static void Main(string[] args)
{
DataTable table = LoadData("dataset.txt");
LGPConfig config = new LGPConfig();
LGPPop pop = new LGPPop(config);
pop.OperatorSet.AddOperator(new LGPOperator_Plus());
pop.OperatorSet.AddOperator(new LGPOperator_Minus());
pop.OperatorSet.AddOperator(new LGPOperator_Division());
pop.OperatorSet.AddOperator(new LGPOperator_Multiplication());
pop.OperatorSet.AddOperator(new LGPOperator_Sin());
pop.OperatorSet.AddOperator(new LGPOperator_Cos());
pop.OperatorSet.AddIfgtOperator();
pop.CreateFitnessCase += (index) =>
{
SpiralFitnessCase fitness_case = new SpiralFitnessCase();
fitness_case.X = double.Parse(table.Rows[index]["X"].ToString());
fitness_case.Y = double.Parse(table.Rows[index]["Y"].ToString());
fitness_case.Label = int.Parse(table.Rows[index]["Label"].ToString());
return(fitness_case);
};
pop.GetFitnessCaseCount += () =>
{
return(table.Rows.Count);
};
pop.EvaluateCostFromAllCases += (fitness_cases) =>
{
double fitness = 0;
for (int i = 0; i < fitness_cases.Count; i++)
{
SpiralFitnessCase fitness_case = (SpiralFitnessCase)fitness_cases[i];
int correct_y = fitness_case.Label;
int computed_y = fitness_case.ComputedLabel;
fitness += (correct_y == computed_y) ? 0 : 1;
}
return(fitness);
};
pop.BreedInitialPopulation();
while (!pop.IsTerminated)
{
pop.Evolve();
Console.WriteLine("Spiral Classification Generation: {0}", pop.CurrentGeneration);
Console.WriteLine("Global Fitness: {0}\tCurrent Fitness: {1}", pop.GlobalBestProgram.Fitness, pop.FindFittestProgramInCurrentGeneration().Fitness);
}
Console.WriteLine(pop.GlobalBestProgram.ToString());
}
static void Main(string[] args)
{
DataTable table = LoadData();
LGPConfig config = new LGPConfig();
LGPPop pop = new LGPPop(config);
pop.OperatorSet.AddOperator(new LGPOperator_Plus());
pop.OperatorSet.AddOperator(new LGPOperator_Minus());
pop.OperatorSet.AddOperator(new LGPOperator_Division());
pop.OperatorSet.AddOperator(new LGPOperator_Multiplication());
pop.OperatorSet.AddOperator(new LGPOperator_Power());
pop.OperatorSet.AddIfltOperator();
pop.CreateFitnessCase += (index) =>
{
MexicanHatFitnessCase fitness_case = new MexicanHatFitnessCase();
fitness_case.X1 = double.Parse(table.Rows[index]["X1"].ToString());
fitness_case.X2 = double.Parse(table.Rows[index]["X2"].ToString());
fitness_case.Y = double.Parse(table.Rows[index]["Y"].ToString());
return(fitness_case);
};
pop.GetFitnessCaseCount += () =>
{
return(table.Rows.Count);
};
pop.EvaluateCostFromAllCases += (fitness_cases) =>
{
double cost = 0;
for (int i = 0; i < fitness_cases.Count; i++)
{
MexicanHatFitnessCase fitness_case = (MexicanHatFitnessCase)fitness_cases[i];
double correct_y = fitness_case.Y;
double computed_y = fitness_case.PredictedY;
cost += (correct_y - computed_y) * (correct_y - computed_y);
}
return(cost);
};
pop.BreedInitialPopulation();
while (!pop.IsTerminated)
{
pop.Evolve();
Console.WriteLine("Mexican Hat Symbolic Regression Generation: {0}", pop.CurrentGeneration);
Console.WriteLine("Global Fitness: {0}\tCurrent Fitness: {1}", pop.GlobalBestProgram.Fitness.ToString("0.000"), pop.FindFittestProgramInCurrentGeneration().Fitness.ToString("0.000"));
}
Console.WriteLine(pop.GlobalBestProgram.ToString());
}
public LGPEnvironment(LGPConfig config)
{
Config = config;
}