modify COMPARE_N in tool, modify attribute of vehicle by using tensor

deepQlearn_0/src/deepQlearning/learn_to_drive.c

@@ -47,14 +47,16 @@ struct networks_qlearning * create_nework_qlearning(
 
 }
 
-struct reward_lists * create_reward_lists (){
+struct status_qlearning * create_status_qlearning (){
-  struct reward_lists * rwrd_l = malloc(sizeof(struct reward_lists));
+  struct status_qlearning * status_ql = malloc(sizeof(struct status_qlearning));
   
-  rwrd_l->list_main_cumul = create_var_list_TYPE_L_INT();
+  status_ql->list_main_cumul = create_var_list_TYPE_L_INT();
-  rwrd_l->list_target_cumul = create_var_list_TYPE_L_INT();
+  status_ql->list_target_cumul = create_var_list_TYPE_L_INT();
-  rwrd_l->progress_best_cumul = create_var_list_TYPE_L_INT();
+  status_ql->progress_best_cumul = create_var_list_TYPE_L_INT();
   
-  return rwrd_l;
+  status_ql->nb_training_after_updated_weight_in_target = 0;
+
+  return status_ql;
 }
 
 struct delay_params * create_delay_params (
@@ -71,7 +73,8 @@ struct delay_params * create_delay_params (
 struct qlearning_params * create_qlearning_params  (
   double learning_rate,
   double discount_factor,
-  double exploration_factor
+  double exploration_factor,
+  long int nb_training_before_update_weight_in_target
 ){
   struct qlearning_params * qparams = malloc(sizeof(struct qlearning_params));
 
@@ -79,13 +82,15 @@ struct qlearning_params * create_qlearning_params  (
   qparams->discount_factor = discount_factor ;
   qparams->exploration_factor = exploration_factor ;
   
+  qparams->nb_training_before_update_weight_in_target = nb_training_before_update_weight_in_target;
+
   return qparams;
 }
 
 struct RL_agent * create_RL_agent (
   struct networks_qlearning * networks,
   struct vehicle * car,
-  struct reward_lists * rewards,
+  struct status_qlearning * status,
   struct delay_params * delay,
   struct qlearning_params *qlearnParams
 ){
@@ -93,7 +98,7 @@ struct RL_agent * create_RL_agent (
 
   rlagent->networks = networks ;
   rlagent->car = car ;
-  rlagent->rewards = rewards ;
+  rlagent->status = status ;
   rlagent->delay = delay ;
   rlagent->qlearnParams = qlearnParams ;
 
@@ -101,18 +106,52 @@ struct RL_agent * create_RL_agent (
 }
 
 void free_networks_qlearning (struct networks_qlearning * networks){
-
+  free_neurons_TYPE_FLOAT(networks->main_net);
+  free_neurons_TYPE_FLOAT(networks->target_net);
+  free_neurons_TYPE_FLOAT(networks->best_net);
+  free_config_layers(networks->config);
+  free(networks);
 }
-void free_reward_lists(struct reward_lists *rwd_l){
+void free_status_qlearning(struct status_qlearning *status_ql){
-
+  free_all_var_list_TYPE_L_INT(status_ql->list_main_cumul);
+  free_all_var_list_TYPE_L_INT(status_ql->list_target_cumul);
+  free_all_var_list_TYPE_L_INT(status_ql->progress_best_cumul);
+  free(status_ql);
 }
 void free_delay_params (struct delay_params *dly_p){
-
+  free(dly_p);
 }
 void free_qlearning_params(struct qlearning_params *q_params){
-
+  free(q_params);
 }
 void free_RL_agent(struct RL_agent *rlAgent){
+  free(rlAgent->qlearnParams);
+  free(rlAgent->delay);
+  free_status_qlearning(rlAgent->status);
+  free_networks_qlearning(rlAgent->networks);
+  free_vehicle(rlAgent->car);
 
+  free(rlAgent);
 }
 
+void train_qlearning(struct RL_agent * rlAgent, 
+  int action /*  */, 
+  tensor_TYPE_FLOAT * new_state /*input*/, 
+  tensor_TYPE_FLOAT * state /*input*/, 
+  long reward){
+  tensor_TYPE_FLOAT * action_value = NULL;
+  tensor_TYPE_FLOAT * next_action_value = NULL;
+  neurons_TYPE_FLOAT * net_main = rlAgent->networks->main_net;
+  neurons_TYPE_FLOAT * net_target = rlAgent->networks->target_net;
+  calculate_output_by_network_neurons_TYPE_FLOAT(net_main, state, &action_value);
+  calculate_output_by_network_neurons_TYPE_FLOAT(net_target, state, &next_action_value);
+  tensor_TYPE_FLOAT * experimental_values = CREATE_TENSOR_FROM_CPY_DIM_TYPE_FLOAT(action_value->dim);
+  
+  struct game_status * car_status = rlAgent->car->status;
+  if( copy_tensor_TYPE_FLOAT(experimental_values, action_value) == 0 /* done */){
+    if(status->done){
+
+    }
+  }
+  
+}

deepQlearn_0/src/deepQlearning/learn_to_drive.h

@@ -17,15 +17,21 @@
 
 struct qlearning_params {
   double learning_rate;
+  double factor_update_learning_rate;
+  double minimum_threshold_learning_rate;
   double discount_factor;
   double exploration_factor;
+  double factor_update_exploration_factor;
+  double minimum_threshold_exploration_factor;
+  long int nb_training_before_update_weight_in_target;
 };
 
 
-struct reward_lists {
+struct status_qlearning {
   struct main_list_TYPE_L_INT * list_main_cumul;
   struct main_list_TYPE_L_INT * list_target_cumul;
   struct main_list_TYPE_L_INT * progress_best_cumul;
+  long int nb_training_after_updated_weight_in_target;
 };
 
 struct delay_params {
@@ -43,7 +49,7 @@ struct networks_qlearning {
 struct RL_agent {
   struct networks_qlearning * networks;
   struct vehicle * car;
-  struct reward_lists * rewards;
+  struct status_qlearning * status;
   struct delay_params * delay;
   struct qlearning_params *qlearnParams;
 
@@ -53,7 +59,7 @@ struct networks_qlearning * create_nework_qlearning(
   struct config_layers * config,
   bool randomize, float minR, float maxR,  int randomRange
 );
-struct reward_lists * create_reward_lists ();
+struct status_qlearning * create_status_qlearning ();
 struct delay_params * create_delay_params (
   size_t delay_between_episodes,
   size_t delay_between_games
@@ -62,19 +68,20 @@ struct delay_params * create_delay_params (
 struct qlearning_params * create_qlearning_params (
   double learning_rate,
   double discount_factor,
-  double exploration_factor
+  double exploration_factor,
+  long int nb_training_before_update_weight_in_target
 );
 
 struct RL_agent * create_RL_agent (
   struct networks_qlearning * networks,
   struct vehicle * car,
-  struct reward_lists * rewards,
+  struct status_qlearning * status,
   struct delay_params * delay,
   struct qlearning_params *qlearnParams
 );
 
 void free_networks_qlearning (struct networks_qlearning * networks);
-void free_reward_lists(struct reward_lists *rwd_l);
+void free_status_qlearning(struct status_qlearning *status_ql);
 void free_delay_params (struct delay_params *dly_p);
 void free_qlearning_params(struct qlearning_params *q_params);
 void free_RL_agent(struct RL_agent *rlAgent);
@@ -82,5 +89,10 @@ void free_RL_agent(struct RL_agent *rlAgent);
 void copy_weight_in_networks_from_main_to_target(struct networks_qlearning * networks);
 void copy_weight_in_networks_from_main_to_best(struct networks_qlearning * networks);
 
+void train_qlearning(struct RL_agent * rlAgent,
+  int action ,
+  tensor_TYPE_FLOAT * new_state /*input*/,
+  tensor_TYPE_FLOAT * state /*input*/,
+  long reward);
 
 #endif /* __LEARNING_VEHICLE__C_H____ */

deepQlearn_0/src/deepQlearning/vehicle.c

@@ -326,7 +326,7 @@ void print2D_blocks_indexOne_withPoint(struct blocks *blk, float scale_x, float
         if(in)
           printf("%d",in);
         else 
-          printf(" ");
+          printf("."); //printf(" ");
         printf("\033[0;37m"); // white
       }
       printf("\n");
@@ -478,7 +478,7 @@ void step(struct vehicle *v, int action){
   status->reward = 0;
   status->done =false;
   struct blocks * path = v->path;
-    printf(" center : %f vs %f direction: %f\n",v->sensor->value[CENTER], LIMIT_DISTANCE, v->direction);
+    //printf(" center : %f vs %f direction: %f\n",v->sensor->value[CENTER], LIMIT_DISTANCE, v->direction);
   if( v->sensor->value[CENTER]<= LIMIT_DISTANCE ){
     status->reward = REWARD_STOP;
     status->done = true;

deepQlearn_0/src/deepQlearning/vehicle.h

@@ -16,6 +16,7 @@
 
 #include "tools_t/tools_t.h"
 #include "dimension_t/dimension_t.h"
+#include "tensor_t/tensor_t.h"
 
 #define LOG_LENTH 128
 
@@ -35,10 +36,12 @@ struct game_status {
   int cur_log;
 };
 
-struct coordinate {
+//struct coordinate {
-  size_t dimension_size;
+//  size_t dimension_size;
-  float *x;
+//  float *x;
-};
+//};
+
+typedef tensor_TYPE_FLOAT coordinate;
 
 /*
   +-----------------------+ <-- upper_bound_block (coordinate (6,5) for example) 
@@ -55,32 +58,34 @@ struct coordinate {
 */
 struct blocks {
   size_t nb_blocks;
-  struct coordinate **lower_bound_block; 
+  coordinate **lower_bound_block; 
-  struct coordinate **upper_bound_block;
+  coordinate **upper_bound_block;
-  struct coordinate **bounds_all_blocks;
+  coordinate **bounds_all_blocks;
   bool all_updated;
-  size_t dimension_size;
+  dimension *dim;
   bool *marker;
   //float step: // size of subdivision of the lowest large
 };
 
 
-struct sensors {
+//struct sensors {
-  size_t nb_values;
+//  size_t nb_values;
-  float *value;
+//  float *value;
-};
+//  tensor_TYPE_FLOAT * sensor;
+//};
+typedef tensor_TYPE_FLOAT sensors;
 
 struct vehicle {
-  struct coordinate *coord;
+  coordinate *coord;
   float direction;
   float speed;
-  struct sensors *sensor;
+  sensors *sensor;
   struct blocks *path;
   struct game_status *status;
 };
 
 struct game_status * greate_game_status();
-struct coordinate * create_coordinate(size_t dim_size);
+coordinate * create_coordinate(size_t dim_size);
 struct blocks * create_blocks(size_t nb_blocks, size_t dim_size);
 
 struct sensors * create_sensors(size_t nb_values);
@@ -89,18 +94,18 @@ struct vehicle * create_vehicle(
 );
 
 void free_game_status(struct game_status *status);
-void free_coordinate(struct coordinate *coord);
+void free_coordinate(coordinate *coord);
 void free_blocks(struct blocks *blk);
 
-void free_sensors(struct sensors *snsr);
+void free_sensors(sensors *snsr);
 
 void free_vehicle(struct vehicle * vhcl);
 
 void update_bounds_limits_blocks(struct blocks * blk);
 
-int is_in_blocks(struct blocks *blk, struct coordinate *coord);
+int is_in_blocks(struct blocks *blk, coordinate *coord);
 
-void copy_coordinate(struct coordinate *coord, float *x);
+void copy_coordinate(coordinate *coord, float *x);
 
 void move_vehicle(struct vehicle *v);
 void read_sensor(struct vehicle *v);
@@ -111,9 +116,9 @@ void reset(struct vehicle *v);
 void print2D_blocks_indexOne_withPoint(struct blocks *blk, float scale_x, float scale_y, struct coordinate *coordPoint);
 void print_vehicle_n_path(struct vehicle *v, float scale_x, float scale_y);
 
-float distance2_coordinate(struct coordinate *c0, struct coordinate *c1);
+float distance2_coordinate(coordinate *c0, coordinate *c1);
 
 void print2D_blocks(struct blocks *blk, float scale_x, float scale_y, char pad);
-void print2D_blocks_withPoint(struct blocks *blk, float scale_x, float scale_y, char pad, struct coordinate *coordPoint);
+void print2D_blocks_withPoint(struct blocks *blk, float scale_x, float scale_y, char pad, coordinate *coordPoint);
 
 #endif /* __VEHICLE__C_H__  */

deepQlearn_0/test/is_good.c

@@ -165,7 +165,7 @@ TEST(first_vehicle){
   copy_coordinate(path->upper_bound_block[0], (float[]){2,7});
   copy_coordinate(path->lower_bound_block[1], (float[]){2,0});
   copy_coordinate(path->upper_bound_block[1], (float[]){4,2});
-  copy_coordinate(path->lower_bound_block[2], (float[]){4,1});
+  copy_coordinate(path->lower_bound_block[2], (float[]){4,0.5});
   copy_coordinate(path->upper_bound_block[2], (float[]){8,3});
   copy_coordinate(path->lower_bound_block[3], (float[]){8,0});
   copy_coordinate(path->upper_bound_block[3], (float[]){16,2});
@@ -204,7 +204,9 @@ TEST(first_vehicle){
 }
 
 TEST(reward_list){
-  struct reward_lists * l_reward = create_reward_lists ();
+  struct status_qlearning * l_reward = create_status_qlearning();
+
+  free_status_qlearning(l_reward);
 }
 
 int main(int argc, char **argv){

neuron_t/src/neuron_t/neuron_t.c

@@ -747,6 +747,19 @@ size_t learning_online2_neurons_##type(neurons_##type *base, data_set_##type *da
   return nbreps;\
 }\
 \
+void calculate_output_by_network_neurons_##type(neurons_##type *base, tensor_##type *input, tensor_##type **output_link){\
+  for(size_t i=0; i<(input->dim)->rank; ++i) (base->output)->x[i]=input->x[i];\
+  neurons_##type * tmp=base->next_layer;\
+  while(tmp){\
+    calc_out_neurons_##type(tmp);\
+    if(tmp->next_layer==NULL){\
+      /*print_tensor_msg_##type(tmp->output,"retult");*/\
+      *output_link = tmp->output;\
+    }\
+    tmp = tmp->next_layer;\
+  }\
+\
+}\
 void print_predict_by_network_neurons_##type(neurons_##type *base, tensor_##type *input){\
     for(size_t i=0; i<(input->dim)->rank; ++i) (base->output)->x[i]=input->x[i];\
       neurons_##type * tmp=base->next_layer;\

neuron_t/src/neuron_t/neuron_t.h

@@ -106,7 +106,7 @@ void print_data_set_msg_##type(data_set_##type *ds, char *msg);\
 \
 size_t learning_online_neurons_##type(neurons_##type *base, data_set_##type *dataset, bool (*condition)(type, size_t));\
 size_t learning_online2_neurons_##type(neurons_##type *base, data_set_##type *dataset, bool (*condition)(type, size_t));\
-\
+void calculate_output_by_network_neurons_##type(neurons_##type *base, tensor_##type *input, tensor_##type **output_link);\
 void print_predict_by_network_neurons_##type(neurons_##type *base, tensor_##type *input);\
 void print_predict_by_network_with_error_neurons_##type(neurons_##type *base, tensor_##type *input, tensor_##type *target);\
 \

neuron_t/test/is_good.c

@@ -21,6 +21,7 @@
 
 #define VALGRIND_ 1
 
+
 float L(float t, float o){
   return (o - t) * (o - t)/2;
 }
@@ -356,13 +357,30 @@ TEST(copy_weight_in_neurons){
 
   size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
   
+  setup_all_layers_functions_TYPE_FLOAT(cpyn,
+    tensorContractnProdThread_TYPE_FLOAT,
+    tensorProdThread_TYPE_FLOAT,
+    DL,
+    L,
+    f,
+    df);
+
+  setup_all_layers_params_TYPE_FLOAT(cpyn, 5, 1 ,  0.1);
+
+
   copy_weight_in_neurons_TYPE_FLOAT(cpyn, bn);
  
   char msg[256];
+  tensor_TYPE_FLOAT * linked_tens = NULL;
   for(size_t i=0; i<ds->size; ++i){
-    print_predict_by_network_with_error_neurons_TYPE_FLOAT(bn,ds->input[i],ds->target[i]);
+//    print_predict_by_network_with_error_neurons_TYPE_FLOAT(bn,ds->input[i],ds->target[i]);
-    print_predict_by_network_with_error_neurons_TYPE_FLOAT(cpyn,ds->input[i],ds->target[i]);
+ //   print_predict_by_network_with_error_neurons_TYPE_FLOAT(cpyn,ds->input[i],ds->target[i]);
-    
+    calculate_output_by_network_neurons_TYPE_FLOAT(bn,ds->input[i],&linked_tens);
+    sprintf(msg," output base %ld ",i);
+    print_tensor_msg_TYPE_FLOAT(linked_tens,msg);
+    calculate_output_by_network_neurons_TYPE_FLOAT(cpyn,ds->input[i],&linked_tens);
+    sprintf(msg," output copy %ld ",i);
+    print_tensor_msg_TYPE_FLOAT(linked_tens,msg);
   }
  
 

tensor_t/src/tensor_t/tensor_t.c

@@ -130,11 +130,16 @@ tensor_##type* CLONE_TENSOR_##type(tensor_##type *tens){\
   return NULL;\
 }\
 \
-void copy_tensor_##type(tensor_##type * dst, tensor_##type * src){\
+int copy_tensor_##type(tensor_##type * dst, tensor_##type * src){\
- if(dst!=NULL && src!=NULL && dst->dim->rank == src->dim->rank){ \
+  if(dst!=NULL && src!=NULL){ \
-   for(size_t i=0; i<(dst->dim)->rank;++i)\
+    int diff = dst->dim->rank - src->dim->rank;\
+    if(diff == 0) \
+      for(size_t i=0; i<(src->dim)->rank;++i)\
         dst->x[i]=src->x[i];\
+    return diff;\
+    \
   }\
+  return -1;\
 }\
 \
   void free_tensor_##type(tensor_##type *  tens){\

tensor_t/test/is_good.c

@@ -1705,6 +1705,58 @@ TEST(copy_tensor){
 }
 
 
+TEST(tensorContractnProd_TYPE_DOUBLE_2_2 ){
+  dimension *d0=create_dim(3);
+  dimension *d1=create_dim(3);
+#if VALGRIND_
+  d0->perm[0]=1;
+  d0->perm[1]=2; //3;
+  d0->perm[2]=3; //3;
+
+  d1->perm[0]=2;
+  d1->perm[1]=3; //3;
+  d1->perm[2]=1; //3;
+
+#else
+
+  d0->perm[0]=1;
+  d0->perm[1]=22; //3;
+  d0->perm[2]=52; //3;
+  d1->perm[0]=52;
+  d1->perm[1]=22; //3;
+  d1->perm[2]=1; //3;
+
+#endif
+
+  updateRankDim(d0);
+  updateRankDim(d1);
+
+
+  tensor_TYPE_DOUBLE *M0 = CREATE_TENSOR_TYPE_DOUBLE(d0);
+  tensor_TYPE_DOUBLE *M1 = CREATE_TENSOR_TYPE_DOUBLE(d1);
+
+  for(size_t i=0; i<M0->dim->rank;++i) M0->x[i]=2 ;
+  for(size_t i=0; i<M1->dim->rank;++i) M1->x[i]=3;
+
+  print_tensor_double(M0,"M0");
+  print_tensor_double(M1,"M1");
+
+  tensor_TYPE_DOUBLE *M=NULL;
+
+  tensorContractnProd_TYPE_DOUBLE(&M, M0,M1,2);
+
+  print_tensor_double(M,"M");
+ 
+  // for(size_t i=0;i<M->dim->rank;++i)
+  //    EXPECT_EQ_TYPE_DOUBLE(M->x[i],MnO->x[i]);
+    
+
+  free_tensor_TYPE_DOUBLE(M);
+  free_tensor_TYPE_DOUBLE(M0);
+  free_tensor_TYPE_DOUBLE(M1);
+
+}
+
 
 
 

ytools_t/src/tools_t/tools_t.c

@@ -115,14 +115,29 @@ long int PRECISION_TYPE_L_DOUBLE  = 100000000000000;
 
 #define GENERATE_FUNCTION_NUMERIC(type)\
   int COMPARE_N_##type(const void *a, const  void *b){              \
-    type diff = (*(type*)a - *(type*)b) * PRECISION_##type;           \
+    type diff = 0;\
+     if((*(type*)a > *(type*)b)){           \
+      diff =(*(type*)a - *(type*)b) * PRECISION_##type;           \
+    /*char *str_diff = type##_TO_STR(diff), *str_a = type##_TO_STR(*(type*)a), *str_b = type##_TO_STR(*(type*)b);\
+    PRINT_DEBUG_(" diff = %s a=%s b=%s PRECISION : %ld\n",str_diff, str_a, str_b, PRECISION_##type);\
+    free(str_diff); free(str_a); free(str_b);\
+     */ \
+      if(diff >= 1) return 1;\
+      return 0;\
+     }else{\
+      diff =(*(type*)b - *(type*)a) * PRECISION_##type;           \
     /*char *str_diff = type##_TO_STR(diff), *str_a = type##_TO_STR(*(type*)a), *str_b = type##_TO_STR(*(type*)b);\
     PRINT_DEBUG_(" diff = %s a=%s b=%s PRECISION : %ld\n",str_diff, str_a, str_b, PRECISION_##type);\
     free(str_diff); free(str_a); free(str_b);\
     */\
-    if (diff <= -1) return -1;                                                    \
+      if(diff >= 1) return -1;\
+      return 0;\
+     }\
+    \
+    /*if (diff <= -1) return -1;                                                    \
     if (diff >= 1) return 1;                                                    \
     return 0;                                                    \
+  */\
   }                                                                 \
                                                                     \
   void COPY_ARRAY_##type(type *dst, const type *src, size_t size){  \