From 8bdd15f9a225190648a0e720c6337ee372a10025 Mon Sep 17 00:00:00 2001
From: fanasina <rafanasin@gmail.com>
Date: Thu, 22 Feb 2024 10:05:50 +0100
Subject: [PATCH] add neuron repository

---
 neuron_t/src/neuron_t/neuron_t.c | 222 +++++++++++++++++++++++++++++++
 neuron_t/src/neuron_t/neuron_t.h |  50 +++++++
 2 files changed, 272 insertions(+)
 create mode 100644 neuron_t/src/neuron_t/neuron_t.c
 create mode 100644 neuron_t/src/neuron_t/neuron_t.h

diff --git a/neuron_t/src/neuron_t/neuron_t.c b/neuron_t/src/neuron_t/neuron_t.c
new file mode 100644
index 0000000..dff8a1f
--- /dev/null
+++ b/neuron_t/src/neuron_t/neuron_t.c
@@ -0,0 +1,222 @@
+#include "neuron_t/neuron_t.h"
+
+
+#define PR_LINE printf("===================================================== \n");
+
+
+#define GEN_NEURONS_F_(type)\
+  \
+void calc_net_neurons_##type(neurons_##type *nr){\
+  size_t contractNB= ((nr->input)->dim)->size - ((nr->net)->dim)->size ;\
+  nr->TensorContraction_##type(&(nr->net), nr->weight_in,nr->input, contractNB, nr->nb_thread  );\
+}\
+\
+void calc_out_neurons_##type(neurons_##type *nr, type (*f)(type x) ){\
+  calc_net_neurons_##type(nr);\
+  for(size_t i = 0; i<(nr->net)->dim->rank; ++i){\
+    (nr->output)->x[i]=f((nr->net)->x[i]);\
+  }\
+}\
+void calc_delta_neurons_##type(neurons_##type *nr, type (*df)(type x)){\
+  if(nr->next_layer == NULL){\
+    for(size_t i = 0; i<(nr->net)->dim->rank; ++i){\
+      (nr->delta_out)->x[i]=df((nr->net)->x[i])*(nr->dL)((nr->target)->x[i],(nr->output)->x[i]);\
+    }\
+  }else{\
+    tensor_##type *temp_w_d;\
+    size_t cntrctnb=(((nr->next_layer)->weight_in)->dim)->size-(((nr->next_layer)->delta_out)->dim)->size ;\
+    nr->TensorContraction_##type(&temp_w_d, ((nr->next_layer)->weight_in), (nr->next_layer)->delta_out,cntrctnb,nr->nb_thread);\
+    \
+    for(size_t i = 0; i<(nr->net)->dim->rank; ++i){\
+      (nr->delta_out)->x[i]=df((nr->net)->x[i]) * temp_w_d->x[i] ;\
+    }\
+    free_tensor_##type(temp_w_d);\
+  }\
+}\
+void update_weight_neurons_##type(neurons_##type *nr){\
+  tensor_##type *tmp_e_w;\
+  nr->TensorProduct_##type(&(tmp_e_w), nr->delta_out, nr->input, nr->nb_thread);\
+  \
+  for(size_t i = 0; i<(nr->weight_in)->dim->rank; ++i){\
+    (nr->weight_in)->x[i]= (nr->weight_in)->x[i] - nr->learning_rate *tmp_e_w->x[i] ;\
+  }\
+  free_tensor_##type(tmp_e_w);\
+}\
+void init_in_out_all_networks_##type(neurons_##type *nr, tensor_##type *in, tensor_##type *out){\
+  if(((nr->input)->dim)->rank == (in->dim)->rank)\
+    for(size_t i=0; i<((in)->dim)->rank; ++i) {\
+      (nr->input)->x[i] = in->x[i]; \
+    }\
+  neurons_##type *tmp=nr;\
+  while(tmp->next_layer) tmp=tmp->next_layer;\
+  if(((tmp->target)->dim)->rank == (out->dim)->rank)\
+    for(size_t i=0; i<((out)->dim)->rank; ++i) {\
+      (tmp->target)->x[i] = out->x[i]; \
+    }\
+  \
+}\
+void link_layers_##type(neurons_##type *nPrev, neurons_##type *nNext ){\
+  nPrev->next_layer = nNext;\
+  nNext->prev_layer = nPrev;\
+  size_t pivot, partPiv;\
+  if(endian){\
+    pivot=0; partPiv = 1;\
+    split_tensor_##type(nNext->input, &(nPrev->output), &(nNext->bias), pivot, partPiv);\
+  }else{\
+    pivot = ((nNext->input)->dim)->size - 1;\
+    partPiv = ((nNext->input)->dim)->perm[pivot] - 1 ;\
+    \
+    split_tensor_##type(nNext->input, &(nNext->bias), &(nPrev->output), pivot, partPiv);\
+  }\
+  for(size_t i=0;i<((nNext->bias)->dim)->rank;++i) (nNext->bias)->x[i]=1;\
+}\
+\
+void setup_networks_all_dim_inputs_##type(neurons_##type **base_nr, dimension **dim_in_layers,  size_t nb_layers){\
+  neurons_##type *tmp_l, *ttmp_l=NULL;\
+  for(size_t l=0; l<nb_layers; ++l){\
+    if(l==0){\
+      *base_nr=malloc(sizeof(neurons_##type)); \
+      tmp_l = *base_nr;\
+    }else{\
+      ttmp_l->next_layer = malloc(sizeof(neurons_##type));\
+      tmp_l = ttmp_l->next_layer;\
+    }\
+    /*dimension *dim=init_copy_dim(tab_in_layers[l],sz_layers[l]);\
+    tensor_##type *input=CREATE_TENSOR_##type(dim);*/\
+    tensor_##type *input=CREATE_TENSOR_##type(dim_in_layers[l]);\
+    tmp_l->input = input;\
+    \
+    tmp_l->net = NULL; /* output tensor_prodContract */\
+    tmp_l->output = NULL; \
+    tmp_l->target = NULL; \
+    tmp_l->weight_in = NULL; /* weight link in */\
+    tmp_l->bias = NULL;  /* bias */\
+    tmp_l->weight_out = NULL; /* weight link out */\
+    tmp_l->prev_layer = ttmp_l;\
+    tmp_l->next_layer = NULL;\
+    \
+    if(ttmp_l != NULL){\
+      link_layers_##type(ttmp_l,tmp_l);\
+      dimension *dim_out = (ttmp_l->output)->dim;\
+      ttmp_l->net = CREATE_TENSOR_FROM_CPY_DIM_##type(dim_out);\
+      if(l == nb_layers - 1) ttmp_l->target = CREATE_TENSOR_FROM_CPY_DIM_##type(dim_out);\
+      ttmp_l->delta_out = CREATE_TENSOR_FROM_CPY_DIM_##type(dim_out); /* NULL; */ /* delta */\
+      dimension *d_w_in;  \
+      add_dimension(&d_w_in, (ttmp_l->input)->dim, ((ttmp_l->output)->dim)); \
+      ttmp_l->weight_in = CREATE_TENSOR_##type(d_w_in);\
+      init_random_x_##type(ttmp_l->weight_in,0,1,5000);\
+    }\
+\
+    ttmp_l = tmp_l;\
+\
+  }\
+}\
+\
+\
+void setup_networks_allinputs_##type(neurons_##type **base_nr, size_t **tab_in_layers, size_t *sz_layers, size_t nb_layers){\
+  neurons_##type *tmp_l, *ttmp_l=NULL;\
+  for(size_t l=0; l<nb_layers-1; ++l){\
+    if(l==0){\
+      *base_nr=malloc(sizeof(neurons_##type)); \
+      tmp_l = *base_nr;\
+    }else{\
+      ttmp_l->next_layer = malloc(sizeof(neurons_##type));\
+      tmp_l = ttmp_l->next_layer;\
+    }\
+    dimension *dim=init_copy_dim(tab_in_layers[l],sz_layers[l]);\
+    tensor_##type *input=CREATE_TENSOR_##type(dim);\
+    tmp_l->input = input;\
+    \
+    tmp_l->net = NULL; /* output tensor_prodContract */\
+    tmp_l->output = NULL; \
+    tmp_l->target = NULL; \
+    tmp_l->weight_in = NULL; /* weight link in */\
+    tmp_l->bias = NULL;  /* bias */\
+    tmp_l->weight_out = NULL; /* weight link out */\
+    tmp_l->prev_layer = ttmp_l;\
+    tmp_l->next_layer = NULL;\
+    \
+    if(ttmp_l != NULL){\
+      link_layers_##type(ttmp_l,tmp_l);\
+      dimension *dim_out = (ttmp_l->output)->dim;\
+      ttmp_l->net = CREATE_TENSOR_FROM_CPY_DIM_##type(dim_out);\
+      ttmp_l->delta_out = CREATE_TENSOR_FROM_CPY_DIM_##type(dim_out); /* NULL; */ /* delta */\
+      dimension *d_w_in;  \
+      add_dimension(&d_w_in, (ttmp_l->input)->dim, ((ttmp_l->output)->dim)); \
+      ttmp_l->weight_in = CREATE_TENSOR_##type(d_w_in);\
+      init_random_x_##type(ttmp_l->weight_in,0,1,5000);\
+    }\
+\
+    ttmp_l = tmp_l;\
+    \
+    if(l == nb_layers - 2) {\
+      dimension *dim=init_copy_dim(tab_in_layers[l+1],sz_layers[l+1]);\
+      tensor_##type *input=CREATE_TENSOR_##type(dim);\
+      tmp_l->output= CREATE_TENSOR_FROM_CPY_DIM_##type(dim);\
+      tmp_l->net = CREATE_TENSOR_FROM_CPY_DIM_##type(dim);\
+      tmp_l->target = CREATE_TENSOR_FROM_CPY_DIM_##type(dim);\
+      dimension *d_w_in;  \
+      add_dimension(&d_w_in, (tmp_l->input)->dim, ((tmp_l->output)->dim)); \
+      tmp_l->weight_in = CREATE_TENSOR_##type(d_w_in);\
+      init_random_x_##type(tmp_l->weight_in,0,1,5000);\
+    }\
+ \
+\
+  }\
+}\
+\
+void setup_networks_OneD_##type(neurons_##type **base_nr, size_t *tab_in_layers, size_t nb_layers){\
+  size_t *sz_layers=malloc(nb_layers*sizeof(size_t));\
+  for(size_t i=0; i<nb_layers;++i) sz_layers[i]=1;\
+  size_t **ttab_in_layers=malloc(nb_layers*sizeof(size_t));\
+  for(size_t i=0; i<nb_layers;++i) {\
+    ttab_in_layers[i]=malloc(sizeof(size_t));\
+    ttab_in_layers[i][0]=tab_in_layers[i];\
+  }\
+  setup_networks_allinputs_##type(base_nr, ttab_in_layers, sz_layers, nb_layers);\
+  \
+  for(size_t i=0; i<nb_layers;++i) {\
+    free(ttab_in_layers[i]);\
+  }\
+  free(ttab_in_layers);\
+  free(sz_layers);\
+}\
+void init_in_out_all_networks_OneD_##type(neurons_##type *nr, type *in, size_t sz_in, type *out, size_t sz_out){\
+  if(((nr->input)->dim)->rank == sz_in){\
+    for(size_t i=0;i<sz_in;++i) (nr->input)->x[i]=in[i];\
+  }\
+  neurons_##type *tmp=nr;\
+  while(tmp->next_layer) tmp=tmp->next_layer;\
+  \
+  if(((tmp->target)->dim)->rank == sz_out){\
+    for(size_t i=0; i< sz_out; ++i) {\
+      (tmp->target)->x[i] = out[i]; \
+    }\
+  }\
+}\
+void print_neurons_msg_##type(neurons_##type *nr, char *msg){\
+  size_t l=0;\
+  while(nr){\
+    printf("%s, layer %ld\n",msg,l++); \
+    PR_LINE;\
+    if(nr->input) print_tensor_msg_##type(nr->input," input "); else printf(" input NULL\n");\
+    PR_LINE;\
+    if(nr->output) print_tensor_msg_##type(nr->input," input "); else printf(" output NULL\n");\
+    PR_LINE;\
+    if(nr->net) print_tensor_msg_##type(nr->net," net "); else printf(" net NULL\n");\
+    PR_LINE;\
+    if(nr->weight_in) print_tensor_msg_##type(nr->weight_in," weight_in "); else printf(" weight_in NULL\n");\
+    PR_LINE;\
+    if(nr->weight_out) print_tensor_msg_##type(nr->weight_out," weight_out "); else printf(" weight_out NULL\n");\
+    PR_LINE;\
+    if(nr->delta_out) print_tensor_msg_##type(nr->delta_out," delta_out "); else printf(" delta_out NULL\n");\
+    PR_LINE;\
+    if(nr->target) print_tensor_msg_##type(nr->target," target "); else printf(" target NULL\n");\
+    PR_LINE;\
+\
+    nr=nr->next_layer;\
+  }\
+}\
+
+GEN_NEURONS_F_(TYPE_FLOAT)
+GEN_NEURONS_F_(TYPE_DOUBLE)
diff --git a/neuron_t/src/neuron_t/neuron_t.h b/neuron_t/src/neuron_t/neuron_t.h
new file mode 100644
index 0000000..3d7613e
--- /dev/null
+++ b/neuron_t/src/neuron_t/neuron_t.h
@@ -0,0 +1,50 @@
+#ifndef __NEURON_T_C__H
+#define __NEURON_T_C__H
+
+#include <stdlib.h>
+
+//#include "tools_t/tools_t.h"
+#include "tensor_t/tensor_t.h"
+
+#define GEN_NEURON_(type)\
+\
+struct neurons_##type {/* layer */\
+  size_t id_layer;\
+  size_t nb_thread;\
+  type learning_rate;\
+  tensor_##type *input; \
+  tensor_##type *net; /* output tensor_prodContract */\
+  tensor_##type *output; \
+  tensor_##type *target; \
+  tensor_##type *weight_in; /* weight link in */\
+  tensor_##type *bias; /* bias */\
+  tensor_##type *weight_out; /* weight link out */\
+  tensor_##type *delta_out; /* delta */\
+  struct neurons_##type *prev_layer;\
+  struct neurons_##type *next_layer;\
+  void (*TensorContraction_##type)(tensor_##type **MM, tensor_##type *M0, tensor_##type *M1, size_t contractionNumber, size_t nbthread);/* nbthread is ignored if not required ! */\
+  void (*TensorProduct_##type)(tensor_##type **MM, tensor_##type *M0, tensor_##type *M1, size_t nbthread);/* nbthread is ignored if not required ! */\
+  type (*dL)(type t, type o);\
+};\
+typedef struct neurons_##type neurons_##type;\
+\
+struct func_act_##type {\
+  type (*func_act)(type x); /* function activation */\
+  type (*deriv_func_act)(type x); /* derivate func act */\
+};\
+/*void calc_net_neurons_##type(neurons_##type *nr);*/\
+void calc_out_neurons_##type(neurons_##type *nr, type (*f)(type x) );\
+void calc_delta_neurons_##type(neurons_##type *nr, type (*df)(type x));\
+void update_weight_neurons_##type(neurons_##type *nr);\
+void setup_networks_##type(neurons_##type **base_nr, size_t **tab_in_layers, size_t *tab_sz_layers, size_t nb_layers);\
+void init_in_out_all_networks_##type(neurons_##type *nr, tensor_##type *in, tensor_##type *out);\
+\
+void setup_networks_OneD_##type(neurons_##type **base_nr, size_t *tab_in_layers, size_t nb_layers);\
+void init_in_out_all_networks_OneD_##type(neurons_##type *nr, type *in, size_t sz_in, type *out, size_t sz_out);\
+void print_neurons_msg_##type(neurons_##type *nr, char * msg);\
+
+GEN_NEURON_(TYPE_FLOAT)
+GEN_NEURON_(TYPE_DOUBLE)
+
+
+#endif /*__NEURON_T_C__H*/