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;
}
private static void TestNaiveBayes(FeatureVector training, FeatureVector test)
{
NaiveBayes nb = new NaiveBayes();
NaiveBayesModel nbModel = (NaiveBayesModel)nb.Fit(training);
FeatureVector predictions = nbModel.transform(test);
PrintPredictionsAndEvaluate(predictions);
}
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");
}
/**
* <summary> Training algorithm for Naive Bayes algorithm with a discrete data set.</summary>
* <param name="priorDistribution">Probability distribution of classes P(C_i)</param>
* <param name="classLists">Instances are divided into K lists, where each list contains only instances from a single class</param>
*/
private void TrainDiscreteVersion(DiscreteDistribution priorDistribution, Partition classLists)
{
var classAttributeDistributions =
new Dictionary <string, List <DiscreteDistribution> >();
for (var i = 0; i < classLists.Size(); i++)
{
classAttributeDistributions[((InstanceListOfSameClass)classLists.Get(i)).GetClassLabel()] =
classLists.Get(i).AllAttributesDistribution();
}
model = new NaiveBayesModel(priorDistribution, classAttributeDistributions);
}
/**
* <summary> Training algorithm for Naive Bayes algorithm with a continuous data set.</summary>
*
* <param name="priorDistribution">Probability distribution of classes P(C_i)</param>
* <param name="classLists"> Instances are divided into K lists, where each list contains only instances from a single class</param>
*/
private void TrainContinuousVersion(DiscreteDistribution priorDistribution, Partition classLists)
{
var classMeans = new Dictionary <string, Vector>();
var classDeviations = new Dictionary <string, Vector>();
for (var i = 0; i < classLists.Size(); i++)
{
var classLabel = ((InstanceListOfSameClass)classLists.Get(i)).GetClassLabel();
var averageVector = classLists.Get(i).Average().ToVector();
classMeans[classLabel] = averageVector;
var standardDeviationVector = classLists.Get(i).StandardDeviation().ToVector();
classDeviations[classLabel] = standardDeviationVector;
}
model = new NaiveBayesModel(priorDistribution, classMeans, classDeviations);
}
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");
}
}
static void Main(string[] args)
{
Console.WriteLine("ICM PREDICTION MODEL");
Console.WriteLine("=============================================");
Console.WriteLine("Preparing model...");
IUserDataProvider inMemDataProvider = new InMemoryUserDataProvider();
IDataPointFactory dataPointFactory = new DataPointFactory(inMemDataProvider);
IModel naiveBayesModel = new NaiveBayesModel();
ModelRunner runner = new ModelRunner(naiveBayesModel, dataPointFactory);
runner.Prepare();
Console.WriteLine("Model prepared");
do
{
var userData = new UserData();
Console.WriteLine("Enter Issue details: ");
Console.Write("\tIs it a partner user (y/n): ");
ConsoleKeyInfo key = Console.ReadKey();
userData.IsPartner = ConvertKeyToBool(key);
Console.WriteLine();
Console.Write("\tDoes user have profile in AAD (y/n): ");
key = Console.ReadKey();
userData.ProfileExistsInAAD = ConvertKeyToBool(key);
Console.WriteLine();
Console.Write("\tDoes user have profile in One Profile (y/n): ");
key = Console.ReadKey();
userData.ProfileExistyInOP = ConvertKeyToBool(key);
Console.WriteLine();
Console.Write("\tDoes claims exists for user (y/n): ");
key = Console.ReadKey();
userData.ClaimsExists = ConvertKeyToBool(key);
Console.WriteLine();
Console.Write("\tIs the Login Expired Windows appearing continously for the user (y/n): ");
key = Console.ReadKey();
userData.LoginExpiredMessage = ConvertKeyToBool(key);
Console.WriteLine();
Console.Write("\tDoes application insights have a lot of result code 0 logged for the user (y/n): ");
key = Console.ReadKey();
userData.ResultCodeZero = ConvertKeyToBool(key);
Console.WriteLine();
Console.Write("\tIs the error AADSTS525 logged in Application Insights for the user (y/n): ");
key = Console.ReadKey();
userData.AADSTS525Issue = ConvertKeyToBool(key);
Console.WriteLine();
Console.WriteLine("Is this correct (y/n)?");
Console.WriteLine(JsonConvert.SerializeObject(userData));
key = Console.ReadKey();
if (!ConvertKeyToBool(key))
{
continue;
}
Console.WriteLine();
Console.WriteLine("Predicting issue...");
string issue = runner.PredictIssue(userData);
Console.WriteLine($"PREDICTED ISSUE: {issue}");
Console.WriteLine("=============================================");
Console.WriteLine("Do you want to continue");
key = Console.ReadKey();
if (!ConvertKeyToBool(key))
{
break;
}
} while (true);
}
private void buttonForDataSplitNext_Click(object sender, EventArgs e)
{
trainingSetPercentage = (double)numericUpDownForTrainingSetPercent.Value / 100.0;
numFolds = (int)numericUpDownForNumFolds.Value;
double[] smaOut = null;
double[] wmaOut = null;
double[] emaOut = null;
double[] macdOut = null;
double[] stochasticsOut = null;
double[] williamsROut = null;
double[] rsiOut = null;
double[] closesOut = null;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
if (isSMAChecked)
{
smaOut = IndicatorDataPreprocessor.GetSMAOut(MovingAverage.Simple(code, targetDate, smaPeriod, numberOfData));
}
if (isWMAChecked)
{
wmaOut = IndicatorDataPreprocessor.GetWMAOut(MovingAverage.Weighted(code, targetDate, wmaPeriod, numberOfData));
}
if (isEMAChecked)
{
emaOut = IndicatorDataPreprocessor.GetEMAOut(MovingAverage.Exponential(code, targetDate, emaPeriod, numberOfData));
}
if (isMACDChecked)
{
macdOut = IndicatorDataPreprocessor.GetMACDOut(new MovingAverageConvergenceDivergence(code, targetDate, firstPeriod, secondPeriod, triggerPeriod, numberOfData));
}
if (isStochasticsChecked)
{
stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(new Stochastics(code, targetDate, fastKPeriod, fastDPeriod, slowDPeriod, numberOfData));
}
if (isWilliamsRChecked)
{
williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(WilliamsR.Wsr(code, targetDate, williamsRPeriod, numberOfData));
}
if (isRSIChecked)
{
rsiOut = IndicatorDataPreprocessor.GetRSIOut(RelativeStrengthIndex.Rsi(code, targetDate, rsiPeriod, numberOfData));
}
closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
int minRowCount = 1000000;
if (smaOut != null)
{
minRowCount = smaOut.Length;
}
if (wmaOut != null)
{
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
}
if (emaOut != null)
{
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
}
if (macdOut != null)
{
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
}
if (rsiOut != null)
{
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
}
if (williamsROut != null)
{
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
}
if (stochasticsOut != null)
{
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
}
if (closesOut != null)
{
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
}
var fv = new FeatureVector();
if (isSMAChecked)
{
fv.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWMAChecked)
{
fv.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isEMAChecked)
{
fv.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isMACDChecked)
{
fv.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isRSIChecked)
{
fv.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWilliamsRChecked)
{
fv.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isStochasticsChecked)
{
fv.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
fv.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
var training = new FeatureVector();
var test = new FeatureVector();
int count = fv.Values[0].Length;
for (int i = 0; i < fv.ColumnName.Count; i++)
{
training.AddColumn(fv.ColumnName[i], fv.Values[i].Take((int)(count * trainingSetPercentage)).ToArray());
}
for (int i = 0; i < fv.ColumnName.Count; i++)
{
test.AddColumn(fv.ColumnName[i], fv.Values[i].Skip((int)(count * trainingSetPercentage)).Take(count).ToArray()); // Take(count) means take the rest of all elements, number of the rest of the elements is smaller than count.
}
if (numFolds > 0)
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
CrossValidator cvLinReg = new CrossValidator(linearRegression, bce1, numFolds);
CrossValidatorModel cvLinRegModel = (CrossValidatorModel)cvLinReg.Fit(training);
FeatureVector linRegPredictions = cvLinRegModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logisticRegression = new LogisticRegression();
CrossValidator cvLogReg = new CrossValidator(logisticRegression, bce2, numFolds);
CrossValidatorModel cvLogRegModel = (CrossValidatorModel)cvLogReg.Fit(training);
FeatureVector logRegPredictions = cvLogRegModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
CrossValidator cvNaiBay = new CrossValidator(naiveBayes, bce3, numFolds);
CrossValidatorModel cvNaiBayModel = (CrossValidatorModel)cvNaiBay.Fit(training);
FeatureVector naiBayPredictions = cvNaiBayModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
else
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
LinearRegressionModel linearRegressionModel = (LinearRegressionModel)linearRegression.Fit(training);
FeatureVector linRegPredictions = linearRegressionModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logicticRegression = new LogisticRegression();
LogisticRegressionModel logisticRegressionModel = (LogisticRegressionModel)logicticRegression.Fit(training);
FeatureVector logRegPredictions = logisticRegressionModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
NaiveBayesModel naiveBayesModel = (NaiveBayesModel)naiveBayes.Fit(training);
FeatureVector naiBayPredictions = naiveBayesModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
labelForLinRegAcc.Text = linRegAcc.ToString();
labelForLogRegAcc.Text = logRegAcc.ToString();
labelForNaiBayAcc.Text = naiBayAcc.ToString();
panelForResults.BringToFront();
}
