public void TestIrisDataset()
{
int trainingSamples = IrisDataset.input.Length;
Backprop bprop = new Backprop(1e-2, abstol: 1e-4, reltol: 1e-7, adjustThreshold: 1e-20);
bprop.UnknownCaseFaced += AddRule <GaussianRule2>;
BatchBackprop bbprop = new BatchBackprop(1e-2, abstol: 1e-4, reltol: 1e-7, adjustThreshold: 1e-20);
bbprop.UnknownCaseFaced += AddRule <GaussianRule2>;
QProp qprop = new QProp(abstol: 1e-4, reltol: 1e-7, adjustThreshold: 1e-20, InitialLearningRate: 1e-4);
qprop.UnknownCaseFaced += AddRule <GaussianRule2>;
StochasticBatch sprop = new StochasticBatch(40, 1e-2);
sprop.UnknownCaseFaced += AddRule <GaussianRule2>;
StochasticQprop sqprop = new StochasticQprop(40);
sqprop.UnknownCaseFaced += AddRule <GaussianRule2>;
double[][] x;
double[][] y;
double[][] tx;
double[][] ty;
SampleData(IrisDataset.input, IrisDataset.output, 120, out x, out y, out tx, out ty);
subtestIris(x, y, tx, ty, bprop);
subtestIris(x, y, tx, ty, bbprop);
subtestIris(x, y, tx, ty, qprop);
subtestIris(x, y, tx, ty, sprop);
subtestIris(x, y, tx, ty, sqprop);
}
private async void btnCalc_Click(object sender, EventArgs e)
{
try
{
double[][] x;
double[][] y;
double[][] tx;
double[][] ty;
SampleData(input, output, input.Count() - int.Parse(txtbxTestCount.Text), out x, out y, out tx, out ty);
Backprop bprop = new Backprop(1e-2, abstol: 1e-4, reltol: 1e-7, adjustThreshold: 1e-20);
bprop.UnknownCaseFaced += AddRule <GaussianRule2>;
await Task.Run(() => Solve(x, y, tx, ty, bprop));
}
catch (Exception ex)
{
MessageBox.Show(ex.Message, ex.GetType().Name, MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
void Awake()
{
//Check if there is already an instance of SoundManager
if (instance == null)
{
//if not, set it to this.
instance = this;
}
//If instance already exists:
else if (instance != this)
{
//Destroy this, this enforces our singleton pattern so there can only be one instance of SoundManager.
Destroy(gameObject);
}
//Set SoundManager to DontDestroyOnLoad so that it won't be destroyed when reloading our scene.
DontDestroyOnLoad(gameObject);
Backprop bprop = new Backprop(1e-2);
KMEANSExtractorIO extractor = new KMEANSExtractorIO(5);
fis = ANFISBuilder <GaussianRule> .Build(gameParams, musicParams, extractor, bprop, 5);
}
public void TestOptimization1()
{
Backprop bprop = new Backprop(1e-2);
BatchBackprop bbprop = new BatchBackprop(1e-2);
QProp qprop = new QProp();
StochasticBatch sprop = new StochasticBatch(100, 1e-2);
StochasticQprop sqprop = new StochasticQprop(100);
int trainingSamples = 1000;
double[][] x = new double[trainingSamples][];
double[][] y = new double[trainingSamples][];
double[][] tx = new double[trainingSamples][];
double[][] ty = new double[trainingSamples][];
Random rnd = new Random();
for (int i = 0; i < trainingSamples; i++)
{
double valx = 0.5 - rnd.NextDouble();
double valy = 0.5 - rnd.NextDouble();
x[i] = new double[] { valx, valy };
y[i] = new double[] { 1 };
valx = 0.5 - rnd.NextDouble();
valy = 0.5 - rnd.NextDouble();
tx[i] = new double[] { valx, valy };
ty[i] = new double[] { 1 };
}
subTestOptimization1(bprop, x, y, tx, ty);
subTestOptimization1(bbprop, x, y, tx, ty);
subTestOptimization1(qprop, x, y, tx, ty);
subTestOptimization1(sprop, x, y, tx, ty);
subTestOptimization1(sqprop, x, y, tx, ty);
}
public void TestOptimization2()
{
Backprop bprop = new Backprop(1e-2);
BatchBackprop bbprop = new BatchBackprop(1e-2);
QProp qprop = new QProp();
StochasticBatch sprop = new StochasticBatch(100, 1e-2);
StochasticQprop sqprop = new StochasticQprop(100);
int trainingSamples = 100;
double[][] x = new double[trainingSamples][];
double[][] y = new double[trainingSamples][];
double[][] tx = new double[trainingSamples][];
double[][] ty = new double[trainingSamples][];
Random rnd = new Random();
for (int i = 0; i < trainingSamples; i++)
{
bool isRigth = i % 2 == 0;
double valx = (isRigth ? 1 : -1) + (0.5 - rnd.NextDouble());
x[i] = new double[] { valx };
y[i] = new double[] { isRigth ? 1 : 0, isRigth ? 0 : 1 };
valx = (isRigth ? 1 : -1) + (0.5 - rnd.NextDouble());
tx[i] = new double[] { valx };
ty[i] = new double[] { isRigth ? 1 : 0, isRigth ? 0 : 1 };
}
subTestOptimization2(bprop, x, y, tx, ty);
subTestOptimization2(bbprop, x, y, tx, ty);
subTestOptimization2(qprop, x, y, tx, ty);
subTestOptimization2(sprop, x, y, tx, ty);
subTestOptimization2(sqprop, x, y, tx, ty);
}
public static void Main()
{
Console.WriteLine("Инициализация обучающей выборки");
List <double[]> x = new List <double[]>();
List <double[]> y = new List <double[]>();
double ar;
double dr;
for (double i = 0; i <= 100; i += 1)
{
for (double j = 0; j <= 100; j += 1)
{
ar = i;
dr = j;
// high 100%
if (ar >= 80 && ar <= 100 && dr >= 0 && dr <= 1)
{
x.Add(new double[] { ar, dr });
y.Add(new double[] { 0, 0, 0, 0, 1 });
}
// average 100%
else if ((ar >= 31 && ar <= 70 && dr >= 0 && dr <= 1) || (ar >= 80 && ar <= 100 && dr > 1 && dr <= 30))
{
x.Add(new double[] { ar, dr });
y.Add(new double[] { 0, 0, 0, 1, 0 });
}
// minor 100%
else if ((ar >= 4 && ar <= 29 && dr >= 0 && dr <= 1) || (ar >= 31 && ar <= 70 && dr > 1 && dr <= 30) || (ar >= 80 && ar <= 100 && dr >= 40 && dr <= 70))
{
x.Add(new double[] { ar, dr });
y.Add(new double[] { 0, 0, 1, 0, 0 });
}
// small 100%
else if ((ar >= 0 && ar <= 1 && dr >= 0 && dr <= 1) || (ar >= 4 && ar <= 29 && dr > 1 && dr <= 30) || (ar >= 31 && ar <= 70 && dr >= 40 && dr <= 70) || (ar >= 80 && ar <= 100 && dr >= 90 && dr <= 100))
{
x.Add(new double[] { ar, dr });
y.Add(new double[] { 0, 1, 0, 0, 0 });
}
// No 100%
else if ((ar >= 0 && ar <= 1 && dr > 4 && dr <= 100) || (ar >= 1 && ar <= 25 && dr >= 30 && dr <= 100) || (ar >= 25 && ar <= 45 && dr >= 60 && dr <= 100) || (ar >= 45 && ar <= 70 && dr >= 90 & dr <= 100))
{
x.Add(new double[] { ar, dr });
y.Add(new double[] { 1, 0, 0, 0, 0 });
}
}
}
Backprop bprop = new Backprop(1e-3);
СMEANSExtractorIO extractor = new СMEANSExtractorIO(5);
Console.WriteLine("Инициализация ANFIS");
ANFIS fis = ANFISBuilder <GaussianRule> .Build(x.ToArray(), y.ToArray(), extractor, bprop, 1000);
bool state = true;
while (state)
{
Console.Write("Введите коэффициент атаки: ");
string ars = Console.ReadLine();
if (ars == "q" || ars == "quit" || ars == "Q" || ars == "Quit")
{
state = false;
continue;
}
ar = Convert.ToDouble(ars);
Console.Write("Введите коэффициент защиты: ");
string drs = Console.ReadLine();
if (drs == "q" || drs == "quit" || drs == "Q" || drs == "Quit")
{
state = false;
continue;
}
dr = Convert.ToDouble(drs);
double[] args = new double[] { ar, dr };
double[] res = fis.Inference(args);
int maxI = 0;
double max = res[0];
for (int I = 1; I < res.Length; I++)
{
if (max < res[I])
{
maxI = I;
max = res[I];
}
}
switch (maxI)
{
case 0:
Console.WriteLine("Вероятнее всего урона не будет");
break;
case 1:
Console.WriteLine("Вероятнее всего урон будет очень маленьким");
break;
case 2:
Console.WriteLine("Вероятнее всего урон будет незначительным");
break;
case 3:
Console.WriteLine("Вероятнее всего урон будет средним");
break;
case 4:
Console.WriteLine("Вероятнее всего урон будет большим");
break;
}
}
Console.ReadKey();
}
public void TestLogisticMap()
{
int trainingSamples = 2000;
double[][] x = new double[trainingSamples][];
double[][] y = new double[trainingSamples][];
double[][] tx = new double[trainingSamples][];
double[][] ty = new double[trainingSamples][];
double px = 0.1;
double r = 3.8;//3.56995;
double lx = r * px * (1 - px);
for (int i = 0; i < trainingSamples; i++)
{
x[i] = new double[] { px, lx };
px = lx;
lx = r * lx * (1 - lx);
y[i] = new double[] { lx };
}
for (int i = 0; i < trainingSamples; i++)
{
tx[i] = new double[] { px, lx };
px = lx;
lx = r * lx * (1 - lx);
ty[i] = new double[] { lx };
}
Backprop bprop = new Backprop(1e-2);
bprop.UnknownCaseFaced += AddRule <GaussianRule2>;
BatchBackprop bbprop = new BatchBackprop(1e-2);
bbprop.UnknownCaseFaced += AddRule <GaussianRule2>;
QProp qprop = new QProp();
qprop.UnknownCaseFaced += AddRule <GaussianRule2>;
StochasticBatch sprop = new StochasticBatch(500, 1e-2);
sprop.UnknownCaseFaced += AddRule <GaussianRule2>;
StochasticQprop sqprop = new StochasticQprop(500);
sqprop.UnknownCaseFaced += AddRule <GaussianRule2>;
subtestLogisticsMap <LinearRule>(x, y, tx, ty, bprop);
subtestLogisticsMap <LinearRule>(x, y, tx, ty, bbprop);
subtestLogisticsMap <LinearRule>(x, y, tx, ty, qprop);
subtestLogisticsMap <LinearRule>(x, y, tx, ty, sprop);
subtestLogisticsMap <LinearRule>(x, y, tx, ty, sqprop);
bprop = new Backprop(1e-2);
bprop.UnknownCaseFaced += AddRule <GaussianRule2>;
bbprop = new BatchBackprop(1e-2);
bbprop.UnknownCaseFaced += AddRule <GaussianRule2>;
qprop = new QProp();
qprop.UnknownCaseFaced += AddRule <GaussianRule2>;
sprop = new StochasticBatch(500, 1e-2);
sprop.UnknownCaseFaced += AddRule <GaussianRule2>;
sqprop = new StochasticQprop(500);
sqprop.UnknownCaseFaced += AddRule <GaussianRule2>;
subtestLogisticsMap <GaussianRule>(x, y, tx, ty, bprop);
subtestLogisticsMap <GaussianRule>(x, y, tx, ty, bbprop);
subtestLogisticsMap <GaussianRule>(x, y, tx, ty, qprop);
subtestLogisticsMap <GaussianRule>(x, y, tx, ty, sprop);
subtestLogisticsMap <GaussianRule>(x, y, tx, ty, sqprop);
}
