public void straight()
{
int size = 25;
vnn nn = new vnn(size, 500, size, addon.RandomizeWeights);
var tr = new trainerModern(nn);
List <double[]> history = new List <double[]>();
trainingSet tset;
Stopwatch sw = Stopwatch.StartNew();
int counter = 0;
var rand = new Random();
do
{
var data = new double[size];
data[rand.Next(0, data.Length)] = 1.0;
history.Add(data);
tset = new trainingSet(history.ToArray(), history.ToArray());
tr.TrainEpoch(tset.inputs, tset.outputs, 0.02, 0.9);
//tr.TrainOne(data, data, 0.1, 0.9);
counter++;
Console.Title = $"{counter} ({counter * tset.size})";
}while (tset.getAccuracy(nn, 0.1) < 0.9);
WriteLine("Elapsed = " + sw.ElapsedMilliseconds + " ms.");
WriteLine("MSE = " + tset.getMSE(nn));
WriteLine("Random MSE = " + tset.getRandomMSE(nn, 500));
nn.TestLoop(new Func <double[], double[]>(arr => new double[] { arr[0] > 0.5 ? 1 : 0, Sin(arr[0] + arr[1]) * arr[0] }));
}
protected NeuralGenome(vnn nn)
{
NN = nn;
TR = new trainerModern(NN);
}
public void bounded_substraction()
{
/// <summary>
/// WORKS!!!! :)
/// Testing if hearthstone health and atack should be one in one input neuron or in whole 'one-hot' input vector
/// </summary>
vnn nn = new vnn(2, 100, 1, addon.RandomizeWeights);
var tr = new trainerModern(nn);
var tset = getSet(1000);
Stopwatch sw = Stopwatch.StartNew();
int counter = 0;
do
{
tr.TrainEpoch(tset.inputs, tset.outputs, 0.1, 0.9);
counter++;
Console.Title = $"{counter} ({counter * tset.size})";
}while (tset.getAccuracy(nn, 0.05) < 1);
WriteLine("Elapsed = " + sw.ElapsedMilliseconds + " ms.");
WriteLine("MSE = " + tset.getMSE(nn));
nn.TestLoop(input_translator, output_translator);
//nn.TestLoop();
const int maxVal = 10;
string output_translator(double[] arr)
{
int output = (int)Round(arr[0] * maxVal);
return(output.ToString());
}
double[] input_translator(string text)
{
string[] split = text.Split(' ');
int a = int.Parse(split[0]);
double da = a / (double)maxVal;
int b = int.Parse(split[1]);
double db = b / (double)maxVal;
return(new double[] { da, db });
}
trainingSet getSet(int size)
{
var rand = new Random();
double[][] inp = new double[size][], outp = new double[size][];
for (int i = 0; i < size; i++)
{
int a = rand.Next(0, maxVal);
double da = a / (double)maxVal;
int b = rand.Next(0, maxVal);
double db = b / (double)maxVal;
int answer = Max(0, a - b);
double dansw = answer / (double)maxVal;
inp[i] = new double[] { da, db };
outp[i] = new double[] { dansw };
}
return(new trainingSet(inp, outp));
}
}
