public double Train(TrainingData data)
{
var localError = data.Output - Evaluate(data);
if (localError == 0)
{
return(localError);
}
weightX += learningRate * localError * data.InputX;
weightY += learningRate * localError * data.InputY;
weightB += learningRate * localError * 1;
return(localError);
}
public IEnumerable <TrainingData> NormalizeSchedulingTrainingData(
params SchedulingTrainingData[] schedulingTrainingDatas)
{
ealiestDeliveryDifference = schedulingTrainingDatas.OrderBy(std => std.Latness.Ticks).First().Latness;
latestDeliveryDifference = schedulingTrainingDatas.OrderByDescending(std => std.Latness.Ticks).First().Latness;
minimumOverproduction = schedulingTrainingDatas.OrderBy(std => std.Overproduction).First().Overproduction;
maximumOverproduction = schedulingTrainingDatas.OrderByDescending(std => std.Overproduction).First().Overproduction;
var trainingDatas = new List <TrainingData>();
foreach (var schedulingTrainingData in schedulingTrainingDatas)
{
var data = GetData(schedulingTrainingData);
var trainingData = new TrainingData(data.InputX, data.InputY, schedulingTrainingData.isValid ? 1 : -1);
trainingDatas.Add(trainingData);
}
return(trainingDatas);
}