/// <summary>
/// Save network to JSON file in application directory
/// </summary>
/// <param name="name">name of the saved file. Example - test.json</param>
//public void SaveJSON(string name)
//{
// string text = JsonSerializer.Serialize(this);
// System.IO.File.WriteAllText(Environment.CurrentDirectory + "//" + name, text);
//}
///// <summary>
///// Load network from JSON file in application directory
///// </summary>
///// <param name="filename">name of the uploaded file. Example - test.json</param>
///// <returns></returns>
//public static NeuronNetwork LoadJSON (string filename)
//{
// string text = System.IO.File.ReadAllText(Environment.CurrentDirectory + "//" + filename);
// return JsonSerializer.Deserialize<NeuronNetwork>(text);
//}
public object Clone()
{
NeuronNetwork nw = new NeuronNetwork();
foreach (var a in layers)
{
NeuronLayer l = new NeuronLayer();
nw.AddLayer(l);
foreach (var n in a.neurons)
{
l.AddNeuron((Neuron)Activator.CreateInstance(n.GetType()));
}
}
nw.Create();
return(nw);
}
} //провести рассчет если нейроны уже имеют входные значения
/// <summary>
/// Calculate answer using input data
/// </summary>
/// <param name="IN">Input values for first layer neurons</param>
/// <returns></returns>
public double[] GetAnswer(params double[] IN) //перегрузка с авто передачей входа на входной уровень нс
{
Reset();
NeuronLayer l1 = layers[0];
if (IN.Length != l1.neurons.Count)
{
throw new Exception("Incorrect input");
}
for (int i = 0; i < IN.Length; i++)
{
l1.neurons[i].In = IN[i];
}
return(GetAnswer());
}
/// <summary>
/// Corrects weights using backpropagation algorithm
/// </summary>
/// <param name="koef">learning rate</param>
/// <param name="expectedConclusions">expected conclusions</param>
public void Backward(double koef, params double[] expectedConclusions)
{
ResetErrors();
if (expectedConclusions.Length != layers[layers.Count - 1].neurons.Count)
{
throw new Exception("Error");
}
NeuronLayer last = layers[layers.Count - 1];
for (int i = 0; i < last.neurons.Count; i++)
{
last.neurons[i].error = (expectedConclusions[i] - last.neurons[i].Out);
}
for (int nextlayerIndex = layers.Count - 1; nextlayerIndex >= 1; nextlayerIndex--)
{
NeuronLayer nextLayer = layers[nextlayerIndex];
NeuronLayer curLayer = layers[nextlayerIndex - 1];
for (int nn = 0; nn < nextLayer.neurons.Count; nn++)
{
Neuron nextNeuron = nextLayer.neurons[nn];
if (nextNeuron is BiasNeuron)
{
continue;
}
double errorkoef = nextNeuron.error * koef;
for (int cn = 0; cn < curLayer.neurons.Count; cn++)
{
Neuron curNeuron = curLayer.neurons[cn];
double weight = curNeuron.outWeights[nn];
double deltaWeight = errorkoef * curNeuron.Out;
curNeuron.outWeights[nn] = weight + deltaWeight;
curNeuron.error += weight * nextNeuron.error * curNeuron.Derivative(curNeuron.In);
}
}
}
}
