = context.InformationContent.Include(s => s.Author).Include(a => a.Category).Where(s => s.IsActive && !s.IsDeleted && !readList.Contains(s.InformationContentId)).OrderBy(s => Guid.NewGuid()).Take(1).SingleOrDefault();
if (informationContent != null)
{
DATA.Models.InformationReadLog informationReadLog = new DATA.Models.InformationReadLog()
{
AppUserId = appUserId,
CreateDate = DateTime.Now,
InformationContentId = informationContent.InformationContentId,
IsActive = true,
IsDeleted = false,
};
informationReadLogOperation.InsertInformationContentReadList(informationReadLog);
}
}
else
{
informationContent = context.InformationContent.Where(s => s.IsActive && !s.IsDeleted).OrderBy(s => Guid.NewGuid()).Take(1).SingleOrDefault();
}
return(informationContent);
}
public InformationContent GetCategorySingleInformationContent(int appUserId = 0, int categoryId = 0)
{
List <int> readList = informationReadLogOperation.GetReadedInformationContentByAppUserId(appUserId);
InformationContent informationContent = new InformationContent();
if (readList != null)
{
informationContent = context.InformationContent.Include(s => s.Author).Include(a => a.Category).Where(s => s.IsActive && !s.IsDeleted && !readList.Contains(s.InformationContentId) && s.CategoryId == categoryId).OrderBy(s => Guid.NewGuid()).Take(1).SingleOrDefault();
}
else
{
informationContent = context.InformationContent.Where(s => s.IsActive && !s.IsDeleted && s.CategoryId == categoryId).OrderBy(s => Guid.NewGuid()).Take(1).SingleOrDefault();
}
return(informationContent);
}
public void DoRandomWalkThread(int start, int end, Landscape landscape, ResearchParameters parameters, IOperator op,
StringBuilder dataBuilder, Action <string, float> callback, string connectionId, float step)
{
for (int j = start; j < end; ++j)
{
var rwResult = landscape.RandomWalk(parameters.RandomWalkSteps, op);
float ac = Autocorrelation.Run(rwResult);
float ic = InformationContent.Run(rwResult, parameters.Sensitivity);
float pic = PartialInformationContent.Run(rwResult, parameters.Sensitivity);
float dbi = DensityBasinInformation.Run(rwResult, parameters.Sensitivity);
string line =
(float.IsNaN(ac) ? FLOAT_PATTERN : ac.ToString(FLOAT_PATTERN)) + SEPARATOR +
(float.IsNaN(ic) ? FLOAT_PATTERN : ic.ToString(FLOAT_PATTERN)) + SEPARATOR +
(float.IsNaN(pic) ? FLOAT_PATTERN : pic.ToString(FLOAT_PATTERN)) + SEPARATOR +
(float.IsNaN(dbi) ? FLOAT_PATTERN : dbi.ToString(FLOAT_PATTERN));
dataBuilder.AppendLine(line);
callback(connectionId, step);
}
}
/// <summary>
/// A place for experimenting/running code
/// </summary>
/// <param name="args">Command line arguments</param>
public static void Main(string[] args)
{
Console.WriteLine("Loading resources...");
var tmp = Console.Out;
Console.SetOut(TextWriter.Null);
// setting the culture to en-US (e.g., decimal point is used instead of decimal comma)
Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
#region Usage example code
// Loading the information contents based on word frequencies from a large corpus
InformationContent ic = new InformationContent(ConfigurationManager.AppSettings["other-resources"] + "\\word-freqs.txt");
// Loading the GloVe embeddings into an instance of WordVectorSpace class
WordVectorSpace wvs = new WordVectorSpace();
wvs.Load(ConfigurationManager.AppSettings["other-resources"] + "\\glove-vectors-6b-200d.txt", null);
// Preprocessing tools required for preprocessing the free-text document
AnnotatorChain ac = new AnnotatorChain(TakeLabCore.NLP.Language.English, new List<AnnotatorType> { AnnotatorType.SentenceSplitter, AnnotatorType.POSTagger, AnnotatorType.Morphology, AnnotatorType.NamedEntities });
// Instantiating the lexical simplifier
LexicalSimplifier simplifier = new LexicalSimplifier();
// setting the information content and word vector space properties
simplifier.InformationContent = ic;
simplifier.VectorSpace = wvs;
simplifier.CandidateInPoSLookup = TakeLab.Utilities.IO.StringLoader.LoadDictionaryStrings(ConfigurationManager.AppSettings["other-resources"] + "\\candidate-in-pos-lookup.txt");
// dummy simplification call to force loading of language model resources
var dLoad = new Document { Text = "want wish" };
dLoad.Annotate(ac);
simplifier.Simplify(dLoad, 5, 0.6, 0.5);
Console.SetOut(tmp);
Console.WriteLine("Loading resources done! \n\n [Light-LS v.0.9 Copyright TakeLab]\n Enter the text to simplify...\n\n");
while(true)
{
try
{
var line = Console.ReadLine();
if (string.IsNullOrEmpty(line)) break;
var document = new Document { Text = line };
document.Annotate(ac);
var dSimple = simplifier.Simplify(document, 5, 0.6, 0.5);
var substitutions = simplifier.LastSubstitutions;
foreach (var v in substitutions)
{
Console.WriteLine(v.Item1.Text + " -> " + v.Item2);
}
Console.WriteLine("Simplified text: \n");
Console.WriteLine(dSimple.Text);
Console.WriteLine("\n\n [Light-LS v.0.9 Copyright TakeLab]\n Enter the text to simplify...\n\n");
}
catch {
Console.WriteLine("Something went wrong, check your text for oddities and try again...");
}
}
#endregion
#region Evaluations Code (whole code commented)
//// Evaluating systems via human-assigned scores of grammaticality, simplicity, and meaning preservation
//#region Human evaluation
//Console.WriteLine("Grammaticality");
////var evalResSanja = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var sanjaGrammaticality = HumanEvaluation.AllAnnotatorScores;
////var evalResGoran = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var goranGrammaticality = HumanEvaluation.AllAnnotatorScores;
////Console.WriteLine("\nSanja");
////evalResSanja.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nGoran");
////evalResGoran.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nAgreement: " + Pearson.Correlation(sanjaGrammaticality, goranGrammaticality));
//var evalAvg = HumanEvaluation.EvaluateHumanDual(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
//Console.WriteLine("\nAveraged");
//evalAvg.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine();
//Console.WriteLine("\nSimplicity");
////evalResSanja = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var sanjaSimplicity = HumanEvaluation.AllAnnotatorScores;
////evalResGoran = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var goranSimplicity = HumanEvaluation.AllAnnotatorScores;
////Console.WriteLine("\nSanja");
////evalResSanja.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nGoran");
////evalResGoran.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nAgreement: " + Pearson.Correlation(sanjaSimplicity, goranSimplicity));
//evalAvg = HumanEvaluation.EvaluateHumanDual(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
//Console.WriteLine("\nAveraged");
//evalAvg.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine();
////Console.WriteLine("Meaning preservation");
////evalResSanja = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
////var sanjaMeaningPreservation = HumanEvaluation.AllAnnotatorScores;
////evalResGoran = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
////var goranMeaningPreservation = HumanEvaluation.AllAnnotatorScores;
////Console.WriteLine("\nSanja");
////evalResSanja.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nGoran");
////evalResGoran.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nAgreement: " + Pearson.Correlation(sanjaMeaningPreservation, goranMeaningPreservation));
//evalAvg = HumanEvaluation.EvaluateHumanDual(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
//Console.WriteLine("\nAveraged");
//evalAvg.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
//Console.WriteLine();
////Console.WriteLine("Changed");
////var evalResCh = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\changed.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
////evalResCh.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
//// Loading the information contents based on word frequencies from a large corpus
//InformationContent ic = new InformationContent(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Corpora\word-freqs.txt");
//// Loading the GloVe embeddings into an instance of WordVectorSpace class
//WordVectorSpace wvs = new WordVectorSpace();
//wvs.Load(@"C:\Goran\Korpusi\GloVe-Vectors\glove-vectors-6b-200d\glove-vectors-6b-200d.txt", null);
//// Instantiating the lexical simplifier
//LexicalSimplifier simplifier = new LexicalSimplifier();
//// setting the information content and word vector space properties
//simplifier.InformationContent = ic;
//simplifier.VectorSpace = wvs;
//simplifier.CandidateInPoSLookup = TakeLab.Utilities.IO.StringLoader.LoadDictionaryStrings(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\candidate-in-pos-lookup.txt");
//// Evaluating systems via human-assigned scores of grammaticality, simplicity, and meaning preservation
//#region Human Evaluation Task
//#region Preparing files for evaluation
////var dirPath = @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\";
////var originalLines = (new StreamReader(dirPath + "HA-Wiki-original.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var humanLines = (new StreamReader(dirPath + "HA-Wiki-manualSimplification.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var embeSimpLines = (new StreamReader(dirPath + "HA-Wiki-EmbeSimp.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var biranLines = (new StreamReader(dirPath + "HA-Wiki-Biran-simplified.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var gramSimpWriter = new StreamWriter(dirPath + "simplification-grades.txt");
////var meanPresWriter = new StreamWriter(dirPath + "meaning-preservation-grades.txt");
////Dictionary<Guid, string> codings = new Dictionary<Guid, string>();
////for (int i = 0; i < originalLines.Count; i++)
////{
//// List<AnnotationEntry> entries = new List<AnnotationEntry>();
//// var entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = originalLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "original");
//// entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = humanLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "manual");
//// entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = biranLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "biran");
//// entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = embeSimpLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "embesimp");
//// entries = entries.OrderBy(x => x.ID).ToList();
//// entries.ForEach(x => gramSimpWriter.WriteLine(x.ID + " " + x.Grade + " \"" + x.Sentence + "\""));
//// gramSimpWriter.WriteLine();
//// var original = entries.Where(x => codings[x.ID] == "original").Single();
//// var other = entries.Where(x => codings[x.ID] != "original").ToList();
//// other.ForEach(x =>
//// {
//// meanPresWriter.WriteLine("Original: " + "\"" + original.Sentence + "\"");
//// meanPresWriter.WriteLine("Simplified: " + x.ID + " " + x.Grade + " \"" + x.Sentence + "\"");
//// meanPresWriter.WriteLine();
//// });
//// meanPresWriter.WriteLine();
//// meanPresWriter.WriteLine();
////}
////gramSimpWriter.Close();
////meanPresWriter.Close();
////StreamWriter codingsWriter = new StreamWriter(dirPath + "codings.txt");
////codings.ToList().ForEach(c =>
////{
//// codingsWriter.WriteLine(c.Key.ToString() + " " + c.Value);
////});
////codingsWriter.Close();
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
////var wikiSentences = TakeLab.Utilities.IO.StringLoader.LoadList(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\HA-Wiki-original.txt");
////var wikiWriter = new StreamWriter(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\HA-Wiki-EmbeSimp.txt");
////AnnotatorChain annChain = new AnnotatorChain(TakeLabCore.NLP.Language.English, new List<AnnotatorType> { AnnotatorType.SentenceSplitter, AnnotatorType.POSTagger, AnnotatorType.Morphology, AnnotatorType.NamedEntities });
//////wikiSentences = wikiSentences.OrderBy(x => Guid.NewGuid()).ToList();
////for (int i = 0; i < wikiSentences.Count; i++)
////{
//// var document = new Document { Text = wikiSentences[i] };
//// document.Annotate(annChain);
//// var dSimple = simplifier.Simplify(document, 5, 0.55, 0.55);
//// wikiWriter.WriteLine(dSimple.Text);
//// Console.WriteLine(document.Text);
//// Console.WriteLine(dSimple.Text);
//// Console.WriteLine();
////}
////wikiWriter.Close();
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
//// Automated evaluation on the ranking lexical simplification task from SemEval 2012
//#region Ranking task
////StreamWriter rankingsWriter = new StreamWriter(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\ranking-task-semeval\test-dataset\gorankings.txt");
////var datasetRanking = DatasetLoader.LoadRankingTasksDataset(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\ranking-task-semeval\test-dataset\contexts.xml", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\ranking-task-semeval\test-dataset\substitutions.gold-rankings");
////datasetRanking = datasetRanking.OrderBy(x => x.ID).ToList();
////datasetRanking.ForEach(rt => {
//// Console.WriteLine("Processing example: " + datasetRanking.IndexOf(rt));
//// var candidateRanking = simplifier.RankSimplificationCandidates(rt, 5);
//// rankingsWriter.Write("Sentence " + rt.ID.ToString() + " rankings: ");
//// for (int i = 0; i < candidateRanking.Count; i++)
//// {
//// rankingsWriter.Write("{" + candidateRanking[i] + "} ");
//// //Console.WriteLine(candidateRanking[i]);
//// }
//// rankingsWriter.WriteLine();
//// //Console.WriteLine();
//// //Console.ReadLine();
////});
////rankingsWriter.Close();
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
//// Automated evaluation on the crowdsourced replacement dataset (Kavuhcuk et al., ACL 2014)
//#region Replacement task
//AnnotatorChain ac = new AnnotatorChain(TakeLabCore.NLP.Language.English, new List<AnnotatorType> { AnnotatorType.SentenceSplitter, AnnotatorType.POSTagger, AnnotatorType.Morphology, AnnotatorType.NamedEntities });
//var dataset = DatasetLoader.LoadMechanicalTurkTDataset(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\lex.mturk.txt");//.OrderBy(x => Guid.NewGuid()).ToList();
//List<string> systemReplacements = new List<string>();
//List<List<string>> systemReplacementCandidates = new List<List<string>>();
//StreamWriter replacementOutputWriter = new StreamWriter(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Output\replacements.txt");
//for(int i = 0; i < dataset.Count; i++)
//{
// Console.WriteLine("Simplifying sentence {0}: ", i);
// Document d = new Document { Text = dataset[i].Item1 };
// ac.Apply(d);
// var dSimple = simplifier.Simplify(d, 5, 0, 0, dataset[i].Item2);
// Console.WriteLine("Original: " + d.Text);
// Console.WriteLine("Simplified: " + dSimple.Text);
// Console.WriteLine();
// var targetSimplification = simplifier.LastSubstitutions.Where(s => s.Item1.Text.ToLower() == dataset[i].Item2.ToLower()).FirstOrDefault();
// if (targetSimplification == null)
// {
// systemReplacements.Add(string.Empty);
// systemReplacementCandidates.Add(new List<string>());
// }
// else
// {
// systemReplacements.Add(targetSimplification.Item2);
// systemReplacementCandidates.Add(simplifier.LastSubstitutionCandidates[0].Item2);
// }
// Console.WriteLine("Change: {0} -> {1}", dataset[i].Item2, targetSimplification != null ? targetSimplification.Item2 : "<empty>");
// //Console.ReadLine();
// replacementOutputWriter.WriteLine(d.Text);
// replacementOutputWriter.WriteLine("Target: " + dataset[i].Item2);
// replacementOutputWriter.WriteLine("Simplification: " + ((targetSimplification != null) ? targetSimplification.Item2 : "<empty>"));
// replacementOutputWriter.WriteLine("All candidates: ");
// simplifier.LastSubstitutionCandidates[0].Item2.ForEach(x => replacementOutputWriter.WriteLine(x));
// replacementOutputWriter.WriteLine();
//}
//replacementOutputWriter.Close();
//var evaluator = new ReplacementEvaluator();
//var precision = evaluator.EvaluatePrecision(dataset, systemReplacements);
//var accuracy = evaluator.EvaluateAccuracy(dataset, systemReplacements);
//var changed = evaluator.EvaluateChanged(dataset, systemReplacements);
//var precisionCandidate = evaluator.EvaluateCandidatePrecision(dataset, systemReplacementCandidates);
//var accuracyCandidate = evaluator.EvaluateCandidateAccuracy(dataset, systemReplacementCandidates);
//Console.WriteLine("Precision: {0}\nAccuracy: {1}\nChanged: {2}\n", precision, accuracy, changed);
//Console.WriteLine("Soft precision: {0}\nSoft accuracy: {1}\n Changed: {2}\n ", precisionCandidate, accuracyCandidate, changed);
//TakeLab.Utilities.IO.StringWriter<string>.WriteDictionary<string>(simplifier.CandidateInPoSLookup, @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\candidate-in-pos-lookup.txt");
//#endregion
//#region Wiki preprocessing
////WikipediaPreprocessing wpp = new WikipediaPreprocessing();
////wpp.Preprocess(@"C:\Goran\Korpusi\Wikipedia\preprocessed");
////TakeLabCore.NLP.Annotations.Document doc = new TakeLabCore.NLP.Annotations.Document{ Text = "he doctor who pulled Harrison Ford from the wreck of a plane crash has described how he feared a fireball from the aircraft's leaking fuel. Spine surgeon Sanjay Khurana was playing golf in Los Angeles when the American actor's vintage plane \"belly-flopped\" down on to the eighth hole." };
////(new TakeLabCore.NLP.Annotators.English.EngNER()).Annotate(doc);
////doc.NamedEntities.ForEach(ne => Console.WriteLine(ne.Text + " " + ne.NEType + " " + ne.StartPosition));
////var simnet = new SimilarityNet();
////simnet.SimpleLoad(@"C:\Goran\Korpusi\GloVe-Vectors\glove-vectors-6b-200d\glove-vectors-6b-200d.txt");
////while (true)
////{
//// var line = Console.ReadLine();
//// if (string.IsNullOrEmpty(line)) break;
//// else
//// {
//// var mostSimilar = simnet.GetMostSimilar(line.Trim(), 20);
//// if (mostSimilar != null) mostSimilar.ForEach(ms => Console.WriteLine(ms.Item1 + " " + ms.Item2));
//// else Console.WriteLine("Word not found in vocabulary, try again!");
//// Console.WriteLine();
//// }
////}
//#endregion
#endregion
}
/// <summary>
/// Oredring the candidates for the ranking task, when the candidates are given. The ordering is somewhat different
/// </summary>
/// <param name="document">The document whose text is to be simplified</param>
/// <param name="substitutionCandidates">Substitution candidates</param>
/// <param name="target">Target word</param>
/// <param name="contextSize">The size of the context of the target word to be compared semantically with candidate replacements</param>
/// <returns></returns>
public List <string> OrderGivenSubstitutionCandidates(Document document, List <string> substitutionCandidates, string target, int contextSize)
{
EngMorphology morph = new EngMorphology();
List <Tuple <TokenAnnotation, string> > substitutions = new List <Tuple <TokenAnnotation, string> >();
//List<string> metrics = new List<string> { "sim", "ic-diff", "context-sim", "length", "lm-bigram-pre", "lm-bigram-post", "lm-trigram-pre", "lm-trigram-post" };
List <string> metrics = new List <string> {
"context-sim" /*, "lm-bigram-pre", "lm-bigram-post"*/, "ic-diff" /*, "lm-trigram-pre", "lm-trigram-post"*/
};
//List<string> metrics = new List<string> { "ic-diff" };
Dictionary <string, string> candidateChanges = new Dictionary <string, string>();
var targetToken = document.AllTokens.Where(t => t.Text == target).Last();
var preceedingSentencePart = document.Text.Substring(0, targetToken.StartPositionSentence);
var followingSentencePart = document.Text.Substring(targetToken.StartPositionSentence + targetToken.Text.Length);
var targetLemmaIC = InformationContent.GetRelativeInformationContent(targetToken.Lemma.ToLower());
var targetWordIC = InformationContent.GetRelativeInformationContent(targetToken.Text.ToLower());
var targetContextTokens = document.AllTokens.Where(t => Math.Abs(document.AllTokens.IndexOf(t) - targetToken.SentenceIndex) > 0 && Math.Abs(document.AllTokens.IndexOf(t) - targetToken.SentenceIndex) <= contextSize && t.IsContent()).ToList();
Dictionary <string, Dictionary <string, double> > scores = new Dictionary <string, Dictionary <string, double> >();
substitutionCandidates.ForEach(candidate => {
var candidateText = candidate;
if (candidateText.Contains(","))
{
var splitCand = candidateText.Split(new char[] { ',' }, StringSplitOptions.RemoveEmptyEntries).ToList();
string selected = string.Empty;
double maxIC = double.MinValue;
splitCand.ForEach(sc =>
{
var scic = InformationContent.GetRelativeInformationContent(sc.Trim().ToLower());
if (scic > maxIC)
{
selected = sc.Trim();
maxIC = scic;
}
});
candidateText = selected;
}
if (candidateText.Trim().Contains(" "))
{
var tokens = (new EngPOSTagger()).Annotate(candidateText.Trim()).ToList();
var contentTokens = tokens.Where(x => ((TokenAnnotation)x).IsContent()).ToList();
var change = contentTokens.Count > 0 ? ((TokenAnnotation)(contentTokens.Last())).Text.Trim() : ((TokenAnnotation)(tokens.First())).Text.Trim();
candidateText = change;
}
var candidateLemmaIC = InformationContent.GetRelativeInformationContent(candidateText.ToLower());
var candidateWordIC = !string.IsNullOrEmpty(candidateText) ? InformationContent.GetRelativeInformationContent(candidateText.ToLower()) : 1;
var candidateIC = candidateWordIC == 1 ? candidateLemmaIC : candidateWordIC;
var targetIC = targetWordIC == 1 ? targetLemmaIC : targetWordIC;
var candidateContextSimilarities = targetContextTokens.Select(x => VectorSpace.Similarity(x.Lemma.ToLower(), candidateText.ToLower())).Where(x => x >= -1).ToList();
var candidateContextSimilarity = candidateContextSimilarities.Count > 0 ? candidateContextSimilarities.Average() : 0;
scores.Add(candidate, new Dictionary <string, double>());
var sim = VectorSpace.Similarity(targetToken.Text.ToLower().Trim(), candidateText.ToLower().Trim());
if (sim < 1)
{
scores[candidate].Add("sim", sim);
}
scores[candidate].Add("ic-diff", candidateIC);
scores[candidate].Add("context-sim", candidateContextSimilarity);
scores[candidate].Add("length", candidateText.Length);
var tokenIndex = document.AllTokens.IndexOf(targetToken);
// bigram LM
if (tokenIndex > 0)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(document.AllTokens[document.AllTokens.IndexOf(targetToken) - 1].Text.ToLower(), candidateText);
scores[candidate].Add("lm-bigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-bigram-pre", 0);
}
if (tokenIndex < document.AllTokens.Count - 1)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(candidateText, document.AllTokens[document.AllTokens.IndexOf(targetToken) + 1].Text.ToLower());
scores[candidate].Add("lm-bigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-bigram-pre", 0);
}
// trigram LM
if (tokenIndex > 1)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(document.AllTokens[document.AllTokens.IndexOf(targetToken) - 2].Text.ToLower(), document.AllTokens[document.AllTokens.IndexOf(targetToken) - 1].Text.ToLower(), candidateText);
scores[candidate].Add("lm-trigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-trigram-pre", 0);
}
if (tokenIndex < document.AllTokens.Count - 2)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(candidateText, document.AllTokens[document.AllTokens.IndexOf(targetToken) + 1].Text.ToLower(), document.AllTokens[document.AllTokens.IndexOf(targetToken) + 2].Text.ToLower());
scores[candidate].Add("lm-trigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-trigram-post", 0);
}
});
LastSubstitutionCandidates = new List <Tuple <TokenAnnotation, List <string> > >();
LastSubstitutionCandidates.Add(new Tuple <TokenAnnotation, List <string> >(targetToken, scores.Select(x => x.Key).ToList()));
var allRanks = new List <Dictionary <string, int> >();
metrics.ForEach(m =>
{
var featDict = scores.Where(x => x.Value.ContainsKey(m)).ToDictionary(x => x.Key, x => x.Value[m]);
allRanks.Add(TrainingExample.RankExamplesByNumericFeature(featDict, m == "length" || m == "ic-diff"));
});
var allCandidates = scores.Select(x => x.Key).ToList();
Dictionary <string, double> averageRankings = allCandidates.ToDictionary(x => x, x => allRanks.Where(y => y.ContainsKey(x)).Select(r => r[x]).Average());
return(averageRankings.OrderBy(r => r.Value).Select(x => x.Key).ToList());
}
/// <summary>
/// Choosing the substitutions for target words. This means we rank the candidates according to several features (similarity with target word, information content reduction, similarity with context words, ...)
/// </summary>
/// <param name="document">The document whose text is to be simplified</param>
/// <param name="substCandidates">All the pairs of target words and collected candidate replacements</param>
/// <param name="contextSize">The size of the context of the target word to be considered for measuring the similarity between candidate words and target word context</param>
/// <param name="noSubstitutionWords">Stopwords, never to be considered for simplification</param>
/// <param name="similarityTreshold">The treshold for semantic similarity between target word and candidate replacement</param>
/// <param name="icReplacementCandidateTreshold">Information content treshold for replacing the target word</param>
/// <param name="word"></param>
/// <returns>The list of substitutions (tuple of target token and candidate replacement word)</returns>
public List <Tuple <TokenAnnotation, string> > GetSubstitutions(Document document, List <Tuple <TokenAnnotation, List <Tuple <string, double> > > > substCandidates, int contextSize, List <string> noSubstitutionWords, double similarityTreshold, double icReplacementCandidateTreshold, string word = null)
{
EngMorphology morph = new EngMorphology();
List <Tuple <TokenAnnotation, string> > substitutions = new List <Tuple <TokenAnnotation, string> >();
List <string> metrics = new List <string> {
"sim", "ic-diff", "context-sim", "lm-bigram-pre", "lm-bigram-post", "lm-trigram-pre", "lm-trigram-post"
};
//List<string> metrics = new List<string> { "sim", "lm-bigram-pre", "lm-bigram-post", /*"ic-diff", "lm-trigram-pre", "lm-trigram-post"*/ };
substCandidates.ForEach(sc => {
if (!noSubstitutionWords.Contains(sc.Item1.Text.ToLower()))
{
if (word == null || sc.Item1.Text.ToLower() == word)
{
Dictionary <string, Dictionary <string, double> > scores = new Dictionary <string, Dictionary <string, double> >();
var targetToken = sc.Item1;
var sentence = document.Sentences.Where(s => s.Tokens.Any(t => t.StartPosition == targetToken.StartPosition && t.Text == targetToken.Text)).Single();
var targetTokenCopy = sentence.Tokens.Where(t => t.StartPosition == targetToken.StartPosition && t.Text == targetToken.Text).Single();
var preceedingSentencePart = sentence.Text.Substring(0, targetTokenCopy.StartPositionSentence);
var followingSentencePart = sentence.Text.Substring(targetTokenCopy.StartPositionSentence + targetTokenCopy.Text.Length);
var targetLemmaIC = InformationContent.GetRelativeInformationContent(targetToken.Lemma.ToLower());
var targetWordIC = InformationContent.GetRelativeInformationContent(targetToken.Text.ToLower());
var targetContextTokens = sentence.Tokens.Where(t => Math.Abs(sentence.Tokens.IndexOf(t) - targetTokenCopy.SentenceIndex) > 0 && Math.Abs(sentence.Tokens.IndexOf(t) - targetTokenCopy.SentenceIndex) <= contextSize && t.IsContent()).ToList();
var targetCtxtSimilarities = targetContextTokens.Select(x => VectorSpace.Similarity(x.Lemma.ToLower(), targetToken.Lemma.ToLower())).Where(x => x >= -1).ToList();
var targetContextSimilarity = targetCtxtSimilarities.Count > 0 ? targetCtxtSimilarities.Average() : 0;
if (sc.Item2 != null)
{
sc.Item2.ForEach(candidate =>
{
try
{
var candidateLemmaIC = InformationContent.GetRelativeInformationContent(candidate.Item1.ToLower());
string key = candidate.Item1 + "<->" + targetToken.POSTag;
//var candidateInPOS = EngMorphology.GetForm(candidate.Item1, targetToken.POSTag);
//if (!CandidateInPoSLookup.ContainsKey(key)) CandidateInPoSLookup.Add(key, candidateInPOS);
var candidateInPOS = CandidateInPoSLookup.ContainsKey(key) ? CandidateInPoSLookup[key] : candidate.Item1;
var candidateWordIC = !string.IsNullOrEmpty(candidateInPOS) ? InformationContent.GetRelativeInformationContent(candidateInPOS.ToLower()) : 1;
var candidateIC = candidateWordIC == 1 ? candidateLemmaIC : candidateWordIC;
var targetIC = targetWordIC == 1 ? targetLemmaIC : targetWordIC;
if (!string.IsNullOrEmpty(candidateInPOS) && targetLemmaIC > icReplacementCandidateTreshold && (candidateIC < targetIC /*|| Math.Abs(targetIC - candidateIC) < 0.05*/))
{
var artificialSentence = preceedingSentencePart + candidateInPOS + followingSentencePart;
var artTokens = (new EngPOSTagger()).Annotate(artificialSentence).Select(x => (TokenAnnotation)x).ToList();
morph.AnnotateMorphology(artTokens);
var candidateToken = artTokens.Where(x => x.StartPositionSentence == targetTokenCopy.StartPositionSentence /*&& x.Text == candidateInPOS*/).Single();
var candidateContextSimilarities = targetContextTokens.Select(x => VectorSpace.Similarity(x.Lemma.ToLower(), candidateToken.Lemma.ToLower())).Where(x => x >= -1).ToList();
var candidateContextSimilarity = candidateContextSimilarities.Count > 0 ? candidateContextSimilarities.Average() : targetContextSimilarity;
// POS-tag compatibility is a second prerequisite
bool sameWord = candidate.Item1.Contains(targetToken.Text) || targetToken.Text.Contains(candidate.Item1) ||
candidate.Item1.Contains(targetToken.Lemma) || targetToken.Lemma.Contains(candidate.Item1) ||
candidateInPOS.Contains(targetToken.Text) || targetToken.Text.Contains(candidateInPOS) ||
candidateInPOS.Contains(targetToken.Lemma) || targetToken.Lemma.Contains(candidateInPOS);
bool sameAsContext = targetContextTokens.Any(ct =>
candidate.Item1.Contains(ct.Text) || ct.Text.Contains(candidate.Item1) ||
candidate.Item1.Contains(ct.Lemma) || ct.Lemma.Contains(candidate.Item1) ||
candidateInPOS.Contains(ct.Text) || ct.Text.Contains(candidateInPOS) ||
candidateInPOS.Contains(ct.Lemma) || ct.Lemma.Contains(candidateInPOS));
if (candidate.Item2 >= similarityTreshold && (candidateToken.POSTag == targetToken.POSTag) && !sameWord && !sameAsContext)
{
if (!scores.ContainsKey(candidateInPOS))
{
scores.Add(candidateInPOS, new Dictionary <string, double>());
scores[candidateInPOS].Add("sim", candidate.Item2);
scores[candidateInPOS].Add("ic-diff", targetIC - candidateIC);
scores[candidateInPOS].Add("context-sim", candidateContextSimilarity);
scores[candidateInPOS].Add("length", candidateInPOS.Length);
var tokenIndex = sentence.Tokens.IndexOf(targetTokenCopy);
// bigram LM
if (tokenIndex > 0)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) - 1].Text.ToLower(), candidateInPOS);
scores[candidateInPOS].Add("lm-bigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-bigram-pre", 0);
}
if (tokenIndex < sentence.Tokens.Count - 1)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(candidateInPOS, sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) + 1].Text.ToLower());
scores[candidateInPOS].Add("lm-bigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-bigram-post", 0);
}
// trigram LM
if (tokenIndex > 1)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) - 2].Text.ToLower(), sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) - 1].Text.ToLower(), candidateInPOS);
scores[candidateInPOS].Add("lm-trigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-trigram-pre", 0);
}
if (tokenIndex < sentence.Tokens.Count - 2)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(candidateInPOS, sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) + 1].Text.ToLower(), sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) + 2].Text.ToLower());
scores[candidateInPOS].Add("lm-trigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-trigram-post", 0);
}
}
}
}
}
catch { }
});
}
LastSubstitutionCandidates = new List <Tuple <TokenAnnotation, List <string> > >();
LastSubstitutionCandidates.Add(new Tuple <TokenAnnotation, List <string> >(targetToken, scores.Select(x => x.Key).ToList()));
if (scores.Count > 0)
{
var allRanks = new List <Dictionary <string, int> >();
metrics.ForEach(m => {
var featDict = scores.ToDictionary(x => x.Key, x => x.Value[m]);
allRanks.Add(TrainingExample.RankExamplesByNumericFeature(featDict, m == "length"));
});
var allCandidates = scores.Select(x => x.Key).ToList();
Dictionary <string, double> averageRankings = allCandidates.ToDictionary(x => x, x => allRanks.Select(r => r[x]).Average());
var finalRanking = averageRankings.OrderBy(r => r.Value).ToList();
double topScore = finalRanking[0].Value;
var equal = new List <string>();
finalRanking.ForEach(fr => {
if (fr.Value == topScore)
{
equal.Add(fr.Key);
}
});
var finalChoice = equal.Where(eq => equal.Where(eq2 => eq2 != eq).All(eq2 => scores[eq]["sim"] >= scores[eq2]["sim"])).First();
substitutions.Add(new Tuple <TokenAnnotation, string>(targetToken, finalChoice));
}
}
}
});
return(substitutions);
}
public void Insert(InformationContent entity)
{
context.InformationContent.Add(entity);
context.SaveChanges();
}
/// <summary>
/// A place for experimenting/running code
/// </summary>
/// <param name="args">Command line arguments</param>
public static void Main(string[] args)
{
Console.WriteLine("Loading resources...");
var tmp = Console.Out;
Console.SetOut(TextWriter.Null);
// setting the culture to en-US (e.g., decimal point is used instead of decimal comma)
Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
#region Usage example code
// Loading the information contents based on word frequencies from a large corpus
InformationContent ic = new InformationContent(ConfigurationManager.AppSettings["other-resources"] + "\\word-freqs.txt");
// Loading the GloVe embeddings into an instance of WordVectorSpace class
WordVectorSpace wvs = new WordVectorSpace();
wvs.Load(ConfigurationManager.AppSettings["other-resources"] + "\\glove-vectors-6b-200d.txt", null);
// Preprocessing tools required for preprocessing the free-text document
AnnotatorChain ac = new AnnotatorChain(TakeLabCore.NLP.Language.English, new List <AnnotatorType> {
AnnotatorType.SentenceSplitter, AnnotatorType.POSTagger, AnnotatorType.Morphology, AnnotatorType.NamedEntities
});
// Instantiating the lexical simplifier
LexicalSimplifier simplifier = new LexicalSimplifier();
// setting the information content and word vector space properties
simplifier.InformationContent = ic;
simplifier.VectorSpace = wvs;
simplifier.CandidateInPoSLookup = TakeLab.Utilities.IO.StringLoader.LoadDictionaryStrings(ConfigurationManager.AppSettings["other-resources"] + "\\candidate-in-pos-lookup.txt");
// dummy simplification call to force loading of language model resources
var dLoad = new Document {
Text = "want wish"
};
dLoad.Annotate(ac);
simplifier.Simplify(dLoad, 5, 0.6, 0.5);
Console.SetOut(tmp);
Console.WriteLine("Loading resources done! \n\n [Light-LS v.0.9 Copyright TakeLab]\n Enter the text to simplify...\n\n");
while (true)
{
try
{
var line = Console.ReadLine();
if (string.IsNullOrEmpty(line))
{
break;
}
var document = new Document {
Text = line
};
document.Annotate(ac);
var dSimple = simplifier.Simplify(document, 5, 0.6, 0.5);
var substitutions = simplifier.LastSubstitutions;
foreach (var v in substitutions)
{
Console.WriteLine(v.Item1.Text + " -> " + v.Item2);
}
Console.WriteLine("Simplified text: \n");
Console.WriteLine(dSimple.Text);
Console.WriteLine("\n\n [Light-LS v.0.9 Copyright TakeLab]\n Enter the text to simplify...\n\n");
}
catch {
Console.WriteLine("Something went wrong, check your text for oddities and try again...");
}
}
#endregion
#region Evaluations Code (whole code commented)
//// Evaluating systems via human-assigned scores of grammaticality, simplicity, and meaning preservation
//#region Human evaluation
//Console.WriteLine("Grammaticality");
////var evalResSanja = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var sanjaGrammaticality = HumanEvaluation.AllAnnotatorScores;
////var evalResGoran = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var goranGrammaticality = HumanEvaluation.AllAnnotatorScores;
////Console.WriteLine("\nSanja");
////evalResSanja.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nGoran");
////evalResGoran.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nAgreement: " + Pearson.Correlation(sanjaGrammaticality, goranGrammaticality));
//var evalAvg = HumanEvaluation.EvaluateHumanDual(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\grammaticality.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
//Console.WriteLine("\nAveraged");
//evalAvg.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine();
//Console.WriteLine("\nSimplicity");
////evalResSanja = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var sanjaSimplicity = HumanEvaluation.AllAnnotatorScores;
////evalResGoran = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
////var goranSimplicity = HumanEvaluation.AllAnnotatorScores;
////Console.WriteLine("\nSanja");
////evalResSanja.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nGoran");
////evalResGoran.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nAgreement: " + Pearson.Correlation(sanjaSimplicity, goranSimplicity));
//evalAvg = HumanEvaluation.EvaluateHumanDual(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\simplicity.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt");
//Console.WriteLine("\nAveraged");
//evalAvg.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine();
////Console.WriteLine("Meaning preservation");
////evalResSanja = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
////var sanjaMeaningPreservation = HumanEvaluation.AllAnnotatorScores;
////evalResGoran = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
////var goranMeaningPreservation = HumanEvaluation.AllAnnotatorScores;
////Console.WriteLine("\nSanja");
////evalResSanja.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nGoran");
////evalResGoran.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("\nAgreement: " + Pearson.Correlation(sanjaMeaningPreservation, goranMeaningPreservation));
//evalAvg = HumanEvaluation.EvaluateHumanDual(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\sanja\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\meaning-preservation.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
//Console.WriteLine("\nAveraged");
//evalAvg.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
//Console.WriteLine();
////Console.WriteLine("Changed");
////var evalResCh = HumanEvaluation.EvaluateHuman(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\human-evaluation\goran\changed.txt", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\codings.txt", true);
////evalResCh.ToList().ForEach(er => Console.WriteLine(er.Key + " " + er.Value));
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
//// Loading the information contents based on word frequencies from a large corpus
//InformationContent ic = new InformationContent(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Corpora\word-freqs.txt");
//// Loading the GloVe embeddings into an instance of WordVectorSpace class
//WordVectorSpace wvs = new WordVectorSpace();
//wvs.Load(@"C:\Goran\Korpusi\GloVe-Vectors\glove-vectors-6b-200d\glove-vectors-6b-200d.txt", null);
//// Instantiating the lexical simplifier
//LexicalSimplifier simplifier = new LexicalSimplifier();
//// setting the information content and word vector space properties
//simplifier.InformationContent = ic;
//simplifier.VectorSpace = wvs;
//simplifier.CandidateInPoSLookup = TakeLab.Utilities.IO.StringLoader.LoadDictionaryStrings(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\candidate-in-pos-lookup.txt");
//// Evaluating systems via human-assigned scores of grammaticality, simplicity, and meaning preservation
//#region Human Evaluation Task
//#region Preparing files for evaluation
////var dirPath = @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\";
////var originalLines = (new StreamReader(dirPath + "HA-Wiki-original.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var humanLines = (new StreamReader(dirPath + "HA-Wiki-manualSimplification.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var embeSimpLines = (new StreamReader(dirPath + "HA-Wiki-EmbeSimp.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var biranLines = (new StreamReader(dirPath + "HA-Wiki-Biran-simplified.txt")).ReadToEnd().Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries).ToList();
////var gramSimpWriter = new StreamWriter(dirPath + "simplification-grades.txt");
////var meanPresWriter = new StreamWriter(dirPath + "meaning-preservation-grades.txt");
////Dictionary<Guid, string> codings = new Dictionary<Guid, string>();
////for (int i = 0; i < originalLines.Count; i++)
////{
//// List<AnnotationEntry> entries = new List<AnnotationEntry>();
//// var entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = originalLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "original");
//// entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = humanLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "manual");
//// entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = biranLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "biran");
//// entry = new AnnotationEntry { ID = Guid.NewGuid(), Sentence = embeSimpLines[i], Grade = 0 };
//// entries.Add(entry);
//// codings.Add(entry.ID, "embesimp");
//// entries = entries.OrderBy(x => x.ID).ToList();
//// entries.ForEach(x => gramSimpWriter.WriteLine(x.ID + " " + x.Grade + " \"" + x.Sentence + "\""));
//// gramSimpWriter.WriteLine();
//// var original = entries.Where(x => codings[x.ID] == "original").Single();
//// var other = entries.Where(x => codings[x.ID] != "original").ToList();
//// other.ForEach(x =>
//// {
//// meanPresWriter.WriteLine("Original: " + "\"" + original.Sentence + "\"");
//// meanPresWriter.WriteLine("Simplified: " + x.ID + " " + x.Grade + " \"" + x.Sentence + "\"");
//// meanPresWriter.WriteLine();
//// });
//// meanPresWriter.WriteLine();
//// meanPresWriter.WriteLine();
////}
////gramSimpWriter.Close();
////meanPresWriter.Close();
////StreamWriter codingsWriter = new StreamWriter(dirPath + "codings.txt");
////codings.ToList().ForEach(c =>
////{
//// codingsWriter.WriteLine(c.Key.ToString() + " " + c.Value);
////});
////codingsWriter.Close();
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
////var wikiSentences = TakeLab.Utilities.IO.StringLoader.LoadList(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\HA-Wiki-original.txt");
////var wikiWriter = new StreamWriter(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\HA-Wiki-EmbeSimp.txt");
////AnnotatorChain annChain = new AnnotatorChain(TakeLabCore.NLP.Language.English, new List<AnnotatorType> { AnnotatorType.SentenceSplitter, AnnotatorType.POSTagger, AnnotatorType.Morphology, AnnotatorType.NamedEntities });
//////wikiSentences = wikiSentences.OrderBy(x => Guid.NewGuid()).ToList();
////for (int i = 0; i < wikiSentences.Count; i++)
////{
//// var document = new Document { Text = wikiSentences[i] };
//// document.Annotate(annChain);
//// var dSimple = simplifier.Simplify(document, 5, 0.55, 0.55);
//// wikiWriter.WriteLine(dSimple.Text);
//// Console.WriteLine(document.Text);
//// Console.WriteLine(dSimple.Text);
//// Console.WriteLine();
////}
////wikiWriter.Close();
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
//// Automated evaluation on the ranking lexical simplification task from SemEval 2012
//#region Ranking task
////StreamWriter rankingsWriter = new StreamWriter(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\ranking-task-semeval\test-dataset\gorankings.txt");
////var datasetRanking = DatasetLoader.LoadRankingTasksDataset(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\ranking-task-semeval\test-dataset\contexts.xml", @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\ranking-task-semeval\test-dataset\substitutions.gold-rankings");
////datasetRanking = datasetRanking.OrderBy(x => x.ID).ToList();
////datasetRanking.ForEach(rt => {
//// Console.WriteLine("Processing example: " + datasetRanking.IndexOf(rt));
//// var candidateRanking = simplifier.RankSimplificationCandidates(rt, 5);
//// rankingsWriter.Write("Sentence " + rt.ID.ToString() + " rankings: ");
//// for (int i = 0; i < candidateRanking.Count; i++)
//// {
//// rankingsWriter.Write("{" + candidateRanking[i] + "} ");
//// //Console.WriteLine(candidateRanking[i]);
//// }
//// rankingsWriter.WriteLine();
//// //Console.WriteLine();
//// //Console.ReadLine();
////});
////rankingsWriter.Close();
////Console.WriteLine("Done!");
////Console.ReadLine();
//#endregion
//// Automated evaluation on the crowdsourced replacement dataset (Kavuhcuk et al., ACL 2014)
//#region Replacement task
//AnnotatorChain ac = new AnnotatorChain(TakeLabCore.NLP.Language.English, new List<AnnotatorType> { AnnotatorType.SentenceSplitter, AnnotatorType.POSTagger, AnnotatorType.Morphology, AnnotatorType.NamedEntities });
//var dataset = DatasetLoader.LoadMechanicalTurkTDataset(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\lex.mturk.txt");//.OrderBy(x => Guid.NewGuid()).ToList();
//List<string> systemReplacements = new List<string>();
//List<List<string>> systemReplacementCandidates = new List<List<string>>();
//StreamWriter replacementOutputWriter = new StreamWriter(@"C:\Goran\Repos\EmbeSimp\EmbeSimp\Output\replacements.txt");
//for(int i = 0; i < dataset.Count; i++)
//{
// Console.WriteLine("Simplifying sentence {0}: ", i);
// Document d = new Document { Text = dataset[i].Item1 };
// ac.Apply(d);
// var dSimple = simplifier.Simplify(d, 5, 0, 0, dataset[i].Item2);
// Console.WriteLine("Original: " + d.Text);
// Console.WriteLine("Simplified: " + dSimple.Text);
// Console.WriteLine();
// var targetSimplification = simplifier.LastSubstitutions.Where(s => s.Item1.Text.ToLower() == dataset[i].Item2.ToLower()).FirstOrDefault();
// if (targetSimplification == null)
// {
// systemReplacements.Add(string.Empty);
// systemReplacementCandidates.Add(new List<string>());
// }
// else
// {
// systemReplacements.Add(targetSimplification.Item2);
// systemReplacementCandidates.Add(simplifier.LastSubstitutionCandidates[0].Item2);
// }
// Console.WriteLine("Change: {0} -> {1}", dataset[i].Item2, targetSimplification != null ? targetSimplification.Item2 : "<empty>");
// //Console.ReadLine();
// replacementOutputWriter.WriteLine(d.Text);
// replacementOutputWriter.WriteLine("Target: " + dataset[i].Item2);
// replacementOutputWriter.WriteLine("Simplification: " + ((targetSimplification != null) ? targetSimplification.Item2 : "<empty>"));
// replacementOutputWriter.WriteLine("All candidates: ");
// simplifier.LastSubstitutionCandidates[0].Item2.ForEach(x => replacementOutputWriter.WriteLine(x));
// replacementOutputWriter.WriteLine();
//}
//replacementOutputWriter.Close();
//var evaluator = new ReplacementEvaluator();
//var precision = evaluator.EvaluatePrecision(dataset, systemReplacements);
//var accuracy = evaluator.EvaluateAccuracy(dataset, systemReplacements);
//var changed = evaluator.EvaluateChanged(dataset, systemReplacements);
//var precisionCandidate = evaluator.EvaluateCandidatePrecision(dataset, systemReplacementCandidates);
//var accuracyCandidate = evaluator.EvaluateCandidateAccuracy(dataset, systemReplacementCandidates);
//Console.WriteLine("Precision: {0}\nAccuracy: {1}\nChanged: {2}\n", precision, accuracy, changed);
//Console.WriteLine("Soft precision: {0}\nSoft accuracy: {1}\n Changed: {2}\n ", precisionCandidate, accuracyCandidate, changed);
//TakeLab.Utilities.IO.StringWriter<string>.WriteDictionary<string>(simplifier.CandidateInPoSLookup, @"C:\Goran\Repos\EmbeSimp\EmbeSimp\Data\candidate-in-pos-lookup.txt");
//#endregion
//#region Wiki preprocessing
////WikipediaPreprocessing wpp = new WikipediaPreprocessing();
////wpp.Preprocess(@"C:\Goran\Korpusi\Wikipedia\preprocessed");
////TakeLabCore.NLP.Annotations.Document doc = new TakeLabCore.NLP.Annotations.Document{ Text = "he doctor who pulled Harrison Ford from the wreck of a plane crash has described how he feared a fireball from the aircraft's leaking fuel. Spine surgeon Sanjay Khurana was playing golf in Los Angeles when the American actor's vintage plane \"belly-flopped\" down on to the eighth hole." };
////(new TakeLabCore.NLP.Annotators.English.EngNER()).Annotate(doc);
////doc.NamedEntities.ForEach(ne => Console.WriteLine(ne.Text + " " + ne.NEType + " " + ne.StartPosition));
////var simnet = new SimilarityNet();
////simnet.SimpleLoad(@"C:\Goran\Korpusi\GloVe-Vectors\glove-vectors-6b-200d\glove-vectors-6b-200d.txt");
////while (true)
////{
//// var line = Console.ReadLine();
//// if (string.IsNullOrEmpty(line)) break;
//// else
//// {
//// var mostSimilar = simnet.GetMostSimilar(line.Trim(), 20);
//// if (mostSimilar != null) mostSimilar.ForEach(ms => Console.WriteLine(ms.Item1 + " " + ms.Item2));
//// else Console.WriteLine("Word not found in vocabulary, try again!");
//// Console.WriteLine();
//// }
////}
//#endregion
#endregion
}
public void PlaceObjectInInfoUI(InformationContent contentId)
{
PlaceObjectInInfoUI((int)contentId);
}
// GET: Information
public ActionResult ListPaging(int pageNum = 1, string p_priority = "ALL", string p_read_yn = "N", string p_query_type = "999", string p_data = "N")
{
try
{
//int memberID = 1;
CMember member = Session[CDictionary.welcome] as CMember;
//modify by Jony 109-12-15
int memberID = 0;
if (member != null)
{
memberID = member.fMemberId;
}
int pageSize = 5;
int currentPage = pageNum < 1 ? 1 : pageNum;
ViewBag.Read_YN = p_read_yn; //將partialview資料傳給 主要view
ViewBag.Priority = p_priority;
ViewBag.Query_Type = p_query_type;
ViewBag.Query_Data = p_data;
Func <Information, bool> myWhere = null;
SingleApartmentEntities db = new SingleApartmentEntities();
IEnumerable <Information> table = null;
//myWhere = p => p.Status != "User_Deleted"; //todo:ok Linq多重where
//if (string.IsNullOrEmpty(read_yn))
//p_priority == "ALL" 顯示全部資料
if (p_query_type == "999" && p_priority == "ALL")
{
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted";
}
else if (p_query_type == "999" && p_priority == "Null")
{ //p_priority == "Null" 顯示已讀或未讀資料
//移除 p.Priority == p_priority
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.Read_YN == p_read_yn;
}
else if (p_query_type == "999" && p_read_yn == "Null")
{ //p_read_yn == "Null" 顯示優先等級資料
//移除 p.Read_YN == p_read_yn
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.Priority == p_priority;
}
//<option value="999">請選擇查詢項目</option>
// <option value="100">系統分類</option>
// <option value="200">個人分類</option>
// <option value="300">關鍵字</option>
else if (p_query_type == "100")
{//100 = 系統分類
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.InformationCategoryID == Convert.ToInt32(p_data);
}
else if (p_query_type == "200")
{//200 = 個人分類
if (p_data == "999")
{
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.MemberCategoryID == null;
}
else
{
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.MemberCategoryID == Convert.ToInt32(p_data);
}
}
else if (p_query_type == "300")
{//300 = 關鍵字
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.InformationContent.Contains(p_data);
}
else if (p_query_type == "400")
{//400 = 依訊息內容分類
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.InformationSource == Convert.ToInt32(p_data);
}
else if (p_query_type == "500")
{//500 = 依當日新訊息
if (p_data == "N")
{
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.InformationDate.ToString("yyyy-MM-dd") == DateTime.Today.ToString("yyyy-MM-dd");
}
else
{
myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.InformationDate.ToString("yyyy-MM-dd") == DateTime.Today.ToString("yyyy-MM-dd") && p.InformationSource == Convert.ToInt32(p_data);
}
}
else
{ //no use
//myWhere = p => p.MemberID == memberID && p.Status != "User_Deleted" && p.Status != "Admin_Deleted" && p.Read_YN == p_read_yn && p.Priority == p_priority;
}
//modify by Jony 1091208 增加 .OrderBy(a=>a.InformationID)
//modify by Jony 1091218 增加 .OrderByDescending(a=>a.InformationID)
table = db.Information.Where(myWhere).OrderByDescending(a => a.InformationID);
// myWhere = p => p.InformationContent.Contains(keyword) && p.Status != "User_Deleted" && p.Status != "Admin_Deleted";
// table = db.Information.Where(myWhere);
// //table = from p in db.Information
// // where p.InformationContent.Contains(keyword) && p.Status != "User_Deleted" && p.Status != "Admin_Deleted"
// // select p;
//}
List <CInformation> list = new List <CInformation>();
foreach (Information item in table)
{
//list.Add(new CInformation()
//{
// information_entity = item,
// InformationCategoryName = item.InformationCategory.InformationCategoryName,
// //三元運算子
// UserCategoryName = item.MemberCategoryID == null ? "未分類" : item.MemberInformationCategory.MemberCategoryName,
//});
CInformation x = new CInformation();
x.information_entity = item;
x.InformationCategoryName = item.InformationCategory.InformationCategoryName;
x.UserCategoryName = item.MemberCategoryID == null ? "未分類" : item.MemberInformationCategory.MemberCategoryName;
x.InformationSourceName = "";
if (x.InformationSource != null)
{
InformationContent c = db.InformationContent.Where(p => p.ContentID == item.InformationSource).FirstOrDefault();
//沒設關聯, 為了取得 UserCategoryName = MemberInformationCategory.MemberCategoryName
if (c != null)
{
x.InformationSourceName = c.ContentName; //取得 訊息來源名稱
}
}
list.Add(x);
}
//return View(list);
var pagedlist = list.ToPagedList(currentPage, pageSize);
//return View(pagedlist); //Page_原始ok 此cshtml使用
return(PartialView(pagedlist));//使用部分顯示
}
catch
{
return(View());
}
}
public void ActivateInformationContent(InformationContent _info)
{
infoHolder.PlaceObjectInInfoUI(_info);
}
public JsonResult GetSingleContent(InformationContentPostModel informationContentPostModel)
{
InfrastructureModel <InformationContentSingleDataModel> response = new InfrastructureModel <InformationContentSingleDataModel>();
InformationContentSingleDataModel resultModel = new InformationContentSingleDataModel();
try
{
InformationContent informationContext = informationContentOperation.GetSingleInformationContent(informationContentPostModel.AppUserId);
if (informationContext != null)
{
resultModel.AuthorFullName = informationContext.Author != null?informationContext.Author.Name + " " + informationContext.Author.Surname:"Eski Sevgilim :)";
resultModel.Explanation = informationContext.Explanation;
resultModel.ImagePath = UrlHelper.InformationPhotoPath + "/" + informationContext.PostImagePath;
resultModel.LikeCount = informationContext.LikeCount.ToString();
resultModel.Title = informationContext.Title;
for (int i = 0; i <= 4; i++)
{
Random rnd = new Random();
Color randomColor = Color.FromArgb(rnd.Next(256), rnd.Next(256), rnd.Next(256));
if (i == 1)
{
resultModel.ColorCode1 = "#" + randomColor.R.ToString("X2") + randomColor.G.ToString("X2") + randomColor.B.ToString("X2");
}
if (i == 2)
{
resultModel.ColorCode2 = "#" + randomColor.R.ToString("X2") + randomColor.G.ToString("X2") + randomColor.B.ToString("X2");
}
if (i == 3)
{
resultModel.ColorCode3 = "#" + randomColor.R.ToString("X2") + randomColor.G.ToString("X2") + randomColor.B.ToString("X2");
}
if (i == 4)
{
resultModel.ColorCode4 = "#" + randomColor.R.ToString("X2") + randomColor.G.ToString("X2") + randomColor.B.ToString("X2");
}
response.ResultModel = resultModel;
response.ResultModel.IsAllView = false;
response.ResultStatus = true;
}
}
else
{
response.ResultModel = resultModel;
response.ResultModel.IsAllView = true;
resultModel.ImagePath = UrlHelper.InformationPhotoPath + "/" + "33350e5ae-56d8-4707-ac40-8b4c60ddb115.jpg";
resultModel.Title = "Tüm İçerikleri Gördünüz";
resultModel.Explanation = "Bunları biliyor musunuz ? takipçileri içerik bittiğinde görüyorsa bir yöneticiye mail gitmiştir ve sizin için yeni içerik oluşturma talebi oluşur";
response.ResultStatus = true;
}
}
catch (Exception ex)
{
response.ResultStatus = false;
throw ex;
}
return(new JsonResult(response));
}
public JsonResult InsertInformationContent([FromForm] InformationApiContentCRUDModel model)
{
InfrastructureModel response;
try
{
if (model != null)
{
InformationContent informationContent = new InformationContent()
{
CategoryId = model.CategoryId,
AuthorId = model.AuthorId,
CreateDate = DateTime.Now,
IsActive = true,
IsDeleted = false,
Explanation = model.Explanation,
LikeCount = model.LikeCount,
Title = model.Title,
};
long size = model.PostImageFile.Length;
if (model.PostImageFile != null)
{
string filePath = Path.Combine(_env.WebRootPath, "Content", "Information");
string imagePath = string.Empty;
string fileExtension = Path.GetExtension(model.PostImageFile.FileName);
string fileName = (Guid.NewGuid() + fileExtension);
if (model.PostImageFile.Length > 0)
{
using (var ms = new MemoryStream())
{
model.PostImageFile.CopyTo(ms);
var fileBytes = ms.ToArray();
imagePath = Convert.ToBase64String(fileBytes);
}
var bytes = Convert.FromBase64String(imagePath);
using (var imageFile = new FileStream(Path.Combine(filePath, fileName), FileMode.Create))
{
imageFile.Write(bytes, 0, bytes.Length);
imageFile.Flush();
}
}
informationContent.PostImagePath = fileName;
}
informationContentOperation.Insert(informationContent);
response = new InfrastructureModel()
{
ResultStatus = true
};
}
else
{
response = new InfrastructureModel()
{
ResultStatus = false
};
}
}
catch (Exception ex)
{
throw;
}
return(Json(response));
}
public void Test1()
{
AbstractChromosomeFactory factory = new SolutionFactory();
int[] routeWeights = new int[]
{
20000, 50000, 120000, 200000, 350000
};
int distanceWeight = 1;
string[] customerTypes = new string[] { "C1", "C2", "R1", "R2", "RC1", "RC2" };
Dictionary <string, int> customerNumbers = new Dictionary <string, int>()
{
{ "2", 20000 }, { "4", 50000 }, { "6", 120000 }, { "8", 200000 }, { "10", 350000 }
};
string[] customerInstances = new string[] { "1", "2", "3", "4", "5", "6", "7", "8", "9", "10" };
CrossoverOperator[] crossoverOps = new CrossoverOperator[]
{
new OrderCrossover(), new PartiallyMatchedCrossover(), new CycleCrossover(), new UniformBasedOrderCrossover()
};
MutationOperator[] mutationOps = new MutationOperator[]
{
new SwapOperator(), new InsertionOperator(), new InversionOperator(), new DisplacementOperator()
};
int randomWalkNumber = 2000, randomWalkSteps = 5000;
string floatPattern = "0.000", separator = ",";
float epsilon = 0.05f;
foreach (var type in customerTypes)
{
foreach (var number in customerNumbers)
{
foreach (var instance in customerInstances)
{
string instanceId = type + '_' + number.Key + '_' + instance;
VrptwProblem problem = reader.ReadFromFile(FILE_PATH + @"\" + instanceId + ".txt");
FitnessFunction ff = new FitnessFunction(number.Value, distanceWeight);
Landscape landscape = new Landscape(problem, factory, ff);
foreach (var op in crossoverOps)
{
string path = RESULT_PATH + @"\" + instanceId + "_" + op.GetId() + ".csv";
if (!File.Exists(path))
{
File.Create(path).Close();
File.ReadAllText(path);
using (TextWriter tw = new StreamWriter(path))
{
tw.WriteLine("AC, IC, PIC, DBI");
for (int i = 0; i < randomWalkNumber; ++i)
{
var rwResult = landscape.RandomWalk(randomWalkSteps, op);
float ac = Autocorrelation.Run(rwResult);
float ic = InformationContent.Run(rwResult, epsilon);
float pic = PartialInformationContent.Run(rwResult, epsilon);
float dbi = DensityBasinInformation.Run(rwResult, epsilon);
string line =
ac.ToString(floatPattern) + separator +
ic.ToString(floatPattern) + separator +
pic.ToString(floatPattern) + separator +
dbi.ToString(floatPattern);
tw.WriteLine(line);
}
}
}
}
}
}
}
}
