Merge de la branche GP et syntaxerror

EaseaLex.l

@@ -5,7 +5,7 @@ Lexical analyser for the EASEA language (EAsy Specification for Evolutionary Alg
 
 Pierre COLLET (Pierre.Collet@polytechnique.fr)
 Ecole Polytechnique
-Centre de Mathmatiques Appliques
+Centre de Math�matiques Appliqu�es
 91128 Palaiseau cedex
   ****************************************************************************/
 #ifdef WIN32
@@ -327,6 +327,8 @@ exponent  ([Ee][+-]?[0-9]+)
   opDesc[iNoOp]->opcode = new string(yytext);
  }
 
+<GP_RULE_ANALYSIS>"//".*"\n" {}
+
 <GP_RULE_ANALYSIS>"\""([a-zA-Z0-9]|\*|\+|\-|\/|\%)*"\"" {
   if( iGP_OPCODE_FIELD != 1 ){
     fprintf(stderr,"Error, op code real name must be given at the second place\n");
@@ -369,7 +371,7 @@ exponent  ([Ee][+-]?[0-9]+)
   if( bVERBOSE ) printf("begining of the code part\n");
   accolade_counter=1;
 
-  printf("arity : %d\n",opDesc[iNoOp]->arity);
+//  printf("arity : %d\n",opDesc[iNoOp]->arity);
   if( opDesc[iNoOp]->arity>=2 )
     opDesc[iNoOp]->gpuCodeStream << "OP2 = stack[--sp];\n      ";
   if( opDesc[iNoOp]->arity>=1 )
@@ -404,7 +406,7 @@ exponent  ([Ee][+-]?[0-9]+)
 <GP_COPY_OPCODE_CODE>"INPUT["[0-9]*"]" {
   char* endptr;
   unsigned no_input = strtol(yytext+strlen("INPUT["),&endptr,10);
-  printf("input no : %d\n",no_input);
+//  printf("input no : %d\n",no_input);
   opDesc[iNoOp]->cpuCodeStream << "input["<< no_input <<"]" ;
   opDesc[iNoOp]->gpuCodeStream << "input["<< no_input << "]";  
  }
@@ -413,7 +415,7 @@ exponent  ([Ee][+-]?[0-9]+)
   opDesc[iNoOp]->isERC = true;
   opDesc[iNoOp]->cpuCodeStream << "root->erc_value" ;
   opDesc[iNoOp]->gpuCodeStream << "k_progs[start_prog++];" ;
-  printf("ERC matched\n");
+//  printf("ERC matched\n");
 }
 
 <GP_COPY_OPCODE_CODE>\n {
@@ -446,9 +448,26 @@ exponent  ([Ee][+-]?[0-9]+)
   iCOPY_GP_EVAL_STATUS = EVAL_BDY;
   if( bVERBOSE ) printf("Insert GP eval body\n");
   fprintf(fpOutputFile,"      ");
+  bCOPY_GP_EVAL_GPU = false;
   BEGIN COPY_GP_EVAL;
  }
 
+
+
+<TEMPLATE_ANALYSIS>"\\INSERT_GENOME_EVAL_BDY_GPU" {
+  yyreset();
+  rewind(fpGenomeFile);
+  yyin = fpGenomeFile;
+  bIsCopyingGPEval = false;
+  iCOPY_GP_EVAL_STATUS = EVAL_BDY;
+  if( bVERBOSE ) printf("Insert GP eval body\n");
+  fprintf(fpOutputFile,"      ");
+  bCOPY_GP_EVAL_GPU = true;
+  BEGIN COPY_GP_EVAL;
+ }
+
+
+
 <TEMPLATE_ANALYSIS>"\\INSERT_GENOME_EVAL_FTR" {
   yyreset();
   rewind(fpGenomeFile);
@@ -523,7 +542,11 @@ exponent  ([Ee][+-]?[0-9]+)
  }
 
 <COPY_GP_EVAL>"OUTPUT" {
-  if( bIsCopyingGPEval) fprintf(fpOutputFile, "outputs[i]" );
+  if( bIsCopyingGPEval) 
+    if( bCOPY_GP_EVAL_GPU )
+      fprintf(fpOutputFile, "outputs[i*NUMTHREAD2+tid]" );
+    else fprintf(fpOutputFile, "outputs[i]" );
+  
  }
 
 <COPY_GP_EVAL>"return " {
@@ -1308,7 +1331,7 @@ if(OPERATING_SYSTEM=WINDOWS)
   bFunction=1; bInitFunction=1;
   if( bLINE_NUM_EZ_FILE )
     fprintf(fpOutputFile,"#line %d \"%s.ez\"\n",lineCounter, sRAW_PROJECT_NAME);
-  
+      
   BEGIN COPY;
  }
 <COPY_INITIALISATION_FUNCTION><<EOF>> {bInitFunction=0; // No before everything else function was found in the .ez file
@@ -1649,7 +1672,7 @@ if(OPERATING_SYSTEM=WINDOWS)
 <GENOME_ANALYSIS>"GPNode"            {
   yylval.pSymbol = new CSymbol("GPNode");
   //yylval.pSymbol->ObjectType = oPointer;
-  printf("match gpnode\n");
+//  printf("match gpnode\n");
   return GPNODE;
  }
 
@@ -2120,7 +2143,7 @@ if(OPERATING_SYSTEM=WINDOWS)
 <GET_PARAMETERS>"Evaluator"[ \t\n]+"goal"[ \t\n]*":"[ \t\n]* {if (bVERBOSE) printf ("\tMinMax...\n");return MINIMAXI;}
 <GET_PARAMETERS>"Number"[ \t\n]+"of"[ \t\n]+"optimisation"[ \t\n]+"iterations"[ \t\n]*":"[ \t\n]* {if (bVERBOSE) printf ("\tNb of Optimisation It...\n");return NB_OPT_IT;}
 <GET_PARAMETERS>"Baldwinism"[ \t\n]*":"[ \t\n]* {if (bVERBOSE) printf ("\tBaldwinism...\n");return BALDWINISM;}
-
+ 
 <GET_PARAMETERS>"Remote"[ \t\n]+"island"[ \t\n]+"model"[ \t\n]*":"[ \t\n]* {if(bVERBOSE) printf ("\tRemote Island Model...\n"); return REMOTE_ISLAND_MODEL;}
 <GET_PARAMETERS>"IP"[ \t\n]+"file"[ \t\n]*":"[ \t\n]* {if(bVERBOSE) printf("\tIP File...\n"); return IP_FILE;}
 

EaseaParse.h

@@ -123,7 +123,9 @@ public:
   double divide(double dDividend, double dDivisor);
   CSymbol* insert() const;
 
-#line 127 "EaseaParse.h"
+  virtual void yysyntaxerror();
+
+#line 129 "EaseaParse.h"
 };
 
 #ifndef YYPARSENAME

EaseaParse.y

@@ -5,7 +5,7 @@ Parser for the EASEA language (EAsy Specification for Evolutionary Algorithms)
 
 Pierre COLLET (Pierre.Collet@polytechnique.fr)
 Ecole Polytechnique
-Centre de Mathmatiques Appliques
+Centre de Math�matiques Appliqu�es
 91128 Palaiseau cedex
 ****************************************************************************/
 
@@ -160,7 +160,7 @@ class CSymbol;
 }
 
 // parser name and class definition
-%name CEASEAParser
+%name CEASEAParser 
 {
 protected:
   CEASEALexer EASEALexer;       // the lexical analyser
@@ -173,6 +173,8 @@ public:
   double assign(CSymbol* pIdentifier, double dValue);
   double divide(double dDividend, double dDivisor);
   CSymbol* insert() const;
+
+  virtual void yysyntaxerror();
 }
 
 // constructor
@@ -317,8 +319,8 @@ Objects
   | Objects ',' Object
   ;
 
-// Attention : il reste  grer correctement les tableaux de pointeurs
-// les indirections multiples et les tableaux  plusieurs dimensions.
+// Attention : il reste � g�rer correctement les tableaux de pointeurs
+// les indirections multiples et les tableaux � plusieurs dimensions.
 // Je sais, il faudrait aussi utiliser un peu de polymorphisme pour les symboles
   
 Object
@@ -835,3 +837,10 @@ double CEASEAParser::divide(double a, double b)
     return a / b;
   }
 }
+
+
+void CEASEAParser::yysyntaxerror(){
+
+  printf("Syntax Error at line : %d\n",EASEALexer.yylineno);
+}
+

Makefile

@@ -11,7 +11,7 @@ $(EXEC):EaseaSym.o EaseaParse.o EaseaLex.o alexyacc/libalex.a EaseaYTools.o boos
 #else
 #$(EXEC):EaseaSym.o EaseaParse.o EaseaLex.o alexyacc/libalex.a EaseaYTools.o libeasea/libeasea.a
 #endif
-	$(CPPC) $(CPPFLAGS) $(LDFLAGS) $^ -o $@
+	$(CPPC) $(CPPFLAGS) $(LDFLAGS) $^ -o $@ 
 	#
 	# Congratulations !  It looks like you compiled EASEA successfully.
 	# 
@@ -81,7 +81,7 @@ endif
 
 
 EaseaParse.o: EaseaParse.cpp EaseaLex.cpp
-	$(CPPC) $(CPPFLAGS) $< -o $@ -c
+	$(CPPC) $(CPPFLAGS) $< -o $@ -c 2>/dev/null
 
 %.o:%.cpp
 	$(CPPC) $(CPPFLAGS) -c -o $@ $<

dev/c4.5_geo/card.ez

@@ -8,6 +8,110 @@ __________________________________________________________*/
 
 \User functions:
 
+float evaluation_fonction(int k, IndividualImpl* Genome,struct base** k_tables,unsigned* packets_size){
+  struct base* k_tmp_tables[k];
+
+  unsigned error = 0;
+  unsigned tree_size = 0;
+
+  // generate tmp tables from genome, for every packets
+  float fitness_value = 0;
+  for( unsigned i=0 ; i<k ; i++ ){
+    k_tmp_tables[i] = table_from_genome(Genome->x,k_tables[i],t2,GENOME_SIZE,GENE_SIZE);
+  }
+  
+  // cross validation
+  for( unsigned i=0 ; i<k ; i++ ){
+    
+    struct base* tmp_learning_table= (struct base*)malloc(sizeof(*tmp_learning_table));
+    struct base* tmp_test_table = (struct base*)malloc(sizeof(*tmp_test_table));
+    
+    tmp_learning_table->instances = 
+      (float**)malloc(sizeof(*tmp_learning_table->instances)*t1->no_instances-k_tables[i]->no_instances);
+
+    tmp_learning_table->hdr = ba_partial_copy_hdr(k_tmp_tables[i]->hdr);
+    tmp_test_table->hdr = ba_partial_copy_hdr(k_tmp_tables[i]->hdr);
+    
+
+    // create a learning set with k-1 packets
+    unsigned copied_instances = 0;
+    for( unsigned j=0 ; j<k ; j++ ){
+      if(j==i)continue;
+      memcpy( (tmp_learning_table->instances)+copied_instances,
+	      k_tmp_tables[j]->instances,
+	      sizeof(*tmp_learning_table->instances)*(k_tmp_tables[j]->no_instances));
+      copied_instances += k_tmp_tables[j]->no_instances;
+
+    }
+    tmp_learning_table->no_instances = copied_instances;
+    
+
+    // create the test set with 1 packet
+    tmp_test_table->instances = (float**)malloc(sizeof(*tmp_test_table->instances)*k_tmp_tables[i]->no_instances);
+    memcpy(tmp_test_table->instances,k_tmp_tables[i]->instances,
+	   sizeof(*tmp_test_table->instances)*k_tmp_tables[i]->no_instances);
+    tmp_test_table->no_instances = k_tmp_tables[i]->no_instances;
+
+
+    // set threshold, from t1 for standard attribute
+    for( unsigned j=0 ; j<t1->hdr->no_attributes ; j++ ){
+      tmp_test_table->hdr->attributes[j]->threshold = t1->hdr->attributes[j]->threshold;
+      tmp_test_table->hdr->attributes[j]->no_threshold = t1->hdr->attributes[j]->no_threshold;
+    }
+    // compute threshold for attribute generated by genome
+    ba_compute_threshold_from(tmp_test_table,t1->hdr->no_attributes);
+
+    // set threshold, from t1 for standard attribute
+    for( unsigned j=0 ; j<t1->hdr->no_attributes ; j++ ){
+      tmp_learning_table->hdr->attributes[j]->threshold = t1->hdr->attributes[j]->threshold;
+      tmp_learning_table->hdr->attributes[j]->no_threshold = t1->hdr->attributes[j]->no_threshold;
+    }
+    // compute threshold for attribute generated by genome
+    ba_compute_threshold_from(tmp_learning_table,t1->hdr->no_attributes);
+
+
+    cTreeNode* t = genereate_decision_tree(tmp_learning_table);
+
+    //show_tree(k_tmp_tables[0],t,0);
+    
+    //DBG_print_instances(tmp_test_table->instances,tmp_test_table->no_instances,tmp_test_table->hdr->no_attributes);
+    for( unsigned j=0 ; j<tmp_test_table->no_instances ; j++ ){
+      unsigned predicted_class = t->classify_instance(tmp_test_table->instances[j]);
+      unsigned real_class = (unsigned)tmp_test_table->instances[j][t1->hdr->whichis_class];
+      
+      //printf("%3.0f : %d, %d\n",tmp_test_table->instances[j][1],predicted_class,real_class);
+      // here compute classification error, or any quality measurment
+      if( predicted_class!=real_class ){
+	error++;
+      }
+    }
+    
+    //printf("err : %f %d\n",fitness_value,t->tree_depth());    
+    tree_size += t->tree_depth();
+
+    delete t;
+
+
+    // free current sets
+    // first un-assignate instances
+    for( unsigned i=0 ; i<tmp_learning_table->no_instances ; i++ ){tmp_learning_table->instances[i] = NULL;}
+    for( unsigned i=0 ; i<tmp_test_table->no_instances ; i++ ){tmp_test_table->instances[i] = NULL;}
+    tmp_learning_table->no_instances = 0;
+    tmp_test_table->no_instances = 0;
+    // then delete tmp tables
+    ba_partial_copy_delete(tmp_learning_table);
+    ba_partial_copy_delete(tmp_test_table);
+    printf("f at this packet %d\n",error);  
+  }
+
+  fitness_value = (((float)error) / t1->no_instances)*100;// + (((float)tree_size)/k)
+  
+  for( unsigned i=0 ; i<k ; i++ ){
+    ba_delete(k_tmp_tables[i]);
+  }
+  return fitness_value;
+}
+
 \end
 
 \At the beginning of each generation function:
@@ -47,6 +151,7 @@ struct base* t1 = NULL;
 struct base* t2 = NULL;
 
 float* uniq_instances[2];
+float* mutation_step;
 unsigned uniq_cnt[2];
 
 struct base* k_tables[K];
@@ -69,12 +174,11 @@ GenomeClass {
   cout << "Seed : " << globalRandomGenerator->get_seed() << endl;
   srand(globalRandomGenerator->get_seed());
 
-  t1 = ba_postgres_load_ilot(1);
-  t2 = ba_postgres_load_batiment(1);
+  t1 = ba_postgres_load_ilot(4);
+  t2 = ba_postgres_load_batiment(4);
   ba_sort_instances(t1,0,0);
   ba_set_links(t1,t2);
-  //ba_print_links(t1);
-  //printf("+ %d - %d\n",t1->class_repartition[1],t1->class_repartition[0]);
+
   for( unsigned i=0 ; i<t1->hdr->attributes[t1->hdr->whichis_class]->no_values; i++ )
     printf("%s : %d\n",t1->hdr->attributes[t1->hdr->whichis_class]->values[i],t1->class_repartition[i]);
   
@@ -107,6 +211,34 @@ GenomeClass {
 #endif
   //exit(-1);
 
+
+  // here we can compute the extremum of the annex table
+  float* mins = new float[t2->hdr->no_attributes-2];
+  float* maxs = new float[t2->hdr->no_attributes-2];
+
+  unsigned no_usefull_attributs = t2->hdr->no_attributes-2;
+
+  mutation_step = new float[no_usefull_attributs];
+
+  for( unsigned i=1 ; i<t2->hdr->no_attributes-1 ; i++ ){
+    mins[i-1] = INFINITY;
+    maxs[i-1] = -INFINITY;
+    for( unsigned j=0 ; j<t2->no_instances ; j++ ){
+      if( t2->instances[j][i] < mins[i-1] )
+	mins[i-1] = t2->instances[j][i];
+      if( t2->instances[j][i] > maxs[i-1] )
+	maxs[i-1] = t2->instances[j][i];
+
+    }
+  }
+
+  for( unsigned i=0 ; i< no_usefull_attributs ; i++ ){
+    mutation_step[i] = (maxs[i]-mins[i])/50;
+    printf("%d : M : %f m : %f ms %f\n",i,maxs[i],mins[i],mutation_step[i]);
+  }
+
+  delete mins;
+  delete maxs;
 }
 \end
 
@@ -146,6 +278,16 @@ GenomeClass {
   printf(" error on the whole set : %d\n",error);
 #endif
 
+
+
+  struct base* k_tables_f[10];
+  unsigned packets_size_f[10];
+  
+
+  generate_k_fold(10,packets_size_f,t1,k_tables_f,t2);
+  //evaluation_fonction(10,
+  printf("validation on 10 folds %f\n",evaluation_fonction(10, best,k_tables_f,packets_size_f));
+
   delete root;
   ba_delete( tmp_table );
 }
@@ -213,24 +355,10 @@ GenomeClass {
 	else{
 	  float value;
 	  if( __isinf(Genome.x[i+j]) ) Genome.x[i+j] = random(0,1000);
-	  switch(j){
-	  case 0:
-	    value = random_gauss(Genome.x[i+j],100);
-	    Genome.x[i+j] = roundf(value);
-	    Genome.x[i+j] = ba_nearest_table_value(t2,j+1,value);
-	    break;
-	  case 1:
-	    value = random_gauss(Genome.x[i+j],20);
-	    Genome.x[i+j] = roundf(value);
-	    Genome.x[i+j] = ba_nearest_table_value(t2,j+1,value);
-	    break;
-	  case 2:
-	    value = random_gauss(Genome.x[i+j],10);
-	    Genome.x[i+j] = roundf(value);
-	    Genome.x[i+j] = ba_nearest_table_value(t2,j+1,value);
-	    break;	    
-	  }
-	  //}
+	  value = random_gauss(Genome.x[i+j],mutation_step[i]);
+	  Genome.x[i+j] = roundf(value);
+	  Genome.x[i+j] = ba_nearest_table_value(t2,j+1,value);
+	  
 	  NbMut++;
 	}
       }
@@ -359,14 +487,15 @@ GenomeClass {
 \end
 
 \User Makefile options: 
+LIBANTLR_DIR = ~/lib/libantlr3c-3.3-SNAPSHOT/
 CXXFLAGS+=-I/home/maitre/sources/c4.5/include/ -fopenmp 
-LDFLAGS+= -lpq  -lm  -fopenmp ../c4.5_common/libc45.a 
+LDFLAGS+= -lpq  -lm  -fopenmp ../c4.5_common/libc45.a $(LIBANTLR_DIR)/.libs/libantlr3c.a
 \end
 
 \Default run parameters :        // Please let the parameters appear in this order
   Number of generations : 200     	// NB_GEN
   Time limit: 0 			// In seconds, 0 to deactivate
-  Population size : 100			//POP_SIZE
+  Population size : 200			//POP_SIZE
   Offspring size : 100 // 40% 
   Mutation probability : 1       // MUT_PROB
   Crossover probability : 1      // XOVER_PROB

dev/c4.5_toy/card.ez

@@ -88,9 +88,9 @@ GenomeClass {
 #if 1
   // try the perfect solution
   IndividualImpl* i = new IndividualImpl();
-  i->x[0] = 30;
-  i->x[1] = 39;
-  i->x[GENE_SIZE-1] = 0;
+  i->x[0] = 60;
+  i->x[1] = 60;
+  i->x[GENE_SIZE-1] = 3;
   
   for( unsigned j=3 ; j<GENE_SIZE-1 ; j++ )
     i->x[j] = INFINITY;
@@ -142,7 +142,7 @@ GenomeClass {
   printf(" error on the whole set : %d\n",error);
 #endif
 
-  
+  ba_to_arff(tmp_table,"output_table.arff");
   delete root;
   ba_delete( tmp_table );
 }
@@ -152,7 +152,7 @@ GenomeClass {
 {
   for( unsigned i=0; i<GENOME_SIZE ; i+=GENE_SIZE ) {
     for( unsigned j=0 ; j<GENE_SIZE-1 ; j++ ){
-      Genome.x[i+j] = random(0,100);
+      Genome.x[i+j] = t2->instances[random(0,t2->no_instances)][j+1]; //random(0,100);
     }
     Genome.x[i+GENE_SIZE-1] = random(0,5);
   }
@@ -162,7 +162,7 @@ GenomeClass {
 \GenomeClass::crossover :
 {
   
-#if 0
+#if 1
   for (int i=0; i<GENOME_SIZE; i+=GENE_SIZE){
     if( tossCoin(0.5) )
       for( unsigned j=0 ; j<GENE_SIZE ; j++ )
@@ -339,8 +339,9 @@ GenomeClass {
 \end
 
 \User Makefile options: 
+LIBANTLR_DIR = ~/lib/libantlr3c-3.3-SNAPSHOT/
 CXXFLAGS+=-I/home/maitre/sources/c4.5/include/ -fopenmp 
-LDFLAGS+= -lpq  -lm  -fopenmp ../c4.5_common/libc45.a 
+LDFLAGS+= -lpq  -lm  -fopenmp ../c4.5_common/libc45.a $(LIBANTLR_DIR)/.libs/libantlr3c.a
 \end
 
 \Default run parameters :        // Please let the parameters appear in this order

dev/c4.5_toy/run.sh

 
 
-for(( i=0 ; i<1 ; i++ ))
+for(( i=0 ; i<20 ; i++ ))
 do
     suf=`date +%s`
     echo $suf

tpl/CUDA_GP.tpl

 \TEMPLATE_START
 #ifdef WIN32
 #define _CRT_SECURE_NO_WARNINGS
-#pragma comment(lib, "libEasea.lib")
+#pragma comment(lib, "libEasea.lib")
 #endif
 /**
  This is program entry for TreeGP template for EASEA
@@ -84,7 +84,10 @@ int main(int argc, char** argv){
 #include "CIndividual.h"
 #include "CCuda.h"
 #include "CGPNode.h"
+#include <iostream>
+#include <sstream>
 
+unsigned aborded_crossover;
 float* input_k;
 float* output_k;
 int* indexes_k;
@@ -95,7 +98,7 @@ int* hits_k;
 using namespace std;
 
 #include "EASEAIndividual.hpp"
-bool INSTEAD_EVAL_STEP = false;
+bool INSTEAD_EVAL_STEP = true;
 
 int fitnessCasesSetLength;
 
@@ -112,6 +115,8 @@ extern CEvolutionaryAlgorithm* EA;
 #define CUDA_GP_TPL
 #define GROW_FULL_RATIO 0.5
 
+struct gpu_argument* t_args;
+unsigned evaluation_threads_status;
 
 #define NUMTHREAD2 128
 #define MAX_STACK 50
@@ -125,10 +130,10 @@ extern CEvolutionaryAlgorithm* EA;
 /* Insert declarations about opcodes*/
 \INSERT_GP_OPCODE_DECL
 
-float recEvaleDrone(GPNode* root, float* input) {
+float recEval(GPNode* root, float* input) {
   float OP1=0, OP2= 0, RESULT = 0;
-  if( opArity[root->opCode]>=1) OP1 = recEvaleDrone(root->children[0],input);
-  if( opArity[root->opCode]>=2) OP2 = recEvaleDrone(root->children[1],input);
+  if( opArity[root->opCode]>=1) OP1 = recEval(root->children[0],input);
+  if( opArity[root->opCode]>=2) OP2 = recEval(root->children[1],input);
   switch( root->opCode ){
 \INSERT_GP_CPU_SWITCH
   default:
@@ -160,11 +165,29 @@ __device__ float eval_tree_gpu(unsigned fc_id, const float * k_progs, const floa
 }
 
 
+
+
+int flattening_tree_rpn( GPNode* root, float* buf, int* index){
+  int i;
+
+  for( i=0 ; i<opArity[(int)root->opCode] ; i++ ){
+    flattening_tree_rpn(root->children[i],buf,index);
+  }
+  if( (*index)+2>MAX_PROGS_SIZE )return 0;
+  buf[(*index)++] = root->opCode;
+  if( root->opCode == OP_ERC ) buf[(*index)++] = root->erc_value;
+  return 1;
+}
+
+
+
 /**
    Send input and output data on the GPU memory.
    Allocate
  */
 void initialDataToGPU(float* input_f, int length_input, float* output_f, int length_output){
+  
+
   // allocate and copy input/output arrays
   CUDA_SAFE_CALL(cudaMalloc((void**)(&input_k),sizeof(float)*length_input));
   CUDA_SAFE_CALL(cudaMalloc((void**)(&output_k),sizeof(float)*length_output));
@@ -177,10 +200,12 @@ void initialDataToGPU(float* input_f, int length_input, float* output_f, int len
   CUDA_SAFE_CALL( cudaMalloc((void**)&progs_k,sizeof(*progs_k)*MAX_PROGS_SIZE));
 
   // allocate hits and results arrays
-  CUDA_SAFE_CALL(cudaMalloc((void**)&results_k,sizeof(*indexes_k)*maxPopSize));
-  CUDA_SAFE_CALL(cudaMalloc((void**)&hits_k,sizeof(*indexes_k)*maxPopSize));
+  CUDA_SAFE_CALL(cudaMalloc((void**)&results_k,sizeof(*results_k)*maxPopSize));
+  CUDA_SAFE_CALL(cudaMalloc((void**)&hits_k,sizeof(*hits_k)*maxPopSize));
 }
 
+
+
 /**
    Free gpu memory from the input and ouput arrays.
  */
@@ -193,12 +218,6 @@ void free_gpu(){
 }
 
 
-
-
-
-
-
-
 __global__ static void 
 EvaluatePostFixIndividuals_128(const float * k_progs,
 			       const int maxprogssize,
@@ -222,7 +241,6 @@ EvaluatePostFixIndividuals_128(const float * k_progs,
   float sum = 0.0;
   int hits = 0 ; // hits number
 
-  float currentOutput;
   float ERROR;
 
   index = bid; // one program per block => block ID = program number
@@ -249,10 +267,10 @@ EvaluatePostFixIndividuals_128(const float * k_progs,
     // are we on a busy thread?
     if (i*NUMTHREAD2+tid >= trainingSetSize) // no!
       continue;
-    currentOutput = outputs[i*NUMTHREAD2+tid];
-
+         
     float EVOLVED_VALUE = eval_tree_gpu(i, k_progs+k_indexes[index], k_inputs+(i*NUMTHREAD2+tid));
-\INSERT_GENOME_EVAL_BDY
+ 
+\INSERT_GENOME_EVAL_BDY_GPU
 
     
     if (!(ERROR < BIG_NUMBER))
@@ -279,26 +297,12 @@ EvaluatePostFixIndividuals_128(const float * k_progs,
     }    
     ERROR = tmpresult[0];
     \INSERT_GENOME_EVAL_FTR_GPU
-    k_results[index] = sqrtf(tmpresult[0]/512);
+      
     k_hits[index] = tmphits[0];
   }  
   // here results and hits have been stored in their respective array: we can leave
 }
 
-int flattening_tree_rpn( GPNode* root, float* buf, int* index){
-  int i;
-
-  for( i=0 ; i<opArity[(int)root->opCode] ; i++ ){
-    flattening_tree_rpn(root->children[i],buf,index);
-  }
-  if( (*index)+2>MAX_PROGS_SIZE )return 0;
-  buf[(*index)++] = root->opCode;
-  if( root->opCode == OP_ERC ) buf[(*index)++] = root->erc_value;
-  return 1;
-}
-
-
-
 GPNode* pickNthNode(GPNode* root, int N, int* childId){
 
   GPNode* stack[TREE_DEPTH_MAX*MAX_ARITY];
@@ -335,13 +339,14 @@ GPNode* pickNthNode(GPNode* root, int N, int* childId){
 }
 
 
+
+
 void simple_mutator(IndividualImpl* Genome){
 
   // Cassical  mutation
   // select a node
   int mutationPointChildId = 0;
   int mutationPointDepth = 0;
-  //toDotFile( Genome.root[tree], "out/mutation/p", tree);
   GPNode* mutationPointParent = selectNode(Genome->root, &mutationPointChildId, &mutationPointDepth);
   
   
@@ -350,12 +355,62 @@ void simple_mutator(IndividualImpl* Genome){
     mutationPointDepth = 0;
   }
   delete mutationPointParent->children[mutationPointChildId] ;
-  mutationPointParent->children[mutationPointChildId] = NULL;
   mutationPointParent->children[mutationPointChildId] =
     construction_method( VAR_LEN+1, OPCODE_SIZE , 1, TREE_DEPTH_MAX-mutationPointDepth ,0,opArity,OP_ERC);
+}
+
 
+/**
+   This function handles printing of tree.
+   Every node is identify by its address, in memory,
+   and labeled by the actual opCode.
+