public NeuralPredictionModel(MiniNetwork n) //New Random Prediction Model
{
shows = n.shows;
isMutated = false;
InputCount = n.factors.Count + 2;
NeuronCount = Convert.ToInt32(Math.Round(InputCount * 2.0 / 3.0 + 1, 0));
FirstLayer = new Neuron[NeuronCount];
SecondLayer = new Neuron[NeuronCount];
for (int i = 0; i < NeuronCount; i++)
{
FirstLayer[i] = new Neuron(InputCount);
SecondLayer[i] = new Neuron(NeuronCount);
}
Output = new Neuron(NeuronCount);
Random r = new Random();
mutationrate = r.NextDouble();
mutationintensity = r.NextDouble();
neuralintensity = r.NextDouble();
}
public PredictionContainer(Show s, MiniNetwork n, double average, bool FromNetwork = false, bool?year = null, bool final = false)
{
network = n;
UseNetwork = FromNetwork;
var model = network.model;
show = s;
odds = s.PredictedOdds;
var threshold = model.GetThreshold(s);
if (s.year == NetworkDatabase.MaxYear)
{
threshold = Math.Pow(threshold, s.network.Adjustment);
}
targetrating = model.GetTargetRating(s.year, threshold);
ShowDetails = false;
IsShowPage = false;
IsLoaded = false;
_networkaverage = average;
finalodds = s.Renewed || s.Canceled ? s.FinalPrediction : s.PredictedOdds;
NetworkDatabase.CurrentYearUpdated += NetworkDatabase_CurrentYearUpdated;
DisplayYear = year;
UseFinal = final;
}
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);
}