// Due Dates
public KleineServiceApi(IRepositories repo, INotification notify)
{
this.repo = repo;
this.notify = notify;
// currently static date, we'll add this in the future to be dynamic from the database
this.outcome = new PredictionOutcome
{
Gender = "Female",
Date = new DateTime(2013, 12, 10),
Time = new DateTime(2013, 12, 10, 17, 42, 0),
Weight = 7.2M,
Length = 20.75M
};
}
public int PointsFor(PredictionOutcome predictionOutcome)
{
switch (predictionOutcome)
{
case PredictionOutcome.CorrectScore:
return(CorrectScorePoints);
case PredictionOutcome.CorrectOutcome:
return(CorrectOutcomePoints);
case PredictionOutcome.IncorrectOutcome:
return(IncorrectOutcomePoints);
case PredictionOutcome.NoFixtureScore:
return(NoFixtureScore);
default:
return(0);
}
}
private void Button1_Click(object sender, EventArgs e)
{
Point[] startPoints = GeneratePoints(int.Parse(p_textBox.Text));
NeuralNetwork network = NeuralNetwork.CreateNetwork(startPoints);
double alpha = double.Parse(alpha_textBox.Text);
double desiredError = double.Parse(e_textBox.Text);
int trainPoints = int.Parse(train_textBox.Text);
Point[] points = GeneratePoints(trainPoints);
network.TrainNetwork(alpha, desiredError, points);
Point[] predictedPoints = network.PredictNextPoints(trainPoints + 20);
richTextBox1.Clear();
richTextBox1.Text += $"Alpha = {network.LearningOutcome.alpha}\n";
Point[] correctPoints = GeneratePoints(trainPoints + 20);
PredictionOutcome predictionOutcome = new PredictionOutcome();
predictionOutcome.X = new double[20];
predictionOutcome.RealValue = new double[20];
predictionOutcome.ReferenceValue = new double[20];
for (int i = 0, j = trainPoints; i < 20; i++, j++)
{
predictionOutcome.X[i] = Math.Round(correctPoints[j].X, 2);
predictionOutcome.RealValue[i] = correctPoints[j].Y;
predictionOutcome.ReferenceValue[i] = predictedPoints[j].Y;
richTextBox1.Text += $"X = {predictionOutcome.X[i]} | Real = {predictionOutcome.RealValue[i]} | Reference = {predictionOutcome.ReferenceValue[i]}\n";
}
string jsonString = JsonSerializer.Serialize(predictionOutcome);
File.WriteAllText("predictedPoints.json", jsonString);
jsonString = JsonSerializer.Serialize(network.LearningOutcome);
File.WriteAllText("learningOutcome.json", jsonString);
}
private PredictionScore getScore(Prediction prediction, PredictionOutcome outcome)
{
PredictionScore score = new PredictionScore();
if (prediction.FinishDate != null)
{
var date = (DateTime)prediction.Date;
var time = ((DateTime)prediction.Time).ToLocalTime();
time = new DateTime(2013, 12, 10, time.Hour, time.Minute, 0);
if (prediction.Gender == outcome.Gender)
score.Gender = 3;
if (date.Month == outcome.Date.Month && date.Day == outcome.Date.Day)
score.Date = 5;
var timedif = (outcome.Time - time);
if (timedif.TotalHours <= 4 && timedif.TotalHours >= 0)
score.Time = 4;
if (prediction.Weight <= outcome.Weight && prediction.Weight <= (outcome.Weight + 1.5M))
score.Weight = 2;
if (prediction.Length <= outcome.Length && outcome.Length <= prediction.Length + 2M)
score.Length = 1;
}
return score;
}
