public NeuroNet neuroNet;// = NeuroNet.CreateNeuroNet(new int[] {400, 15, 1 }, new NeuroData(1000, 0.3f));
public NeuroWork(int offset, int number_of_passes, float edukationK)
{
neuroNet = NeuroNet.CreateNeuroNet(new int[] { offset *offset, offset, 1 }, new NeuroData(number_of_passes, edukationK));
this.offset = offset;
PreShow = new Bitmap(offset, offset);
start_signals = new int[offset * offset];
}
public static NeuroNet LoadNeuroNet(string filePath)
{
NeuroNet neuroNet;
using (StreamReader reader = new StreamReader(filePath))
{
NeuroData neuroData = NeuroData.LoadNeuroData(reader);
int number_of_layers = Convert.ToInt32(reader.ReadLine());
int[] layers = new int[number_of_layers];
for (int i = 0; i < number_of_layers; i++)
{
layers[i] = Convert.ToInt32(reader.ReadLine());
}
neuroNet = NeuroNet.CreateNeuroNet(layers, neuroData);
for (int i = 0; i < neuroNet.neuroLayer.Length; i++)
{
for (int j = 0; j < neuroNet.neuroLayer[i].Neurons.Length; j++)
{
for (int k = 0; k < neuroNet.neuroLayer[i].Neurons[j].weights.Length; k++)
{
neuroNet.neuroLayer[i].Neurons[j].weights[k] = Convert.ToSingle(reader.ReadLine());
}
}
if (i < neuroNet.neuroLayer.Length - 1)
{
for (int k = 0; k < neuroNet.neuroLayer[i + 1].Neurons.Length; k++)
{
neuroNet.neuroLayer[i].AdditionalNeuron.weights[k] = Convert.ToSingle(reader.ReadLine());
}
}
}
}
return(neuroNet);
}
