public static List <TrainingDataDto> GetTestingSet(NeuralNetworkTrainingConfig trainingConfig)
{
List <TrainingDataDto> trainingSet = new List <TrainingDataDto>();
string line;
// Read the file and display it line by line.
System.IO.StreamReader file =
new System.IO.StreamReader(System.IO.Path.Combine(trainingConfig.TrainingDatabaseFileRoute, trainingConfig.TrainingDatabaseTestFileName));
while ((line = file.ReadLine()) != null)
{
var trainingRow = new TrainingDataDto();
var lines = line.Split(" ", StringSplitOptions.RemoveEmptyEntries);
trainingRow.XEntries =
Array.ConvertAll(lines.Take(trainingConfig.InputSize).ToArray(),
System.Convert.ToDouble)
.ToList();
trainingRow.YExpected = Array.ConvertAll(lines.Skip(trainingConfig.InputSize)
.Take(trainingConfig.PredictedObjects.Count).ToArray(),
System.Convert.ToDouble)
.ToList();
trainingSet.Add(trainingRow);
}
file.Close();
return(trainingSet);
}
public static PredictedObjectResultDto GetNetworkPredictionOptimized(IList <NeuronForManipulation> HiddenLayer,
IList <NeuronForManipulation> OutputLayer,
NeuralNetworkTrainingConfig neuralNetworkConfig,
IList <double> input)
{
List <double> hiddenOutput = new List <double>();
foreach (NeuronForManipulation hiddenNeuron in HiddenLayer)
{
CalculateNeuronOutput(hiddenNeuron, neuralNetworkConfig.HiddenActivationFunction, Alpha, input);
hiddenOutput.Add(hiddenNeuron.Output);
}
foreach (NeuronForManipulation outputNeuron in OutputLayer)
{
CalculateNeuronOutput(outputNeuron, neuralNetworkConfig.OutputActivationFunction, Alpha, hiddenOutput);
}
var maxNeuralNetwork = OutputLayer.OrderByDescending(s => s.Output).FirstOrDefault();
var predictedObject = neuralNetworkConfig.PredictedObjects.Where(po => po.Index == maxNeuralNetwork.Index).FirstOrDefault();
return(new PredictedObjectResultDto {
ObjectName = predictedObject.ObjectName, Index = maxNeuralNetwork.Index, Accuracy = maxNeuralNetwork.Output
});
}