public void Filter(bool UseNetwork = true, bool UseFinal = false, bool UseYear = false, List <Year> years = null)
{
Predictions.Clear();
FilteredShows.Clear();
var year = NetworkDatabase.YearList[NetworkDatabase.CurrentYear];
//var Adjustments = model.GetAdjustments(true);
var average = model.GetNetworkRatingsThreshold(year);
if (!UseYear)
{
years = new List <Year>()
{
new Year(NetworkDatabase.YearList[NetworkDatabase.CurrentYear])
}
}
;
shows.Where(x => years.Select(y => y.year).Contains(x.year)).ToList().ForEach(x => FilteredShows.Add(x));
if (UseYear || !UseNetwork)
{
var p = new ListOfPredictions {
Category = "Predictions"
};
foreach (Show s in FilteredShows)
{
p.Add(new PredictionContainer(s, this, average, false, UseYear, UseFinal));
}
if (Predictions.Count > 0)
{
throw new Exception("Predictions were not empty!");
}
Predictions.Add(p);
pendingFilter = true;
}
else
{
if (Application.Current.Properties.ContainsKey("PredictionSort"))
{
switch (Application.Current.Properties["PredictionSort"] as string)
{
case "Ratings":
{
var TempList = FilteredShows.AsParallel().Select(x => new PredictionContainer(x, this, average, true, false, UseFinal)).OrderByDescending(x => x.show.AverageRating).ToList();
MiniNetwork.AddPredictions_Ratings(TempList, ref Predictions);
break;
}
case "Name":
{
var TempList = shows.AsParallel().Where(x => x.year == year).Select(x => new PredictionContainer(x, this, average, true, false, UseFinal)).OrderBy(x => x.Name).ThenBy(x => x.Season).ToList();
MiniNetwork.AddPredictions_Name(TempList, ref Predictions);
break;
}
default:
{
Filter_Odds(average, UseFinal);
break;
}
}
}
else
{
Filter_Odds(average, UseFinal);
}
}
}
void Filter_Odds(double average, bool UseFinal = false)
{
var tempPredictions = FilteredShows.AsParallel().Select(x => new PredictionContainer(x, this, average, true, false, UseFinal)).OrderByDescending(x => x.odds);
MiniNetwork.AddPredictions_Odds(tempPredictions, ref Predictions);
}
public static void AddPredictions_Ratings(List <PredictionContainer> TempList, ref ObservableCollection <ListOfPredictions> Predictions)
{
Predictions.Clear();
var count = TempList.Count() - 1;
//Determine Percentile rating scores
double
P100 = (count > -1) ? Math.Round(TempList[0].show.AverageRating, 2) : 0,
P80 = (count > -1) ? Math.Round(TempList[count / 5].show.AverageRating, 2) : 0,
P60 = (count > -1) ? Math.Round(TempList[count * 2 / 5].show.AverageRating, 2) : 0,
P40 = (count > -1) ? Math.Round(TempList[count * 3 / 5].show.AverageRating, 2) : 0,
P20 = (count > -1) ? Math.Round(TempList[count * 4 / 5].show.AverageRating, 2) : 0,
P0 = (count > -1) ? Math.Round(TempList[count].show.AverageRating, 2) : 0;
ListOfPredictions
HighRatings = new ListOfPredictions {
Category = (P80 == P100) ? P80.ToString("N2") : P80.ToString("N2") + " - " + P100.ToString("N2")
},
GoodRatings = new ListOfPredictions {
Category = (P60 == P80 - 0.01) ? P60.ToString("N2") : P60.ToString("N2") + " - " + (P80 - 0.01).ToString("N2")
},
MediumRatings = new ListOfPredictions {
Category = (P40 == P60 - 0.01) ? P40.ToString("N2") : P40.ToString("N2") + " - " + (P60 - 0.01).ToString("N2")
},
PoorRatings = new ListOfPredictions {
Category = (P20 == P40 - 0.01) ? P20.ToString("N2") : P20.ToString("N2") + " - " + (P40 - 0.01).ToString("N2")
},
LowRatings = new ListOfPredictions {
Category = (P0 == P20 - 0.01) ? P0.ToString("N2") : P0.ToString("N2") + " - " + (P20 - 0.01).ToString("N2")
};
foreach (PredictionContainer p in TempList)
{
var rating = Math.Round(p.show.AverageRating, 2);
if (rating >= P80)
{
HighRatings.Add(p);
}
else if (rating >= P60)
{
GoodRatings.Add(p);
}
else if (rating >= P40)
{
MediumRatings.Add(p);
}
else if (rating >= P20)
{
PoorRatings.Add(p);
}
else
{
LowRatings.Add(p);
}
}
if (HighRatings.Count > 0)
{
Predictions.Add(HighRatings);
}
if (GoodRatings.Count > 0)
{
Predictions.Add(GoodRatings);
}
if (MediumRatings.Count > 0)
{
Predictions.Add(MediumRatings);
}
if (PoorRatings.Count > 0)
{
Predictions.Add(PoorRatings);
}
if (LowRatings.Count > 0)
{
Predictions.Add(LowRatings);
}
}
public static void AddPredictions_Name(List <PredictionContainer> TempList, ref ObservableCollection <ListOfPredictions> Predictions)
{
Predictions.Clear();
var count = TempList.Count() - 1;
//Determine Percentile Letters
char
P100 = (count > -1) ? TempList[0].Name.ToUpper()[0] : 'A',
P80 = (count > -1) ? TempList[count / 5].Name.ToUpper()[0] : 'F',
P60 = (count > -1) ? TempList[count * 2 / 5].Name.ToUpper()[0] : 'K',
P40 = (count > -1) ? TempList[count * 3 / 5].Name.ToUpper()[0] : 'P',
P20 = (count > -1) ? TempList[count * 4 / 5].Name.ToUpper()[0] : 'U',
P0 = (count > -1) ? TempList[count].Name.ToUpper()[0] : 'Z';
ListOfPredictions
First = new ListOfPredictions {
Category = (P100 == P80) ? P80.ToString() : P100 + " - " + P80
},
Second = new ListOfPredictions {
Category = (P80 + 1 == P60) ? P60.ToString() : (char)(P80 + 1) + " - " + P60
},
Third = new ListOfPredictions {
Category = (P60 + 1 == P40) ? P40.ToString() : (char)(P60 + 1) + " - " + P40
},
Fourth = new ListOfPredictions {
Category = (P40 + 1 == P20) ? P20.ToString() : (char)(P40 + 1) + " - " + P20
},
Fifth = new ListOfPredictions {
Category = (P20 + 1 == P0) ? P0.ToString() : (char)(P20 + 1) + " - " + P0
};
foreach (PredictionContainer p in TempList)
{
if (p.Name[0] <= P80)
{
First.Add(p);
}
else if (p.Name[0] <= P60)
{
Second.Add(p);
}
else if (p.Name[0] <= P40)
{
Third.Add(p);
}
else if (p.Name[0] <= P20)
{
Fourth.Add(p);
}
else
{
Fifth.Add(p);
}
}
if (First.Count > 0)
{
Predictions.Add(First);
}
if (Second.Count > 0)
{
Predictions.Add(Second);
}
if (Third.Count > 0)
{
Predictions.Add(Third);
}
if (Fourth.Count > 0)
{
Predictions.Add(Fourth);
}
if (Fifth.Count > 0)
{
Predictions.Add(Fifth);
}
}
public static void AddPredictions_Odds(OrderedParallelQuery <PredictionContainer> tempPredictions, ref ObservableCollection <ListOfPredictions> Predictions, bool UseFinal = false)
{
Predictions.Clear();
ListOfPredictions
CertainRenewed = new ListOfPredictions {
Category = "Certain Renewal"
},
LikelyRenewed = new ListOfPredictions {
Category = "Likely Renewal"
},
LeaningRenewed = new ListOfPredictions {
Category = "Leaning Towards Renewal"
},
LeaningCanceled = new ListOfPredictions {
Category = "Leaning Towards Cancellation"
},
LikelyCanceled = new ListOfPredictions {
Category = "Likely Cancellation"
},
CertainCanceled = new ListOfPredictions {
Category = "Certain Cancellation"
};
foreach (PredictionContainer p in tempPredictions)
{
var odds = UseFinal? p.finalodds : p.odds;
if (odds > 0.8)
{
CertainRenewed.Add(p);
}
else if (odds > 0.6)
{
LikelyRenewed.Add(p);
}
else if (odds > 0.5)
{
LeaningRenewed.Add(p);
}
else if (odds == 0.5)
{
if (p.show.ShowIndex > p.show._calculatedThreshold)
{
LeaningRenewed.Add(p);
}
else
{
LeaningCanceled.Add(p);
}
}
else if (odds > 0.4)
{
LeaningCanceled.Add(p);
}
else if (odds > 0.2)
{
LikelyCanceled.Add(p);
}
else
{
CertainCanceled.Add(p);
}
}
if (CertainRenewed.Count > 0)
{
Predictions.Add(CertainRenewed);
}
if (LikelyRenewed.Count > 0)
{
Predictions.Add(LikelyRenewed);
}
if (LeaningRenewed.Count > 0)
{
Predictions.Add(LeaningRenewed);
}
if (LeaningCanceled.Count > 0)
{
Predictions.Add(LeaningCanceled);
}
if (LikelyCanceled.Count > 0)
{
Predictions.Add(LikelyCanceled);
}
if (CertainCanceled.Count > 0)
{
Predictions.Add(CertainCanceled);
}
}
