// loading the network from the computer memory.
public void Load(neuralnetwork data)
{
for (int i = 0; i < entryLevel.Length; i++)
{
for (int z = 0; z < entryLevel[i].Weights.Length; z++)
{
entryLevel[i].Weights[z] = data.entryLevel[i].Weights[z];
}
entryLevel[i].bias = data.entryLevel[i].bias;
}
for (int i = 0; i < numOfneuronsRows; i++)
{
for (int j = 0; j < length; j++)
{
for (int z = 0; z < neurons[i, j].Weights.Length; z++)
{
neurons[i, j].Weights[z] = data.neurons[i, j].Weights[z];
}
neurons[i, j].bias = data.neurons[i, j].bias;
}
}
for (int i = 0; i < outputLevel.Length; i++)
{
for (int z = 0; z < outputLevel[i].Weights.Length; z++)
{
outputLevel[i].Weights[z] = data.outputLevel[i].Weights[z];
}
outputLevel[i].bias = data.outputLevel[i].bias;
}
}
public void AIPrep(snake[] snakes)
{
IsAI = true;
XsnakesHeads = new double[snakes.Length];
YsnakesHeads = new double[snakes.Length];
nn = new neuralnetwork(numOfneuronsRows, length, inputLength, outputLength);
}
// saving the network to the computer memory.
public void Save(string x)
{
BinaryFormatter bf = new BinaryFormatter();
int y = 0;
FileStream file;
if (File.Exists(x + length + "_" + (numOfneuronsRows) + "_" + y))
{
while (File.Exists(x + length + "_" + (numOfneuronsRows) + "_" + y))
{
y++;
}
file = File.Create(x + length + "_" + (numOfneuronsRows) + "_" + y);
}
else
{
file = File.Create(x + length + "_" + (numOfneuronsRows) + "_" + 0);
}
// "c:\\4inRow\\_neuralnetwork.dat"
neuralnetwork data = new neuralnetwork(numOfneuronsRows, length, entryLevel.Length, outputLength);
for (int i = 0; i < entryLevel.Length; i++)
{
for (int z = 0; z < entryLevel[i].Weights.Length; z++)
{
data.entryLevel[i].Weights[z] = entryLevel[i].Weights[z];
}
data.entryLevel[i].bias = entryLevel[i].bias;
}
for (int i = 0; i < numOfneuronsRows; i++)
{
for (int j = 0; j < length; j++)
{
for (int z = 0; z < neurons[i, j].Weights.Length; z++)
{
data.neurons[i, j].Weights[z] = neurons[i, j].Weights[z];
}
data.neurons[i, j].bias = neurons[i, j].bias;
}
}
for (int i = 0; i < outputLevel.Length; i++)
{
for (int z = 0; z < outputLevel[i].Weights.Length; z++)
{
data.outputLevel[i].Weights[z] = outputLevel[i].Weights[z];
}
data.outputLevel[i].bias = outputLevel[i].bias;
}
data.score = score;
bf.Serialize(file, data);
file.Close();
}
// loading a network from a given network (duplicating the given network into this network).
public void Load(int fileNum, string x)
{
if (File.Exists(x + length + "_" + (numOfneuronsRows) + "_" + fileNum))
{
BinaryFormatter bf = new BinaryFormatter();
FileStream file = File.Open(x + length + "_" + (numOfneuronsRows) + "_" + fileNum, FileMode.Open);
neuralnetwork data = (neuralnetwork)bf.Deserialize(file);
file.Close();
for (int i = 0; i < entryLevel.Length; i++)
{
for (int z = 0; z < entryLevel[i].Weights.Length; z++)
{
entryLevel[i].Weights[z] = data.entryLevel[i].Weights[z];
}
entryLevel[i].bias = data.entryLevel[i].bias;
}
for (int i = 0; i < numOfneuronsRows; i++)
{
for (int j = 0; j < length; j++)
{
for (int z = 0; z < neurons[i, j].Weights.Length; z++)
{
neurons[i, j].Weights[z] = data.neurons[i, j].Weights[z];
}
neurons[i, j].bias = data.neurons[i, j].bias;
}
}
for (int i = 0; i < outputLevel.Length; i++)
{
for (int z = 0; z < outputLevel[i].Weights.Length; z++)
{
outputLevel[i].Weights[z] = data.outputLevel[i].Weights[z];
}
outputLevel[i].bias = data.outputLevel[i].bias;
}
}
}
public void Load(neuralnetwork nn2)
{
nn.Load(nn2);
}
