public bool TrainClusteringModels(MySqlDataManipulator manipulator, int companyId, List <string> examplesIn, bool training = false)
{
List <KeywordExample> trainingData = new List <KeywordExample>();
foreach (string sentence in examplesIn)
{
List <string> tokens = SentenceTokenizer.TokenizeSentence(sentence);
List <List <string> > taggedTokens = KeywordTagger.Tag(tokens);
List <string> keywords = KeywordPredictor.PredictKeywords(taggedTokens);
KeywordExample example = new KeywordExample();
foreach (string keyword in keywords)
{
example.AddKeyword(keyword);
}
trainingData.Add(example);
}
KeywordClusterer.Train(trainingData);
if (!training)
{
return(KeywordClusterer.Save(manipulator, companyId));
}
else
{
return(true);
}
}
public List <string> ProcessQuery(MechanicQuery queryIn)
{
List <List <string> > complaintTokens = PartOfSpeechTagger.Tag(
SentenceTokenizer.TokenizeSentence(queryIn.Complaint)
);
List <string> keywords = KeywordPredictor.PredictKeywords(complaintTokens);
KeywordExample ex = new KeywordExample();
foreach (string s in keywords)
{
ex.AddKeyword(s);
}
List <int> complaintGroups = KeywordClusterer.PredictTopNSimilarGroups(ex, NUMBER_COMPLAINT_GROUPS);
List <object> queryDataPoint = new List <object> {
queryIn.Make, queryIn.Model
};
foreach (int x in complaintGroups)
{
queryDataPoint.Add(x);
}
List <object> predictedProblems = ProblemPredictor.PredictTopN(queryDataPoint, CalculateDistance, NUMBER_QUERIES_OUT);
List <string> returnProblems = new List <string>();
foreach (object o in predictedProblems)
{
returnProblems.Add((string)o);
}
return(returnProblems);
}
private static float CalcSimilarity(RepairJobEntry query, RepairJobEntry other)
{
IKeywordPredictor keyPred = NaiveBayesKeywordPredictor.GetGlobalModel();
AveragedPerceptronTagger tagger = AveragedPerceptronTagger.GetTagger();
List <String> tokened = SentenceTokenizer.TokenizeSentence(query.Complaint);
List <List <String> > tagged = tagger.Tag(tokened);
List <String> InputComplaintKeywords = keyPred.PredictKeywords(tagged);
tokened = SentenceTokenizer.TokenizeSentence(query.Problem);
tagged = tagger.Tag(tokened);
List <String> InputProblemKeywords = keyPred.PredictKeywords(tagged);
float score = 0;
tokened = SentenceTokenizer.TokenizeSentence(other.Complaint);
tagged = tagger.Tag(tokened);
List <String> JobComplaintKeywords = keyPred.PredictKeywords(tagged);
tokened = SentenceTokenizer.TokenizeSentence(other.Problem);
tagged = tagger.Tag(tokened);
List <String> JobProblemKeywords = keyPred.PredictKeywords(tagged);
foreach (String keyword in JobComplaintKeywords)
{
if (InputComplaintKeywords.Contains(keyword))
{
score++;
}
}
foreach (String keyword in JobProblemKeywords)
{
if (InputProblemKeywords.Contains(keyword))
{
score++;
}
}
return(score / (JobComplaintKeywords.Count + JobProblemKeywords.Count));
}
