Linear Genetic Programming implemented in C#
Install-Package cs-linear-genetic-programming -Version 1.0.2The sample codes below show how to use the LinearGP to solve the Mexican Hat symbolic regression problem:
class Program
{
static double FunctionXY(double x1, double x2)
{
return (1 - x1 * x1 / 4 - x2 * x2 / 4) * System.Math.Exp(- x1 * x2 / 8 - x2 * x2 / 8);
}
static DataTable LoadData()
{
DataTable table = new DataTable();
table.Columns.Add("X1");
table.Columns.Add("X2");
table.Columns.Add("Y");
double lower_bound=-4;
double upper_bound=4;
int period=16;
double interval=(upper_bound - lower_bound) / period;
for(int i=0; i<period; i++)
{
double x1=lower_bound + interval * i;
for(int j=0; j<period; j++)
{
double x2=lower_bound + interval * j;
table.Rows.Add(x1, x2, FunctionXY(x1, x2));
}
}
return table;
}
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());
}
}Where the fitness case MexicanHatFitnessCase is defined as follows:
public class MexicanHatFitnessCase : LGPFitnessCase
{
private double[] mX = new double[2];
private double mY;
private double mPredictedY;
public double PredictedY
{
get { return mPredictedY; }
}
public double X1
{
get { return mX[0]; }
set { mX[0] = value; }
}
public double X2
{
get { return mX[1]; }
set { mX[1] = value; }
}
public double Y
{
get { return mY; }
set { mY = value; }
}
public override void RunLGPProgramCompleted(double[] result)
{
mPredictedY = result[0];
}
public override bool QueryInput(int index, out double input)
{
input = 0;
if (index < mX.Length)
{
input = mX[index];
return true;
}
return false;
}
public override int GetInputCount()
{
return mX.Length;
}
}The sample codes below show how to use the LinearGP to solve the Spiral classification problem:
class Program
{
static DataTable LoadData(string filename)
{
DataTable table = new DataTable();
table.Columns.Add("X");
table.Columns.Add("Y");
table.Columns.Add("Label");
int line_count = 0;
using (StreamReader reader = new StreamReader(filename))
{
string line=reader.ReadLine();
int.TryParse(line, out line_count);
while ((line = reader.ReadLine()) != null)
{
string[] elements=line.Split(new char[]{'\t'});
double x, y;
int label;
double.TryParse(elements[0].Trim(), out x);
double.TryParse(elements[1].Trim(), out y);
int.TryParse(elements[2].Trim(), out label);
table.Rows.Add(x, y, label);
}
}
return table;
}
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());
}
}Where the fitness case SpiralFitnessCase is defined as follows:
public class SpiralFitnessCase : LGPFitnessCase
{
private double[] mCoordinates = new double[2];
private int mLabel;
private int mComputedLabel;
public int ComputedLabel
{
get { return mComputedLabel; }
}
public int Label
{
get { return mLabel; }
set { mLabel = value; }
}
public double X
{
get { return mCoordinates[0]; }
set { mCoordinates[0] = value; }
}
public double Y
{
get { return mCoordinates[1]; }
set { mCoordinates[1] = value; }
}
public override void RunLGPProgramCompleted(double[] result)
{
if (result[0] < 0.5)
{
mComputedLabel = -1;
}
else
{
mComputedLabel = 1;
}
}
public override bool QueryInput(int index, out double input)
{
input = 0;
if (index < mCoordinates.Length)
{
input = mCoordinates[index];
return true;
}
return false;
}
public override int GetInputCount()
{
return mCoordinates.Length;
}
}