static void Main(string[] args)
{
// Some example documents.
//string[] documents =
//{
// //"The sun in the Stars sky is bright.",
// //"We can see the shining sun, the bright sun."
// "How much is my balance",
// "I need my balance",
// "balance for account",
// "payment for my account",
// "hello need my balance"
//};
//// Apply TF*IDF to the documents and get the resulting vectors.
//double[][] inputs = TFIDF.Transform(documents, 0);
//inputs = TFIDF.Normalize(inputs);
//double[] result = inputs[inputs.Length - 1];
//for (int index = 0; index < inputs.Length-1; index++)
//{
// Console.WriteLine(documents[index]);
// int counter = 0;
// double cosineValue = 0;
// foreach (double value in inputs[index])
// {
// cosineValue = cosineValue + (value * result[counter]);
// counter++;
// }
// Console.WriteLine(cosineValue);
// Console.WriteLine("\n");
//}
//Console.WriteLine("------------------------------");
//for (int index = 0; index < inputs.Length; index++)
//{
// Console.WriteLine(documents[index]);
// foreach (double value in inputs[index])
// {
// Console.Write(value + ", ");
// }
// Console.WriteLine("\n");
//}
Console.WriteLine("Levinstine Algorithm");
Console.WriteLine("----------------------------------");
//Console.WriteLine(LevenshteinDistance.Compute("Payments for Account", "Pyment for Account"));
//Console.WriteLine(LevenshteinDistance.Compute("Payments for Account", "Balance for Account"));
Console.WriteLine("TF IDF Cosine Similarity");
Console.WriteLine("----------------------------------");
int threshold = 0;
try
{
// Create instance of calculator
SimilarityCalculator sc = new SimilarityCalculator();
sc.CompareString("Payments for Account", "Payment Balance for Account", vocabularyThreshold: threshold);
sc.CompareString("Payments for Account", "Balance for Account", vocabularyThreshold: threshold);
Console.WriteLine("Press any key...");
Console.ReadKey();
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
Console.ReadKey();
}
//ChaT Bot Reponse Main Entry
public ChatIntent ChatResponseMain()
{
string responseMessage = contentManager.NoIntentMatchedResponse;
TFIDF getVocab = new TFIDF();
Dictionary <string, string> reponseDict = new Dictionary <string, string>();
List <ChatIntent> intentListAll = db.ChatIntent.ToList();
ChatIntent responseIntent = intentListAll.Where(x => x.ChatIntentId == 0).FirstOrDefault();
#region 1.CheckIntentGreetingOrGoodbye
if (hiBye.Greet())
{
return(UpdateIntent(Node, contentManager.GreetResponse, responseIntent));
}
else if (hiBye.GoodBye())
{
return(UpdateIntent(Node, contentManager.GoodbyeResponse, responseIntent));
}
#endregion
List <ChatIntent> intentList = (from intention in intentListAll
where intention.ChatIntentId > 2 && intention.ParentId == Node
select intention).ToList();
#region 2.CheckIntentFullMatchbySuggestion
KeyValuePair <int, bool> fullMatch = suggestionMatch.FullSuggestionMatch(intentList);
if (fullMatch.Value)
{
ChatIntent fullMatchIntent = intentList.Where(x => x.ChatIntentId == fullMatch.Key).FirstOrDefault();
responseMessage = fullMatchIntent.Response;
var hasEntity = (from ent in db.ChatEntity where ent.ChatIntentId == fullMatchIntent.ChatIntentId
select ent);
if (hasEntity.Any())
{
AskMeEntityExtraction entity = new AskMeEntityExtraction(Message, fullMatchIntent.ChatIntentId, SessionId);
return(entity.GetEntityforIntentfromNLP(fullMatchIntent));
}
return(fullMatchIntent);
}
KeyValuePair <int, bool> partialMatch = suggestionMatch.PartialSuggestionMatch(intentList);
if (partialMatch.Value)
{
ChatIntent partialMatchIntent = intentList.Where(x => x.ChatIntentId == partialMatch.Key).FirstOrDefault();
responseMessage = partialMatchIntent.Response;
var hasEntity = (from ent in db.ChatEntity
where ent.ChatIntentId == partialMatchIntent.ChatIntentId
select ent);
if (hasEntity.Any())
{
AskMeEntityExtraction entity = new AskMeEntityExtraction(Message, partialMatchIntent.ChatIntentId, SessionId);
return(entity.GetEntityforIntentfromNLP(partialMatchIntent));
}
return(partialMatchIntent);
}
#endregion
List <string> vocabList = getVocab.GetVocabulary(Message);
if (vocabList.Count == 0)
{
return(UpdateIntent(Node, contentManager.NoIntentMatchedResponse, responseIntent));
}
if (Message.ToLower() == "yes" || Message.ToLower() == "no")
{
return(UpdateIntent(Node, contentManager.NoIntentMatchedResponse, responseIntent));
}
#region 3.TFIDF Match Process
SimilarityCalculator similarityCalculator = new SimilarityCalculator();
List <ChatIntentQuestion> questionList = db.ChatIntentQuestion.ToList();
Dictionary <int, double> scoreDict = new Dictionary <int, double>();
foreach (ChatIntentQuestion question in questionList)
{
double compare = similarityCalculator.CompareString(Message, question.QuestionDesc, 1);
KeyValuePair <int, double> score = new KeyValuePair <int, double>(question.ChatIntentId, compare);
if (scoreDict.ContainsKey(score.Key))
{
if (scoreDict[score.Key] < compare)
{
scoreDict[score.Key] = compare;
}
}
else
{
scoreDict.Add(score.Key, score.Value);
}
}
if (scoreDict.Where(x => x.Value > 0.45).Any())
{
int maxScoreChatIntentId = scoreDict.OrderByDescending(x => x.Value).Select(y => y.Key).FirstOrDefault();
ChatIntent maxIntent = intentListAll.Where(x => x.ChatIntentId == maxScoreChatIntentId).FirstOrDefault();
Node = maxScoreChatIntentId;
var hasEntity = (from ent in db.ChatEntity
where ent.ChatIntentId == maxIntent.ChatIntentId
select ent);
if (hasEntity.Any())
{
AskMeEntityExtraction entity = new AskMeEntityExtraction(Message, maxIntent.ChatIntentId, SessionId);
return(entity.GetEntityforIntentfromNLP(maxIntent));
}
//KeyValuePair<int, string> responseIntent = GetEntityforIntent(Node, maxIntent.Response);
return(maxIntent);
}
else if (scoreDict.Where(x => x.Value >= 0.23).Any())
{
List <int> possibeMatch = scoreDict.OrderByDescending(x => x.Value).Where(x => x.Value >= 0.23).Select(y => y.Key).ToList();
responseMessage = contentManager.IntentPossibleMatchedResponse;
foreach (int match in possibeMatch)
{
responseMessage = responseMessage + ", ";
string suggestion = intentListAll.Where(x => x.ChatIntentId == match).Select(y => y.IntentDescription).FirstOrDefault();
responseMessage = responseMessage + suggestion;
}
responseMessage = responseMessage + ", " + contentManager.IntentSuggestionResponse;
return(UpdateIntent(Node, responseMessage, responseIntent));
}
#endregion
#region 4.Probable Match Process
KeyValuePair <string, bool> probableMatchCorrect = zPossibleMatch.ProbableMatchCorrectSpelling(vocabList, intentListAll);
if (probableMatchCorrect.Value)
{
common.LogFailureResponse();
responseMessage = probableMatchCorrect.Key;
return(UpdateIntent(Node, responseMessage, responseIntent));
}
KeyValuePair <string, bool> probableMatchTypo = zPossibleMatch.ProbableMatchTypoError(vocabList, intentListAll);
if (probableMatchTypo.Value)
{
common.LogFailureResponse();
responseMessage = probableMatchTypo.Key;
return(UpdateIntent(Node, responseMessage, responseIntent));
}
#endregion
#region 4.Synonym Match Process
KeyValuePair <string, bool> synMatch = synonymMatch.SynonymMatch(vocabList, intentListAll);
if (synMatch.Value)
{
common.LogFailureResponse();
responseMessage = synMatch.Key;
return(UpdateIntent(Node, responseMessage, responseIntent));
}
#endregion
if (responseIntent != null)
{
responseIntent.ChatIntentId = Node;
responseIntent.Response = responseMessage;
}
else
{
responseIntent = new ChatIntent();
responseIntent.ChatIntentId = Node;
responseIntent.Response = "Sorry I did not understand, Please enter one of the suggestions";
}
return(responseIntent);
}
