//计算网络输出
public List <double> Update(List <double> _inputs)
{
List <double> inputs = new List <double>(_inputs);
List <double> outputs = new List <double>();
int cWeight = 0;
//添加噪声
if (Useful.WITH_NOISE)
{
for (int k = 0; k < inputs.Count; k++)
{
inputs[k] += Useful.RandomClamped() * Useful.MAX_NOISE_TO_ADD;
}
}
//验证输入长度
if (inputs.Count != NumInputs)
{
return(outputs);
}
//对于每一层执行
for (int i = 0; i < NumHiddenLayers + 1; i++)
{
if (i > 0)
{
inputs = new List <double>(outputs);
}
outputs.Clear();
cWeight = 0;
//对于每个神经元执行
for (int n = 0; n < Layers[i].NumNeurons; n++)
{
double netinput = 0;
int num = Layers[i].Neurons[n].NumInputs;
//对于每个权重执行
for (int k = 0; k < num - 1; k++)
{
netinput += Layers[i].Neurons[n].Weights[k] * inputs[cWeight++];
}
netinput += Layers[i].Neurons[n].Weights[num - 1] * Useful.BIAS;
Layers[i].Neurons[n].Activation = Sigmoid(netinput, Useful.ACTIVATION_RESPONSE);
outputs.Add(Layers[i].Neurons[n].Activation);
cWeight = 0;
}
}
return(outputs);
}
//构造函数
public Neuron(int _NumInputs)
{
NumInputs = _NumInputs + 1;
Activation = 0;
Error = 0;
Weights = new List <double>();
PrevUpdate = new List <double>();
//生成随机权重
for (int i = 0; i < NumInputs; i++)
{
Weights.Add(Useful.RandomClamped());
PrevUpdate.Add(0.0);
}
}
//将所有权重设置为随机的小值
private void InitializeNetwork()
{
//对于每一层执行
for (int i = 0; i < NumHiddenLayers + 1; i++)
{
//对于每个神经元执行
for (int n = 0; n < Layers[i].NumNeurons; n++)
{
//对于每个权重执行
for (int k = 0; k < Layers[i].Neurons[n].NumInputs; k++)
{
Layers[i].Neurons[n].Weights[k] = Useful.RandomClamped();
}
}
}
ErrorSum = 9999;
NumEpochs = 0;
}
