From d9b21e7fa1cef59977d0aabb99db68592cfa258a Mon Sep 17 00:00:00 2001
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 =?UTF-8?q?=D0=B0=D0=BF=D0=BE=D0=B2?= <ns.potapov@yandex.ru>
Date: Sat, 10 May 2025 20:12:35 +0400
Subject: [PATCH] =?UTF-8?q?=D0=B3=D0=BE=D1=82=D0=BE=D0=B2=D0=BE=D0=B5?=
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---
 lab_10/lab10.ipynb | 688 +++++++++++++++++++++++++++++++--------------
 1 file changed, 481 insertions(+), 207 deletions(-)

diff --git a/lab_10/lab10.ipynb b/lab_10/lab10.ipynb
index 7f4bf29..a6a9979 100644
--- a/lab_10/lab10.ipynb
+++ b/lab_10/lab10.ipynb
@@ -1,5 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "044e75de",
+   "metadata": {},
+   "source": [
+    "# Лабортароная работа №10. Генетический алгоритм\n",
+    "\n",
+    "Задача оптимизации: подбор оптимального рациона на день (завтрак, обед и ужин) по содержанию белков, жиров и углеводов (БЖУ)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -9,63 +19,72 @@
    "source": [
     "import random\n",
     "import numpy as np\n",
+    "from pprint import pprint\n",
     "from deap import base, creator, tools, algorithms\n",
     "import matplotlib.pyplot as plt"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f5ebf2cd",
+   "metadata": {},
+   "source": [
+    "Список с продуктами и информацией по содержанию в них БЖУ (а также цене и предположительной разовой порции)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "ebd621c8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 1. База данных продуктов (калории, белки, жиры, углеводы, цена)\n",
     "products = [\n",
-    "    # Мясо и птица (категория \"meat\")\n",
-    "    {\"name\": \"Курица (грудка)\", \"calories\": 165, \"protein\": 31, \"fat\": 3.6, \"carbs\": 0, \"price\": 30, \"category\": \"meat\"},\n",
-    "    {\"name\": \"Говядина\", \"calories\": 250, \"protein\": 26, \"fat\": 15, \"carbs\": 0, \"price\": 93, \"category\": \"meat\"},\n",
-    "    {\"name\": \"Индейка\", \"calories\": 135, \"protein\": 29, \"fat\": 1.7, \"carbs\": 0, \"price\": 70, \"category\": \"meat\"},\n",
-    "    {\"name\": \"Свинина\", \"calories\": 242, \"protein\": 21, \"fat\": 17, \"carbs\": 0, \"price\": 34, \"category\": \"meat\"},\n",
+    "    {\"name\": \"Курица (грудка)\", \"calories\": 165, \"protein\": 31, \"fat\": 3.6, \"carbs\": 0, \"price\": 30, \"category\": \"meat\", \"portion\": 150},\n",
+    "    {\"name\": \"Говядина\", \"calories\": 250, \"protein\": 26, \"fat\": 15, \"carbs\": 0, \"price\": 93, \"category\": \"meat\", \"portion\": 120},\n",
+    "    {\"name\": \"Индейка\", \"calories\": 135, \"protein\": 29, \"fat\": 1.7, \"carbs\": 0, \"price\": 70, \"category\": \"meat\", \"portion\": 150},\n",
+    "    {\"name\": \"Свинина\", \"calories\": 242, \"protein\": 21, \"fat\": 17, \"carbs\": 0, \"price\": 34, \"category\": \"meat\", \"portion\": 120},\n",
     "    \n",
-    "    # Рыба и морепродукты (категория \"fish\")\n",
-    "    {\"name\": \"Минтай\", \"calories\": 72, \"protein\": 16, \"fat\": 0.9, \"carbs\": 0, \"price\": 36, \"category\": \"fish\"},\n",
-    "    {\"name\": \"Треска\", \"calories\": 69, \"protein\": 16, \"fat\": 0.6, \"carbs\": 0, \"price\": 110, \"category\": \"fish\"},\n",
-    "    {\"name\": \"Скумбрия\", \"calories\": 191, \"protein\": 18, \"fat\": 13.2, \"carbs\": 0, \"price\": 106, \"category\": \"fish\"},\n",
+    "    {\"name\": \"Минтай\", \"calories\": 72, \"protein\": 16, \"fat\": 0.9, \"carbs\": 0, \"price\": 36, \"category\": \"fish\", \"portion\": 180},\n",
+    "    {\"name\": \"Треска\", \"calories\": 69, \"protein\": 16, \"fat\": 0.6, \"carbs\": 0, \"price\": 110, \"category\": \"fish\", \"portion\": 180},\n",
+    "    {\"name\": \"Скумбрия\", \"calories\": 191, \"protein\": 18, \"fat\": 13.2, \"carbs\": 0, \"price\": 106, \"category\": \"fish\", \"portion\": 150},\n",
     "    \n",
-    "    # Крупы и злаки (категория \"grains\")\n",
-    "    {\"name\": \"Рис\", \"calories\": 325, \"protein\": 6.9, \"fat\": 1, \"carbs\": 72.2, \"price\": 6.8, \"category\": \"grains\"},\n",
-    "    {\"name\": \"Гречка\", \"calories\": 330, \"protein\": 13, \"fat\": 2.5, \"carbs\": 68, \"price\": 3.5, \"category\": \"grains\"},\n",
-    "    {\"name\": \"Овсянка\", \"calories\": 352, \"protein\": 12.3, \"fat\": 6.2, \"carbs\": 61.8, \"price\": 4.5, \"category\": \"grains\"},\n",
-    "    {\"name\": \"Перловка\", \"calories\": 317, \"protein\": 10.4, \"fat\": 1.3, \"carbs\": 67, \"price\": 3, \"category\": \"grains\"},\n",
+    "    {\"name\": \"Рис\", \"calories\": 325, \"protein\": 6.9, \"fat\": 1, \"carbs\": 72.2, \"price\": 6.8, \"category\": \"grains\", \"portion\": 100},\n",
+    "    {\"name\": \"Гречка\", \"calories\": 330, \"protein\": 13, \"fat\": 2.5, \"carbs\": 68, \"price\": 3.5, \"category\": \"grains\", \"portion\": 100},\n",
+    "    {\"name\": \"Овсянка\", \"calories\": 352, \"protein\": 12.3, \"fat\": 6.2, \"carbs\": 61.8, \"price\": 4.5, \"category\": \"grains\", \"portion\": 80},\n",
+    "    {\"name\": \"Перловка\", \"calories\": 317, \"protein\": 10.4, \"fat\": 1.3, \"carbs\": 67, \"price\": 3, \"category\": \"grains\", \"portion\": 100},\n",
     "    \n",
-    "    # Овощи (категория \"vegetables\")\n",
-    "    {\"name\": \"Брокколи\", \"calories\": 34, \"protein\": 2.8, \"fat\": 0.4, \"carbs\": 7.0, \"price\": 40, \"category\": \"vegetables\"},\n",
-    "    {\"name\": \"Морковь\", \"calories\": 41, \"protein\": 0.9, \"fat\": 0.2, \"carbs\": 10, \"price\": 0.9, \"category\": \"vegetables\"},\n",
-    "    {\"name\": \"Картофель\", \"calories\": 77, \"protein\": 2, \"fat\": 0.1, \"carbs\": 17, \"price\": 11, \"category\": \"grains\"},\n",
-    "    {\"name\": \"Капуста белокочаная\", \"calories\": 28, \"protein\": 1.8, \"fat\": 0.2, \"carbs\": 4.7, \"price\": 7, \"category\": \"vegetables\"},\n",
-    "    {\"name\": \"Помидоры\", \"calories\": 18, \"protein\": 0.9, \"fat\": 0.2, \"carbs\": 2.7, \"price\": 40, \"category\": \"vegetables\"},\n",
-    "    {\"name\": \"Огурцы\", \"calories\": 15, \"protein\": 0.7, \"fat\": 0.1, \"carbs\": 3.1, \"price\": 38, \"category\": \"vegetables\"},\n",
+    "    {\"name\": \"Брокколи\", \"calories\": 34, \"protein\": 2.8, \"fat\": 0.4, \"carbs\": 7.0, \"price\": 40, \"category\": \"vegetables\", \"portion\": 200},\n",
+    "    {\"name\": \"Морковь\", \"calories\": 41, \"protein\": 0.9, \"fat\": 0.2, \"carbs\": 10, \"price\": 0.9, \"category\": \"vegetables\", \"portion\": 150},\n",
+    "    {\"name\": \"Картофель\", \"calories\": 77, \"protein\": 2, \"fat\": 0.1, \"carbs\": 17, \"price\": 11, \"category\": \"grains\", \"portion\": 150},\n",
+    "    {\"name\": \"Капуста белокочаная\", \"calories\": 28, \"protein\": 1.8, \"fat\": 0.2, \"carbs\": 4.7, \"price\": 7, \"category\": \"vegetables\", \"portion\": 200},\n",
+    "    {\"name\": \"Помидоры\", \"calories\": 18, \"protein\": 0.9, \"fat\": 0.2, \"carbs\": 2.7, \"price\": 40, \"category\": \"vegetables\", \"portion\": 200},\n",
+    "    {\"name\": \"Огурцы\", \"calories\": 15, \"protein\": 0.7, \"fat\": 0.1, \"carbs\": 3.1, \"price\": 38, \"category\": \"vegetables\", \"portion\": 200},\n",
     "    \n",
-    "    # Фрукты (категория \"fruits\")\n",
-    "    {\"name\": \"Яблоки\", \"calories\": 47, \"protein\": 0.4, \"fat\": 0.4, \"carbs\": 9.8, \"price\": 18, \"category\": \"fruits\"},\n",
-    "    {\"name\": \"Бананы\", \"calories\": 96, \"protein\": 1.5, \"fat\": 0.5, \"carbs\": 21, \"price\": 19, \"category\": \"fruits\"},\n",
-    "    {\"name\": \"Апельсины\", \"calories\": 43, \"protein\": 0.9, \"fat\": 0.2, \"carbs\": 8.1, \"price\": 15, \"category\": \"fruits\"},\n",
+    "    {\"name\": \"Яблоки\", \"calories\": 47, \"protein\": 0.4, \"fat\": 0.4, \"carbs\": 9.8, \"price\": 18, \"category\": \"fruits\", \"portion\": 150},\n",
+    "    {\"name\": \"Бананы\", \"calories\": 96, \"protein\": 1.5, \"fat\": 0.5, \"carbs\": 21, \"price\": 19, \"category\": \"fruits\", \"portion\": 120},\n",
+    "    {\"name\": \"Апельсины\", \"calories\": 43, \"protein\": 0.9, \"fat\": 0.2, \"carbs\": 8.1, \"price\": 15, \"category\": \"fruits\", \"portion\": 150},\n",
     "    \n",
-    "    # Молочные продукты (категория \"dairy\")\n",
-    "    {\"name\": \"Творог 5%\", \"calories\": 120, \"protein\": 16, \"fat\": 5, \"carbs\": 3, \"price\": 54, \"category\": \"dairy\"},\n",
-    "    {\"name\": \"Творог 0.5%\", \"calories\": 90, \"protein\": 18, \"fat\": 0.5, \"carbs\": 3.5, \"price\": 38, \"category\": \"dairy\"},\n",
-    "    {\"name\": \"Молоко\", \"calories\": 60, \"protein\": 3, \"fat\": 3.2, \"carbs\": 4.7, \"price\": 9.5, \"category\": \"dairy\"},\n",
-    "    {\"name\": \"Сыр твердый\", \"calories\": 340, \"protein\": 26.3, \"fat\": 26.1, \"carbs\": 0, \"price\": 126, \"category\": \"dairy\"},\n",
-    "    {\"name\": \"Йогурт 5%\", \"calories\": 120, \"protein\": 5.7, \"fat\": 4.8, \"carbs\": 13.9, \"price\": 38, \"category\": \"dairy\"},\n",
+    "    {\"name\": \"Творог 5%\", \"calories\": 120, \"protein\": 16, \"fat\": 5, \"carbs\": 3, \"price\": 54, \"category\": \"dairy\", \"portion\": 150},\n",
+    "    {\"name\": \"Творог 0.5%\", \"calories\": 90, \"protein\": 18, \"fat\": 0.5, \"carbs\": 3.5, \"price\": 38, \"category\": \"dairy\", \"portion\": 150},\n",
+    "    {\"name\": \"Молоко\", \"calories\": 60, \"protein\": 3, \"fat\": 3.2, \"carbs\": 4.7, \"price\": 9.5, \"category\": \"dairy\", \"portion\": 200},\n",
+    "    {\"name\": \"Сыр твердый\", \"calories\": 340, \"protein\": 26.3, \"fat\": 26.1, \"carbs\": 0, \"price\": 126, \"category\": \"dairy\", \"portion\": 50},\n",
+    "    {\"name\": \"Йогурт 5%\", \"calories\": 120, \"protein\": 5.7, \"fat\": 4.8, \"carbs\": 13.9, \"price\": 38, \"category\": \"dairy\", \"portion\": 125},\n",
     "    \n",
-    "    # Другое\n",
-    "    {\"name\": \"Яйца (1 шт. ~60г)\", \"calories\": 157, \"protein\": 12.7, \"fat\": 11.5, \"carbs\": 0.7, \"price\": 15.8, \"category\": \"eggs\"},\n",
-    "    {\"name\": \"Масло сливочное\", \"calories\": 662, \"protein\": 1, \"fat\": 72.5, \"carbs\": 1.4, \"price\": 86, \"category\": \"fats\"},\n",
-    "    {\"name\": \"Масло подсолнечное\", \"calories\": 899, \"protein\": 0, \"fat\": 100, \"carbs\": 0, \"price\": 16, \"category\": \"fats\"},\n",
+    "    {\"name\": \"Яйца (1 шт. ~60г)\", \"calories\": 157, \"protein\": 12.7, \"fat\": 11.5, \"carbs\": 0.7, \"price\": 15.8, \"category\": \"eggs\", \"portion\": 60},\n",
+    "    {\"name\": \"Масло сливочное\", \"calories\": 662, \"protein\": 1, \"fat\": 72.5, \"carbs\": 1.4, \"price\": 86, \"category\": \"fats\", \"portion\": 10},\n",
+    "    {\"name\": \"Масло подсолнечное\", \"calories\": 899, \"protein\": 0, \"fat\": 100, \"carbs\": 0, \"price\": 16, \"category\": \"fats\", \"portion\": 10},\n",
     "]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3f75c690",
+   "metadata": {},
+   "source": [
+    "Указываем целевые значения и дополнительные списки с несочетаемыми продуктами "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -73,22 +92,38 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 2. Целевые значения (суточные нормы)\n",
     "TARGET_CALORIES = 2000\n",
-    "TARGET_PROTEIN = 100\n",
-    "TARGET_FAT = 70\n",
-    "TARGET_CARBS = 250\n",
-    "MEAL_PRODUCTS = {\"breakfast\": 3, \"lunch\": 4, \"dinner\": 3}  # Кол-во продуктов на прием пищи\n",
+    "TARGET_PROTEIN = 75\n",
+    "TARGET_FAT = 67\n",
+    "TARGET_CARBS = 275\n",
+    "MEAL_PRODUCTS = {\n",
+    "    \"breakfast\": 3,\n",
+    "    \"lunch\": 4,\n",
+    "    \"dinner\": 3,\n",
+    "}  # Кол-во продуктов на прием пищи\n",
     "\n",
     "# Категории, которые не должны повторяться в одном приеме пищи\n",
     "EXCLUSIVE_CATEGORIES = [\"meat\", \"fish\", \"grains\", \"dairy\"]\n",
     "\n",
     "# Категории, которые не должны встречаться в одном приеме пищи\n",
-    "INCOMPATIBLE_CATEGORIES = [[\"meat\", \"fish\"], [\"fish\", \"dairy\"], [\"vegetables\", \"dairy\"], [\"fats\", \"dairy\"]]\n",
+    "INCOMPATIBLE_CATEGORIES = [\n",
+    "    [\"meat\", \"fish\"],\n",
+    "    [\"fish\", \"dairy\"],\n",
+    "    [\"vegetables\", \"dairy\"],\n",
+    "    [\"fats\", \"dairy\"],\n",
+    "]\n",
     "\n",
     "INCOMPATIBLE_CATEGORIES = [set(x) for x in INCOMPATIBLE_CATEGORIES]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ce8b1155",
+   "metadata": {},
+   "source": [
+    "Особь (хромосома) у нас будет являться списком списков (приемов пищи). Внутри списоков приемов пищи будут находиться гены - продукты питания."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -96,54 +131,60 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 3. Создаем структуру особи\n",
+    "# Создаем структуру особи\n",
     "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))  # Минимизируем функцию\n",
     "creator.create(\"Individual\", list, fitness=creator.FitnessMin)\n",
     "\n",
     "toolbox = base.Toolbox()\n",
     "\n",
+    "\n",
     "# Генерация случайного приема пищи с учетом ограничений\n",
     "def create_meal(meal_type):\n",
     "    meal = []\n",
     "    categories_in_meal = set()\n",
-    "    \n",
+    "\n",
     "    for _ in range(MEAL_PRODUCTS[meal_type]):\n",
     "        # Фильтруем продукты, чтобы не было конфликтов категорий\n",
     "        available_products = [\n",
-    "            p for p in products \n",
-    "            if (p[\"category\"] not in EXCLUSIVE_CATEGORIES or \n",
-    "                p[\"category\"] not in categories_in_meal)\n",
+    "            p\n",
+    "            for p in products\n",
+    "            if (\n",
+    "                p[\"category\"] not in EXCLUSIVE_CATEGORIES\n",
+    "                or p[\"category\"] not in categories_in_meal\n",
+    "            )\n",
     "        ]\n",
-    "        \n",
+    "\n",
     "        if not available_products:\n",
     "            break\n",
-    "            \n",
+    "\n",
     "        product = random.choice(available_products)\n",
     "        meal.append(product)\n",
-    "        \n",
+    "\n",
     "        if product[\"category\"] in EXCLUSIVE_CATEGORIES:\n",
     "            categories_in_meal.add(product[\"category\"])\n",
-    "    \n",
+    "\n",
     "    return meal\n",
     "\n",
+    "\n",
     "# Создание особи: [завтрак, обед, ужин]\n",
     "def create_individual():\n",
     "    individual = []\n",
     "    used_products = set()\n",
-    "    \n",
+    "\n",
     "    for meal_type in [\"breakfast\", \"lunch\", \"dinner\"]:\n",
     "        while True:\n",
     "            meal = create_meal(meal_type)\n",
     "            meal_product_names = {p[\"name\"] for p in meal}\n",
-    "            \n",
+    "\n",
     "            # Проверяем, что продукты не повторяются в течение дня\n",
     "            if not meal_product_names & used_products:\n",
     "                individual.append(meal)\n",
     "                used_products.update(meal_product_names)\n",
     "                break\n",
-    "    \n",
+    "\n",
     "    return individual\n",
     "\n",
+    "\n",
     "toolbox.register(\"individual\", tools.initIterate, creator.Individual, create_individual)\n",
     "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)"
    ]
@@ -151,11 +192,64 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "662390bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Сыр твердый',\n",
+       "  'calories': 340,\n",
+       "  'protein': 26.3,\n",
+       "  'fat': 26.1,\n",
+       "  'carbs': 0,\n",
+       "  'price': 126,\n",
+       "  'category': 'dairy',\n",
+       "  'portion': 50},\n",
+       " {'name': 'Перловка',\n",
+       "  'calories': 317,\n",
+       "  'protein': 10.4,\n",
+       "  'fat': 1.3,\n",
+       "  'carbs': 67,\n",
+       "  'price': 3,\n",
+       "  'category': 'grains',\n",
+       "  'portion': 100},\n",
+       " {'name': 'Индейка',\n",
+       "  'calories': 135,\n",
+       "  'protein': 29,\n",
+       "  'fat': 1.7,\n",
+       "  'carbs': 0,\n",
+       "  'price': 70,\n",
+       "  'category': 'meat',\n",
+       "  'portion': 150}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# пример создания случайного приема пищи\n",
+    "create_meal(\"breakfast\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "946ea3a8",
+   "metadata": {},
+   "source": [
+    "В фитнес-функции будем высчитывать общее содержание БЖУ для всех приемов пищи за день и высчитывать отклонение от целевых параметров. Также будем учитывать цену продуктов питания и содержание в одном приеме пищи несочетаемых продуктов (например, и рыба и мясо в одном приеме пищи, или молочная продукция и овощи). Так как у нас есть заданные оптимальные параметры БЖУ, то нам будет удобнее взять эти параметры за нулевую точку и отталкиваться от нее - *будем искать минимум отклонения* от целевых значений."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "ffd94b14",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 4. Фитнес-функция\n",
+    "# Фитнес-функция\n",
     "def evaluate(individual):\n",
     "    total_calories = 0\n",
     "    total_protein = 0\n",
@@ -211,12 +305,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
+   "id": "996243ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[{'calories': 60,\n",
+      "   'carbs': 4.7,\n",
+      "   'category': 'dairy',\n",
+      "   'fat': 3.2,\n",
+      "   'name': 'Молоко',\n",
+      "   'portion': 200,\n",
+      "   'price': 9.5,\n",
+      "   'protein': 3},\n",
+      "  {'calories': 191,\n",
+      "   'carbs': 0,\n",
+      "   'category': 'fish',\n",
+      "   'fat': 13.2,\n",
+      "   'name': 'Скумбрия',\n",
+      "   'portion': 150,\n",
+      "   'price': 106,\n",
+      "   'protein': 18},\n",
+      "  {'calories': 47,\n",
+      "   'carbs': 9.8,\n",
+      "   'category': 'fruits',\n",
+      "   'fat': 0.4,\n",
+      "   'name': 'Яблоки',\n",
+      "   'portion': 150,\n",
+      "   'price': 18,\n",
+      "   'protein': 0.4}],\n",
+      " [{'calories': 90,\n",
+      "   'carbs': 3.5,\n",
+      "   'category': 'dairy',\n",
+      "   'fat': 0.5,\n",
+      "   'name': 'Творог 0.5%',\n",
+      "   'portion': 150,\n",
+      "   'price': 38,\n",
+      "   'protein': 18},\n",
+      "  {'calories': 242,\n",
+      "   'carbs': 0,\n",
+      "   'category': 'meat',\n",
+      "   'fat': 17,\n",
+      "   'name': 'Свинина',\n",
+      "   'portion': 120,\n",
+      "   'price': 34,\n",
+      "   'protein': 21},\n",
+      "  {'calories': 96,\n",
+      "   'carbs': 21,\n",
+      "   'category': 'fruits',\n",
+      "   'fat': 0.5,\n",
+      "   'name': 'Бананы',\n",
+      "   'portion': 120,\n",
+      "   'price': 19,\n",
+      "   'protein': 1.5},\n",
+      "  {'calories': 28,\n",
+      "   'carbs': 4.7,\n",
+      "   'category': 'vegetables',\n",
+      "   'fat': 0.2,\n",
+      "   'name': 'Капуста белокочаная',\n",
+      "   'portion': 200,\n",
+      "   'price': 7,\n",
+      "   'protein': 1.8}],\n",
+      " [{'calories': 34,\n",
+      "   'carbs': 7.0,\n",
+      "   'category': 'vegetables',\n",
+      "   'fat': 0.4,\n",
+      "   'name': 'Брокколи',\n",
+      "   'portion': 200,\n",
+      "   'price': 40,\n",
+      "   'protein': 2.8},\n",
+      "  {'calories': 18,\n",
+      "   'carbs': 2.7,\n",
+      "   'category': 'vegetables',\n",
+      "   'fat': 0.2,\n",
+      "   'name': 'Помидоры',\n",
+      "   'portion': 200,\n",
+      "   'price': 40,\n",
+      "   'protein': 0.9},\n",
+      "  {'calories': 43,\n",
+      "   'carbs': 8.1,\n",
+      "   'category': 'fruits',\n",
+      "   'fat': 0.2,\n",
+      "   'name': 'Апельсины',\n",
+      "   'portion': 150,\n",
+      "   'price': 15,\n",
+      "   'protein': 0.9}]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# пример генерации хромосомы (рациона на день)\n",
+    "daily_meals = create_individual()\n",
+    "pprint(daily_meals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f2110fe5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(14453.0,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# расчет фитнес-функции\n",
+    "val = evaluate(daily_meals)\n",
+    "val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d49623dd",
+   "metadata": {},
+   "source": [
+    "Опишем функции мутации, будем с определенной вероятностью менять ген (продукт) в приеме пищи"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "05e8df95",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 5. Генетические операторы\n",
+    "# функция мутации с учетом несочетаемых продуктов\n",
     "def mutate_individual(individual, indpb):\n",
     "    for i, meal in enumerate(individual):\n",
     "        if random.random() < indpb:\n",
@@ -234,14 +458,54 @@
     "    \n",
     "    return individual,\n",
     "\n",
-    "toolbox.register(\"mate\", tools.cxTwoPoint)\n",
-    "toolbox.register(\"mutate\", mutate_individual, indpb=0.3)\n",
-    "toolbox.register(\"select\", tools.selTournament, tournsize=3)"
+    "\n",
+    "# функция мутации без учета сочетаемости\n",
+    "def mutate_individual2(individual, indpb):\n",
+    "    for i, meal in enumerate(individual):\n",
+    "        if random.random() < indpb:\n",
+    "            # Создаем новый прием пищи\n",
+    "            meal_type = [\"breakfast\", \"lunch\", \"dinner\"][i]\n",
+    "            new_meal = create_meal(meal_type)\n",
+    "            \n",
+    "            individual[i] = new_meal\n",
+    "    \n",
+    "    return individual,\n",
+    "\n",
+    "toolbox.register(\"mutate\", mutate_individual2, indpb=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "457e9260",
+   "metadata": {},
+   "source": [
+    "Функции кроссинговера и селекции возьмем стандартные, которые предлагает библиотека.\n",
+    "\n",
+    "В кроссинговере происходит обмен приемами пищи, а в селекции будем отбирать 3 лучших индивида."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
+   "id": "38bf0e0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toolbox.register(\"mate\", tools.cxTwoPoint)\n",
+    "toolbox.register(\"select\", tools.selTournament, tournsize=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70a6f50d",
+   "metadata": {},
+   "source": [
+    "Запускаем работу алгоритма и указываем количество особей в популяции 100 штук. Количество поколений - 100."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "bf02bfed",
    "metadata": {},
    "outputs": [
@@ -250,112 +514,112 @@
      "output_type": "stream",
      "text": [
       "gen\tnevals\tmin   \n",
-      "0  \t100   \t1180.5\n",
-      "1  \t79    \t1903.4\n",
-      "2  \t76    \t500.6 \n",
-      "3  \t82    \t500.6 \n",
-      "4  \t74    \t500.6 \n",
-      "5  \t87    \t500.6 \n",
-      "6  \t85    \t500.6 \n",
-      "7  \t82    \t500.6 \n",
-      "8  \t75    \t500.6 \n",
-      "9  \t80    \t500.6 \n",
-      "10 \t77    \t500.6 \n",
-      "11 \t75    \t500.6 \n",
-      "12 \t81    \t500.6 \n",
-      "13 \t69    \t500.6 \n",
-      "14 \t64    \t500.6 \n",
-      "15 \t84    \t500.6 \n",
-      "16 \t86    \t500.6 \n",
-      "17 \t80    \t500.6 \n",
-      "18 \t73    \t500.6 \n",
-      "19 \t72    \t500.6 \n",
-      "20 \t77    \t500.6 \n",
-      "21 \t77    \t500.6 \n",
-      "22 \t88    \t500.6 \n",
-      "23 \t87    \t500.6 \n",
-      "24 \t80    \t500.6 \n",
-      "25 \t86    \t500.6 \n",
-      "26 \t74    \t500.6 \n",
-      "27 \t90    \t500.6 \n",
-      "28 \t79    \t500.6 \n",
-      "29 \t87    \t500.6 \n",
-      "30 \t74    \t500.6 \n",
-      "31 \t79    \t500.6 \n",
-      "32 \t85    \t500.6 \n",
-      "33 \t81    \t500.6 \n",
-      "34 \t75    \t500.6 \n",
-      "35 \t90    \t500.6 \n",
-      "36 \t83    \t500.6 \n",
-      "37 \t86    \t500.6 \n",
-      "38 \t76    \t500.6 \n",
-      "39 \t86    \t500.6 \n",
-      "40 \t83    \t500.6 \n",
-      "41 \t77    \t500.6 \n",
-      "42 \t93    \t500.6 \n",
-      "43 \t78    \t500.6 \n",
-      "44 \t84    \t500.6 \n",
-      "45 \t79    \t500.6 \n",
-      "46 \t79    \t500.6 \n",
-      "47 \t83    \t500.6 \n",
-      "48 \t73    \t500.6 \n",
-      "49 \t79    \t500.6 \n",
-      "50 \t75    \t500.6 \n",
-      "51 \t77    \t500.6 \n",
-      "52 \t87    \t500.6 \n",
-      "53 \t70    \t500.6 \n",
-      "54 \t79    \t500.6 \n",
-      "55 \t66    \t500.6 \n",
-      "56 \t73    \t500.6 \n",
-      "57 \t81    \t500.6 \n",
-      "58 \t73    \t500.6 \n",
-      "59 \t84    \t500.6 \n",
-      "60 \t87    \t500.6 \n",
-      "61 \t77    \t500.6 \n",
-      "62 \t80    \t500.6 \n",
-      "63 \t76    \t500.6 \n",
-      "64 \t81    \t500.6 \n",
-      "65 \t76    \t500.6 \n",
-      "66 \t83    \t500.6 \n",
-      "67 \t92    \t500.6 \n",
-      "68 \t80    \t500.6 \n",
-      "69 \t79    \t500.6 \n",
-      "70 \t92    \t500.6 \n",
-      "71 \t79    \t500.6 \n",
-      "72 \t74    \t500.6 \n",
-      "73 \t78    \t500.6 \n",
-      "74 \t83    \t500.6 \n",
-      "75 \t84    \t500.6 \n",
-      "76 \t84    \t500.6 \n",
-      "77 \t82    \t500.6 \n",
-      "78 \t81    \t500.6 \n",
-      "79 \t79    \t500.6 \n",
-      "80 \t88    \t500.6 \n",
-      "81 \t76    \t500.6 \n",
-      "82 \t79    \t500.6 \n",
-      "83 \t85    \t500.6 \n",
-      "84 \t83    \t500.6 \n",
-      "85 \t81    \t500.6 \n",
-      "86 \t78    \t500.6 \n",
-      "87 \t81    \t500.6 \n",
-      "88 \t80    \t500.6 \n",
-      "89 \t88    \t500.6 \n",
-      "90 \t73    \t500.6 \n",
-      "91 \t81    \t500.6 \n",
-      "92 \t87    \t500.6 \n",
-      "93 \t88    \t500.6 \n",
-      "94 \t85    \t374.9 \n",
-      "95 \t83    \t374.9 \n",
-      "96 \t82    \t374.9 \n",
-      "97 \t83    \t374.9 \n",
-      "98 \t86    \t374.9 \n",
-      "99 \t83    \t374.9 \n",
-      "100\t77    \t374.9 \n"
+      "0  \t100   \t1444.7\n",
+      "1  \t79    \t1349.9\n",
+      "2  \t76    \t703.8 \n",
+      "3  \t83    \t703.8 \n",
+      "4  \t81    \t703.8 \n",
+      "5  \t79    \t703.8 \n",
+      "6  \t71    \t703.8 \n",
+      "7  \t76    \t703.8 \n",
+      "8  \t73    \t703.8 \n",
+      "9  \t78    \t703.8 \n",
+      "10 \t75    \t703.8 \n",
+      "11 \t82    \t703.8 \n",
+      "12 \t73    \t703.8 \n",
+      "13 \t80    \t586   \n",
+      "14 \t69    \t586   \n",
+      "15 \t87    \t586   \n",
+      "16 \t76    \t586   \n",
+      "17 \t82    \t586   \n",
+      "18 \t80    \t586   \n",
+      "19 \t70    \t586   \n",
+      "20 \t84    \t586   \n",
+      "21 \t79    \t586   \n",
+      "22 \t71    \t586   \n",
+      "23 \t79    \t586   \n",
+      "24 \t73    \t586   \n",
+      "25 \t83    \t586   \n",
+      "26 \t72    \t586   \n",
+      "27 \t82    \t586   \n",
+      "28 \t86    \t586   \n",
+      "29 \t82    \t586   \n",
+      "30 \t78    \t586   \n",
+      "31 \t78    \t586   \n",
+      "32 \t68    \t586   \n",
+      "33 \t71    \t586   \n",
+      "34 \t82    \t586   \n",
+      "35 \t70    \t586   \n",
+      "36 \t76    \t586   \n",
+      "37 \t87    \t586   \n",
+      "38 \t76    \t586   \n",
+      "39 \t75    \t586   \n",
+      "40 \t86    \t586   \n",
+      "41 \t89    \t586   \n",
+      "42 \t82    \t586   \n",
+      "43 \t77    \t586   \n",
+      "44 \t80    \t586   \n",
+      "45 \t82    \t586   \n",
+      "46 \t82    \t551   \n",
+      "47 \t80    \t551   \n",
+      "48 \t81    \t551   \n",
+      "49 \t83    \t551   \n",
+      "50 \t77    \t551   \n",
+      "51 \t79    \t551   \n",
+      "52 \t78    \t551   \n",
+      "53 \t76    \t551   \n",
+      "54 \t79    \t551   \n",
+      "55 \t83    \t551   \n",
+      "56 \t81    \t551   \n",
+      "57 \t80    \t551   \n",
+      "58 \t79    \t551   \n",
+      "59 \t85    \t551   \n",
+      "60 \t86    \t551   \n",
+      "61 \t83    \t551   \n",
+      "62 \t74    \t551   \n",
+      "63 \t77    \t442.3 \n",
+      "64 \t78    \t442.3 \n",
+      "65 \t78    \t442.3 \n",
+      "66 \t89    \t442.3 \n",
+      "67 \t84    \t442.3 \n",
+      "68 \t83    \t442.3 \n",
+      "69 \t78    \t442.3 \n",
+      "70 \t76    \t442.3 \n",
+      "71 \t78    \t442.3 \n",
+      "72 \t84    \t442.3 \n",
+      "73 \t81    \t442.3 \n",
+      "74 \t76    \t442.3 \n",
+      "75 \t84    \t442.3 \n",
+      "76 \t72    \t442.3 \n",
+      "77 \t85    \t442.3 \n",
+      "78 \t83    \t442.3 \n",
+      "79 \t82    \t442.3 \n",
+      "80 \t80    \t442.3 \n",
+      "81 \t80    \t442.3 \n",
+      "82 \t80    \t442.3 \n",
+      "83 \t76    \t442.3 \n",
+      "84 \t74    \t442.3 \n",
+      "85 \t78    \t442.3 \n",
+      "86 \t79    \t442.3 \n",
+      "87 \t73    \t442.3 \n",
+      "88 \t78    \t442.3 \n",
+      "89 \t82    \t442.3 \n",
+      "90 \t74    \t442.3 \n",
+      "91 \t75    \t442.3 \n",
+      "92 \t78    \t442.3 \n",
+      "93 \t87    \t442.3 \n",
+      "94 \t82    \t442.3 \n",
+      "95 \t76    \t442.3 \n",
+      "96 \t75    \t442.3 \n",
+      "97 \t83    \t442.3 \n",
+      "98 \t71    \t442.3 \n",
+      "99 \t87    \t442.3 \n",
+      "100\t86    \t442.3 \n"
      ]
     }
    ],
    "source": [
-    "# 6. Запуск алгоритма\n",
+    "# Запуск алгоритма\n",
     "population = toolbox.population(n=100)\n",
     "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
     "stats.register(\"min\", np.min)\n",
@@ -365,9 +629,17 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "43c11006",
+   "metadata": {},
+   "source": [
+    "Вывод лучшего решения"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "6d1448dd",
    "metadata": {},
    "outputs": [
@@ -379,35 +651,34 @@
       "=== Оптимальный рацион на день ===\n",
       "\n",
       "Завтрак:\n",
-      "  Бананы: 150 г (144 ккал, 28.5 руб.)\n",
-      "  Сыр твердый: 200 г (680 ккал, 252.0 руб.)\n",
-      "  Овсянка: 100 г (352 ккал, 4.5 руб.)\n",
-      "  Итого: 1176 ккал, 67.2 г белков, 59.2 г жиров, 93.3 г углеводов, 285.0 руб.\n",
+      "  Перловка: 100 г (317 ккал, 3.0 руб.)\n",
+      "  Помидоры: 200 г (36 ккал, 80.0 руб.)\n",
+      "  Капуста белокочаная: 200 г (56 ккал, 14.0 руб.)\n",
+      "  Итого: 409 ккал, 15.8 г белков, 2.1 г жиров, 81.8 г углеводов, 97.0 руб.\n",
       "\n",
       "Обед:\n",
-      "  Рис: 100 г (325 ккал, 6.8 руб.)\n",
-      "  Морковь: 200 г (82 ккал, 1.8 руб.)\n",
-      "  Свинина: 150 г (363 ккал, 51.0 руб.)\n",
-      "  Яйца (1 шт. ~60г): 50 г (78 ккал, 7.9 руб.)\n",
-      "  Итого: 848 ккал, 46.6 г белков, 32.6 г жиров, 92.5 г углеводов, 67.5 руб.\n",
+      "  Курица (грудка): 150 г (248 ккал, 45.0 руб.)\n",
+      "  Яблоки: 150 г (70 ккал, 27.0 руб.)\n",
+      "  Овсянка: 80 г (282 ккал, 3.6 руб.)\n",
+      "  Морковь: 150 г (62 ккал, 1.4 руб.)\n",
+      "  Итого: 661 ккал, 58.3 г белков, 11.3 г жиров, 79.1 г углеводов, 76.9 руб.\n",
       "\n",
       "Ужин:\n",
+      "  Рис: 100 г (325 ккал, 6.8 руб.)\n",
+      "  Масло сливочное: 10 г (66 ккал, 8.6 руб.)\n",
       "  Апельсины: 150 г (64 ккал, 22.5 руб.)\n",
-      "  Минтай: 150 г (108 ккал, 54.0 руб.)\n",
-      "  Гречка: 100 г (330 ккал, 3.5 руб.)\n",
-      "  Итого: 502 ккал, 38.4 г белков, 4.2 г жиров, 80.2 г углеводов, 80.0 руб.\n",
+      "  Итого: 456 ккал, 8.3 г белков, 8.6 г жиров, 84.5 г углеводов, 37.9 руб.\n",
       "\n",
       "=== Суточная сумма ===\n",
-      "• Калории: 2527 (цель: 2000)\n",
-      "• Белки: 152.1 г (цель: 100)\n",
-      "• Жиры: 96.0 г (цель: 70)\n",
-      "• Углеводы: 266.0 г (цель: 250)\n",
-      "• Стоимость: 432.5 руб.\n"
+      "• Калории: 1526 (цель: 2000)\n",
+      "• Белки: 82.4 г (цель: 75)\n",
+      "• Жиры: 21.9 г (цель: 67)\n",
+      "• Углеводы: 245.4 г (цель: 275)\n",
+      "• Стоимость: 211.8 руб.\n"
      ]
     }
    ],
    "source": [
-    "# 7. Вывод лучшего решения с реалистичными порциями\n",
     "best_individual = tools.selBest(result, k=1)[0]\n",
     "print(\"\\n=== Оптимальный рацион на день ===\")\n",
     "\n",
@@ -425,30 +696,34 @@
     "    meal_fat = 0\n",
     "    meal_carbs = 0\n",
     "    meal_cost = 0\n",
-    "    \n",
+    "\n",
     "    for product in meal:\n",
-    "        portion = PORTION_SIZES.get(product[\"category\"], 100)  # Берем рекомендуемый размер порции\n",
-    "        calories = (portion/100) * product[\"calories\"]\n",
-    "        protein = (portion/100) * product[\"protein\"]\n",
-    "        fat = (portion/100) * product[\"fat\"]\n",
-    "        carbs = (portion/100) * product[\"carbs\"]\n",
-    "        cost = (portion/100) * product[\"price\"]\n",
-    "        \n",
-    "        print(f\"  {product['name']}: {portion:.0f} г ({calories:.0f} ккал, {cost:.1f} руб.)\")\n",
-    "        \n",
+    "        portion = product[\"portion\"]\n",
+    "        calories = (portion / 100) * product[\"calories\"]\n",
+    "        protein = (portion / 100) * product[\"protein\"]\n",
+    "        fat = (portion / 100) * product[\"fat\"]\n",
+    "        carbs = (portion / 100) * product[\"carbs\"]\n",
+    "        cost = (portion / 100) * product[\"price\"]\n",
+    "\n",
+    "        print(\n",
+    "            f\"  {product['name']}: {portion:.0f} г ({calories:.0f} ккал, {cost:.1f} руб.)\"\n",
+    "        )\n",
+    "\n",
     "        meal_calories += calories\n",
     "        meal_protein += protein\n",
     "        meal_fat += fat\n",
     "        meal_carbs += carbs\n",
     "        meal_cost += cost\n",
-    "    \n",
+    "\n",
     "    total_cost += meal_cost\n",
     "    total_calories += meal_calories\n",
     "    total_protein += meal_protein\n",
     "    total_fat += meal_fat\n",
     "    total_carbs += meal_carbs\n",
-    "    \n",
-    "    print(f\"  Итого: {meal_calories:.0f} ккал, {meal_protein:.1f} г белков, {meal_fat:.1f} г жиров, {meal_carbs:.1f} г углеводов, {meal_cost:.1f} руб.\")\n",
+    "\n",
+    "    print(\n",
+    "        f\"  Итого: {meal_calories:.0f} ккал, {meal_protein:.1f} г белков, {meal_fat:.1f} г жиров, {meal_carbs:.1f} г углеводов, {meal_cost:.1f} руб.\"\n",
+    "    )\n",
     "\n",
     "print(\"\\n=== Суточная сумма ===\")\n",
     "print(f\"• Калории: {total_calories:.0f} (цель: {TARGET_CALORIES})\")\n",
@@ -458,15 +733,23 @@
     "print(f\"• Стоимость: {total_cost:.1f} руб.\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c6545309",
+   "metadata": {},
+   "source": [
+    "График сходимости"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "a99d926e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -476,7 +759,6 @@
     }
    ],
    "source": [
-    "# 8. График сходимости\n",
     "gen = logbook.select(\"gen\")\n",
     "fit_min = logbook.select(\"min\")\n",
     "plt.plot(gen, fit_min, \"b-\", label=\"Минимальная ошибка\")\n",
@@ -486,14 +768,6 @@
     "plt.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ba3b8f3",
-   "metadata": {},
-   "source": [
-    "![супер мега детокс на ужин](docs/img/Снимок%20экрана%202025-05-10%20172044.png)"
-   ]
   }
  ],
  "metadata": {