public static void Main()
{
double EPS = Pow(10, -6);
//
Func <vector, double> f = (z) => Pow(1 - z[0], 2) + 100 * Pow(z[1] - Pow(z[0], 2), 2);
vector x01 = new vector(0, 0);
int StepCounts = Quasi.qnewton(f, ref x01);
WriteLine("");
x01.print("Starting point: ");
WriteLine($"steps = {StepCounts}");
WriteLine($"Found minimum = ({x01[0]:f5}, {x01[1]:f5},)");
WriteLine($"Exact minimum = (1,1)");
WriteLine($"Tolerance for gradient = {EPS}");
//
Func <vector, double> f2 = (z) => Pow((Pow(z[0], 2) + z[1] - 11), 2) + Pow(z[0] + Pow(z[1], 2) - 7, 2);
vector x02 = new vector(-2, 3);
int StepCounts2 = Quasi.qnewton(f2, ref x02);
x02.print("Starting point: ");
WriteLine($"steps = {StepCounts2}");
WriteLine($"The minimum was found at minimum = ({x02[0]:f5}, {x02[1]:f5})");
WriteLine($"Where the analytical minimum = (-2.805118, 3.131312)");
WriteLine($"The tolorence for gradient is = {EPS}");
}
public double UniformDouble(int seriesIndex)
{
if (seriesIndex < m_haltonStateArray.Length)
{
double value = Quasi.QuasiHaltonWithIndex(
seriesIndex, m_haltonStateArray[seriesIndex]);
m_haltonStateArray[seriesIndex] = value;
return(value);
}
else
{
return(m_randomUniform.UniformDouble());
}
}
public void train(vector x, vector y)
{
double res1 = 0;
Func <vector, double> deltaP = (k) => {
p = k;
double res = 0;
for (int i = 0; i < x.size; i++)
{
res += Pow((feedforward(x[i]) - y[i]), 2);
}
res1 = res;
return(res);
};
vector pa = p.copy();
int ncounts = Quasi.qnewton(deltaP, ref pa);
p = pa;
double TH = 0.01; //threshhold
if (res1 > TH)
{
calls++;
res1 = 0;
for (int i = 0; i < 3 * n; i++)
{
double u1 = 1.0 - rand.NextDouble();
double u2 = 1.0 - rand.NextDouble();
p[i] = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2);
}
train(x, y);
}
}
