private void Current_Data_btn_Click(object sender, RoutedEventArgs e)
{//show the user the current data we will use for forcasting from the hashtag file the user choose
if (check_user_input(1))
{
Popup.IsOpen = false;
//clear the charts if it has points alrady
Current_Data.Clear();
Next_Data.Clear();
Predicted_Data.Clear();
Negetive_Predicted_Data.Clear();
//create the arff data file from 90% of the current data from the hashtag that the user choose
//and save in arff.next_data an array with the next data point (the last 10% of the current data)
SignalToSignalArff arff = new SignalToSignalArff("D:\\Twist_DB\\hashtag_signal\\" + listBox.SelectedItem.ToString() + ".txt", data_precentage_split);
//fill the next data points to the next_data prop
if (arff.next_data != null)
{
next_data = new Tuple <double, double> [arff.next_data.Length];
next_data = arff.next_data;
}
//fixing chart prespective
Next_Data.Add(new ObservablePoint(0, 0));
Predicted_Data.Add(new ObservablePoint(0, 0));
Negetive_Predicted_Data.Add(new ObservablePoint(0, 0));
// path to the signal data - found in the project folder ..WekaForecasting\bin\Debug
String pathToData = ".\\Signal.arff";
// load the signal data
Instances data = new Instances(new java.io.BufferedReader(new java.io.FileReader(pathToData)));
//fill Current_Data series with the data from the hashtag file the user choose
for (int i = 0; i < data.numInstances(); i++)
{
Current_Data.Add(new ObservablePoint(data.instance(i).value(0), data.instance(i).value(1)));
}
}
}
private void Forecast_Calc_Fill()
{//using WekaForcaster to culclate the predicted data for our current signal data.
//and show it in a chart for the user
try
{
// path to the signal data - found in the project folder ..WekaForecasting\bin\Debug
String pathToData = ".\\Signal.arff";
// load the signal data
Instances data = new Instances(new java.io.BufferedReader(new java.io.FileReader(pathToData)));
// new forecaster
WekaForecaster forecaster = new WekaForecaster();
// set the target we want to forecast - the attribute in our arff file.
forecaster.setFieldsToForecast("frequency");
// default underlying classifier is SMOreg (SVM) - we'll use
// MultilayerPerceptron for regression instead
forecaster.setBaseForecaster(new MultilayerPerceptron());
// set the attribute in our arff file that will be our time stamp.
forecaster.getTSLagMaker().setTimeStampField("time");
// set the lag creation to the data
forecaster.getTSLagMaker().setMinLag(1);
forecaster.getTSLagMaker().setMaxLag(returnBestMaxLagFit(forecaster, data));
// build the model
forecaster.buildForecaster(data);
// prime the forecaster with enough recent historical data
forecaster.primeForecaster(data);
//xFactor will help us to calculate the forecast data after the last data piont in the next_data
int xFactor = next_data.Length;
// forecast for the number of forecastUnits we set beyond the end of the training data
int forecastUnits = Convert.ToInt32(TextBox.Text) + xFactor;
double[] PredectedList = new double[forecastUnits];
var mylist = forecaster.forecast(forecastUnits);
//get the forcasted data and place it in the PredectedList
for (int i = 0; i < forecastUnits; i++)
{
String predict = mylist.get(i).ToString().Split(' ')[2];
PredectedList[i] = Convert.ToDouble(predict);
}
//lag represent our fixed lag between the time stamp of the predictions
double lag = forecaster.getTSLagMaker().getDeltaTime();
//DeltaTime will represent our changing lag between each time stamp at each prediction
double DeltaTime = 0;
//getting the last time stamp in our last point in the current data
double lastValidTime = data.lastInstance().value(0);
//clear Predicted_Data if alredy have data inside
Predicted_Data.Clear();
Negetive_Predicted_Data.Clear();
// output the predictions with their time stamp in the chart.
for (int i = 0; i < forecastUnits; i++)
{
if (i < next_data.Length - 1)
{//we still in the next data points and not in the predected points the user want to predict
Predicted_Data.Add(new ObservablePoint(next_data[i].Item1, PredectedList[i]));
//checks if the predected point is negetive if is negetive add the the black points series
if (PredectedList[i] < 0)
{
Negetive_Predicted_Data.Add(new ObservablePoint(next_data[i].Item1, PredectedList[i]));
}
//get the x from the last point in our next data to be the starting point in the predecred data
lastValidTime = next_data[i].Item1;
}
else
{//we are in the range of the predected points the user want to predict
//advance the current time to correspond to the forecasted values
DeltaTime = DeltaTime + lag;
//append the predicted data to the chart
Predicted_Data.Add(new ObservablePoint(lastValidTime + DeltaTime, PredectedList[i]));
//checks if the predected point is negetive if is negetive add the the black points series
if (PredectedList[i] < 0)
{
Negetive_Predicted_Data.Add(new ObservablePoint(lastValidTime + DeltaTime, PredectedList[i]));
}
}
}
//clear progressBar in the end of the progress
ProgressBar.Value = 0;
//checks if their wasn't negetive predected point and add the (0,0) point to the black points series
//for preserving the chart prespective
if (Negetive_Predicted_Data.Count == 0)
{
Negetive_Predicted_Data.Add(new ObservablePoint(0, 0));
}
}
catch (Exception ex) { ex.ToString(); }
}
