static void Main(string[] args)
{
int[] array = { 2, 8, 4 };
string str = File.ReadAllText("TD1.txt");
float[][] vs1 = JsonConvert.DeserializeObject <float[][]>(str);
NNModel model = new NNModel(array);
BackPropagation backPropagation = new BackPropagation();
backPropagation.Learn(model, vs1);
for (int i = 0; i < vs1.Length; i += 2)
{
var inp = vs1[i];
Array.ForEach(model.Compute(inp).Neurons, x =>
{
Console.WriteLine(x.Value + " ");
});
Console.WriteLine();
}
Console.ReadKey();
}
static void TestingOpen1()
{
int[] array = { 9, 20, 1 };
NNModel model = NNModel.Open("nnmodel.dat");
string str1 = File.ReadAllText("TD2.txt");
double[][] vs2 = JsonConvert.DeserializeObject <double[][]>(str1);
for (int i = 0; i < vs2.Length; i++)
{
var inp = new double[9];
var tmp = vs2[i];
for (int j = 0; j < 9; j++)
{
inp[j] = tmp[j] / tmp[j + 1];
}
model.Compute(inp).Neurons.ForEach(x =>
{
Console.WriteLine(x.Value + " ");
});
Console.WriteLine();
}
Console.ReadKey();
//model.Save();
}
static void NewNN(string[] args)
{
int[] array = new int[args.Length - 1];
for (int i = 1; i < args.Length; i++)
{
if (Int32.TryParse(args[i], out int res))
{
array[i] = res;
}
else
{
return;
}
}
string str = File.ReadAllText(args[0]);
double[][] vs1 = JsonConvert.DeserializeObject <double[][]>(str);
NNModel model = new NNModel(array);
BackPropagation backPropagation = new BackPropagation();
backPropagation.Learn(model, vs1);
for (int i = 0; i < vs1.Length; i += 2)
{
var inp = vs1[i];
model.Compute(inp).Neurons.ForEach(x =>
{
Console.WriteLine(x.Value + " ");
});
Console.WriteLine();
}
Console.ReadKey();
}
static void Testing()
{
int[] array = { 10, 20, 2 };
string str = File.ReadAllText("TD1.txt");
double[][] vs1 = JsonConvert.DeserializeObject <double[][]>(str);
NNModel model = new NNModel(array);
Console.Beep();
var watch = System.Diagnostics.Stopwatch.StartNew();
BackPropagation backPropagation = new BackPropagation();
backPropagation.Learn(model, vs1);
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
string str1 = File.ReadAllText("TD2.txt");
double[][] vs2 = JsonConvert.DeserializeObject <double[][]>(str1);
for (int i = 0; i < vs2.Length; i++)
{
model.Compute(NNModel.ReculcArray(vs2[i])).Neurons.ForEach(x =>
{
Console.WriteLine(x.Value + " ");
});
Console.WriteLine();
}
Console.WriteLine("_______");
model.Save();
Console.Beep();
Console.ReadKey();
}
static void TestingOpen()
{
int[] array = { 9, 20, 1 };
NNModel model = NNModel.Open("nnmodel.dat");
string str1 = File.ReadAllText("TD2.txt");
double[][] vs2 = JsonConvert.DeserializeObject <double[][]>(str1);
for (int i = 0; i < vs2.Length; i++)
{
model.Compute(NNModel.ReculcArray(vs2[i])).Neurons.ForEach(x =>
{
Console.WriteLine(x.Value + " ");
});
Console.WriteLine();
}
Console.WriteLine("_______");
Console.ReadKey();
}
static void Testing1()
{
int[] array = { 9, 20, 1 };
string str = File.ReadAllText("TD1.txt");
double[][] vs1 = JsonConvert.DeserializeObject <double[][]>(str);
for (int i = 0; i < vs1.Length; i += 2)
{
var tmp = vs1[i];
vs1[i] = new double[9];
for (int j = 0; j < 9; j++)
{
vs1[i][j] = tmp[j] / tmp[j + 1];
}
}
NNModel model = new NNModel(array);
//NNModel model = NNModel.Open("nnmodel.dat");
Console.Beep();
var watch = System.Diagnostics.Stopwatch.StartNew();
BackPropagation backPropagation = new BackPropagation();
backPropagation.Learn(model, vs1);
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
string str1 = File.ReadAllText("TD2.txt");
double[][] vs2 = JsonConvert.DeserializeObject <double[][]>(str1);
for (int i = 0; i < vs2.Length; i++)
{
var inp = new double[9];
var tmp = vs2[i];
for (int j = 0; j < 9; j++)
{
inp[j] = tmp[j] / tmp[j + 1];
}
model.Compute(inp).Neurons.ForEach(x =>
{
Console.WriteLine(x.Value + " ");
});
Console.WriteLine();
}
Console.WriteLine("_______");
//for (int i = 90; i < vs1.Length; i += 90)
//{
// for (int j = 0; j < 3; j++)
// {
// var inp = new double[9];
// var tmp = vs1[i + j];
// for (int k = 0; k < 9; k++)
// {
// inp[k] = tmp[k] / tmp[k + 1];
// }
// model.Compute(inp).Neurons.ForEach(x =>
// {
// Console.WriteLine(x.Value + " ");
// });
// Console.WriteLine();
// }
//}
model.Save();
Console.Beep();
Console.ReadKey();
}