tmp(root): transfere files

Dockerfile

@@ -1,17 +1,18 @@
 # Use an official image as a parent image
-FROM ubuntu:20.04
+FROM ubuntu:latest
 
 # Set the working directory
 WORKDIR /app
 
 # Install necessary libraries and tools
-RUN apt-get update && apt-get install -y gcc libcsfml-dev make
+RUN apt-get update && apt-get install -y gcc python3 libsfml-dev libcsfml-dev make python3-pip libglib2.0-0
 
 # Copy your source code to the container
 COPY . .
 
 # Compile your C project
-RUN make
+RUN make re
 
 # Set the entry point for your application
-CMD ["./bin/engine.out"]
+CMD ["./launcher"]
+# CMD ["./bin/engine.out", "Projects/New/config.xml"]

Engine/AI/.vscode/tasks.json

@@ -0,0 +1,29 @@
+{
+    "tasks": [
+        {
+            "type": "cppbuild",
+            "label": "C/C++: gcc générer le fichier actif",
+            "command": "/usr/bin/gcc",
+            "args": [
+                "-fdiagnostics-color=always",
+                "-g",
+                "${file}",
+                "-o",
+                "${fileDirname}/${fileBasenameNoExtension}",
+                "-lm"
+            ],
+            "options": {
+                "cwd": "${fileDirname}"
+            },
+            "problemMatcher": [
+                "$gcc"
+            ],
+            "group": {
+                "kind": "build",
+                "isDefault": true
+            },
+            "detail": "Tâche générée par le débogueur."
+        }
+    ],
+    "version": "2.0.0"
+}

Engine/AI/gomoku_model.c

@@ -0,0 +1,69 @@
+/*
+** EPITECH PROJECT, 2023
+** Engine-3D
+** File description:
+** gomoku_model
+*/
+
+#define MH_IMPLEMENTATION
+#include "machine_learning.h"
+
+// 8x8=64 for the input board, 2 for the output coordinates
+float gomoku_model[66] = {0};
+
+typedef unsigned char uint8_t;
+
+uint8_t random_between_0_2(void)
+{
+    return rand() % 3 + '0';
+}
+
+int main(void)
+{
+    srand(time(NULL));
+
+    size_t stride = 8*8+2;
+    size_t n = ARRAY_SIZE(gomoku_model)/stride;
+    matrix_t in = {
+        .rows = n,
+        .cols = 1,
+        .stride = stride,
+        .ptr = gomoku_model
+    };
+
+    matrix_t out = {
+        .rows = n,
+        .cols = 2,
+        .stride = stride,
+        .ptr = (float []){gomoku_model[64], gomoku_model[65]}
+    };
+
+    size_t arch[] = {64, 64, 2};
+    neural_n_t nn = neural_link_create(arch, ARRAY_SIZE(arch));
+    neural_n_t gradient = neural_link_create(arch, ARRAY_SIZE(arch));
+    neural_link_randomize(nn, 0, 1);
+
+    size_t epochs = 100*1000;
+    float rate = 1;
+
+    printf("Training...\n");
+
+    printf("c = %f\n", neural_link_cost(nn, in, out));
+    neural_link_train(nn, gradient, in, out, epochs, rate);
+    printf("c = %f\n", neural_link_cost(nn, in, out));
+
+    printf("Done!\n");
+
+    printf("Saving neural network to xor.nn\n");
+    neural_link_save(nn, "xor.nn");
+
+    neural_link_print_result(nn, (matrix_t) {
+        .rows = 1,
+        .cols = 2,
+        .ptr = (float[]){0, 1}
+    }, 1);
+    neural_link_free(nn);
+    return EXIT_SUCCESS;
+}
+
+// Build: gcc -Wall -Wextra -Werror gomoku_model.c -o gomoku_model -lm