/*_________________________________________________________

  This is a standard GP implementation on EASEA, 
  aimed for regression.

  use : easea -cuda_gp regression.ez
        make

  in order to generate and compile this code.
__________________________________________________________*/

\User declarations :
#define PI (3.141592653589793)
\end

\User functions:
#define POLY(x) x*x*x-3*x*x+x
/**
   This function generates data NO_FITNESS_CASES fitness cases,
   from the polynome POLY(X) with X randomly picked between (-1,1)

   @inputs address of the inputs array. (array will be allocated here)
   @outputs adddress of the outputs array. (array will be allocated here)

   @ret number of loaded fitness cases (should be equal to NO_FITNESS_CASES).   
 */
int generateData(float*** inputs, float** outputs){
  int i=0;

  (*inputs) = new float*[NO_FITNESS_CASES];
  (*outputs) = new float[NO_FITNESS_CASES];
  
  for( i=0 ; i<NO_FITNESS_CASES ; i++ ){
    (*inputs)[i]=new float[VAR_LEN];
    float x = random(-10.,+10.);
    (*inputs)[i][0] = x;
    (*outputs)[i] = POLY(x);
  }

  return NO_FITNESS_CASES;
}


void free_data(){
  for( int i=0 ; i<fitnessCasesSetLength ;i++ )
    delete[] inputs[i] ;
  delete[] outputs;
  delete[] inputs;
} 
\end



\Before everything else function:
{
  generateData(&inputs,&outputs);
}
\end

\After everything else function:
{
  toDotFile( ((IndividualImpl*)EA->population->Best)->root, "best", 0);
  free_data();
}
\end

\At the beginning of each generation function:
{
  //cout << "At the beginning of each generation function called" << endl;
}		    
\end

\At the end of each generation function:
{		 
  //cout << "At the end of each generation function called" << endl;
}
\end

\At each generation before reduce function:
 //cout << "At each generation before replacement function called" << endl;
\end


\User classes :

GenomeClass {
  GPNode* root;
}
\end

\GenomeClass::display:
\end

\GenomeClass::initialiser :
{
  Genome.root = RAMPED_H_H(INIT_TREE_DEPTH_MIN,INIT_TREE_DEPTH_MAX,EA->population->actualParentPopulationSize,EA->population->parentPopulationSize,GROW_FULL_RATIO, VAR_LEN, OPCODE_SIZE,opArity, OP_ERC);
}
\end

\GenomeClass::crossover :
{
  simpleCrossOver(parent1,parent2,child);
  child.valid = false;
}
\end

\GenomeClass::mutator : // Must return the number of mutations
{

  simple_mutator(&Genome);

  return 1;
}
\end

\begin operator description :
OP_X, "x", 0, {RESULT=INPUT[0];};
OP_ADD, "+", 2, {RESULT=OP1+OP2;};
OP_SUB, "-", 2, {RESULT=OP1-OP2;};
OP_MUL, "*", 2, {RESULT=OP1*OP2;};
OP_DIV, "/", 2, {
  if( !OP2 ) RESULT = 1;
  else RESULT = OP1/OP2;
};
OP_ERC, "ERC", 0, {RESULT=ERC;};
\end

\GenomeClass::evaluator header:
\end

\GenomeClass::evaluator for each fc :
float expected_value = OUTPUT;
ERROR = powf(expected_value-EVOLVED_VALUE,2);
\end

\GenomeClass::evaluator accumulator :
return sqrtf(ERROR/NO_FITNESS_CASES);
\end

\User Makefile options: 

CXXFLAGS+=-I/usr/local/cuda/common/inc/ -I/usr/local/cuda/include/
LDFLAGS+=
\end

\Default run parameters :         // Please let the parameters appear in this order
  Number of generations : 100       // NB_GEN
  Time limit: 0 		  // In seconds, 0 to deactivate
  Population size : 4096	          //POP_SIZE
  Offspring size : 4096              // 40% 
  Mutation probability : 0.2        // MUT_PROB
  Crossover probability : 0.9       // XOVER_PROB
  Evaluator goal : minimise       // Maximise
  Selection operator: Tournament 2
  Surviving parents: 100%//percentage or absolute  
  Surviving offspring: 100%
  Reduce parents operator: Tournament 2
  Reduce offspring operator: Tournament 2
  Final reduce operator: Tournament 2

  Elitism: Strong			//Weak or Strong
  Elite: 1

  //  Print stats:1				//Default: 1
  //  Generate csv stats file:0			
  //  Generate gnuplot script:0
  //  Generate R script:0
  //  Plot stats:0				//Default: 0

  max init tree depth : 9
  min init tree depth : 4

  max tree depth : 12

  nb of GPUs : 1
  size of prog buffer : 20000000

  nb of fitness cases : 128
\end