public static void Run(CostFunction f, int max_iterations = 200)
{
BFGS s = new BFGS();
double[] x_0 = f.CreateRandomSolution();
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
s.Minimize(x_0, f, max_iterations);
}
static void Main(string[] args)
{
RandomGenerator generator = new RandomGenerator();
//RandomGenerator generator = new RandomGenerator(3);
ReadData(@"d:\Projects\HeroesModel\HeroesModel\data.csv");
//GenerateModelData(generator, 1000);
data.Sort((x, y) =>
{
if (x.red != y.red)
{
return(x.red.CompareTo(y.red));
}
return(x.blue.CompareTo(y.blue));
});
PrintData();
BFGS bfgs = new BFGS(TOTAL_PARAMS + 1 + TOWN_TYPES * TOWN_TYPES * 4, LogLikelyhood, LogLikelyHoodGradient);
//BFGS bfgs = new BFGS(TOTAL_PARAMS + 1, LogLikelyhood, LogLikelyHoodGradient);
//bfgs.FunctionTolerance = EPS;
DateTime startTime = DateTime.Now;
ManualResetEvent interrupted = new ManualResetEvent(false);
Thread reportThread = new Thread(() =>
{
while (!interrupted.WaitOne(10000))
{
Console.WriteLine($"Time elapsed {(DateTime.Now - startTime)}, iterations complete {bfgs.IterationsDone}");
}
});
reportThread.Start();
//bfgs.Minimize(new Vector(TOTAL_PARAMS, 0.01));
//RandomGenerator generator = new RandomGenerator(1);
//RandomGenerator generator = new RandomGenerator(1);
Normal normal = new Normal(generator, 0, 0.1);
bfgs.Minimize(normal.Sample(TOTAL_PARAMS + 1 + TOWN_TYPES * TOWN_TYPES * 4));
//bfgs.Minimize(normal.Sample(TOTAL_PARAMS + 1));
interrupted.Set();
reportThread.Join();
Vector result = bfgs.MinimumPoint;
double min = -LogLikelyhood(result);
Console.WriteLine($"Converged. Time elapsed {(DateTime.Now - startTime)}. Iterations: {bfgs.IterationsDone}");
for (double money = -0.5; money <= 0.5; money += 0.1)
{
Console.WriteLine($"Red money {(money + 1) * 10000}");
PrintProbabilities(result, money);
}
PrintCoeficients(result);
EstimateInputData(result, @"d:\Projects\HeroesModel\display\data.js");
PrintCoefficients(result, @"d:\Projects\HeroesModel\display\coefficients.js");
//GaussKronrodSingular integration = new GaussKronrodSingular();
//integration.Integrate(x => ConditionalDensity(x, 0, 1, 0, 1), Double.NegativeInfinity, Double.PositiveInfinity, 100);
//Console.WriteLine(integration.Result);
}