public void Evaluate(List <int> lst, GPFunctionSet gpFunctionSet, GPTerminalSet gpTerminalSet, GPChromosome c)
{
c.Fitness = 0;
double rowFitness = 0.0;
double val1 = 0;
double y;
// copy constants
//Translate chromosome to list expressions
int indexOutput = gpTerminalSet.NumConstants + gpTerminalSet.NumVariables;
for (int i = 0; i < gpTerminalSet.RowCount; i++)
{
// evalue the function
y = gpFunctionSet.Evaluate(lst, gpTerminalSet, i);
// check for correct numeric value
if (double.IsNaN(y) || double.IsInfinity(y))
{
y = 0;
}
val1 += System.Math.Pow(((y - gpTerminalSet.TrainingData[i][indexOutput]) / gpTerminalSet.TrainingData[i][indexOutput]), 2.0);
}
rowFitness = val1 / gpTerminalSet.RowCount;
if (double.IsNaN(rowFitness) || double.IsInfinity(rowFitness))
{
//if output is not a number return infinity fitness
c.Fitness = 0;
return;
}
//Fitness
c.Fitness = (float)((1.0 / (1.0 + rowFitness)) * 1000.0);
}
public void Evaluate(List <int> lst, GPFunctionSet gpFunctionSet, GPTerminalSet gpTerminalSet, GPChromosome c)
{
c.Fitness = 0;
double rowFitness = 0.0;
double y, temp;
//Translate chromosome to list expressions
int indexOutput = gpTerminalSet.NumConstants + gpTerminalSet.NumVariables;
for (int i = 0; i < gpTerminalSet.RowCount; i++)
{
// evalue the function
y = gpFunctionSet.Evaluate(lst, gpTerminalSet, i);
// check for correct numeric value
if (double.IsNaN(y) || double.IsInfinity(y))
{
y = 0;
}
temp = y - gpTerminalSet.TrainingData[i][indexOutput];
rowFitness += temp * temp;
}
//Fitness
c.Fitness = (float)((1.0 / (1.0 + rowFitness / indexOutput)) * 1000.0);
}
public double Evaluate(List <int> tokens, GPTerminalSet gpTerminalSet, int numRow)
{
int countT = tokens.Count;
//Stack fr evaluation
Stack <double> arguments = new Stack <double>();
for (int i = 0; i < countT; i++)
{
//Debug.Assert(tokens[i] != 2004 || countT <= 2);
// Ako je token argument onda na osnovu naziv tog argumenta
//izvlacimo mu vrijednost iz skupa terminala i konstanti
if (tokens[i] >= 1000 && tokens[i] < 2000)
{
arguments.Push(gpTerminalSet.TrainingData[numRow][tokens[i] - 1000]);
}
else//Ako je token funkcija tada ubacene argumente evaluiramo preko odredjene funkcije
{
//Evaluacija funkcije. Svaka funkcija ima bar 1 argument
int count = functions[tokens[i] - 2000].Aritry;
//Ovdje moramo unazad zapisati varijable zbog Staka
double[] val = new double[count];
for (int j = count; j > 0; j--)
{
Debug.Assert(arguments.Count > 0);
val[j - 1] = arguments.Pop();
}
//Ako je neka funkcija statistička distribucija ili neka druga funkcija koja
// zahtjeva ulazne varijable da bi koristila neku statisticku osobinu
//
double[] values = null;
if (functions[tokens[i] - 2000].IsDistribution)
{
values = new double[gpTerminalSet.NumVariables];
for (int k = 0; k < gpTerminalSet.NumVariables; k++)
{
values[k] = gpTerminalSet.TrainingData[numRow][k];
}
}
double result = Evaluate(functions[tokens[i] - 2000], values, val);
if (double.IsNaN(result) || double.IsInfinity(result))
{
return(double.NaN);
}
//Izracunavanje izraza
arguments.Push(result);
}
}
// return the only value from stack
Debug.Assert(arguments.Count == 1);
return(arguments.Pop());
}
public void Evaluate(List <int> lst, GPFunctionSet gpFunctionSet, GPTerminalSet gpTerminalSet, GPChromosome c)
{
c.Fitness = 0;
double rowFitness = 0.0;
double CovTP = 0.0;
double SigmaP = 0.0;
double SigmaT = 0.0;
//number of sample case
int indexOutput = gpTerminalSet.NumConstants + gpTerminalSet.NumVariables;
double[] y = new double[gpTerminalSet.RowCount];
double ymean = 0;
//Calculate output
for (int i = 0; i < gpTerminalSet.RowCount; i++)
{
// evalue the function
y[i] = gpFunctionSet.Evaluate(lst, gpTerminalSet, i);
// check for correct numeric value
if (double.IsNaN(y[i]) || double.IsInfinity(y[i]))
{
//if output is not a number return infinity fitness
c.Fitness = 0;
return;
}
ymean += y[i];
}
//calculate AverageValue output
ymean = ymean / gpTerminalSet.RowCount;
//Calculate Corelation coeficient
for (int i = 0; i < gpTerminalSet.RowCount; i++)
{
CovTP = CovTP + ((gpTerminalSet.TrainingData[i][indexOutput] - gpTerminalSet.AverageValue) * (y[i] - ymean));
SigmaP += System.Math.Pow((y[i] - ymean), 2);
SigmaT += System.Math.Pow((gpTerminalSet.TrainingData[i][indexOutput] - gpTerminalSet.AverageValue), 2);
}
rowFitness = CovTP / (System.Math.Sqrt(SigmaP * SigmaT));
if (double.IsNaN(rowFitness) || double.IsInfinity(rowFitness))
{
//if output is not a number return zero fitness
c.Fitness = 0;
return;
}
//Fitness
c.Fitness = (float)(rowFitness * rowFitness * 1000.0);
}
public string DecodeExpressionInExcellForm(List <int> tokens, GPTerminalSet gpTerminalSet)
{
//Prepare chromoseme for evaluation
// List<int> tokens = new List<int>();
// FunctionTree.ToListExpression(tokens, c.Root);
int countT = tokens.Count;
//Stack fr evaluation
Stack <string> expression = new Stack <string>();
for (int i = 0; i < countT; i++)
{
//Debug.Assert(tokens[i] != 2004 || countT <= 2);
// Ako je token argument onda na osnovu naziv tog argumenta
//izvlacimo mu vrijednost iz skupa terminala i konstanti
if (tokens[i] >= 1000 && tokens[i] < 2000)
{
string varaiable = terminals[tokens[i] - 1000].Name;
expression.Push(varaiable);
}
else//Ako je token funkcija tada ubacene argumente evaluiramo preko odredjene funkcije
{
//Evaluacija funkcije. Svaka funkcija ima bar 1 argument
int count = functions[tokens[i] - 2000].Aritry;
string function = functions[tokens[i] - 2000].ExcelDefinition;
//Ovdje moramo unazad zapisati varijable zbog Staka
double[] val = new double[count];
for (int j = count; j > 0; j--)
{
string oldStr = "x" + (j).ToString();
string newStr = expression.Pop();
function = function.Replace(oldStr, newStr);
}
if (functions[tokens[i] - 2000].IsDistribution)
{
string oldStr = "xn";
string newStr = "X" + gpTerminalSet.NumVariables.ToString();
function = function.Replace(oldStr, newStr);
function = function.Replace("x0", "X1");
}
//Izracunavanje rezultata
expression.Push("(" + function + ")");
//Izracunavanje izraza
}
}
// return the only value from stack
Debug.Assert(expression.Count == 1);
// return arguments.Pop();
return(expression.Pop());
}
public void Evaluate(List <int> lst, GPFunctionSet gpFunctionSet, GPTerminalSet gpTerminalSet, GPChromosome c)
{
c.Fitness = 0;
double rowFitness = 0.0;
double SS_err = 0.0;
double SS_tot = 0.0;
double y;
// copy constants
//Translate chromosome to list expressions
int indexOutput = gpTerminalSet.NumConstants + gpTerminalSet.NumVariables;
for (int i = 0; i < gpTerminalSet.RowCount; i++)
{
// evalue the function
y = gpFunctionSet.Evaluate(lst, gpTerminalSet, i);
// check for correct numeric value
if (double.IsNaN(y) || double.IsInfinity(y))
{
y = 0;
}
//Calculate square error
rowFitness += System.Math.Pow(y - gpTerminalSet.TrainingData[i][indexOutput], 2);
//Calculate square error
SS_err += System.Math.Pow(y - gpTerminalSet.TrainingData[i][indexOutput], 2);
SS_tot += System.Math.Pow(gpTerminalSet.TrainingData[i][indexOutput] - gpTerminalSet.AverageValue, 2);
}
if (double.IsNaN(rowFitness) || double.IsInfinity(rowFitness))
{
//if output is not a number return zero fitness
c.Fitness = 0;
return;
}
//Fitness
c.Fitness = (float)((1.0 / (1.0 + System.Math.Sqrt(rowFitness / gpTerminalSet.RowCount))) * 1000.0);
}
public string DecodeWithOptimisationExpression(List <int> tokens, GPTerminalSet gpTerminalSet)
{
// return "";
//Prepare chromoseme for evaluation
// List<int> tokens = new List<int>();
// FunctionTree.ToListExpression(tokens, c.Root);
int countT = tokens.Count;
//Stack fr evaluation
Stack <double> arguments = new Stack <double>();
Stack <string> expression = new Stack <string>();
for (int i = 0; i < countT; i++)
{
//Debug.Assert(tokens[i] != 2004 || countT <= 2);
// Ako je token argument onda na osnovu naziv tog argumenta
//izvlacimo mu vrijednost iz skupa terminala i konstanti
if (tokens[i] >= 1000 && tokens[i] < 2000)
{
double var;
string varaiable = terminals[tokens[i] - 1000].Name;
if (terminals[tokens[i] - 1000].IsConstant)
{
var = gpTerminalSet.TrainingData[0][tokens[i] - 1000];
}
else
{
var = double.NaN;
}
arguments.Push(var);
expression.Push(varaiable);
}
else//Ako je token funkcija tada ubacene argumente evaluiramo preko odredjene funkcije
{
//Evaluacija funkcije. Svaka funkcija ima bar 1 argument
int count = functions[tokens[i] - 2000].Aritry;
string function = functions[tokens[i] - 2000].Definition;
//Ovdje moramo unazad zapisati varijable zbog Staka
double[] val = new double[count];
for (int j = count; j > 0; j--)
{
val[j - 1] = arguments.Pop();
string oldStr = "x" + (j).ToString();
string newStr = expression.Pop();
function = function.Replace(oldStr, newStr);
}
/*
* //Izracunavanje rezultata
* //Ako je neka funkcija statistička distribucija ili neka druga funkcija koja
* // zahtjeva ulazne varijable da bi koristila neku statisticku osobinu
* //
* double[] values = null;
* if (functions[tokens[i] - 2000].IsDistribution)
* {
* values = new double[gpTerminalSet.NumVariables];
* for (int k = 0; k < gpTerminalSet.NumVariables; k++)
* values[k] = gpTerminalSet.TrainingData[numRow][k];
* }*/
double result = Evaluate(functions[tokens[i] - 2000], val);
if (!double.IsNaN(result))
{
expression.Push(result.ToString());
}
else
{
expression.Push("(" + function + ")");
}
//Izracunavanje izraza
arguments.Push(result);
}
}
// return the only value from stack
Debug.Assert(expression.Count == 1);
// return arguments.Pop();
return(expression.Pop());
}
/// <summary>
/// Constructor for population.
/// </summary>
/// <param name="size">Size of population.Number must be greather than 0.</param>
/// <param name="terminalSet">Terminal set.Must be nonnull.</param>
/// <param name="functionSet">Function set. Must be nonnull.</param>
/// <param name="parameters">Parameter of GP. If it pass a null value default parameter is initialized.</param>
/// <param name="paralelComp">True for parallel multy core evaluation. False for secvencial computing.</param>
public GPPopulation(int size, GPTerminalSet terminalSet,
GPFunctionSet functionSet,
GPParameters parameters,
bool paralelComp)
{
ParalelComputing = paralelComp;
//During population creating the size must be grether than 1,
//and papameters must be non null
if (size < 1)
{
return;
}
//Ako nisu definisani parametri definisi defaultne
if (parameters == null)
{
gpParameters = new GPParameters();
}
else
{
gpParameters = parameters;
}
if (functionSet == null)
{
return;
}
if (terminalSet == null)
{
return;
}
//
gpFunctionSet = functionSet;
gpTerminalSet = terminalSet;
//Extend capasity three time cause crossover and mutation
population = new List <GPChromosome>(3 * size);
if (ParalelComputing)
{
tempCrossover1 = new List <GPChromosome>(size);
tempCrossover2 = new List <GPChromosome>(size);
tempMutation = new List <GPChromosome>();
tempPermutation = new List <GPChromosome>();
temCross = new ConcurrentStack <GPChromosome>();
}
//Creating starting Chromosome
GPChromosome ancestor = new GPChromosome();
//Initialize population
InitPopulation(size);
//Evaluacija pocetne populacije
if (ParalelComputing)
{
EvaluationParallel();
}
else
{
EvaluationSec();
}
//Calculate population statistic parameters
CalculatePopulation();
}
