public bool CategorizePayments(int customerID)
{
NaiveBayesModel <Operation> model = new NaiveBayesModel <Operation>();
var operations = _operationServices.GetOperationsByCustomerID(customerID);
var notCategorized = operations.Where(x => x.Tag == null || x.Tag.Name == "NotSet");
var categorized = operations.Where(x => x.Tag != null && x.Tag.Name != "NotSet");
var predictor = model.Generate(categorized);
foreach (var operation in notCategorized)
{
var newOperation = predictor.Predict(operation);
Debug.Write(newOperation.Tag.Name);
using (TransactionScope scope = new TransactionScope())
{
_repository.Update <Operation>(newOperation);
scope.Complete();
}
}
return(true);
}
public bool CategorizePayments(int customerID)
{
NaiveBayesModel<Operation> model = new NaiveBayesModel<Operation>();
var operations = _operationServices.GetOperationsByCustomerID(customerID);
var notCategorized = operations.Where(x => x.Tag == null || x.Tag.Name == "NotSet");
var categorized = operations.Where(x => x.Tag != null && x.Tag.Name!="NotSet");
var predictor = model.Generate(categorized);
foreach (var operation in notCategorized)
{
var newOperation = predictor.Predict(operation);
Debug.Write(newOperation.Tag.Name);
using (TransactionScope scope = new TransactionScope())
{
_repository.Update<Operation>(newOperation);
scope.Complete();
}
}
return true;
}
public void NaiveBayes_Main_Test()
{
var data = Payment.GetData();
NaiveBayesModel<Payment> model = new NaiveBayesModel<Payment>();
var predictor = model.Generate(data);
var item = predictor.Predict(new Payment { Amount = 110, Description = "Monop try it" });
Assert.AreEqual(item.Category, "Household");
}
public static string FindSentimentType(int sentimentCount, bool isRetweet, string language, List <TweetClassification> tweetClassificationList)
{
var model = new NaiveBayesModel <TweetClassification>();
var predictor = model.Generate(tweetClassificationList);
var result = predictor.Predict(new TweetClassification {
SentimentCount = sentimentCount, Language = language, IsRetweet = isRetweet
});
return(result.SentimentType);
}
public void NaiveBayes_Main_Test()
{
var data = Payment.GetData();
NaiveBayesModel <Payment> model = new NaiveBayesModel <Payment>();
var predictor = model.Generate(data);
var item = predictor.Predict(new Payment {
Amount = 110, Description = "Monop try it"
});
Assert.AreEqual(item.Category, "Household");
}
public void NaiveBayesPredictor_Serialization_Test()
{
var data = Payment.GetData();
NaiveBayesModel<Payment> model = new NaiveBayesModel<Payment>();
var predictor = model.Generate(data);
XmlSerializer ser = new XmlSerializer(predictor.GetType());
using (var stream = new MemoryStream())
{
ser.Serialize(stream, predictor);
stream.Position = 0;
var newPredictor = model.Load(stream);
var item = newPredictor.Predict(new Payment { Amount = 110, Description = "Monop try it" });
Assert.AreEqual(item.Category, "Household");
}
}
public void NaiveBayesPredictor_Serialization_Test()
{
var data = Payment.GetData();
NaiveBayesModel <Payment> model = new NaiveBayesModel <Payment>();
var predictor = model.Generate(data);
XmlSerializer ser = new XmlSerializer(predictor.GetType());
using (var stream = new MemoryStream())
{
ser.Serialize(stream, predictor);
stream.Position = 0;
var newPredictor = model.Load(stream);
var item = newPredictor.Predict(new Payment {
Amount = 110, Description = "Monop try it"
});
Assert.AreEqual(item.Category, "Household");
}
}