wip: set_neurons : transfert training

dimension_t/Makefile

@@ -3,10 +3,11 @@ CC=gcc
 ROOTPROJECTDIR:=$(realpath ..)
 TOOLDIR=$(ROOTPROJECTDIR)/ytools_t
 PERMDIR=$(ROOTPROJECTDIR)/ypermutation_t
+LISTDIR=$(ROOTPROJECTDIR)/list_t
 
 INCLUDE_PERMDIR=$(PERMDIR)/src 
 INCLUDE_TOOLDIR=$(TOOLDIR)/include
-CFLAGS=-I$(INCLUDE_TOOLDIR) -I$(INCLUDE_PERMDIR) -I./src 
+CFLAGS=-I$(INCLUDE_TOOLDIR) -I$(INCLUDE_PERMDIR) -I./src -I$(LISTDIR)/src
 
 #SRC_DIR=$(ROOT_DIR)/src
 #SRC=$(wildcard */*/*.c) 

dimension_t/src/dimension_t/dimension_t.c

@@ -209,7 +209,7 @@ void min_dimension(dimension **d, dimension *d0, dimension *d1) {
 
 void printDebug_dimension(dimension *d,char *msg){
 
-  printf("(%s)->size = %ld | (%s)->rank = %ld \n[",msg,d->size,msg,d->rank);
+  printf("<%p>(%s)->size = %ld | (%s)->rank = %ld \n[",d,msg,d->size,msg,d->rank);
   for(size_t i=0; i<d->size; ++i)
     printf(" %ld,", d->perm[i]);
   printf("] \n");
@@ -451,3 +451,11 @@ void free_list_perm_in_dim(list_perm_in_dim *l_p){
   }
 }
 
+GEN_LIST_ALL(dimension)
+
+GEN_LIST_ALL(ptr_DIMENSION)
+GEN_FUNC_PTR_LIST_FREE(ptr_DIMENSION){
+  dimension *pdim=(dimension*)arg;
+  free_dimension(pdim);
+  //free(pdim);
+}

dimension_t/src/dimension_t/dimension_t.h

@@ -2,6 +2,7 @@
 #define __DIMENSION_T__H__
 
 #include "permutation_t/permutation_t.h"
+#include "list_t/list_t.h"
 
 extern bool endian;
 
@@ -74,5 +75,11 @@ dimension * create_binary_dim(size_t dimension_size);
 
 void free_list_perm_in_dim(list_perm_in_dim *l_p);
 
+GENERATE_LIST_ALL(dimension)
+typedef dimension * ptr_DIMENSION;
+
+GENERATE_LIST_ALL(ptr_DIMENSION)
+GEN_HEAD_PTR_LIST(ptr_DIMENSION)
+
 #endif /* __DIMENSION_T__H__ */
 //int compare_dimension(dimension *d1, dimension *d2);

neuron_t/src/neuron_t/neuron_t.c

@@ -39,9 +39,98 @@ void free_config_layers(config_layers *pconf){
   free(pconf);
 } 
 
+long int cmp_config_layers(config_layers *c1, config_layers *c2){
+  long int diff_nb=c1->nb_layers - c2->nb_layers;
+  if(diff_nb) return diff_nb;
+
+  for(long int i=0; i<c1->nb_layers; ++i){
+    long int diff_sz_layers = c1->sz_layers[i] - c2->sz_layers[i];
+    if(diff_sz_layers) return diff_sz_layers;
+    for(long int j=0; j<c1->sz_layers[i]; ++j){
+      long int diff_dim = c1->array_dim_in_layers[i][j] -  c2->array_dim_in_layers[i][j]; 
+      if(diff_dim) return diff_dim;
+    }
+  }
+  return 0;
+}
+
+config_layers * create_config_layers_from_m_list_ptr_DIMENSION(struct main_list_ptr_DIMENSION *m_l_dim){
+  config_layers * pconf=malloc(sizeof(struct config_layers));
+  pconf->nb_layers=m_l_dim->size;
+  //printf("debug: pconf->nb_layers=%ld\n",pconf->nb_layers);
+  pconf->sz_layers=malloc(pconf->nb_layers * sizeof(size_t));
+  pconf->array_dim_in_layers=malloc((pconf->nb_layers)*sizeof(size_t*));
+  for(struct list_ptr_DIMENSION *local_l_dim=m_l_dim->begin_list; local_l_dim; local_l_dim=local_l_dim->next){
+    size_t i = local_l_dim->index;
+    pconf->sz_layers[i]=local_l_dim->value->size;
+  //printf("debug: pconf->sz_layers[%ld]=%ld\n",i,pconf->sz_layers[i]);
+    pconf->array_dim_in_layers[i]=malloc((pconf->sz_layers[i])*sizeof(size_t));
+    for(size_t j=0; j< pconf->sz_layers[i];++j){
+      pconf->array_dim_in_layers[i][j] = local_l_dim->value->perm[j];
+  //printf("debug: pconf->array_dim_in_layers[%ld][%ld]=%ld\n",i,j,pconf->array_dim_in_layers[i][j]);
+    
+    }
+  }
+  return pconf;
+}
+
+
+config_layers * create_config_layers_from_m_list_dimension(struct main_list_dimension * m_l_dim){
+  config_layers * pconf=malloc(sizeof(struct config_layers));
+  pconf->nb_layers=m_l_dim->size;
+  printf("debug: pconf->nb_layers=%ld\n",pconf->nb_layers);
+  pconf->sz_layers=malloc(pconf->nb_layers * sizeof(size_t));
+  pconf->array_dim_in_layers=malloc((pconf->nb_layers)*sizeof(size_t*));
+  for(struct list_dimension *local_l_dim=m_l_dim->begin_list; local_l_dim; local_l_dim=local_l_dim->next){
+    size_t i = local_l_dim->index;
+    char msg[50]; sprintf(msg, "dim[%ld] ",i);
+    printDebug_dimension(&(local_l_dim->value), msg);
+    pconf->sz_layers[i]=local_l_dim->value.size;
+  printf("debug: pconf->sz_layers[%ld]=%ld\n",i,pconf->sz_layers[i]);
+    pconf->array_dim_in_layers[i]=malloc((pconf->sz_layers[i])*sizeof(size_t));
+    for(size_t j=0; j< pconf->sz_layers[i];++j){
+      pconf->array_dim_in_layers[i][j] = local_l_dim->value.perm[j];
+  printf("debug: pconf->array_dim_in_layers[%ld][%ld]=%ld\n",i,j,pconf->array_dim_in_layers[i][j]);
+    
+    }
+  }
+  return pconf;
+}
+
+void print_config_layers(config_layers * pconf){
+  for(size_t i=0;i<pconf->nb_layers; ++i){
+  printf("debug: pconf->sz_layers[%ld]=%ld\n",i,pconf->sz_layers[i]);
+    for(size_t j=0; j< pconf->sz_layers[i];++j){
+  printf(" [%ld][%ld]=%ld  | ",i,j,pconf->array_dim_in_layers[i][j]);
+    
+    }
+  printf("debug: pconf->nb_layers=%ld\n",pconf->nb_layers);
+  }
+}
 bool randomizeInitWeight=true;
 
 #define GEN_NEURONS_F_(type)\
+config_layers * create_config_layers_from_weight_in_neurons_##type(neurons_##type *base){\
+  config_layers *pconf=malloc(sizeof(struct config_layers));\
+  neurons_##type *tmp=base->next_layer;\
+  pconf->nb_layers=0;\
+  while(tmp){ ++(pconf->nb_layers); tmp=tmp->next_layer;}\
+  tmp=base->next_layer;\
+  pconf->sz_layers=malloc((pconf->nb_layers)*sizeof(size_t));\
+  pconf->array_dim_in_layers=malloc((pconf->nb_layers)*sizeof(size_t*));\
+  printf("debug: pconf->nb_layers=%ld\n",pconf->nb_layers);\
+  size_t layer=0;\
+  while(tmp){\
+    pconf->sz_layers[layer]=tmp->weight_in->dim->size;\
+  printf("debug: pconf->sz_layers[%ld]=%ld\n",layer,pconf->sz_layers[layer]);\
+    pconf->array_dim_in_layers[layer]=malloc((pconf->sz_layers[layer])*sizeof(size_t));\
+    for(size_t j=0;j<pconf->sz_layers[layer];++j){\
+      pconf->array_dim_in_layers[layer][j]=tmp->weight_in->dim->perm[j];\
+    }\
+    ++layer; tmp=tmp->next_layer;\
+  }\
+  return pconf;\
+}\
   \
 void do_not_update_learnig_rate_##type(neurons_##type *N){}\
 \
@@ -1034,14 +1123,66 @@ size_t learning_cloneuronset_##type(cloneuronset_##type *clnrnst, data_set_##typ
 }  \
 \
 \
-GEN_LIST_ALL(ptr_set_NEURONS_##type)\
-GEN_FUNC_PTR_LIST_FREE(ptr_set_NEURONS_##type){\
-  ptr_set_NEURONS_##type p_s_nn = (struct set_neurons_##type *)arg;\
+struct set_neurons_##type * create_set_neurons_##type(config_layers *pconf, struct neurons_##type *base, ssize_t score, size_t dateid){\
+  struct set_neurons_##type *p_set_n=malloc(sizeof(struct set_neurons_##type));\
+  p_set_n->pconf=pconf;\
+  p_set_n->base=base;\
+  p_set_n->score=score;\
+  p_set_n->dateid=dateid;\
+  return p_set_n;\
+}\
+\
+void free_set_neurons_##type(struct set_neurons_##type *p_s_nn){\
   free_config_layers(p_s_nn->pconf);\
   free_neurons_##type(p_s_nn->base);\
   free(p_s_nn);\
 }\
 \
+GEN_LIST_ALL(ptr_set_NEURONS_##type)\
+GEN_FUNC_PTR_LIST_FREE(ptr_set_NEURONS_##type){\
+  ptr_set_NEURONS_##type p_s_nn = (struct set_neurons_##type *)arg;\
+  free_set_neurons_##type(p_s_nn);\
+}\
+\
+ssize_t sum_score_set_neurons_##type(struct main_list_ptr_set_NEURONS_##type *m_set_nrns){\
+  ssize_t sum_score=0;\
+  for(struct list_ptr_set_NEURONS_##type *local_set_n=m_set_nrns->begin_list; local_set_n ; local_set_n = local_set_n->next ){\
+    sum_score+=(local_set_n->value->score);\
+  }\
+  return sum_score;\
+}\
+void put_meaning_of_weitgh_in_set_neurons_##type(struct set_neurons_##type *dst_nrns, struct main_list_ptr_set_NEURONS_##type * m_set_nrns)\
+{\
+  ssize_t sum_score=0;\
+  for(struct list_ptr_set_NEURONS_##type *local_set_n=m_set_nrns->begin_list; local_set_n ; local_set_n = local_set_n->next ){\
+    sum_score+=(local_set_n->value->score);\
+    if(cmp_config_layers(dst_nrns->pconf, local_set_n->value->pconf)){\
+      printf("debug: config_layers not match in inex = %ls \n",local_set_n->index);\
+      return;\
+    }\
+  }\
+  struct neurons_##type *tmp_dst=dst_nrns->base;\
+  while(tmp_dst){\
+    for(size_t i=0; i<tmp_dst->weight_in->dim->size;++i){\
+    tmp_dst->weight_in->x[i]=0;\
+    }\
+    tmp_dst=tmp_dst->next_layer;\
+  }\
+  for(struct list_ptr_set_NEURONS_##type *local_set_n=m_set_nrns->begin_list; local_set_n ; local_set_n = local_set_n->next ){\
+    local_set_n->value->cur_neurons=local_set_n->value->base;\
+    tmp_dst=dst_nrns->base;\
+    while(tmp_dst){\
+      for(size_t i=0; i<tmp_dst->weight_in->dim->size;++i){\
+        tmp_dst->weight_in->x[i] +=(((local_set_n->value->score) * (local_set_n->value->cur_neurons->weight_in->x[i]))/sum_score);\
+      }\
+      tmp_dst=tmp_dst->next_layer;\
+      local_set_n->value->cur_neurons=local_set_n->value->cur_neurons->next_layer;\
+    }\
+  }\
+}\
+\
+\
+
  
   
   

neuron_t/src/neuron_t/neuron_t.h

@@ -20,7 +20,10 @@ typedef struct config_layers config_layers;
 config_layers *create_config_layers(size_t nb_layers, size_t *sz_layers, size_t **array_dim_in_layers);
 config_layers *create_config_layers_from_OneD(size_t nb_layers, size_t *array_dim_in_layers);
 void free_config_layers(config_layers *pconf);
-
+long int cmp_config_layers(config_layers *c1, config_layers *c2);
+config_layers * create_config_layers_from_m_list_ptr_DIMENSION(struct main_list_ptr_DIMENSION *m_l_dim);
+config_layers * create_config_layers_from_m_list_dimension(struct main_list_dimension *m_l_dim);
+void print_config_layers(config_layers * pconf);
 
 #define GEN_NEURON_(type)\
 \
@@ -53,6 +56,8 @@ struct neurons_##type {/* layer */\
 };\
 typedef struct neurons_##type neurons_##type;\
 \
+config_layers * create_config_layers_from_weight_in_neurons_##type(neurons_##type *base);\
+\
 struct func_act_##type {\
   type (*func_act)(type x); /* function activation */\
   type (*deriv_func_act)(type x); /* derivate func act */\
@@ -131,10 +136,14 @@ size_t learning_cloneuronset_##type(cloneuronset_##type *clnrnst, data_set_##typ
 struct set_neurons_##type{\
   struct config_layers *pconf;\
   struct neurons_##type *base;\
+  struct neurons_##type *cur_neurons;\
   ssize_t score;\
+  size_t dateid;\
 };\
 typedef struct set_neurons_##type * ptr_set_NEURONS_##type;\
 \
+struct set_neurons_##type * create_set_neurons_##type(config_layers *pconf, struct neurons_##type *base, ssize_t score, size_t dateid);\
+\
 GENERATE_LIST_ALL(ptr_set_NEURONS_##type)\
 GEN_HEAD_PTR_LIST(ptr_set_NEURONS_##type)\
 \

neuron_t/src/neuron_t/nneuron_t_file.h

@@ -447,9 +447,200 @@ j=0;\
 \
 \
 \
-\
-\
 
 
 
+#define EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(type, neuronDst, attribute, file_name_input, m_l_dim) \
+do{\
+  int fd_input;\
+  fd_input=open(file_name_input, O_RDONLY);\
+  if ( fd_input == -1 ) {\
+    fprintf( stderr, "Cannot open file: %s for reading\n",file_name_input );\
+    exit( -1 );\
+  }\
+  size_t buf_size=820;/*need to be more than the nb of char representation of the type*/\
+  char *input=malloc(buf_size + 1);\
+  char *recInput=malloc(buf_size + 1);\
+  memset(recInput,0, buf_size + 1);\
+  char *iinput=malloc(buf_size * 2);\
+  /*bool size_unknown=false, broken=false*/; \
+  bool Done=false;\
+  int retread = 0, curIn=0, lastNonNumber=0, lenRecIn=0;\
+\
+  list_perm_in_dim *l_p=NULL;\
+  dimension *dim=NULL;\
+  size_t ss;\
+  char *ttmp=NULL;\
+  char *ppEnd=NULL;\
+  bool bracketsDown=false/*, endTensor = false*/;\
+  size_t j=0;\
+  neurons_##type * tmpNN = neuronDst;\
+  tensor_##type * T=NULL;\
+  while(tmpNN /*&& !endTensor*/){\
+    bracketsDown = false;\
+    Done = false;\
+   /* T = tmpNN->attribute;\
+    if(T == NULL){\
+      Done = true;\
+    }*/\
+            T = tmpNN->attribute;\
+            while((T == NULL) && (tmpNN!=NULL)){\
+              tmpNN = tmpNN->next_layer;\
+              if(tmpNN)\
+                T = tmpNN->attribute;\
+            }\
+        /*printf("debug : dd ttmp = %s, T == NULL?=%d %s\n",ttmp,(T==NULL),#attribute);*/\
+            if(T == NULL){\
+              Done = true;\
+            } \
+j=0;\
+    while(!Done /*&& !endTensor*/){\
+      if(ttmp == NULL || *ttmp=='\0'){\
+        for(curIn=0; curIn<lenRecIn; ++curIn){\
+          iinput[curIn]=recInput[lenRecIn-curIn-1];\
+        }\
+        retread = read(fd_input, input, buf_size) ;\
+        /*endTensor = (retread != buf_size);*/\
+        /*printf("debug: ************************* ------>input = |%s|, retread=%d, input[ret-1]={%c}\n", input,retread,input[retread-1]);*/\
+        lenRecIn = 0;\
+        for(lastNonNumber=retread-1; lastNonNumber>=0; --lastNonNumber){ \
+          if(((input[lastNonNumber] >='0') && (input[lastNonNumber] <='9'))||(input[lastNonNumber] =='-')||(input[lastNonNumber] =='.')||(input[lastNonNumber] =='E')||(input[lastNonNumber] =='e')){\
+            recInput[lenRecIn++]=input[lastNonNumber];\
+          }\
+          else break;  \
+        }\
+        recInput[lenRecIn]='\0';\
+        /*printf("recInput = |%s|\n", recInput);*/\
+        for(int ii=0; ii<=lastNonNumber; ++ii){\
+          iinput[curIn++]=input[ii];\
+        }\
+        \
+        iinput[curIn]='\0';\
+        /*printf("iinput = |%s|\nDone=%d\n", iinput,Done);*/\
+        ttmp=iinput;\
+      }\
+      while(!Done && (*ttmp != '\0') /*&& !endTensor*/){\
+        /*printf("debug : >> ttmp = %s, bracketsDown=%d\n",ttmp, bracketsDown);*/\
+        if(*ttmp=='[') {\
+          bracketsDown=false;\
+        }\
+        ppEnd=ttmp;\
+        if( !bracketsDown){\
+          while(*ttmp!='\0' && *ppEnd!=']' ){\
+            if(*ttmp=='['){\
+        /*printf("debug : [[ ttmp = %s\n",ttmp);\
+   if(dim)printDebug_dimension(dim,"[DIM]");*/\
+              if(l_p != NULL){\
+                free_dimension(dim);\
+                free_list_perm_in_dim(l_p);\
+                l_p=NULL;\
+              }\
+   /*if(dim)printDebug_dimension(dim,"{DIM}");*/\
+            }\
+            ss = strtoul(ttmp, &ppEnd, 10);\
+            while(ttmp == ppEnd && *ttmp!='\0'  && ppEnd[0] !=']'){\
+             \
+             if(*ttmp=='['){\
+        /*printf("debug : [[ ttmp = %s\n",ttmp);\
+   if(dim)printDebug_dimension(dim,"[DIM]");*/\
+              if(l_p != NULL){\
+                free_dimension(dim);\
+                free_list_perm_in_dim(l_p);\
+                l_p=NULL;\
+              }\
+   /*if(dim)printDebug_dimension(dim,"{DIM}");*/\
+            }\
+             \
+        /*printf("debug : aa ttmp = %s\n",ttmp);*/\
+              ttmp++;\
+              ss = strtoul(ttmp, &ppEnd, 10);\
+        /*printf("debug : bb ttmp = %s\n",ttmp);*/\
+            }\
+            if(ppEnd !=ttmp ){\
+              append_in_list_perm(&l_p,ss);\
+            }\
+            ttmp=ppEnd;\
+          }\
+        /*printf("debug : cc ttmp = %s\n",ttmp);*/\
+          if( *ttmp ==']'){\
+            dim=create_dim_from_list_perm(l_p);\
+            push_back_list_ptr_DIMENSION(m_l_dim, clone_dim(dim));\
+            /*push_back_list_dimension(m_l_dim, *dim);*/\
+            bracketsDown = true;\
+     printf("debug: dim ptr: %p sizeof(*dim)=%ld sizeof(dimension)=%ld\n",dim,sizeof(*dim), sizeof(dimension));\
+   if(dim){printDebug_dimension(dim,"{DIM}");}\
+            j=0;\
+            \
+          }\
+        \
+        }\
+        /*printf("debug : <<---->> ttmp = %s, bracketsDown=%d T==NULL? =%d, done?=%d\n",ttmp, bracketsDown, (T==NULL), Done);*/\
+        if(!Done && bracketsDown){\
+        /*printf("debug : ee ttmp = %s, T==NULL ? = %d\n %ld vs %ld\n",ttmp,(T==NULL),T->dim->rank,dim->rank);\
+   printDebug_dimension(dim," DIM");*/\
+          if((T->dim->rank == dim->rank)){\
+            \
+            \
+          \
+            type x;\
+            while(strlen(ttmp) && (*ttmp!='[') && (j<dim->rank)){ \
+              x = strto_##type(ttmp, &ppEnd);\
+              while(ttmp == ppEnd && strlen(ttmp) && *ttmp!='[' ){\
+        /*printf("debug : dd ttmp = %s\n",ttmp);*/\
+                ttmp++;\
+                x = strto_##type(ttmp, &ppEnd);\
+        /*printf("debug : ww ttmp = %s\n",ttmp);*/\
+              }\
+              if((*ttmp!='[') && (ttmp != ppEnd)){\
+                T->x[j++]=x;\
+                /*printf("debug : x=%lf ===> %d\n",x,(j==dim->rank));*/\
+              }\
+              else if ( *ttmp =='[') {\
+                bracketsDown = false;\
+                Done=true;\
+                break;\
+              }\
+              ttmp=ppEnd;\
+              Done=(j==dim->rank);\
+              /*endTensor=(j==dim->rank);*/\
+            }\
+            if(Done) break;\
+            if(j == dim->rank ){\
+              Done = true;\
+            }\
+          }else {\
+            /*endTensor = true;*/\
+            /*Done = true;\
+            bracketsDown = false;*/\
+            break;\
+          }\
+        }\
+          if(Done){\
+                /*printf("debug : done=%d , l_p==NULL?=%d, endTensor=%d\n",Done, (l_p==NULL), endTensor);*/\
+            if(l_p != NULL){\
+              free_dimension(dim);\
+							dim=NULL;\
+              free_list_perm_in_dim(l_p);\
+              l_p=NULL;\
+            }\
+          }\
+          \
+      }\
+      if(Done) break;\
+  \
+    }\
+    tmpNN = tmpNN->next_layer;\
+  }\
+  free(input);\
+  free(iinput);\
+  free(recInput);\
+  close(fd_input);\
+	if(dim) free_dimension(dim);\
+	if(l_p) free_list_perm_in_dim(l_p);\
+\
+}while(0);\
+\
+\
+\
+
 #endif /* NNEURONE_T_FILE_H__C_ */

y_network_neural_network_/test/Makefile

@@ -23,8 +23,9 @@ YLISTDIR=$(ROOTPROJECTDIR)/list_t
 YWORKDIR=$(ROOTPROJECTDIR)/y_worker_t
 YJSONDIR=$(ROOTPROJECTDIR)/yjson_t
 YSOCKET_DIR=$(ROOTPROJECTDIR)/y_socket_t
+#DIMENSIONDIR=$(ROOTPROJECTDIR)/dimension_t
 
-INCLUDE_SOCKET=-I$(YSOCKET_DIR)/include -I$(YLISTDIR)/src -I$(YWORKDIR)/include -I$(YJSONDIR)/src -I$(YSOCKET_DIR)/include 
+INCLUDE_SOCKET=-I$(YSOCKET_DIR)/include -I$(YLISTDIR)/src -I$(YWORKDIR)/include -I$(YJSONDIR)/src -I$(YSOCKET_DIR)/include #-I$(DIMENSIONDIR)/src
 
 LIB_SOCKET=$(YSOCKET_DIR)/libysocket.so
 

y_network_neural_network_/test/is_good.c

@@ -14,6 +14,7 @@
 #include "fmock/fmock.h"
 
 #include "neuron_t/neuron_t.h"
+//#include "dimension_t/dimension_t.h"
 
 #include "vehicle.h"
 #include "learn_to_drive.h"
@@ -772,8 +773,240 @@ struct status_qlearning *qlstatus = create_status_qlearning ();
 
 }
 
+
+TEST(extract_with_pconf){
+  size_t nb_block = 7;
+  size_t dim= 2;
+  struct blocks * path = create_blocks(nb_block, dim);
+
+  LOG("debug: f_name = %s\n", __func__);
+
 #if 1
-TEST(_first_learn_vehicle_50__11){
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){100,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,80});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,0});
+  copy_coordinate(path->upper_bound_block[2], (float[]){360,140});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,140});
+  copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[4], (float[]){720,300});
+  copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[5], (float[]){720,350});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
+ 
+
+
+/*
+
+  copy_coordinate(path->lower_bound_block[4], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[4], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[3], (float[]){150,40});
+  copy_coordinate(path->upper_bound_block[3], (float[]){250,150});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,80});
+  copy_coordinate(path->upper_bound_block[2], (float[]){360,200});
+  copy_coordinate(path->lower_bound_block[1], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[1], (float[]){600,150});
+  copy_coordinate(path->lower_bound_block[0], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[0], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[6], (float[]){260,300});
+  copy_coordinate(path->upper_bound_block[6], (float[]){760,360});
+  copy_coordinate(path->lower_bound_block[5], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[5], (float[]){410,300});
+
+
+
+
+copy_coordinate(path->lower_bound_block[0], (float[]){0,0});
+  copy_coordinate(path->upper_bound_block[0], (float[]){150,250});
+  copy_coordinate(path->lower_bound_block[1], (float[]){150,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){250,150});
+  copy_coordinate(path->lower_bound_block[2], (float[]){250,80});
+  copy_coordinate(path->upper_bound_block[2], (float[]){360,200});
+  copy_coordinate(path->lower_bound_block[3], (float[]){360,70});
+  copy_coordinate(path->upper_bound_block[3], (float[]){600,170});
+  copy_coordinate(path->lower_bound_block[4], (float[]){600,90});
+  copy_coordinate(path->upper_bound_block[4], (float[]){760,300});
+  copy_coordinate(path->lower_bound_block[5], (float[]){300,300});
+  copy_coordinate(path->upper_bound_block[5], (float[]){760,350});
+  copy_coordinate(path->lower_bound_block[6], (float[]){0,250});
+  copy_coordinate(path->upper_bound_block[6], (float[]){410,300});
+
+
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,300});
+  copy_coordinate(path->upper_bound_block[0], (float[]){400,700});
+  copy_coordinate(path->lower_bound_block[1], (float[]){100,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){1000,300});
+  copy_coordinate(path->lower_bound_block[2], (float[]){1000,50});
+  copy_coordinate(path->upper_bound_block[2], (float[]){1400,500});
+  copy_coordinate(path->lower_bound_block[3], (float[]){1400,200});
+  copy_coordinate(path->upper_bound_block[3], (float[]){1800,700});
+  copy_coordinate(path->lower_bound_block[4], (float[]){1100,700});
+  copy_coordinate(path->upper_bound_block[4], (float[]){1700,1000});
+  copy_coordinate(path->lower_bound_block[5], (float[]){800,600});
+  copy_coordinate(path->upper_bound_block[5], (float[]){1100,975});
+  copy_coordinate(path->lower_bound_block[6], (float[]){100,700});
+  copy_coordinate(path->upper_bound_block[6], (float[]){800,975});
+ */
+
+#else 
+
+  copy_coordinate(path->lower_bound_block[0], (float[]){0,3});
+  copy_coordinate(path->upper_bound_block[0], (float[]){4,7});
+  copy_coordinate(path->lower_bound_block[1], (float[]){1,0});
+  copy_coordinate(path->upper_bound_block[1], (float[]){10,3});
+  copy_coordinate(path->lower_bound_block[2], (float[]){10,0.5});
+  copy_coordinate(path->upper_bound_block[2], (float[]){14,5});
+  copy_coordinate(path->lower_bound_block[3], (float[]){14,2});
+  copy_coordinate(path->upper_bound_block[3], (float[]){18,7});
+  copy_coordinate(path->lower_bound_block[4], (float[]){11,7});
+  copy_coordinate(path->upper_bound_block[4], (float[]){17,10});
+  copy_coordinate(path->lower_bound_block[5], (float[]){8,6});
+  copy_coordinate(path->upper_bound_block[5], (float[]){11,9.75});
+  copy_coordinate(path->lower_bound_block[6], (float[]){1,7});
+  copy_coordinate(path->upper_bound_block[6], (float[]){8,9.75});
+ 
+ 
+#endif
+
+  update_bounds_limits_blocks(path);
+
+  struct vehicle *car = create_vehicle(path);
+
+  config_layers *pconf = create_config_layers_from_OneD(4,(size_t[]){3,24,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  //config_layers *pconf = create_config_layers_from_OneD(3,(size_t[]){3,24,3}); /* 3 input , 3 target; 2 hidden layer with 24 neurons each */
+  
+  bool randomize=true; 
+  float minR = -0.5, maxR = 0.5;  
+  //float minR = 0, maxR = 1;  
+  int randomRange = 500;
+  size_t nb_prod_thread = 2;
+  size_t nb_calc_thread = 4;
+  float learning_rate = 0.0007 /*0.001*//* 0.0001*/; /* 0.000001*/ /* 0.001*/;  
+  struct networks_qlearning *nnetworks = create_network_qlearning(
+    pconf,
+    randomize, minR, maxR,  randomRange,
+    nb_prod_thread, nb_calc_thread,
+    learning_rate
+  );
+/*
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20240717_01h42m16s_5300.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20240717_01h42m16s_5300.txt");
+*/
+  struct main_list_ptr_DIMENSION *m_l_dim=create_var_list_ptr_DIMENSION();
+  //struct main_list_dimension *m_l_dim=create_var_list_dimension();
+
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS_PCONF(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_TEST__first_learn_vehicle_50__11____9.symlink",m_l_dim);
+ 
+ for(struct list_ptr_DIMENSION *local_l_dim=m_l_dim->begin_list; local_l_dim; local_l_dim=local_l_dim->next){
+   size_t i=local_l_dim->index;
+   char msg[50]; sprintf(msg, " DIM[%ld] ",i);
+   printDebug_dimension((local_l_dim->value), msg);
+ }
+ LOG("%s","=========================================="); 
+  config_layers *base_conf=create_config_layers_from_weight_in_neurons_TYPE_FLOAT(nnetworks->main_net);
+  config_layers *p_conf=create_config_layers_from_m_list_ptr_DIMENSION(m_l_dim);
+  //config_layers *p_conf=create_config_layers_from_m_list_dimension(m_l_dim);
+
+  if(cmp_config_layers(p_conf, base_conf)==0){
+    LOG("base_conf == %s\n","p_conf");
+    
+  }else{
+    LOG("base_conf != %s\n","p_conf");
+
+  }
+LOG("%s ", "base_conf"); print_config_layers(base_conf);
+LOG("%s ", "p_conf"); print_config_layers(p_conf);
+free_config_layers(base_conf);
+free_config_layers(p_conf);
+//free_all_var_list_dimension(m_l_dim);
+///free_all_var_list_ptr_DIMENSION(m_l_dim);
+//remove_all_ptr_type_list_ptr_DIMENSION(m_l_dim);
+purge_ptr_type_list_ptr_DIMENSION(m_l_dim);
+
+//EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20250508_17h50m56s_26300.txt");
+///EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_.symlink");
+///EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_.symlink");
+/*
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->main_net, weight_in, ".ff_main_20250508_23h02m40s_29000.txt");
+EXTRACT_FILE_TO_TENSOR_ATTRIBUTE_NNEURONS(TYPE_FLOAT, nnetworks->target_net, weight_in, ".ff_target_20250508_23h02m40s_29000.txt");
+*/
+struct status_qlearning *qlstatus = create_status_qlearning ();
+  struct delay_params *dly = create_delay_params (
+    500/*size_t delay_between_episodes*/,
+    50/*size_t delay_between_games*/
+  );
+  
+  struct qlearning_params *qlparams = create_qlearning_params (
+    0.95/*float gamma*/,
+    learning_rate,
+    0 /* (not used!)float discount_factor*/,
+    1.0/*0.99*//*0.0001*//*0.99*/  /*float exploration_factor*/,
+    20/*long int nb_training_before_update_weight_in_target*/,
+    10000/*size_t number_episodes*/
+  );
+/*   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->main_net, f_act, f );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, d_f_act , df );
+   UPDATE_ATTRIBUTE_NEURONE_IN_ALL_LAYERS(TYPE_FLOAT, nnetworks->target_net, f_act , f );
+*/
+  qlparams->caller_func_name=malloc(strlen(__func__)+1);
+  strcpy(qlparams->caller_func_name, __func__);
+
+
+  struct print_params *pprint = create_print_params(
+    12/*float scale_x*/,12 /*float scale_y*/,  
+    dly/*struct delay_params * dly_p*/
+  );
+
+  struct RL_agent *rlAgent = create_RL_agent (
+    nnetworks /*struct networks_qlearning * networks*/,
+    car /*struct vehicle *  car*/,
+    qlstatus /*struct status_qlearning * status*/,
+    pprint /*struct print_params * pprint*/,
+    qlparams/*struct qlearning_params *qlearnParams*/
+  );
+
+
+  //learn_to_drive(rlAgent);
+ 	//learn_to_drive(rlAgent);
+  
+	struct arg_bash *bash_arg= create_arg_bash();
+
+  struct arg_run_qlearn_bprint *argQL_BP =   create_arg_run_qlearn_bprint(bash_arg, rlAgent);
+
+  struct arg_var_ * var = create_arg_var_(y_nnn_manager_handle_input, argQL_BP);
+  struct y_socket_t *argS = y_socket_create("1600", 2, 3, var);
+ 
+
+  pthread_t pollTh;
+  pthread_create(&pollTh, NULL, y_socket_poll_fds, (void*)argS);
+
+
+
+
+
+
+
+  pthread_join(pollTh, NULL);
+	//pthread_join(thread_learn, NULL);
+  
+  y_socket_free(argS);
+  free_arg_var_(var);
+  free_arg_run_qlearn_bprint(argQL_BP);
+  //free_RL_agent(rlAgent);
+
+
+
+
+}
+
+#if 1
+HIDE_TEST(_first_learn_vehicle_50__11){
   size_t nb_block = 7;
   size_t dim= 2;
   struct blocks * path = create_blocks(nb_block, dim);