private void RunRl()
{
var list = GetAllModelForWeekday(selectedWeekday);
var takeNumber = list.Count;
var comeHomingValues =
list.OrderByDescending(u => u.Date)
.Select(z => new TimeSpan(0, int.Parse(z.Hour), int.Parse(z.Minutes), 0))
.Take(takeNumber)
.ToList();
var xs = new double[takeNumber];
var value = 0.1;
for (var i = 0; i < xs.Length; i++)
{
xs[i] = value;
value += 0.1;
}
var comeHomingHourValues =
comeHomingValues.Select(y => TimeConverter.ConvertFromTimeToDouble(y.TotalMinutes)).ToArray();
double r;
double yintercept;
double slope;
LinearRegression.Execute(xs, comeHomingHourValues, 1, takeNumber - 1, out r, out yintercept, out slope);
var predictionValue = slope * (value + 0.1) + yintercept;
_predictRL = predictionValue;
}
private void RunANN()
{
var list = GetAllModelForWeekday(selectedWeekday);
var takeNumber = HowManyTake(list.Count);
var comeHomingValues =
list.OrderByDescending(u => u.Date)
.Select(z => new TimeSpan(0, int.Parse(z.Hour), int.Parse(z.Minutes), 0))
.Take(takeNumber)
.ToList();
if (takeNumber < 40)
{
var random = new Random();
for (var i = takeNumber; i < 40; i++)
{
comeHomingValues.Add(comeHomingValues[random.Next(takeNumber)]);
}
}
var comeHomingHourValues =
comeHomingValues.Select(y => TimeConverter.ConvertFromTimeToDouble(y.TotalMinutes)).ToArray();
_predict = new NuralNetworkPredictionAlgorithm(comeHomingHourValues);
_predict.Execute();
_predict.PredictValue();
_predictANN = _predict.GetPredictedValue();
}
private void RunWma()
{
var list = GetAllModelForWeekday(selectedWeekday);
var takeNumber = list.Count;
var comeHomingValues =
list.OrderByDescending(u => u.Date)
.Select(z => new TimeSpan(0, int.Parse(z.Hour), int.Parse(z.Minutes), 0))
.Take(takeNumber)
.ToList();
var prediction =
WMA.WeightedMovingAverage(
comeHomingValues.Select(y => TimeConverter.ConvertFromTimeToDouble(y.TotalMinutes)).ToArray(), 1,
0.05, 0.15, 0.20, 0.25, 0, 0.35);
_predictWma = prediction.Value;
}