private void NeuralNet(bool _training)
{
inputs[0] = Horses[0].age;
inputs[1] = Horses[0]._speed;
inputs[2] = Horses[0]._movesPerRace;
if (_training == true)
{
Mn.Train(Horses[0]._previousRaceMoves);
}
Mn.StartNN(inputs);
prediction = Mn._FinalOutPut[0] * 100;
Console.WriteLine("Prediction for next race is: " + Math.Round(prediction));
Console.ReadKey();
}
private void RunNeuralNet()
{
bool run = true;
while (run == true)
{
bool IsValid = false;
int InputLayerSize = 0;
int NumHiddenLayers = 0;
char c1 = '0';
Console.Clear();
Console.WriteLine("Basic Neural Network, or Multilayer? 1/2");
while (IsValid == false)
{
string input = Console.ReadLine();
if (Tools.Validation.validMenuFormat12(input) == true)
{
IsValid = true;
c1 = char.Parse(input);
Console.Clear();
Console.WriteLine("How many Inputs?");
InputLayerSize = int.Parse(Console.ReadLine());
if (c1 == '2')
{
Console.WriteLine("How many Hidden Layers?");
NumHiddenLayers = int.Parse(Console.ReadLine());
}
else if (c1 == '1')
{
NumHiddenLayers = 0;
}
}
}
Console.WriteLine("How many Hidden Neurons per Layer?");
int HiddenLayerSize = int.Parse(Console.ReadLine());
Console.WriteLine("How many Outputs?");
int OutputLayerSize = int.Parse(Console.ReadLine());
Console.WriteLine("Display Weights at end? Y/N");
char c2 = Console.ReadKey().KeyChar;
Console.Clear();
Display d = new Display();
switch (c1)
{
case '1':
Standard(InputLayerSize, HiddenLayerSize, OutputLayerSize, c2, d);
break;
case '2':
Experimental(InputLayerSize, NumHiddenLayers, HiddenLayerSize, OutputLayerSize, c2, d);
break;
}
Console.WriteLine(" ");
Console.WriteLine("Quit? Y/N");
char q = Console.ReadKey().KeyChar;
switch (q)
{
case 'y':
run = false;
break;
case 'n':
run = true;
break;
}
}
Console.ReadKey();
void Standard(int _InputLayerSize, int _HiddenLayerSize, int _OutputLayerSize, char _c2, Display _d)
{
SingleHiddenLayerNN nn = new SingleHiddenLayerNN(_InputLayerSize, _HiddenLayerSize, _OutputLayerSize);
Console.ReadKey();
if (_c2 == 'y')
{
_d.StandardNN(nn._inputNeurons, nn._hiddenNeurons);
}
}
void Experimental(int _InputLayerSize, int _NumHiddenLayers, int _HiddenLayerSize, int _OutputLayerSize, char _c2, Display _d)
{
bool IsValid;
double[] inputs = new double[_InputLayerSize];
for (int i = 0; i < _InputLayerSize; i++)
{
Console.Clear();
Console.WriteLine("Please enter input values: " + (i + 1));
inputs[i] = double.Parse(Console.ReadLine());
}
MultilayerNeuralNet Enn = new MultilayerNeuralNet(_InputLayerSize, _NumHiddenLayers, _HiddenLayerSize, _OutputLayerSize);
Enn.StartNN(inputs);
double[] outputValues = Enn._FinalOutPut;
int j = 1;
foreach (double value in outputValues)
{
Console.Clear();
foreach (double ivalue in inputs)
{
Console.WriteLine("Input: " + ivalue);
}
Console.WriteLine("Final Output " + j + ": " + (value * 100));
j++;
}
Console.WriteLine("Train? Y/N");
IsValid = false;
while (IsValid == false)
{
string input = Console.ReadLine();
if (Tools.Validation.validMenuFormatYN(input) == true)
{
IsValid = true;
char c = char.Parse(input);
switch (c)
{
case 'y':
case 'Y':
Console.WriteLine("How many training iterations?");
int t = int.Parse(Console.ReadLine());
Console.WriteLine("What was the target value?");
int target = int.Parse(Console.ReadLine());
for (int tr = 0; tr < t; tr++)
{
Enn.Train(target);
Enn.StartNN(inputs);
outputValues = Enn._FinalOutPut;
int i = 1;
foreach (double value in outputValues)
{
foreach (double ivalue in inputs)
{
Console.WriteLine("Input: " + ivalue);
}
Console.WriteLine("Final Output " + i + ": " + (value * 100));
j++;
}
}
break;
case 'n':
case 'N':
break;
}
}
}
Console.ReadKey();
if (_c2 == 'y')
{
_d.ExperimentalNN(Enn._Neurons);
}
}
}