public ANN()
{
Console.Write("Input the level of hidden layer: ");
Console.WriteLine("This is a sample for Git hub TEST! Again!");
int layer_num = Int32.Parse(Console.ReadLine()) + 2;
Initial_DATA initial = new Initial_DATA(layer_num);
for (int num = 0; (Math.Abs((double)(initial.layers[layer_num - 1].original[0] - initial.layers[layer_num - 1].values[initial.layers[layer_num - 1].values.Count - 1])) > 10); num++)
{
Back_Propagation(ref initial);
for (int i = 1; i < layer_num; i++)
{
initial.layers[i].Result_Values(initial.layers[i].original, initial.layers[i].weights);
}
for (int i = 1; i < layer_num; i++)
{
for (int j = 0; j < initial.layers[i].weights.Count; j++)
{
for (int k = 0; k < initial.layers[i].weights[j].Length; k++)
{
Console.Write($"a[{j}][{k}]={initial.layers[i].weights[j][k]} ");
}
Console.WriteLine();
}
}
}
}
void Back_Propagation(ref Initial_DATA list)
{
int size = list.layer_num;
List <float> original_tmp = new List <float>();
for (int i = size - 1; i > 0; i--)
{
if (i == size - 1)
{
float[] sub_val = new float[list.layers[i].neurons];
for (int j = 0; j < list.layers[i].neurons; j++)
{
sub_val[j] = list.layers[i].original[j] - list.layers[i].values[j];
for (int k = 0; k < list.layers[i].weights[j].Length; k++)
{
if (sub_val[j] != 0)
{
list.layers[i].weights[j][k] += list.layers[i].weights[j][k] / sub_val[j];
}
}
}
}
else
{
float[] sub_val = new float[list.layers[i].neurons];
for (int j = 0; j < list.layers[i].neurons; j++)
{
List <float> original = new List <float>();
for (int k = 0; k < list.layers[i + 1].original.Count; k++)
{
original.Add(list.layers[i + 1].original[k] / list.layers[i + 1].weights[j][k]);
}
list.layers[i].original.Add(original.Sum());
}
for (int j = 0; j < list.layers[i].neurons; j++)
{
sub_val[j] = list.layers[i].original[j] - list.layers[i].values[j];
for (int k = 0; k < list.layers[i].weights[j].Length; k++)
{
if (sub_val[j] != 0)
{
list.layers[i].weights[j][k] += list.layers[i].weights[j][k] / sub_val[j];
}
}
}
}
}
}
