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https://github.com/augustin64/projet-tipe
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Changes in matrix module
This commit is contained in:
Julien Chemillier
parent
cc283be86e
commit
c44cd6e8f8
35
matrix_2d.c
35
matrix_2d.c
@ -1,12 +1,3 @@
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/*
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Version du module Matrice avec des matrices 2d
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Une version 3d doit s'inspirer de celle-ci
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*/
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#include <stdint.h>
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#include <stdlib.h>
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#include <stdio.h>
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@ -21,6 +12,18 @@ typedef struct Matrix {
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} Matrix;
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// Mis ici jusqu'à le rassemblement des fichiers
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typedef struct Neuron{
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float* weights; // Liste de tous les poids des arêtes sortants du neurone
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float bias; // Caractérise le bias du neurone
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float z; // Sauvegarde des calculs faits sur le neurone (programmation dynamique)
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float *back_weights; // Changement des poids sortants lors de la backpropagation
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float *last_back_weights; // Dernier changement de d_poid_sortants
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float back_bias; // Changement du bias lors de la backpropagation
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float last_back_bias; // Dernier changement de back_bias
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} Neuron;
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@ -480,14 +483,16 @@ void average_pooling_step_forward(Matrix** layer_input, Matrix*** layer_kernel,
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}
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void reshape_step_forward(Matrix** layer_input, Matrix*** layer_kernel, Matrix** layer_bias, Matrix** layer_output, int len_layer, int depth_kernel, int stride) {
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void reshape_step_forward(Matrix** layer_input, Neuron** output, int len_layer) {
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/* Effectue une étape de la forward-propagation
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en redimensionnant la matrice */
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for (int i=0; i < depth_kernel; i++) {
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copy_matrix(layer_bias[i], layer_output[i]);
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for (int j=0; j < len_layer; j++) {
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average_pooling_matrix(layer_input[j], layer_kernel[i][j], stride, layer_output[j]);
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int cpt = 0;
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for (int i=0; i < len_layer; i++) {
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for (int j=0; j < layer_input[i]->rows; j++) {
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for (int k=0; k < layer_input[i]->columns; k++) {
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output[cpt]->z = layer_input[i]->value[j][k];
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cpt++;
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}
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}
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}
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}
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467
matrix_3d.c
Normal file
467
matrix_3d.c
Normal file
@ -0,0 +1,467 @@
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#include <stdint.h>
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#include <stdlib.h>
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#include <stdio.h>
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#include <float.h>
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#include <math.h>
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typedef struct Matrix {
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int depths; // Nombre de couches de la matrice
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int rows; // Nombre de lignes de la matrice
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int columns; // Nombre de colonnes de la matrice
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float*** value; // Tableau 2d comportant les valeurs de matrice
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} Matrix;
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float exp_float(float a);
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float max_float(float a, float b);
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float min_float(float a, float b);
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Matrix* create_matrix(int nb_layers, int nb_rows, int nb_columns);
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void uniformise_matrix(Matrix* m, float x);
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float max_in_matrix(Matrix* m);
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void free_matrix(Matrix* m);
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float number_from_matrix(Matrix* m);
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void product_of_a_scalar_matrix(Matrix* m, float scalar);
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void sum_of_a_scalar_matrix(Matrix* m, float scalar);
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Matrix* copy_matrix(Matrix* m);
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Matrix* apply_function_new_matrix(Matrix* m, float (*f)(float));
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void apply_function_matrix(Matrix* m, float (*f)(float));
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Matrix* add_matrix(Matrix* m1, Matrix* m2);
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Matrix* product_matrix(Matrix* m1, Matrix* m2);
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void max_pooling_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out);
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void min_pooling_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out);
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void average_pooling_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out);
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void valid_cross_correlation_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out);
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void full_cross_correlation_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out);
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void softmax_matrix(Matrix* m);
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float quadratic_cost_matrix(Matrix* m, int i_number, int j_number, int k_number);
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void rotation_180_matrix(Matrix* m);
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float exp_float(float a) {
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/* Renvoie l'exponentiel d'un flotant '*/
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return (float)exp(a);
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}
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float max_float(float a, float b) {
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/* Renvoie le max entre les deux flotants */
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return a>b?a:b;
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}
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float min_float(float a, float b) {
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/* Renvoie le min entre les deux flotants */
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return a<b?a:b;
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}
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Matrix* create_matrix(int nb_layers, int nb_rows, int nb_columns) {
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/* Créé une matrice en lui allouant de la mémoire */
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Matrix* m = malloc(sizeof(Matrix));
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m->rows = nb_rows;
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m->columns = nb_columns;
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m->depths = nb_layers;
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m->value = malloc(sizeof(float**)*m->depths);
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for (int i=0; i < m->depths; i++) {
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m->value[i] = malloc(sizeof(float*)*m->rows);
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for (int j=0; j < m->rows; j++) {
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m->value[i][j] = malloc(sizeof(float*)*m->columns);
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}
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}
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return m;
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}
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void uniformise_matrix(Matrix* m, float x) {
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/* Donne la même valeur x à tous les éléments de la matrice */
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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m->value[i][j][k] = x;
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}
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}
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}
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}
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void print_matrix(Matrix* m) {
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/* Affiche la matrice */
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for (int i=0; i < m->depths; i++) {
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if (i!=0)
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printf("-----------------\n");
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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if (k!=0)
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printf(",");
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printf("%f ", m->value[i][j][k]);
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}
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printf("\n");
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}
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}
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}
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float max_in_matrix(Matrix* m) {
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/* Renvoie l'élément maximal de la matrice */
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float max_tmp = FLT_MIN;
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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max_tmp = max_float(max_tmp, m->value[i][j][k]);
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}
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}
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}
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return max_tmp;
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}
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void free_matrix(Matrix* m) {
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/* Libère l'espace mémoire alloué à la matrice */
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m->rows; j++) {
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free(m->value[i][j]);
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}
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free(m->value[i]);
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}
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free(m->value);
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}
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float number_from_matrix(Matrix* m) {
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/* Renvoie la somme des éléments de la matrice */
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float tmp=0;
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for (int i=0; i < m->depths ; i++) {
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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tmp += m->value[i][j][k];
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}
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}
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}
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return tmp;
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}
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void product_of_a_scalar_matrix(Matrix* m, float scalar) {
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/* Multiplie la matrice par un scalaire */
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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m->value[i][j][k] *= scalar;
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}
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}
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}
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}
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void sum_of_a_scalar_matrix(Matrix* m, float scalar) {
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/* Ajoute un scalaire à la matrice */
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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m->value[i][j][k] += scalar;
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}
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}
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}
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}
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Matrix* copy_matrix(Matrix* m) {
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/* Renvoie une copie de la matrice */
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Matrix* new_m = create_matrix(m->depths, m->rows, m->columns);
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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new_m->value[i][j][k] = m->value[i][j][k];
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}
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}
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}
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return new_m;
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}
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Matrix* apply_function_new_matrix(Matrix* m, float (*f)(float)) {
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/* Renvoie une matrice avec une fonction appliquée
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à tous les éléments de l'ancienne matrice */
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Matrix* new_m = create_matrix(m->depths, m->rows, m->columns);
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m ->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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new_m->value[i][j][k] = (*f)(m->value[i][j][k]);
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}
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}
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}
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return new_m;
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}
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void apply_function_matrix(Matrix* m, float (*f)(float)) {
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/* Applique une fonction à tous les éléments de la matrice */
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m ->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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m->value[i][j][k] = (*f)(m->value[i][j][k]);
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}
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}
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}
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}
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Matrix* add_matrix(Matrix* m1, Matrix* m2) {
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/* Renvoie la somme de deux matrices */
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if (m1->depths != m2->depths || m1->rows != m2->rows || m1->columns != m2->columns) {
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printf("Erreur, matrices non compatibles avec la somme");
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return NULL;
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}
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Matrix* m = create_matrix(m1->depths, m1->rows, m2->columns);
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for (int i=0; i < m->depths; i++) {
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for (int j=0; j < m->rows; j++) {
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for (int k=0; k < m->columns; k++) {
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m->value[i][j][k] = m1->value[i][j][k] + m2->value[i][j][k];
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}
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}
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}
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return m;
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}
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/*Matrix* product_matrix(Matrix* m1, Matrix* m2) { // TO DO
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Renvoie une nouvelle matrice produit (classique)
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des deux matrices si les dimensions sont correctes
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if (m1->depths != m2->rows || m1->rows != ) {
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printf("Erreur, matrices non compatibles avec le produit");
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return NULL;
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}
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float cpt;
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Matrix* m = create_matrix(m1->rows, m2->columns);
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for (int i=0; i < m->rows; i++) {
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for (int j=0; j < m->columns; j++) {
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cpt=0;
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for (int k=0; k < m2->rows; k++) {
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cpt += m1->value[i][j]* m2->value[k][j];
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}
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m->value[i][j] = cpt;
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}
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}
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return m;
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}*/
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void max_pooling_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out) {
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/* Insère le résultat de max pooling avec un décalage
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de (stride) éléments dans la matrice m_out */
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if (m_in->depths < kernel->depths || m_in->rows < kernel->rows || m_in->columns < kernel->columns) {
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printf("Erreur, kernel plus grand que la matrice dans max pooling");
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return;
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}
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if (((m_in->depths - kernel->depths)/stride)+1 != m_out->depths || ((m_in->rows - kernel->rows)/stride)+1 != m_out->rows || ((m_in->columns - kernel->columns)/stride)+1 != m_out->columns) {
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printf("Erreur, matrice et kernel non compatibles avec le décalage ou la matrice sortante dans max pooling");
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return;
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}
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int i, j, k, a, b, c;
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float tmp;
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for (i=0; i < m_out->depths; i++) {
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for (j=0; j < m_out->rows; j++) {
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for (k=0; k < m_out->columns; k++) {
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tmp = FLT_MIN;
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for (a=0; a < kernel->depths; a++) {
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for (b=0; b < kernel->rows; b++) {
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for (c=0; c < kernel->columns; c++) {
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tmp = max_float(tmp, m_in->value[i*stride +a][j*stride +b][k*stride +c]);
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}
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}
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}
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m_out->value[i][j][k] = tmp;
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}
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}
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}
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}
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void min_pooling_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out) {
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/* Insère le résultat de min pooling avec un décalage
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de (stride) éléments dans la matrice m_out */
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if (m_in->depths < kernel->depths || m_in->rows < kernel->rows || m_in->columns < kernel->columns) {
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printf("Erreur, kernel plus grand que la matrice dans min pooling");
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return;
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}
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if (((m_in->depths - kernel->depths)/stride)+1 != m_out->depths || ((m_in->rows - kernel->rows)/stride)+1 != m_out->rows || ((m_in->columns - kernel->columns)/stride)+1 != m_out->columns) {
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printf("Erreur, matrice et kernel non compatibles avec le décalage ou la matrice sortante dans min pooling");
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return;
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}
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int i, j, k, a, b, c;
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float tmp;
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for (i=0; i < m_out->depths; i++) {
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for (j=0; j < m_out->rows; j++) {
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for (k=0; k < m_out->columns; k++) {
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tmp = FLT_MAX;
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for (a=0; a < kernel->depths; a++) {
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for (b=0; b < kernel->rows; b++) {
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for (c=0; c < kernel->columns; c++) {
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tmp = min_float(tmp, m_in->value[i*stride +a][j*stride +b][k*stride +c]);
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}
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}
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}
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m_out->value[i][j][k] = tmp;
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}
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}
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}
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}
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void average_pooling_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out) {
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/* Insère le résultat de average pooling avec un décalage
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de (stride) éléments dans la matrice m_out */
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if (m_in->depths < kernel->depths || m_in->rows < kernel->rows || m_in->columns < kernel->columns) {
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printf("Erreur, kernel plus grand que la matrice dans average pooling");
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return;
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}
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if (((m_in->depths - kernel->depths)/stride)+1 != m_out->depths || ((m_in->rows - kernel->rows)/stride)+1 != m_out->rows || ((m_in->columns - kernel->columns)/stride)+1 != m_out->columns) {
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printf("Erreur, matrice et kernel non compatibles avec le décalage ou la matrice sortante dans average pooling");
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return;
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}
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int i, j, k, a, b, c, nb=kernel->depths*kernel->rows*kernel->columns;
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float tmp;
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for (i=0; i < m_out->depths; i++) {
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for (j=0; j < m_out->rows; j++) {
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for (k=0; k < m_out->columns; k++) {
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tmp = 0;
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for (a=0; a < kernel->depths; a++) {
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for (b=0; b < kernel->rows; b++) {
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for (c=0; c < kernel->columns; c++) {
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tmp += m_in->value[i*stride +a][j*stride +b][k*stride +c];
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}
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}
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}
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m_out->value[i][j][k] = tmp/nb;
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}
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}
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}
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}
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void valid_cross_correlation_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out) {
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/* Insère, la cross-correlation valide de m_in et
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kernel avec un décalage de stride, dans m_out */
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if (m_in->depths < kernel->depths || m_in->rows < kernel->rows || m_in->columns < kernel->columns) {
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printf("Erreur, kernel plus grand que la matrice dans valid cross-correlation");
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return;
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}
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if (((m_in->depths - kernel->depths)/stride)+1 != m_out->depths || ((m_in->rows - kernel->rows)/stride)+1 != m_out->rows || ((m_in->columns - kernel->columns)/stride)+1 != m_out->columns) {
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printf("Erreur, matrice et kernel non compatibles avec le décalage ou la matrice sortante dans valid cross-correlation");
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return;
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}
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int i, j, k, a, b, c, new_i, new_j, new_k;
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for (i=0; i < m_out->depths; i++) {
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for (j=0; j < m_out->rows; j++) {
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for (k=0; k < m_out->columns; k++) {
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m_out->value[i][j][k] = 0;
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for (a=0; a < kernel->depths; a++) {
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for (b=0; b < kernel->rows; b++) {
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for (c=0; c < kernel->columns; c++) {
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m_out->value[i][j][k] += m_in->value[i*stride +a][j*stride +b][k*stride +c]*kernel->value[a][b][c];
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}
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}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void full_cross_correlation_matrix(Matrix* m_in, Matrix* kernel, int stride, Matrix* m_out) {
|
||||
/* Insère, la cross-correlation entière de m_in et
|
||||
kernel avec un décalage de stride, dans m_out */
|
||||
int rows_k = kernel->rows-1;
|
||||
int columns_k = kernel->columns-1;
|
||||
int depths_k = kernel->depths-1;
|
||||
if (m_in->depths < kernel->depths || m_in->rows < kernel->rows || m_in->columns < kernel->columns) {
|
||||
printf("Erreur, kernel plus grand que la matrice dans full cross-correlation");
|
||||
return;
|
||||
}
|
||||
if ((m_in->depths + 2*depths_k)/stride != m_out->depths || (m_in->rows + 2*rows_k)/stride != m_out->rows || (m_in->columns + 2*columns_k)/stride != m_out->columns) {
|
||||
printf("Erreur, matrice et kernel non compatibles avec le décalage ou la matrice sortante dans full cross-correlation");
|
||||
return;
|
||||
}
|
||||
int i, j, k, a, b, c, new_i, new_j, new_k;
|
||||
for (i=-depths_k; i < (m_out->depths + depths_k); i++) {
|
||||
for (j=-rows_k; j < (m_out->rows + rows_k); j++) {
|
||||
for (k=--columns_k; k < (m_out->columns + columns_k); k++) {
|
||||
m_out->value[i+rows_k][j+columns_k] = 0;
|
||||
for (a=0; a < kernel->depths; a++) {
|
||||
for (b=0; b < kernel->rows; b++) {
|
||||
for (c=0; c < kernel->columns; c++) {
|
||||
new_i = i*stride +a;
|
||||
new_j = j*stride +b;
|
||||
new_k = k*stride +c;
|
||||
if (new_k >= 0 || new_k < m_in->columns || new_i >= 0 || new_i < m_in->depths || new_j >= 0 || new_j < m_in->rows)
|
||||
m_out->value[i+depths_k][j+rows_k][k+columns_k] += m_in->value[new_i][new_j][new_k]*kernel->value[a][b][c];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void softmax_matrix(Matrix* m) {
|
||||
/* Applique la fonction softmax sur la matrice en changeant ses valeurs */
|
||||
float max = max_in_matrix(m);
|
||||
sum_of_a_scalar_matrix(m, (-1)*max);
|
||||
apply_function_matrix(m, exp_float);
|
||||
float sum = number_from_matrix(m);
|
||||
sum = 1/sum;
|
||||
product_of_a_scalar_matrix(m, sum);
|
||||
}
|
||||
|
||||
|
||||
float quadratic_cost_matrix(Matrix* m, int i_number, int j_number, int k_number) {
|
||||
/* Renvoie l'erreur de la matrice où les valeurs sont des probabailités */
|
||||
float loss = 0;
|
||||
for (int i=0; i < m->depths; i++) {
|
||||
for (int j=0; j < m->rows; j++) {
|
||||
for (int k=0; k < m->columns; k++) {
|
||||
if (i==i_number && j==j_number && k==k_number)
|
||||
loss += (1-m->value[i][j][k])*(1-m->value[i][j][k]);
|
||||
else
|
||||
loss += m->value[i][j][k]*m->value[i][j][k];
|
||||
}
|
||||
}
|
||||
}
|
||||
return loss;
|
||||
}
|
||||
|
||||
|
||||
/*void rotation_180_matrix(Matrix* m) { // TO DO
|
||||
if (m->rows != m-> columns) {
|
||||
printf("Erreur, une matrice non carrée ne peut pas être retourner");
|
||||
return;
|
||||
}
|
||||
float tmp;
|
||||
int half_rows = m->rows/2;
|
||||
int max_r = m->rows-1;
|
||||
int max_c = m->columns-1;
|
||||
for (int i=0; i < m->rows; i++) {
|
||||
for (int j=i; j < m->columns; j++) {
|
||||
if (i!=j || i>=half_rows) {
|
||||
tmp = m->value[i][j];
|
||||
m->value[i][j] = m->value[max_r-i][max_c-j];
|
||||
m->value[max_r-i][max_c-j] = tmp;
|
||||
}
|
||||
}
|
||||
}
|
||||
}*/
|
||||
|
||||
|
||||
|
||||
|
||||
int main() {
|
||||
Matrix* m = create_matrix(3, 3, 3);
|
||||
m->value[0][1][2]=10;
|
||||
softmax_matrix(m);
|
||||
print_matrix(m);
|
||||
free_matrix(m);
|
||||
return 1;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user