public static void Test()
{
GSLFunctions.DToD dtodFnc = delegate(double x)
{
return Math.Cos(x) + 1.0d;
};
Minimization mini = new Minimization(Minimization.SolverType.Brent);
double xValue = 2;
mini.Solve(dtodFnc, 0, 6, 0.001, 0, 100, ref xValue);
Console.WriteLine(xValue + " : " + mini.Iteration());
mini = new Minimization(Minimization.SolverType.GoldenSection);
xValue = 2;
mini.Solve(dtodFnc, 0, 6, 0.001, 0, 100, ref xValue);
Console.WriteLine(xValue + " : " + mini.Iteration());
}
private void SteepestDescent()
{
Minimization minimization = new Minimization()
{
MaxIter = maxIter,
};
int n = multifunc.N;
Single1D p = new Single1D(n);
Single1D df = new Single1D(n);
for (int i = 0; i < n; i++)
{
p[i] = _point[i];
}
minimization.GradientDescent(p, df, (iter) =>
{
return(multifunc.Calculate(p, df));
}, alpha);
Console.WriteLine();
}
public void Learn(
SampleList samples, CalculationArguments arguments)
// samples = yjks
{
arguments.reporter?.WriteStart($"Learning the network using a subset of {samples.Count} random samples...");
Stopwatch timer = new Stopwatch();
timer.Start();
int nSamples = samples.Count; // number of sample rows
int nCoefficients = CoefficientCount();
// Current biasses and weights of the neurons in this network:
Single1D coefficients = new Single1D(nCoefficients);
// The derivatives of the cost with respect to the biasses and weights:
Single1D derivatives = new Single1D(nCoefficients);
Single1D velocities = new Single1D(nCoefficients);
velocities.Clear();
MeasurementList measurements = new MeasurementList(nSamples, Last.Count);
GetCoefficients(coefficients, 0);
Minimization minimization = new Minimization()
{
MaxIter = arguments.settings.MaxIter,
Eps = arguments.settings.Epsilon,
Tol = arguments.settings.Tolerance,
};
float finalCost = minimization.MomentumBasedGradientDescent(coefficients, derivatives, velocities,
(iter) =>
{
SetCoefficients(coefficients, 0);
arguments.reporter?.ReportCoefficients(coefficients);
float cost = GetCostAndDerivatives(samples, derivatives, measurements, arguments);
arguments.reporter?.ReportCostAndDerivatives(cost, derivatives, measurements);
return(cost);
}, arguments.settings.LearningRate, arguments.settings.MomentumCoefficient);
arguments.reporter?.WriteEnd($"The network has learned in {timer.Elapsed.TotalSeconds} s, and the final cost value is {finalCost:F4}.");
}
