add macro MIN MAX in toom_t, and add copy tensor and weight_in neurons

dimension_t/src/dimension_t/dimension_t.c

@@ -433,6 +433,15 @@ dimension * create_dim_from_list_perm( list_perm_in_dim *l_p){
   return NULL;
 }
 
+dimension * create_binary_dim(size_t dimension_size){
+  dimension * dim = create_dim(dimension_size);
+  for(size_t i=0; i<dimension_size; ++i)
+    dim->perm[i]=2;
+  updateRankDim(dim);
+  return dim;
+}
+
+
 void free_list_perm_in_dim(list_perm_in_dim *l_p){
   list_perm_in_dim *tmp=l_p, *ttmp;
   while(tmp){

dimension_t/src/dimension_t/dimension_t.h

@@ -70,6 +70,8 @@ typedef struct list_perm_in_dim list_perm_in_dim;
 void append_in_list_perm(list_perm_in_dim **list_p, size_t perm);
 dimension * create_dim_from_list_perm( list_perm_in_dim *l_p);
 
+dimension * create_binary_dim(size_t dimension_size);
+
 void free_list_perm_in_dim(list_perm_in_dim *l_p);
 
 #endif /* __DIMENSION_T__H__ */

neuron_t/src/neuron_t/neuron_t.c

@@ -1,6 +1,6 @@
 #include "neuron_t/neuron_t.h"
 
-#define MAX(X, Y) (((X) > (Y)) ? (X) : (Y))
+//#define MAX(X, Y) (((X) > (Y)) ? (X) : (Y))
 
 #define ABSMAX(X, Y) ((((X) > (Y)) || ((-X) > (Y))  ) ? (X) : (Y))
 
@@ -828,6 +828,16 @@ void update_cloneuronesets_weight_in_base_##type(cloneuronset_##type * clnrnst){
   free(tmp_c);\
 }\
 \
+void copy_weight_in_neurons_##type(neurons_##type *dst_nrns, neurons_##type *src_nrns){\
+  neurons_##type *tmp_src = src_nrns;\
+  neurons_##type *tmp_dst = dst_nrns;\
+  while(tmp_src){\
+    copy_tensor_##type(tmp_dst->weight_in,tmp_src->weight_in);\
+    tmp_src = tmp_src->next_layer;\
+    tmp_dst = tmp_dst->next_layer;\
+  }\
+}\
+\
 type clon_error_batch_##type(cloneuronset_##type * clnrnst){\
   \
   type err=0;\

neuron_t/src/neuron_t/neuron_t.h

@@ -65,6 +65,7 @@ void setup_learning_rate_params_neurons_##type(neurons_##type *base,type initial
 void calc_out_neurons_##type(neurons_##type *nr);\
 void calc_delta_neurons_##type(neurons_##type *nr);\
 void update_weight_neurons_##type(neurons_##type *nr);\
+void copy_weight_in_neurons_##type(neurons_##type *dst_nrns, neurons_##type *src_nrns);\
 /*void setup_networks_##type(neurons_##type **base_nr, size_t **array_dim_in_layers, size_t *tab_sz_layers, size_t nb_layers);*/\
 void init_copy_in_out_networks_from_tensors_##type(neurons_##type *nr, tensor_##type *input, tensor_##type *target);\
 void init_in_out_networks_from_tensors_##type(neurons_##type *nr, tensor_##type *input, tensor_##type *target, neurons_##type *base);\

neuron_t/test/is_good.c

@@ -330,6 +330,52 @@ TEST(learning_cloneuroneset_LEARN_RATE){
 
 }
 
+TEST(copy_weight_in_neurons){
+   bool rec_randomizeInitWeight = randomizeInitWeight;
+   randomizeInitWeight =false;
+  
+  data_set_TYPE_FLOAT *ds= fill_data_set_from_file_TYPE_FLOAT("xor.txt",1);
+//  print_data_set_msg_TYPE_FLOAT(ds,"data");
+  config_layers *pconf = create_config_layers_from_OneD(3,(size_t[]){2,4,1}); /* 2 input , 1 target; 1 hidden layer with 5 neurons */
+  neurons_TYPE_FLOAT *bn=NULL, *tmp ;
+  neurons_TYPE_FLOAT *cpyn=NULL;
+  //setup_networks_alloutputs_config_GLOBAL_rdm01_TYPE_FLOAT(setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf);bn,pconf);
+     setup_networks_alloutputs_config_TYPE_FLOAT(&bn,pconf,false,0,1,5000);
+     setup_networks_alloutputs_config_TYPE_FLOAT(&cpyn, pconf,false,0,1,5000);
+
+  setup_all_layers_functions_TYPE_FLOAT(bn,
+    tensorContractnProdThread_TYPE_FLOAT,
+    tensorProdThread_TYPE_FLOAT,
+    DL,
+    L,
+    f,
+    df);
+
+  setup_all_layers_params_TYPE_FLOAT(bn, 5, 1 ,  0.1);
+
+
+  size_t reps = learning_online2_neurons_TYPE_FLOAT(bn,ds,cond);
+  
+  copy_weight_in_neurons_TYPE_FLOAT(cpyn, bn);
+ 
+  char msg[256];
+  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(cpyn,ds->input[i],ds->target[i]);
+    
+  }
+ 
+
+
+  free_data_set_TYPE_FLOAT(ds);
+  free_neurons_TYPE_FLOAT(bn); 
+  free_neurons_TYPE_FLOAT(cpyn); 
+
+  LOG("reps = %ld\n",reps);
+  randomizeInitWeight = rec_randomizeInitWeight;
+}
+
+
 
 
 

tensor_t/src/tensor_t/tensor_t.c

@@ -129,6 +129,13 @@ tensor_##type* CLONE_TENSOR_##type(tensor_##type *tens){\
   }\
   return NULL;\
 }\
+\
+void copy_tensor_##type(tensor_##type * dst, tensor_##type * src){\
+ if(dst!=NULL && src!=NULL && dst->dim->rank == src->dim->rank){ \
+   for(size_t i=0; i<(dst->dim)->rank;++i)\
+      dst->x[i]=src->x[i];\
+ }\
+}\
 \
   void free_tensor_##type(tensor_##type *  tens){\
     if(tens){\

tensor_t/src/tensor_t/tensor_t.h

@@ -20,6 +20,7 @@ tensor_##type* CREATE_TENSOR_FROM_CPY_DIM_##type(dimension *dim);\
 void _RECREATE_TENSOR_IF_NOT_THE_SAME_DIM_OR_NULL_##type(tensor_##type **M, dimension *dd);\
 tensor_##type* CLONE_TENSOR_##type(tensor_##type *tens);\
 void free_tensor_##type(tensor_##type *  tens); \
+void copy_tensor_##type(tensor_##type * dst, tensor_##type * src);\
 tensor_##type * sub_minus_tensor_head_##type(tensor_##type *rootens, size_t minuSubdim, size_t rankInDim); \
 tensor_##type * sub_minus_tensor_tail_##type(tensor_##type *rootens, size_t minuSubdim, size_t rankInDim); \
 tensor_##type * sub_tensor_head_##type(tensor_##type *rootens, size_t subdim, size_t rankInDim); \

tensor_t/test/is_good.c

@@ -1676,6 +1676,35 @@ TEST(rec_in_file_tensor){
   free_tensor_TYPE_FLOAT(M0);
 }
 
+TEST(copy_tensor){
+  dimension *d0=create_dim(3);
+
+  d0->perm[0]=2;
+  d0->perm[1]=3;
+  d0->perm[2]=4;
+
+
+  updateRankDim(d0);
+
+
+  tensor_TYPE_FLOAT *M0 = CREATE_TENSOR_TYPE_FLOAT(d0);
+  tensor_TYPE_FLOAT *M1 = CREATE_TENSOR_FROM_CPY_DIM_TYPE_FLOAT(d0);
+
+  LOG("M0->dim->rank = %ld\n",M0->dim->rank);
+
+  init_random_x_TYPE_FLOAT(M0,2.7,5.4,50001);
+  init_random_x_TYPE_FLOAT(M1,2.7,5.4,50001);
+
+  print_tensor_float(M0, "init M0 random");
+
+  copy_tensor_TYPE_FLOAT(M1, M0);
+  print_tensor_float(M1, "M1 copy of M0");
+  
+  free_tensor_TYPE_FLOAT(M0);
+  free_tensor_TYPE_FLOAT(M1);
+}
+
+
 
 
 

ytest_t/include_ytest/include/tools_t/tools_t.h

@@ -95,6 +95,8 @@ void gotoxy(int x, int y);
 #define FOREACH(array, size, function)\
   for(size_t _ind = 0; _ind < size; ++_ind) function(array[_ind]);
 
+#define MIN(X, Y) (((Y) < (X)) ? (Y) : (X))
+#define MAX(X, Y) (((Y) > (X)) ? (Y) : (X))
 
 #define GENERATE_ALL(type)\
   int COMPARE_N_##type(const void *,const    void*);\

ytools_t/include/tools_t/tools_t.h

@@ -95,6 +95,8 @@ void gotoxy(int x, int y);
 #define FOREACH(array, size, function)\
   for(size_t _ind = 0; _ind < size; ++_ind) function(array[_ind]);
 
+#define MIN(X, Y) (((Y) < (X)) ? (Y) : (X))
+#define MAX(X, Y) (((Y) > (X)) ? (Y) : (X))
 
 #define GENERATE_ALL(type)\
   int COMPARE_N_##type(const void *,const    void*);\