SII_Lab_1_(2).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sinrez/pyCoursera/blob/master/SII_Lab_1_(2).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "psa_Yq2epuWE"
      },
      "source": [
        "## Лабораторная работа №1 ##"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnPJP8aBpuWH"
      },
      "source": [
        "### Задание №1: Разработать программу на языке Python, генерирующую определённый набор данных.\n",
        "Характеристика набора данных определяются согласно варианту. Число классов равно двум (трём, если хотите выполнить задание повышенной сложности), число элементов в классе N (согласно варианту), распределение значений в каждом классе – нормальное, данные двумерные (используются только два признака). Классификатор – логистическая регрессия. Разбиение выборки 50%/50% (половина данных используется для обучения классификатора, половина для оценки ошибки классификации). Точность классификации набора данных с использованием логистической регрессии должна попадать в диапазон, соответствующий варианту. <br>\n",
        "Результаты, которые необходимо получить в итоге:\n",
        "1. Параметры генерации данных, обеспечивающие выполнение задания.\n",
        "2. Графическое представления расположения набора данных (двумерный график, в котором каждый класс обозначен своим цветом).\n",
        "3. Значение точности классификации."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAv6ecwApuWI"
      },
      "source": [
        "### Задание №2: Реализовать предикции по аналогии с классификатором \"логистическая регрессия\".\n",
        "Пункты задания (алгоритм):\n",
        "1. Выполнить обучение классификатора из пакета sklearn на обучающей выборки.\n",
        "2. Вывести параметры, определенные классификатором для различения набора данных.\n",
        "3. Реализовать функцию, определяющую принадлежность вектора признаков определённому классу с использованием значений параметров, полученных в пункте 2.\n",
        "4. Провести классификацию тестовой части выборки как с использованием классификатора из пакета sklearn, так и с использованием своей реализации (функция из пункта 3). \n",
        "5. Сравнить результаты."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7sf1CNRBpuWI"
      },
      "source": [
        "### Генерация данных и анализ с использованием логистической регрессии ###"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "TM7MEK6gpuWJ"
      },
      "outputs": [],
      "source": [
        "#Импорт библотек\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "wVNKfAQapuWK"
      },
      "outputs": [],
      "source": [
        "# Подключения модуля для отрисовки графиков непосредственно в интерфейсе\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zItHvZaApuWK"
      },
      "source": [
        "### Инициализация вектора с использованием заданных значений"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LltJhvnFpuWK",
        "outputId": "631723ba-688f-41c8-ee82-b87f8418c0c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 7, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "x = np.array([2,7,5])    \n",
        "x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hM_bTnbxpuWL"
      },
      "source": [
        "### Инициализация вектора с использованием стандартных методов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f6ZBZ6yvpuWL",
        "outputId": "84ffb904-1d1a-4f68-99a3-81c58c1ead90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "y = np.arange(4)\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAjLm2mcpuWM",
        "outputId": "d150d89d-08fd-42a9-d4ce-0fc2be22dba7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4,  5,  6,  7,  8,  9, 10, 11, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "y = np.arange(4, 13)\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UU5XGNa_puWM",
        "outputId": "16bf1842-d9f3-4152-9ed6-09bb436b34ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4,  7, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "y = np.arange(4, 13, 3)\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XPzmtjdpuWM",
        "outputId": "db08e1ce-14b0-4975-cac9-dfc7b3e122c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([13, 10,  7])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "y = np.arange(13, 4, -3)\n",
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpKX-qFApuWN"
      },
      "source": [
        "### Операции над векторами"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kl9VopYQpuWN",
        "outputId": "73c6cba6-f128-4fe0-bc5b-5bf19ccf63c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([26, 70, 35])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "x * y    # Перемножение"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZgS8WvlpuWN",
        "outputId": "34860631-63b6-4abb-d1fa-a107adbd95fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15, 17, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "x + y    # Сложение"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SF2dd7XpuWN",
        "outputId": "b76214d5-07bb-4a2b-9443-a840c5f4da84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.15384615, 0.7       , 0.71428571])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "x / y    # Делене"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQGUZFE8puWN",
        "outputId": "ca1ea20b-d479-4306-9cdb-db76f5a92b35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([     8192, 282475249,     78125])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "x ** y   # Возведение в степень"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gdOjUyp_puWO",
        "outputId": "f4fb9469-20f2-401c-d373-6e1b57ee81e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "x.dot(y) # Скалярное произведение"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhAf0IjopuWO"
      },
      "source": [
        "### Обращение к элементам вектора"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2ek4XKwpuWO",
        "outputId": "79fb9708-b6fe-450b-d641-f35228b9ff37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "x[1]    # Выбор элемента в определённой позиции"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZpA7AFupuWO",
        "outputId": "bafaded6-0f52-4a3d-f6d7-41fd735d12b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "x[1:3]  # Выбор нескольких элементов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Poi4B0T0puWO",
        "outputId": "d5dc2f7e-7599-41fb-e906-5d733c6c618c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "x[-2]  # Можно выбрать элемент, указав его позицию \"с конца\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjY7MDhkpuWO",
        "outputId": "e82e64f5-cf30-4d25-a67c-09ca3b469fd8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "x[np.array([0,1])]  # Можно задать позицию элементов как вектор"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqWqESI-puWO",
        "outputId": "e75392cf-250d-4a77-c4a5-e9ebc91284a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "x[[0,1]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKmq7bBppuWP"
      },
      "source": [
        "### Матрицы"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydM9uNlrpuWP",
        "outputId": "f21e4711-433c-4929-a14e-3de49791ab25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "Z = np.array([\n",
        "    [1, 2, 3],\n",
        "    [4, 5, 6],\n",
        "    [7, 8, 9]\n",
        "])\n",
        "Z"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YPw0ZxipuWP",
        "outputId": "c156157c-8261-40b7-bc7e-8d505f905b5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "Z.shape # Размерность матрицы"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQgJQ-65puWP",
        "outputId": "1a0045d0-1554-47ab-9f5d-fdc8d1f5ea58"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "Z = np.arange(1, 10, 1).reshape(3, 3)\n",
        "Z"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkgF84s1puWP",
        "outputId": "174356e0-8f36-418c-e264-2f4f91ecab4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "Z[2:4, 1:3]    # Индексы считаться с 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2hXcOD1puWP",
        "outputId": "10dba765-8319-47b2-914b-917b56658059"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 3],\n",
              "       [5, 6],\n",
              "       [8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "Z[:, 1:3]    # По всем строкам"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zldKSDkWpuWP"
      },
      "source": [
        "## Генерация данных с заданным распредлением ###"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsKi_hVcpuWP"
      },
      "source": [
        "### Равномерное распределение"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0KDKx1LpuWP",
        "outputId": "389bc7ff-89e8-4df9-dd33-72557c73a4f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.8341094 , 0.60984451, 1.86722387, 0.87621922, 0.6564688 ,\n",
              "       1.65765825, 1.15060716, 0.81708821, 1.86395669, 0.47082545,\n",
              "       1.66327058, 0.72129089, 1.88834803, 0.67105411, 0.03053724,\n",
              "       0.09981665, 0.69414724, 0.07181467, 1.01932658, 1.3112808 ,\n",
              "       0.75551868, 0.09138663, 0.24491212, 1.75929719, 1.72905549,\n",
              "       0.47266552, 0.64282274, 1.94175496, 0.81656253, 1.64018903,\n",
              "       0.33776706, 0.17423209, 1.09953521, 1.39027629, 0.46076825,\n",
              "       1.0638897 , 0.4540175 , 1.97516562, 0.38912298, 1.12801046,\n",
              "       1.0134519 , 1.45841942, 0.40434718, 0.80151851, 1.36303339,\n",
              "       1.46858736, 0.38902846, 1.08581695, 0.42471928, 1.00618194,\n",
              "       0.68743212, 1.72102614, 0.6814047 , 1.40155186, 1.42150309,\n",
              "       0.24116287, 1.25141253, 0.96653381, 1.94054313, 1.33193234,\n",
              "       1.37048715, 1.89497416, 1.21706265, 1.67293687, 0.71696527,\n",
              "       0.36572753, 0.74086908, 0.83138222, 0.52504072, 0.32101228,\n",
              "       1.53141013, 0.56082841, 0.51774561, 1.57003215, 0.83971279,\n",
              "       0.30120534, 1.56371352, 1.38521426, 0.83084638, 0.84297015,\n",
              "       0.31147101, 0.1535021 , 1.99334843, 0.62011664, 0.82864996,\n",
              "       0.39554066, 1.73976   , 0.67076652, 0.27434081, 0.1567311 ,\n",
              "       1.97062159, 0.80246196, 0.48288178, 0.48564969, 1.29388705,\n",
              "       1.14028824, 1.01374339, 0.56135006, 1.93087651, 0.36707988,\n",
              "       0.25471013, 0.95980137, 1.49239696, 1.66221935, 1.76096241,\n",
              "       1.63475432, 1.45596495, 0.61375126, 1.14379922, 0.11745243,\n",
              "       1.00372082, 1.21913872, 0.98234854, 1.71633312, 1.43686524,\n",
              "       0.19936057, 0.10870765, 1.87531553, 1.50556877, 0.24904701,\n",
              "       0.22042454, 0.36130877, 1.44015318, 0.64717542, 1.64547086,\n",
              "       0.9399876 , 1.34625016, 1.15953597, 0.51555059, 1.74231916,\n",
              "       0.9469385 , 0.98790245, 1.14352075, 1.16441088, 0.73070776,\n",
              "       0.34999558, 1.21224802, 1.73612724, 1.48960544, 0.79388798,\n",
              "       1.98468751, 0.04143631, 1.49123171, 0.40267338, 1.46993103,\n",
              "       0.99184474, 0.68441784, 1.12764553, 1.00916866, 0.80479853,\n",
              "       0.79299545, 1.31149615, 1.98679138, 1.93280717, 0.83627473,\n",
              "       0.19661294, 1.6760815 , 0.04665077, 0.28863149, 1.54941763,\n",
              "       0.23018234, 0.01820008, 0.04151001, 1.46431785, 0.30803886,\n",
              "       0.6883542 , 0.90510486, 0.41943436, 0.55474874, 1.73294989,\n",
              "       0.61213415, 1.91020572, 0.82101277, 0.80365118, 0.0681273 ,\n",
              "       1.79191591, 0.10695232, 1.28421384, 0.18415832, 1.81564961,\n",
              "       1.04814423, 1.5535414 , 0.16209135, 0.71654785, 1.35227479,\n",
              "       1.14189755, 1.15454923, 0.23559907, 0.47466017, 0.14840708,\n",
              "       1.71181097, 0.72833943, 0.34856872, 0.72931718, 0.58648303,\n",
              "       1.81637145, 1.2683832 , 0.40030354, 1.59162539, 0.11677303,\n",
              "       0.73693092, 0.25155566, 0.12037627, 0.47737168, 1.61220802,\n",
              "       1.39384477, 1.3994256 , 0.39426685, 0.80546917, 1.70653647,\n",
              "       0.75031154, 0.69922732, 0.06541142, 1.24413416, 1.60972523,\n",
              "       0.42343101, 1.56701034, 0.16759112, 0.98509538, 0.16911025,\n",
              "       0.86576539, 1.39622791, 0.1750221 , 0.14499074, 0.49729289,\n",
              "       0.45832675, 1.9717569 , 1.20940015, 1.51400571, 0.89978248,\n",
              "       0.34355099, 0.09385126, 0.31288669, 1.83053936, 0.27888995,\n",
              "       1.90864077, 1.69086436, 0.39554268, 1.29166784, 1.80197738,\n",
              "       0.95780293, 1.38718087, 0.74220205, 0.3224018 , 1.40831057,\n",
              "       0.08841888, 1.42331559, 1.53855942, 0.62041795, 1.85727991,\n",
              "       1.11496778, 0.44255955, 1.43429303, 0.07976575, 1.74565915,\n",
              "       1.49752563, 0.60109542, 1.54484331, 0.33020563, 1.31458806,\n",
              "       1.22729125, 1.72430922, 0.74823499, 0.94463958, 0.74268163,\n",
              "       1.44180703, 1.5949483 , 0.63027943, 1.32072119, 1.68089523,\n",
              "       0.38373217, 0.29550708, 0.36360138, 0.50124771, 0.56274129,\n",
              "       0.64177157, 0.59967207, 1.64257356, 1.51428793, 1.30767318,\n",
              "       1.43690529, 1.33113653, 1.2760672 , 1.01084205, 1.1877503 ,\n",
              "       1.65626041, 1.77794107, 0.17692635, 1.98520875, 0.12902798,\n",
              "       1.74472631, 0.75738951, 1.60450489, 0.91516796, 0.5240217 ,\n",
              "       0.59669411, 0.67899583, 1.19960791, 0.92674788, 0.18258313,\n",
              "       1.7015042 , 0.96233844, 0.02035439, 0.71813308, 0.6086398 ,\n",
              "       0.95405089, 1.39985598, 1.8289944 , 0.56012044, 0.62769633,\n",
              "       1.63048881, 0.07669074, 0.58989389, 1.49287357, 0.59775661,\n",
              "       0.59278486, 1.29301606, 0.29949965, 1.77461498, 1.89094575,\n",
              "       1.0687912 , 0.00800895, 0.07577507, 1.27496362, 1.81324575,\n",
              "       0.11707398, 0.97623145, 1.18337007, 0.59163477, 1.97482748,\n",
              "       1.43811506, 0.82043298, 1.61788146, 0.60427985, 0.86224655,\n",
              "       1.89093102, 0.439108  , 1.55284844, 1.77022754, 1.22021064,\n",
              "       1.86005511, 1.48868599, 1.04765062, 0.9240827 , 1.01955996,\n",
              "       1.15719179, 0.38519131, 1.5893771 , 1.90684979, 1.20269565,\n",
              "       0.21446668, 0.50333771, 1.19420738, 0.09480554, 1.53780747,\n",
              "       1.2683365 , 0.47607981, 0.37873695, 0.09689565, 1.19916691,\n",
              "       1.4174491 , 0.58061484, 1.87836495, 0.51512308, 0.33728358,\n",
              "       1.38821325, 0.35281118, 1.18981716, 1.13428369, 1.303433  ,\n",
              "       0.95626873, 1.82020368, 0.99699825, 1.28292139, 1.41346805,\n",
              "       1.55552557, 0.48007042, 1.84743522, 1.25799979, 1.54167853,\n",
              "       1.86319233, 1.48152952, 1.90425226, 1.50645097, 1.68763408,\n",
              "       1.88289242, 1.7513726 , 1.94847614, 1.13635935, 1.1435669 ,\n",
              "       0.96289661, 1.53770069, 1.56080384, 0.35616599, 1.15285492,\n",
              "       0.23011664, 0.67169664, 1.74718665, 1.28504115, 1.28166582,\n",
              "       1.15709804, 1.54185099, 1.63592243, 1.72317455, 1.82115053,\n",
              "       1.49392632, 1.04521457, 0.23970219, 0.05216086, 1.54850769,\n",
              "       0.68678893, 0.06079816, 0.35018749, 0.24343265, 0.40471968,\n",
              "       1.07484507, 1.75032279, 1.19689233, 1.29097922, 0.09446655,\n",
              "       1.51234909, 1.20232942, 0.7254606 , 1.42823838, 1.31166316,\n",
              "       1.18777577, 0.11262256, 1.79331286, 1.85790272, 0.92233859,\n",
              "       0.01793277, 1.9554674 , 0.332742  , 1.06920288, 1.91479986,\n",
              "       0.42172187, 0.36683652, 0.69255977, 0.92985527, 0.94671045,\n",
              "       0.18921792, 0.21036555, 0.98511519, 0.08841586, 1.85475477,\n",
              "       1.44326715, 1.67894411, 1.86041317, 0.06996447, 0.24902126,\n",
              "       1.92990122, 1.73217029, 1.97985066, 0.8528718 , 0.75592529,\n",
              "       1.25323363, 1.0533666 , 1.74895459, 0.97509338, 1.75367756,\n",
              "       1.25156401, 0.17135259, 0.69457167, 1.97387151, 0.36815371,\n",
              "       0.6507844 , 0.83805981, 1.72765829, 0.54889205, 0.31175682,\n",
              "       1.45826279, 1.11572819, 0.37481593, 0.86919593, 1.66532173,\n",
              "       1.77539246, 0.52762213, 0.16318822, 1.52647971, 0.87134634,\n",
              "       0.05437022, 1.40129002, 1.7459249 , 0.4699723 , 1.31610559,\n",
              "       1.75877529, 0.33410498, 1.28653198, 1.85537275, 1.12893816,\n",
              "       1.68553798, 0.80144431, 1.59929802, 0.93505468, 0.47326439,\n",
              "       1.9055101 , 1.53158616, 0.06556026, 0.56200156, 1.6323049 ,\n",
              "       0.89267059, 1.42734058, 1.40545169, 1.74344757, 0.24449423,\n",
              "       0.74120417, 1.61941913, 0.59113017, 1.83740371, 0.16351054,\n",
              "       1.46241395, 0.78496608, 1.56533037, 1.45333742, 0.96076322,\n",
              "       0.04025805, 1.09510473, 1.41174818, 0.28151751, 1.64172102,\n",
              "       1.05377002, 0.14592361, 1.00640025, 0.13853769, 0.959044  ,\n",
              "       0.0655587 , 0.402609  ])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "x = np.random.uniform(0.0, 2.0, 527) # (Левая граница, Правая граница, Число элементов)\n",
        "x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtbaRQpdpuWP"
      },
      "source": [
        "### Нормальное распределение"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-lwJVeHpuWP",
        "outputId": "2deeacd5-a31e-4685-cefd-7b55da606c57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2.49207567, -0.53894481, -1.18865777,  4.06876896, -0.43129442,\n",
              "       -0.73837404,  1.1054215 ,  1.11148345, -3.23169627,  2.77005585,\n",
              "        1.80062243,  1.54198443, -1.96948556, -0.20460529, -2.14338296,\n",
              "       -0.76977293, -2.03854314,  0.60446493, -0.28872103,  1.02638156,\n",
              "       -0.34450275,  0.70287173, -0.59953434, -1.9657826 , -1.34222763,\n",
              "       -3.90119736,  2.70351687, -2.64079748,  1.64211433, -3.06298845,\n",
              "       -1.13729441, -1.0284777 , -0.36980928,  3.96401282,  0.56170939,\n",
              "        2.88723644,  1.20427323,  1.23108228, -0.18145751,  0.46093351,\n",
              "       -2.13696758, -2.53887758, -0.7288105 ,  2.73950687, -0.36755576,\n",
              "        1.01049943,  0.28472006, -0.25865937, -2.90007593,  1.69215346,\n",
              "        1.82977322,  0.37541408,  4.11447028, -0.11258912,  2.85286963,\n",
              "        0.13270261,  3.26663502,  1.15969639,  0.4429478 ,  0.35361527,\n",
              "        0.77168142,  0.9331257 , -2.95010694, -0.05780088,  2.63869656,\n",
              "       -0.44040039, -3.66791922,  2.24546772,  2.17960725, -4.26557612,\n",
              "       -1.64344034, -0.87336177, -0.168863  , -0.72266254,  5.71080586,\n",
              "       -0.36780604, -0.65632459,  3.04855981, -1.66515217, -1.56529366,\n",
              "        1.99679339, -2.05280422,  1.93564688, -0.99961987,  1.95717266,\n",
              "        2.54632141, -1.25313974, -0.56235246,  0.18225518, -3.33924938,\n",
              "       -0.88899017,  1.69556355, -3.44118192, -2.33352318, -0.67336122,\n",
              "        3.62153807, -0.44631811, -0.12388585,  0.57845506,  0.85233023,\n",
              "       -0.23925042,  0.96719896, -0.38517373,  2.06965364, -2.33150312,\n",
              "       -3.38765366, -1.29007082,  2.85507222,  2.09731309,  1.64078081,\n",
              "        0.47288671, -2.96073428,  1.36151908,  2.88019572,  1.7926955 ,\n",
              "       -3.54681483,  0.78743985,  0.31438432,  1.07236028,  0.99791036,\n",
              "       -0.88877216, -2.48108882, -0.35819974, -1.05535768,  2.08547174,\n",
              "       -0.57137846, -1.45782492,  2.7562637 , -1.78250498, -2.77798531,\n",
              "        1.91750811, -0.43376689,  0.99731452,  1.74033338,  1.96731249,\n",
              "       -0.40598176,  4.07006866, -3.42765371,  3.44891194, -2.61320265,\n",
              "       -0.48578319, -2.8321787 , -2.00030947,  1.20189443,  2.68242899,\n",
              "       -0.54918709,  3.33356288, -2.59583831, -1.52457898,  4.04229377,\n",
              "        2.87593693, -2.29782369,  0.60473361, -2.18100203,  0.28294679,\n",
              "       -1.34013366, -2.04412463, -0.75326058, -2.7723051 , -0.67204511,\n",
              "        2.62330681, -1.07105784,  1.00300029, -0.37708267,  2.10727541,\n",
              "       -1.19563687, -1.6081156 , -1.4449212 , -0.52124081,  0.17765354,\n",
              "        0.64930435, -2.77114602, -2.92091824, -0.21928639,  0.91695924,\n",
              "        1.08342969,  1.69446574,  2.82932524,  0.90123256, -1.86463737,\n",
              "        2.89177163,  2.61551289, -2.70677024,  2.30809336, -3.23417447,\n",
              "        0.2787411 , -0.42768181,  1.48954623,  2.92631701,  1.35722166,\n",
              "        0.28752891,  1.70739261,  0.00694328,  0.02903582, -4.74433862,\n",
              "        0.10136068,  3.62590222, -2.97827204,  2.00495325, -0.72530606,\n",
              "       -1.06033189, -1.12659514,  0.95113289,  0.04124632,  1.74558897,\n",
              "       -1.79100986, -0.17410894, -0.65438057, -0.76704622,  0.74813063,\n",
              "       -2.77344079,  0.3986403 , -2.05794772, -0.6417712 , -0.99839259,\n",
              "        2.91721546,  0.04881137,  3.0848175 ,  4.05118459, -0.30274531,\n",
              "       -0.77168101, -1.60423341, -2.00845212, -2.72415483,  2.37316967,\n",
              "       -0.8568417 ,  3.12844086, -0.95795547,  0.86139286,  0.12211671,\n",
              "       -0.15012239, -0.73201165, -0.58584192, -0.16469401, -1.67064727,\n",
              "       -2.56296455,  1.00297708,  1.39144474, -2.25734662, -0.94264201,\n",
              "        0.90467411, -0.17444474,  1.40895455,  0.02701873, -1.0138167 ,\n",
              "        0.91499994, -1.37253502, -2.63424382,  0.85662836,  3.10408777,\n",
              "        0.56483666, -0.17651515,  0.11452678,  1.84620487,  1.00097526,\n",
              "        2.15537245,  4.09353827,  0.85415875,  1.52870833,  1.57610653,\n",
              "       -1.07978956,  3.00809991, -0.01297806,  2.63919521,  1.68695741,\n",
              "        1.15028408,  1.72810552,  4.57979561,  2.77015938,  0.99354725,\n",
              "       -1.42763957,  0.18067168,  0.28552671, -3.29530449, -1.90146599,\n",
              "       -1.06803549, -0.95309545, -3.35006635,  0.81979011, -1.6313871 ,\n",
              "       -0.17945879, -0.78350124,  1.57756474,  2.07078785, -1.75420412,\n",
              "       -2.21586313,  0.62893537, -2.03026456, -0.43039308, -2.01984547,\n",
              "       -1.44236462,  0.65508187,  1.40512337,  0.32166219, -0.36030351,\n",
              "       -1.68702705,  1.27543349, -1.56030212, -0.42772252,  0.98292581,\n",
              "        0.99908268, -1.54375255, -2.79469722,  0.70074998, -0.58378377,\n",
              "        0.20577392, -0.65983159, -3.08010073, -0.26616935, -0.7269344 ,\n",
              "       -0.67762377, -4.46293226, -3.24917312, -2.04824126,  1.1397483 ,\n",
              "       -3.53273562,  1.74438604, -3.87238089, -2.69265156, -3.36717838,\n",
              "        0.33542143,  4.95349435, -1.3716947 ,  0.06002982, -3.20351696,\n",
              "       -1.76511877, -2.35402011, -2.51632202,  0.16658583, -0.83157846,\n",
              "       -2.42063284,  0.59639364, -0.17449002, -0.52771003,  1.87134891,\n",
              "        1.27991921,  1.15514333,  0.22510979, -1.90107763,  0.54651193,\n",
              "        3.36029746, -1.49382027, -1.36419986, -1.30770732,  2.50641374,\n",
              "       -0.69068865, -1.63856202,  1.85814045,  0.09807274,  3.80983037,\n",
              "       -2.21386492, -0.34328397, -0.41600648,  1.09321148,  0.24801007,\n",
              "        1.60723086,  1.41790994, -0.11483523,  2.75779415,  1.29963802,\n",
              "        0.45825879, -1.54222683, -1.10794547, -6.0637897 ,  0.9000421 ,\n",
              "        2.07945666,  2.57430064, -1.54801316,  2.38965684, -0.82807266,\n",
              "        2.84369683,  0.2586782 ,  1.25759887,  4.01030507,  0.16713089,\n",
              "       -3.44986879,  2.39416923, -1.81566752, -2.53783209, -3.86503998,\n",
              "       -2.02955059,  3.10850112,  1.35961393, -0.57220432,  0.0411696 ,\n",
              "        0.06755364,  0.26065829, -3.64268609,  0.10966207, -1.3106772 ,\n",
              "        4.70333423, -0.71905419, -1.64766086, -2.86203852, -2.32672431,\n",
              "       -2.48890873,  0.65097865,  1.62532234,  0.5972962 ,  3.51853873,\n",
              "        0.57508231,  0.92929556,  3.79436974, -0.1932165 , -3.04785111,\n",
              "       -1.82771381, -1.43282944,  4.30661757, -3.43335879, -0.84031329,\n",
              "        1.08199556, -4.6226664 , -0.75244274,  0.59036303,  0.15502622,\n",
              "        1.90745805,  0.65854499, -1.88591744,  1.80261227, -2.37520163,\n",
              "        1.62082947, -2.96302522,  2.77452647, -3.05895922, -0.40531937,\n",
              "        0.74153284, -1.81347578, -3.22486343, -0.81526017, -1.97151141,\n",
              "        0.87648435,  0.33066558,  0.38703903,  2.03858287, -2.57130923,\n",
              "       -0.85228286,  0.10764512, -3.5614088 , -1.39254007, -2.29337297,\n",
              "       -0.26924214, -0.92690707, -0.88421201,  3.68108378,  1.56298967,\n",
              "       -0.32159978, -2.56232669, -3.36412824,  1.38033024,  1.47521431,\n",
              "       -2.0769172 ,  0.20668481, -0.07307057,  3.6675677 ,  4.06067077,\n",
              "       -0.55192009,  2.92636545, -1.14944522,  1.13130125, -0.29335796,\n",
              "        1.81691885, -0.50673195,  0.89067416, -0.58013208, -0.35078517,\n",
              "       -0.88789703, -1.65621232,  1.89444984,  3.59810419,  2.37823702,\n",
              "        0.51184776, -0.4684114 ,  0.4913883 , -0.17378212, -0.03609073,\n",
              "        2.51094242,  0.65299456, -0.3085224 , -0.31166462, -0.97237804,\n",
              "        0.67758049,  2.72260519, -2.49477227,  3.1005355 , -0.5477692 ,\n",
              "        1.19702036, -1.59722175,  2.25388273,  1.39990173,  6.20251068,\n",
              "       -5.08548959, -1.13670858, -2.41197248,  1.07413982, -0.83987699,\n",
              "        4.7885586 , -0.09691685, -0.82541639, -0.75210882,  0.04261754,\n",
              "       -2.38887866, -1.8786055 , -0.88266363, -0.27696596,  1.90587076,\n",
              "        2.16520546,  1.50733178, -1.21733067, -4.40266023, -3.95499871,\n",
              "        1.7075038 , -1.03340259, -1.07411746, -1.01850432,  0.48413997,\n",
              "       -0.08290494, -1.38015034, -1.04506429, -0.31891723,  0.94421336,\n",
              "       -1.10588921, -0.44567774,  0.79113611, -0.42799035,  0.15254311,\n",
              "       -2.84672168, -0.35837391])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "y = np.random.normal(0.0, 2.0, 527) # (Среднее, СКО, Число элементов)\n",
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgMLuxncpuWP"
      },
      "source": [
        "### Визуализация точек с использованием графиков"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "wc-Vr4AlpuWQ",
        "outputId": "0ccded19-e747-4fda-d045-0e777bc179fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fccfc4c2310>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Se/kvG7bBLSQKyKW8N2+O2/XSGHd9hQ7QofUfO0v1ndDkGvR8r9f0F4TUh8+5Z2USsnLLGTpaqm9PIQqIZdgMrlnQvg055vwQvuvmIioOm53at7Mb466vUCdlCKXNCDnDJktObnyvFzpIu8qhmWZmrNBVWY6ou4mbbFMUwHJuBudqcDl2efRtKCUb19iKpev6oREduesqhNKOxJS6tzmec09uZtdz+WFCzTS2cminpjN7qJXWjIk7lFMUwGwzuK+wyJvBtRuMK6pg8+awBuYapGwNJdR57CvXVGctPiuwZkSHa3Ao0Qmbiio0vDT2WjFKOsYUNMTGczEDps/kILauUwdwHwUy4mQrKQqIg5vBPZdF3AzOZoroskWvW3dnB6Wr07sGNVunyxmO2GSqs5bYmO6uuiqh0LrKeVZHQ3ZsnwElxhSUc/XSJ1/s5MN1P7HKK3Uy1OezijFLFSBVAZwIPB84a7Z9wxipmAJwDYTthhwTzjfECiB0QB87DK5vgPCN6gk5Z05sIY9DDv4hdu6uNrt+/Z1DbYdcAcbUVakVQKw8M2wKYHXVbRLLKUsHKSagl1JtAPdy4HeBvcDvuPKVSMUUQOhAGDNwDuEDsK0y2ufL4fhKwXVPU5CxD1s5DzWApphQmoPK2D6gUIYo+5gycckVMpEpsFpIUQBXA3dt/H834GpXvhJptBVA6vEzSkcB9cklcuhDMqU6UUjHSQnVG+vZib6ZdCnlZLuXsVdvfZRWKKVXX7GDr02u0LZSwDSbogDOB+7e+P/uwCdc+UqkQX0ANpvuVJ2nq6v2gcFn4EoZ/EPKxHe1Mju3z6CSs166ggK6nkwuNQiXcKKWJib8MlRRlO57feXqCvrok8vWTvrkLqDcUxTAe+sIoLcAb6Z6NeS7gXOBc135c6ZsCsC2FO4aLLsqKmU5XXKW5DNAlRi0cq2iZk8BDyFDH7YJgU/KMQi7/Ey5BsKcbdE2C273kxTZQ2Vur9xm22F0OaZ969h3TIhZsUxsBfB0W3Llz5myKICuxrdu3cHNtvr2SnHtaeLbqMeawZQetGLiumO2Z8gpQx+xZZirLm0DUc6wwtxt0Xc7CJvDNPV+uiZlfVuKNO/XJ/Agpp12Xd+1+d6UfABTSkkKwOVUTO3kpSJ1uu6haZboavChs9ccg5bLYdt1fptMOWUIVSih++jk2vDMdR8x9xJzndhr5OpfzXbb3sGzj75B09dnEyO7z8RidTXu5UQTigK6tpWuA6515SuRohVAjHb3aTSHlnB/ahI7S/W5h+Zspq0oSsetu+QLUZqxZqBcM6eQwSDmRTMubAoop7kwt625RD/zbQspykfEXuYpW7NPxFeTogCuADa3kytfiRStAHLPTLo6SZ+3f+1aP1lcDcL3HvrOM0S4n2uV1RXhk9sMlOM+Qway2E3mbNgckbnu0XadrnryvV7z2L4+4TMrD20Loau2tjy2skiZWEwkWit5M7gppGgFkNI4fBul7dgmsY3J9x7GDgO0ydolm+teSikrF82BzNUOYgct27X72kjuSCfXuVKuZ8sb0td82rRNadp8AFCZZFyKPFbpLsAKYG+9BfRRzeTKVyJNZgXQtSwNqeiYxpS6AoglRlafJXOIT6bPtDWUYrA5LUvN8PruNfeA4irT1Ov1nT+kv/n6x2xK02d78ZWVg6uWtWur/8d2tGdq8ykKYF/D9n/dwvgAUlcFPtfwDRWLvYe+gdL3fC45Yhpvl9ML7rxTY6itePPm8jHmtnvqi+TINSD3ydz+3jWI5Q5BLqXgfM1Audt0yBO7ubbK8K3brrEi02pPo4BCOlLsjKR0hXY5d31eRdh1Hh85YgY3l/3adkyOushpImnSNTvMdb2+c9ic910DskuGGFlzRq21TUtdSjW3Q933fkJeI5pjle1TFxlXeykrgOe2ngS+B/DrrnwlUtYngWMHoZTBpMTyPedj6205YmZ/Pnlsx4TWS1uWoW3yxvQrB19i7tlHKbTvOaZsXPdum936+BeGNun1yZXS5mLwmShlXH2lKIDLO767zJWvRMqqAELMELlCJlMqtKuzxA52vnLkXAE087hmYV2zWd9QvBImi1IRIi6ZbclnNdu+Z1fZhJoqbPc+EednJ035moo7pOxT8Qn1ncgK4EpAGv+vBa5y5SuRsu8F1GwIfbOqnDOT2AoNnbW4BjtfOWJ9ADGRJV330Cz/kmarplxdA13MiiXE+R+6Aoid2edWZLbzTST8sRefNpjiA4h1rjfLMGOIcYoCeBXwLuDUOr0L+F+ufCXSoG8EK7EczW2y6du2wqW4QuSIKRdfB/PsGN+90mMd1+D2j8TOZn0HOtv5Q1ajKbb93DP2XIqxKd8QJqHVVfeM3xUF5PJv+EyCfOo6k08kRQGsAVaA8+r0bGCtK1+JNOg7gUvR1XCa323efDC8cPZ7bMSSTbmMYX/to8QTqaFvB7MNWCsr/U7WHLPvmcyuQcmmxHzrM2aV00fOFUWs4g4lVdn2naN5rG+bGHBrcY0CmgrtDuh697Dvfia2mcxQzrVYhVLCXhxqmnENBO3vTj31YF6fgc5ngHUNTiXt57Ez9r4tsmdt27dN2EwiOaK4fK7je/+uskpZFbryRJKyAnhAPfP/XHNPIFe+EmnuFUBXhfvM7rti4ENSzg7ke1+h4a25QzdzdcK+WXnzYUAf5ec7wK6slBkMXDLG+nt8dtr0wdUPck1kQlfTIU+vz461mWzbq3vbKrJPAUWQogD+obb9XwFspXo15O+58vkk4DH1G8euAX7LdfzcK4DQ2UffYBQSseBqSDlMQTlm8DGmsRwyuWaeMWXad38pTuyUwSDERxDSFnzas6/MPudK8VO5Blpf+W0m2aY5z2fC1g6nLfH8Sk2KArik/ntl+7uUVEcTfRm4H7C+3nLiQbY8c68AUp8+TkldM5lcja5ExIerE+WIRrLJDu5BI/T+fAYqn/DAUEqY2FyyhpaR76DZNSD3PUSXYjrtay99ZdlcEc7k8pmoNe8nZcLjIEUBXFg7gt8NPA94EhneCQycAny48f+LgRfb8hR9I9gQ2BpPaQUQY8vso11+KdvlhpZVipwxphmfGV9O+uRpPiAUSikFHTrAuc7nM2D7PquSmvqcz7a+GnKsrQ4KrAZSFMDJwCbgWKpXQr4b+AlXPo/zPhV4Q+P/Xwb+tOO4M4E9wJ4tW7ZEF8AdFF5qRV276Sxrav5cjbkZbtgcEG2N0hb+1r6HdevubAv2maHbBuecs0vbdX1irW1RQLkp0T5zrgBWV43ZtMmv3fm8+GR2Tl8fV3vGnGOg9xn4XWU5k62ZN3YSU2DFlhwFBBwBHO57vMf5vBRAM2VZAZRaDvvSNCvMZlChs9PQBj4b/H0d0LaN12wzVN9Vlc8gF9p5fO3ALgXssiHnWDX6OGRzrlBzKRWX07edXK8+nOG7MnbZzENTjFIPMU3GmjELrNhSVgDb66eB99VpL3CSK5/HeccxAZWOOd+40W2765tFd0UIpDby2SAZ0slsJh3btXzxUcKpHS33U8I+pCih0ivQks5+W/IxXbnab5fMrhVs6ODfTK6JWUjkToxdf0orgDr65z83/n84cIUrn8d5D6tDSu/bcAI/2JZncisAnxlRaKRHu4HmMAnZ7NjtTmYLQ2x2jq7vfQkJ0Wx2nqbpoblU963TktsT5IryGcof5UuzDmLbX/PeukxwMT4WmzwrK/Z+GaLIbMq5VHuamA/gso7vsrwlDDgN+GIdDbTTdXwWBZBauL7OqpiByNUIY01Crlm9rWzaDdt2Hd/y8936wbfefDuir6Lwnck3j9m4MU/dd20Z0OeLyWEmsp0nxwq0mbr21rHVXYwtvum8t22VHtKX+tplyRWlS/5AUhTAq4E/Bx4J/BTwWuB/AycCJ7ry50yjRwGF2kBDByJXI4y9/qwB5bC7xwzezfLrG1BcStjW2UIGdp89WmKOyVn3s4eebD6LXDb9mC0NutKpp8a1S1tKkT3m3n3rMJcMLvn6XkIUQYoCON+SPuHKnzON/hxA7AzcZyDybYSxsc22yJ4ZvisTm/PMtje+rfxce+i7Zoq+HdFVBn0yNp9ETZk9+ta9bdWWa/M8m0Pftz00Z6Yx7dK33LpIXQX5rqpdK9OcDvsZtteQRpAcBTSFNLoCiLGD9i3pmx3Z58XVswEo1hbr06FCfBPN/2eDd5//YPa7TfaUFUCzXFM7oo+MIXUQ40SMTc2Zqo9SjH0Irq8tpT545dsWcpOyMi2FrXyiTqcKIJ3QTutjU23H6HcNss1jYzqZbyOO9QHMBoS+7anXrPErv1gfQE58ZPRtB67ZWqyN3WcFYPP5+Cig2XGu9tqOWuvbHA66fQBdZTYLevCNmsmh/LsmZmM55VdX7WUUgSqANi7nVztSIWbwbxNiq+7r5Js3uztRe/ANeUWhLWzN1Shd5eHa+dQVPVFqud2+hktGVwdtDpQ+1+ur674Hrk491a4MXfL5KJ1ZXfQN9D6KoWsAd8lmW5U0V5u2+lq/Pus2CoNj62tqAspAV6Nphl2GDLAbN/q/NSgkbMzWEbrMRSnmFV/6GmbIoNj0EbRTSvSEr3LwjfBxyWhbiYUOOn0Dqs8sPjRG3ncjQdvum6nRLynmr3Z0kM+5hjTj5JikuExzEaQ4gTcALwH+b/3/A4DHu/KVSNkUQA77azt80qfSQzqOzRmZInPb3BSy3LXZv22hkF0y5DTn+J4v1FlsOzb39hBdbSg2zjzXNiKlnlS1TcBC+55vvhyhmTH3FdMmXM75CFIUwDuBFwGfrf/fQMeL4odI2RRAjg7i09i7BlrfQaOvMaXKbbPD+jRW27ltM+d22eXc7dBXsYbOXPsUe98AFmJqy3lfvvlm23WEtJeua+WIUGmXbWzf882b62G/mKiqUOXTF1Kb0L5SFMCe+u9lje/2uvKVSJNdAXRhsyXPlIBr4OtqcLbOnUNBuO7L9iTw6mqYkzrX0tx3RmpT/CHk6ugufGaUMTb6kHbSNXBmDlE0xoSH1voETeSsm5SoqhDl41plR5KiAC4E7jZ7+he4P/BpV74SKUkBtJ1TIXZ+n4Gr3RFdA2FsZbo6d2oonquxusol9Ho+itTlrPc9t8uH4UvJLSXauO4/JEqnmS+lfnwUaagt3FemvgfhbD68HBMNH6WfY2Jga88J7StFAfwscAFwANhdbwj3SFe+EilaAdgiBZqNZ5YOO6y78GeV6WNb9u1cMSYQV+dyDYwpA3Ju34StUafMZPuUdI7ZVUhHLxm55FJ+tmulOE9teVdW4m3hrsnL7KEzV/mXKHMfpW/zbfjKYVOuY6wAqrxsBh4HPB442idPiRStAGIaTEgjihlsYxyIMbOqLsWX4gPI7ZuwbSNtqzdXpEufvdRWH76UcDrHELqvkEs23/BJ22x97dr4mbCvUi+1AouJrGrfk20V7lP3uVapLVIVwBOBP67TKBFAxiQogNJL9lCncswsNHYwsSm3WYedXdu3gXWdM9b0ZFuu2+otdvDLZb/3UcalfQU+Ew9Xm4qdKce079DAib77KVGurqiuFKUfImOhAIMUE9ArgI8Dz6zTR4E/cOUrkYqtAFLxibpoDrS2jjPWPaSwuhq/SZ7tnmJXALayiXWsxpBjIHQ9r+BafZXwSxhjDwbI0VZtZZd7ZeVrGkxR+rnrPpDU9wGsafy/Nsf7AGJSVh+AjyM3ZEYcYvuL6SBDOh5D6bufvq0hwO6Ebz6FmhLN0lc2sY7VXOXislWHyBDqCM9F375PGzfmCWMcys6/umr3X4X2L9fKdKQJW6oCOKrx/1FzpwCM8XOcpnT8Zkd02fdjrlXaj5GCSzm1TUS+zry++2r/lrOz5Vxp2QZCWxuIkaG0v6GLlZVuJZ/D0T7E/fhMIkLr3dYex9pYzqQpgF8C9gNvAd4KXAc8zZWvRCq6GVyujh/iLEp16Npmw13O3lINMKfTL0bGnIOFbTAIlck2ENrKLHa1l3NWXCoAIrTNl5zA+Mieoy1C8gtdUkl1At+rdgQ/EbinT54SqagCyGViKWmq6esQIZ0w9xK0zwEcshlayUEr9Px95oA1a9wryObvNqe4zYltezK2vdqD/DtXhirTkACImMi3krhk37gx7rxDrbwDSFkBPKIrufKVSAu1AgjF1qhCO2EuJjrbscoXa0u3KTgfU0K7LdjaSazvI3RA7WpToWa5PoXZbpMFnm5NJofDdk5IUQDvr9PXgffVn9/nylciFVUAY5sjUhyTY60A+q5r20lyKHwjO2bHpjzFHFL+sygWV53GrvZ867fv+q7B0KesZk/rNuX3OW/z+Hb+Eu3JdS9jKqfMJG8HTcfL4YdOxV8IM4YNdXZ8zAA/a6B9ERltB13uJbdt5THm8n511S/KaEaoHbt9Ht8VWDueO6a9ua7lO2u1Ke8YZbd2bfwzEb5KpZQSSDFhzgk5FMClvseWSqO8EawvgiUnrgHe5Vfoy297ytaFz+CUazaam1C5XI/f2/blt10vpfz7yLXHlEt59w2GKQ7q0KinIdtT6uRvgnb/JikmoLPrdEPj89mufCXS4ApgdbX7Aad164aZSbsGeF8FEYqvGcs1cxvLhuqaJbfvw8fp6jLXDBWCaVMAIdd0zcj7BrO+62/c6B4A+yZTISuoKdBlrho6BDeQFAXwsq7kylciDa4AbDOTIWzpvgNQbsezbxSKSNWJ+x74muIKoGvLYp8B3DXDG2oGaBssQ1d4JTZsC50wbNjgf84h25Mtqqzvoc+ptP8OJvVKSOBVwBfqh8zeA9zdJ9/gCsDW2UpH04QMQLlnoKGP4a9bd+eV0hR9AOvX98tUMrQyJ33KbVY3IZTwQdgGQJupbCwfQBe2fYFiHP4TMA3l8AGcVpuBvgrs8M3Xc65HA4fVn/8I+COffMUUQEzExZo1h+6ceOqpBwcO226UoTIMlb+JbQUwlr8hlBjfzZCmnFhCoptKEDoANrEdWyoKKCYgw1a+IQpwVgYTaE85FMDFwA/VW0NncwgDTwJ2+xxbRAHYOn2fD8A35X5F4FDYymQsf8MQ5DallcJ3wC1Bigmkz1S4Zs1wssZuuTK7T9sKzHdc6DKlFl4dZI0CAv7eN5/Hed8PnGH5/UxgD7Bny5Yt6SXRLnRXhEdfmJhPWrs2Xd6xCF0VlfI3DEmKchtyqT92mcU6QW19pQQx5eSKCOtTKrMVi8+4MDMNtSeXNhNlIilO4HPrdFP99zXADR75PgZ8tiOd3jhmZ+0DENf5jMmwAvCJN+7r9KFLv1maiA0wG0P6G4YmdmAdehUzpVVTUyZXOx9KAbhs9ba25eNjsd2rzzhhm3imvFfZQooCeHpXcuXzOO8zgH8CNvjmSVYAIfZLXweWS4lMraPmYCh/w9D0Obhdb8ka4x5S6qAdxeXzFrAcDDHo+UzybPXSZ+LyNee6xolZ/x9KGdZMLQroMcDngGNC8iUrAN9ZfFenD1k9zNKmTdMZ3GyMuUqZ2my2PTh2RTeFbnOQS54cdeNqx6VXLqXNHr4DsI2VlfiADpuPpFl/86IAgG8BtzTSt4BbXPkc57wG+Gfg8jq93idfsRVAM4Klr9PPlEDfzKkrCmhK5o0+pjAAj62A+q7t6/ArFZVTom58VrKxcvvUY+loNx8bvuv8qWXuc49zZAK6zHXMUKmID6BduTmX833nyrFZWq5Bc0ommKFxtYcQv0+JcL8SdeNzTzETlCEmEqX77+qqey+krjwx/XB19c7PquTeYaBBigL4PHA8cBxwpOv4kilLGGjsDCJXp8jROXJ2tnlYpZTCNViE+n1yr2JcDyL6zmh9ot5KTnZylYnP4O5rgmnjMo119YfUfjgPYaDA+cDfA5+pHwS7EtjuylciDfIkcO5ZV8ysYkgZF2EFENuRXMovJeY9x73Y3lXrM+B0yd/1priSE4lckx7bNdqDczMKyHdl5lL2XfUb03dS/AsJZHMCAw/vO1npNIgCKLGUzT3LLr1KmadIpRT5fWeUMTHvue7FJ/UNOL4+rxxRQL6rJVcEjk2Rhw64IcfbFFhf/Yb2w75t2089tfhKIGsU0EKvAIxxN8TQMLrcs+wSq5SxnLCppNp8YwbzUuXlMqP0DVB9A86Q5j1f5dV3bZ+6CK2vkPv3WQGkKiSfFV3OCUWDJAUAPA54EfDSWfLJlzslKYBcnTYmjM634frKOO+z9hj6yiZ1kJuS8nPdS8kZcA58zFepsobUV8j926L2cikkn8G/UB2l+ABeD7ytDtt8We0DeKMrX4kUrQBcjqGQjadiw+h8VhUhjWkkW+Io2MpmEXwYM1z3EtpGxpwolJyt55YhxPSWopD69kLKfd8dpCiAK1p/NwGfcuUrkaIVQGg0R0xDTa28kIEspHNNaYYbi61susrC5+ndKeJrBgmpzzHrv9RsPbcMIeNDysC8caP/dSa0Ari4/nsR8IPAXYBrXPlKpGgFELOPT+hSNbXyctgrZw286Z9oR3zMo6nIJ1qnec9TejdBKCGDZvPYTZsOltM8rgjHXK2EjA8pA7PvdabkAwBeAtwdeApwI9X7AH7fla9EGmwFYNP0MT6AFBljIhYGnl0UJ6RsFskkZMPHbDGkEsix0hhrtdLXZmwhpDGy+kRlTTkKqJ79HxmSJ2fK6gNIGSRDo4BiZQyNWfaNMpi3h7xCymZZHmzzmdQMtR35FIISUpRHn/x9vsE+n6JL4Y5YTikrgF/pSq58JVKWKKAuzZ5rFp9KahRQDuU2VXzLZllWAL7mhCEYu8xzDKw5/BUik/XJpCiA24ELOfgugNcA57rylUjZngNoV0Ku188NSVdD8pkVzpM9PIYpzEaHoPQKIGSgGnvVNYQCapaHa3I1wcCLFAXwI8ArgY8Avwkc7cpTKg3+Uvh5o2vwW78+zEQV03in1uCnJk8JSvoAQpXo2CuA0goo1IQ8wQlIsg+gtv/vAq70zZM7TU4BTHGgKWELdUWiTLDBLwWlooBCB/Sx24DNuVry/H1Kp281NuI4kbICeCDwCuDDwH8PfYlLzjQpBTB2oy9BzExu7Nmfkp+YGfXYzxt0PWTl+7IZl+y+/hbbJnsjjxOpPoB/mtn+Z8mVr0TKrgBSGu0iDXwu/4Gt449t/1XyM3bbjnnYra/tumT2mcj5+lt8ttoeaZxIUQDPoMA7gWNSVgWQOoMPeThpKuahLnzsmzErgEJvNlIGYMzVbcy1YycvtrzNNu/77E/IA2UDT5BSN4NbDzwE+DFgvU+eEimrAkid5biexp0X85BrduPjA+ha+oa863VelOUyMbWHsmz90jbwuvpz6DsGbM/+hPgK5mgF8Lh6I7hPAhcA1wOPdeUrkbIqgBy7SC7CBmWuzuPT8fuWvj7366MsVUEsDzH9MiUuP2df7YvC89mOpXAbT1EAXwB+qPH//YEvuPKVSJNaARjTX2nzZBfPUQ4p9+u6/jytppR0Ytpj7JO5fXlT2lfXmOAa3Ado4ykK4DOt/6X93VBpUj4AG/O0AshRDin3m3sPfGW+iW2PqeHPY64wB2jjKQrgdcDfNpzBHwBeCzwZeLIrf840qSgg13nnadaaWg4p9+tq/PO0mlLSaJpPZ/taLYPJb4A2nqIA3mxJb3Llz5km9RyAi3mYkeS8Tuy5XMpDVwDLwbxNmnIy5RVAyQT8D8D4bi8xSQWQe7AeqiNMqcPZynBKcirlWGZFP3EfwLHAe4Cb6vTXwLGufB7nvU/9dPH+uVUAJSpuqI4wTx1ubButUp5lN/WNFAUk1W/9iMhHgb8A3l5/dQawwxjzs9aMDkTkPOD3gb8Bthtj/tWVZ/v27WbPnj0pl83Ltm2wf/+dv9+6FfbtizvnmjVV028jArffHnfOMa+jKD6U6EvKHYjIJcaY7e3v13jkPcYY82ZjzH/U6S3AMYnCnA58xRizN+U8o3P99WHf+7BlS9j3U7+OovhwzjmwYcOh323YUH2vFMNHAdwsImeIyNo6nQHc7MokIh8Tkc92pNOB3wZe6iOgiJwpIntEZM+BAwd8sgxHiUF0qI6gHU6ZEjt2wK5d1YxfpPq7a1f1vVKOLrtQMwFbgfcBB6h8AO8FtrjyWc73Y/V59tXpP6ieLr6nK+9S+ABm53U9dp4Dta0rylJArA+gNCKyj3n1AQDs3g07d1Zmny1bqhl0rlnL7t1w5plw220Hv9uwQWdGiqIE0ecD6FUAIvIaqhDNTowxz88k2D7mWQGURB1jijJ9Sk4CMxHjBN4DXFKnJzY+z1IWjDHbfAb/Qdi9uxp016yp/u7ePa48JZzMiqLkY7ZK37+/MgLv31/9P/bY4YmXCUhELjPG/PgA8lgpugKYorlFVwCKMm3mpI+mhIGCxRS0MOzceejgD9X/O3eOIw9opI6iTJ05X6X7KoDFZWb26dLiMG5FamicokybOX+e5rC+H0TkWxyc+W8QkVtmPwHGGHNEaeGK02X2aTN2Re7YoQO+okyVc87pNh3PySq9dwVgjDncGHNEnQ5rfD58IQZ/6Db7NJmjihyVqTnPFWUo5nyVPvpzACFkdwL37YcDVUVOMJxrckzRea4oyiGkOoEXkz7zzsyDrwOYmyk6zxVF8WK5FYBG2aQz51EQirLMLLcCmHP73SSY8ygIRVlmllsBQDXY79tX7YGvZp9wdBWlTB0NUuhFFYCShq6ilCkz51s1lGa5o4AURVls5mSrhtJoFJCiKMuHBilYUQWgKMriokEKVlQBKIqyuGiQghVVAIqiLC4apGCldzM4RVGUhUA3VOxFVwCKoihLiioARVGUJUUVgKIoypKiCkBRFGVJUQWgKIqypKgCUBRFWVJUASiKoiwpoykAETlLRL4gIleJyCvHkkNRFGVZGUUBiMijgNOB440xDwb+eAw5FAu6h7qiLDxjPQm8ArzCGPMdAGPMTSPJoXTRftH7bA910CcqFWWBGMsE9EDgP4vIxSJygYic3HegiJwpIntEZM+BAwcGFHGJ0Re9K8pSUGwFICIfA+7Z8dPO+rpHAT8BnAy8S0TuZzreTmOM2QXsguqFMKXkVRroHuqKshQUUwDGmJ/p+01EVoB31wP+p0XkduBoQKf4U2DLlu63KOke6oqyUIxlAnov8CgAEXkgsB7415FkUdroHuqKshSMpQDeBNxPRD4L/CXw9C7zjzISuoe6oiwF+lJ4RVGUBUdfCq8oiqIcgioARVGUJUUVgKIoypKiCkBRFGVJUQWgKIqypMxVFJCIHAA6nlBycjTTfM5gqnLBdGVTucKZqmwqVzixsm01xhzT/nKuFEAsIrKnKwRqbKYqF0xXNpUrnKnKpnKFk1s2NQEpiqIsKaoAFEVRlpRlUQC7xhagh6nKBdOVTeUKZ6qyqVzhZJVtKXwAiqIoyp1ZlhWAoiiK0kIVgKIoypIy9wpARB4jIleLyDUi8lsdv99FRN5Z/36xiGxr/Pbi+vurReTnBpbrbBH5nIhcISIfF5Gtjd++LyKX1+l9A8v1DBE50Lj+f2v89nQR+VKdnj6wXP+nIdMXReQbjd9KltebROSmeuvyrt9FRM6t5b5CRE5s/FasvDxl21HLdKWIXCgixzd+21d/f7mIZN1i10OuR4rINxt19tLGb9Z2UFiuFzZk+mzdro6qfytZXvcRkfPr8eAqEfmNjmPKtDNjzNwmYC3wZeB+VC+V2Qs8qHXMrwOvrz//IvDO+vOD6uPvAty3Ps/aAeV6FLCh/rwyk6v+/9YRy+sZwJ925D0KuLb+e4/68z2Gkqt1/FnAm0qXV33uRwAnAp/t+f004O8AoXrF6cWlyytAtofNrgk8diZb/f8+4OiRyuyRwAdS20FuuVrHPgH4xEDldS/gxPrz4cAXO/plkXY27yuAhwLXGGOuNcZ8l+rlMqe3jjkdeGv9+TzgVBGR+vu/NMZ8xxhzHXBNfb5B5DLGnG+Mmb15/SLg2EzXTpLLws8BHzXGfM0Y83Xgo8BjRpLrl4B3ZLq2FWPM3wNfsxxyOvA2U3ERcHcRuRdly8tLNmPMhfW1Ybg25lNmfaS0z9xyDdnGvmqMubT+/C3g88C9W4cVaWfzrgDuDfxz4/8buHPB3XGMMeY/gG8Cmz3zlpSrybOotPuMu4rIHhG5SER+PpNMIXI9pV5mnici9wnMW1IualPZfYFPNL4uVV4+9MlesrxiaLcxA3xERC4RkTNHkOcUEdkrIn8nIg+uv5tEmYnIBqpB9K8bXw9SXlKZqH8cuLj1U5F2Vuyl8IofInIGsB34qcbXW40xXxGR+wGfEJErjTFfHkik9wPvMMZ8R0SeTbV6+umBru3DLwLnGWO+3/huzPKaPCLyKCoF8PDG1w+vy+wHgI+KyBfqGfIQXEpVZ7eKyGlU7wh/wEDX9uEJwD8aY5qrheLlJSKbqJTObxpjbsl57j7mfQXwFeA+jf+Prb/rPEZEDgOOBG72zFtSLkTkZ4CdwBONMd+ZfW+M+Ur991rgk1QzgkHkMsbc3JDlDcBJvnlLytXgF2ktzQuWlw99spcsL29E5CFU9Xi6Mebm2feNMrsJeA/5zJ9OjDG3GGNurT//LbBORI5mImWGvY0VKS8RWUc1+O82xry745Ay7ayEU2OoRLWCuZbKJDBzGj24dcxzOdQJ/K7684M51Al8LfmcwD5y/TiVw+sBre/vAdyl/nw08CUyOcI85bpX4/OTgIvMQWfTdbV896g/HzWUXPVxx1E542SI8mpcYxv9Ds3Hcahz7tOlyytAti1Uvq2Htb7fCBze+Hwh8JgB5brnrA6pBtLr6/Lzagel5Kp/P5LKT7BxqPKq7/1twKstxxRpZ1kb4xiJyjv+RarBdGf93e9RzaoB7gr8Vd0RPg3cr5F3Z53vauCxA8v1MeBfgMvr9L76+4cBV9aN/0rgWQPL9YfAVfX1zweOa+R9Zl2O1wC/OqRc9f8vB17Ryle6vN4BfBX4HpV99VnAc4Dn1L8L8Ge13FcC24coL0/Z3gB8vdHG9tTf368ur711Xe8cWK7nNdrYRTQUVFc7GEqu+phnUAWHNPOVLq+HU/kYrmjU1WlDtDPdCkJRFGVJmXcfgKIoihKJKgBFUZQlRRWAoijKkqIKQFEUZUlRBaAoirKk6JPASi8i8n2qkLPDqPYnebo5uH+Roihzjq4AFBvfNsacYIz5UeC7VHHJiqIsCKoAFF8+BfwQgIi8t94U66rmxliNffmvEZF31HuYb2vuvy4iTxWRt9Sft4nIJ+TgOxG21N+/RURuEJG19f8rImLqjbIQkTNE5NP1tf68cdytUr034Kr6fMfU339SRLY3ZLi1/isi8qp67/crReRpjWNeKCKfqWX73a4CsVzv1+q8e0Xkr+vNxRCR/yQi76m/3ysiD6vL4NtycB/66xrl8xYReX290d0XReTx9fdra7ln8j27IdP2Wq7LReR6EflTjzzN/flvFJEX1N+fJCIX1HX9Yal2n7SV5yNF5AP156NE5BuNc91fRD5Un+tTInKcvbkpQ6AKQHFS76H0WCpzEMAzjTEnUW1i93wR2Vx//21jzAnAj1G97+DujlO/BnirMeYhwG7g3MZvX6Ha6haqrXCvqWX5EeBpwE/W1/o+sKM+biPV064PBi4AXua4/pOBE4DjgZ8BXiUi9xKRR1NtTvbQ+veTROQRHfn7rvduY8zJxpjjqUxnz6q/Pxe4oP7+RKqnSgG+XK+0TgBe2LrGtlqOxwGvF5G71uf7pjHmZOBk4NdE5L718Wuptgk4AXhp4zyuPBfUeV4Pd+xN8xrgqXVdvwk4p6ccu3gx1RYPM3YBZ9XnegHw2oBzKYVQH4Bi424icnn9+VPAG+vPzxeRJ9Wf70M1WN7cOP5Y4L3GmK+LyJHA/RvnOZJqsAQ4hWoQBng78MrGtd8O/LKIXE+1v89sL/tTqTao+4yIANwNuKn+7XbgnfXnVaC5qdZuEfn27L7qvw+n2vn0+8C/iMgFVIPjI4BHA5fVx22q77G9+2Pf9X5URP4nlQLcBHy4/v6ngV8BqK/5TRG5B3beZYy5HfiSiFxLtR/So4GHiMhT62OOrOW7rr5e1573tjx3A/69dfwPAz9KtfMlVEriq43fu8oTABG5N9V+Ne+p/99EtWXHX9XngmoPLmVkVAEoNmYz+jsQkUdSzZZPMcbcJiKfpNpv6Y7j6xXDR0XkYcD/o57h1vmfCjze49o3AuuoZsR/QrWigGpPlLcaY17scY7mPic7jDF7ahludeQT4A+NMX/ucY2u670F+HljzF4ReQbVG7Biae/VYmr5zjLGfLjj+PtS7XPTxpbnB6nqqX38VcaYU3rkspXny4Dfpxr0obI0fKPdlpTxUROQEsqRwNfrwf84qpneIZjqxTu3Ue3OaeNCqh1aoTLjfKr1+5uBHzD125JqPg48Vap92We25q31b2uA2Qz3vwL/4Lj+p4Cn1fbxY6hm/p+mmrE/s565IiL3nl2vRd/1Dge+WptRdjSO/zjV6z9nNvkjHfIB/IKIrBGR+1NtSnZ1Ld9KfX5E5IEislGq6fVTgA90nKcvz1qqVdg/to6/GjhGRE6pj18nB1/cYuP+wDZjzEdmX5hqb/vrROQX6nOJNN5PrIyHrgCUUD4EPEdEPk81SFzU+G1mAlpHZd/+ENXsso+zgDeLyAuBA8CvNn80xnwQ+GDru8+JyO9QvZ1pDdXOjs8F9gP/Bjy0/v0mKl+BjfdQmaH2Us2sX2SMuRG4sfY1/FNtsrgVOIODpqYZfdd7CdUbnQ7Ufw+vv/8NYJeIPIvKd7HCoWaVLq6nUkpHUO0M+e8i8gYq38Cl9aB/APh54I+o3mR1bxG5nWqr4LtJ5VTuy/N2KhNb8+1XGGO+W6/Wzq0V1WHAqznot+jjOFr1WLMDeF1dVuuoXve413EupTC6G6iyMIjIrcaYTYtyvXrg/oAx5ryA419ujNnX+O55VPvff7KAiMqcoysARVkcXkc1s2/yYar3YCvKndAVgKIoypKiTmBFUZQlRRWAoijKkqIKQFEUZUlRBaAoirKkqAJQFEVZUv4/UPu5DPEVoecAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.xlabel(\"Равномерное распределение\")\n",
        "plt.ylabel(\"Нормальное распределение\")\n",
        "plt.scatter(x, y, marker='o', color='red')  # Настройки отображения графика"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x92ebBY1puWQ"
      },
      "source": [
        "### Гистограмма"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "pWLSyYrfpuWQ",
        "outputId": "998ccb25-478f-468a-c82d-579c626cdc20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  2.,  11.,  56.,  76., 142., 110.,  69.,  43.,  16.,   2.]),\n",
              " array([-6.0637897 , -4.83715967, -3.61052963, -2.38389959, -1.15726955,\n",
              "         0.06936049,  1.29599053,  2.52262056,  3.7492506 ,  4.97588064,\n",
              "         6.20251068]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.subplot(121)    # Выбор области для визуализации\n",
        "plt.hist(x)\n",
        "plt.subplot(122)\n",
        "plt.hist(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16K9k_bEpuWQ"
      },
      "source": [
        "## Generate two Gaussian clouds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66ceTxKIpuWQ"
      },
      "source": [
        "### Генерация наборов данных с нормальным распределением"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "wo104h1SpuWQ",
        "outputId": "42bc76e6-afe3-4435-9508-27c92bd1fe24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fccfc214d30>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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imkwvsiAIFwi910nZL/TkwpkyPbtn1zdyF8z84oXTlTZOGsKCkjo4P7M1vXEXgRxbNPGBy/qiz7K2vnH+mGUuBOO74EyuOLzskovPp2HGiq60bHsFY2o4P7M1vXEXC4KwRd7Ci4X5OV126cWVh4VF7ik5abwQSNIFFQtz22xLjnzz72e1a/fBY5nHNSnoRlNAllCfPxYEoZC8NMb6xqlCS8DzbJ6oyrtYTBr3WMYr94pWrWQJlcdtq4wylhWVCKPpeQQCObbI+8BNqlNedmDvHh248+HMCctJ4wtLXsVC0eOHqENua4vdVLbyRXFN18ETyAcsq9eXd+u3LFXLy8bB6KNfflQ/ydkqdSxUjyVEHXJb9eQx160zUqim6Tp4AvlA5fX6bt93jW7fd82WD9zJX5/ODLibg2yVJexZ933crE6PJa89db44bdVLx1q3zkihnibr4AnkAzWt1/fAwRumLsiRtgbZUFuMrq1vaJuZzrhrsUaPpamA01a9dKx12TGPFIaOQD5QZXp9s4aFVb7gTQ7Rmwo4be33Eeu+IrGOFEAgH6yyvb5pw8KyX/Cmh+hNBZy29vuIdV+RWEcKIJAPVsheX9kveNND9CYDTlv7fYQ6TsiRT6wjBbCyc7BCrsYru0S76SE6S8bPCr1KlxWc8aJHPmChen1lUwFND9Hrpib6UmLXxMiHHQjjRCBHEGW+4G0M0asGnC5L7OpeQCb/Pm9hF5OT/UMgR+tincwbt6mLCpy6F5Csvzcpc38PJif7h0DeIymlBGIdondVgVM3DZL19y5dEMyHOFcwBEx29kTT+x13KfT+6NPk9VarVOCUUXcCOO/3XGJycgDokfdEX1fdtZ2zLpu/D1WBU3cCOO/vFxfmz+8eif6iR94TfV111/SG/JPKltiV7cHnqVsyWebvQ49w2hwxIRs98p7o66q7Li5QXVTg1J0ALvr3eSOc1aef1/1PPFf62GykFYdagdzM3inpI5J+X9Lr3J3b/nSkr6vuYr9AhazAqTsBXOTv80Y4Rx78/vlJ0TLBuK8pvdTU7ZF/W9I+Sf8eoC2oIeaSvjpSuEDFWoGTZdqk6GZFg3FfU3qpqRXI3f1xSTKzMK1BLSkFlKKqXKBSKsNsW9E7QEnFgnHsI6ZJff1stJYjN7P9kvZL0s6dO9s6LBrQ9pehzAWKnO10WSOcOguHUhgxjfX5szEzkJvZVyW9IuOfbnX3LxY9kLsflnRYkkajUdEbqSMyTX0ZQl0ciuRsx8cKdSOLlGSNcK5/9Q7ddXytUjBOKaXX53z+zEDu7m9qoyGop61echNfhpAXh1k528ljnXGvfczUZI1wRq/8rVoVM6FvPt3EZ7nP+XzKD3ugzSFjE1+GkBeHWTnbrGPVPWYfxDK/0uRnObV8fhm1FgSZ2TvM7BlJfyzpmJndG6ZZKKPNRTOhFsBsFvLiMGthzKzn7EPvLGVNfpb7vE99rUDu7l9w9yvd/VJ3/2133xuqYSiuzSFjE1+GkBeHWSszZz1nH3pnKWvys9znG2OQWumBNoeMTUxuha58mJYmyDpWiGMijDqf5SK59VhSSKERyHug7RKw0F+GNisfNh9riFUrsav6We5zaWER5t5+JeBoNPLVVVbzh9TXhQ4Yniqf5aXllUHs/mhmx919NPk4PfKe6OuQEcNT5bPc59LCIgjkQEuGOmpq43X3ubSwCPYjB1rQ5zs4TdPW6+5zaWERBHKgBW3fICMWbb3uPpcWFkFqBWjBUHO4bb7uIc8T0SMHWtDEitgUDPV1t41ADrRgqDncob7utpFaAVqQ0navIQ31dbeNBUGIzlDL9IBZWBCEJAx9qTVQBTlyRGWoZXpAHQRyRGWoZXpAHQRyRIVyNaA8AjmiQrkaUB6TnYgK5WpAeQRyRGfIS62BKkitAEDiCOQAkDhSK+gtVohiKAjk6CVWiGJISK2gl1ghiiEhkKOXWCGKISGQo5dYIYohIZAn4OiJNS0tr2j3wWNaWl7p/Q17Q2CFKIaEyc7IMWlXTd4KUUlaWl6hkgW9QiCP3LRJOwLQdJMrRLkooq9IrUSOSbtwqGRBXxHII8ekXThcFNFXtQK5mR0ysyfM7Ftm9gUzWwjULpzDpF04XBTRV3V75PdJeo27XyvpO5Juqd8kbHbTdYu6fd81WlyYl0laXJjX7fuuIadbARdF9FWtyU53/8qmHx+U9Bf1moMsbOsaBnudo69CVq28R9Ln8v7RzPZL2i9JO3fuDHhYoDguiuijmYHczL4q6RUZ/3Sru3/x3O/cKum0pCN5z+PuhyUdlqTRaOSVWgsAuMDMQO7ub5r272b2bklvlfRGdydAA0DLaqVWzOxGSR+S9KfufjJMkwAAZdStWvmEpJdKus/MHjKzfwvQJgBACXWrVn43VEMAANWwshMAEsemWUAEuL8o6iCQAx1jV0bURWoF6Bi7MqIuAjnQMXZlRF2kVtAbqeaZr1iY11pG0GZXRhRFjxy9MM4zr61vyPWbPHMK9zdlV0bURSBHL6ScZ2arYtRFagW9kHqemV0ZUQc9cvQCd//BkBHI0QvkmTFkpFbQC9z9B0NGIEdvkGfGUJFaAYDEEcgBIHEEcgBIHIEcABJHIAeAxFkXN743s+ckPS3pckk/br0B7eN19guvs19Sep2vdPcdkw92EsjPH9xs1d1HnTWgJbzOfuF19ksfXiepFQBIHIEcABLXdSA/3PHx28Lr7BdeZ78k/zo7zZEDAOrrukcOAKiJQA4Aies8kJvZP5rZt8zsITP7ipld0XWbmmBmh8zsiXOv9QtmttB1m5pgZu80s0fN7EUzS7qkK4uZ3WhmT5rZU2Z2sOv2NMHMPmVmPzKzb3fdliaZ2VVmdr+ZPXbuM/v+rttUVeeBXNIhd7/W3V8r6b8k/UPH7WnKfZJe4+7XSvqOpFs6bk9Tvi1pn6Svdd2Q0Mxsm6RPSnqzpKsl3WxmV3fbqkZ8WtKNXTeiBaclfdDdr5b0R5L+LtX3s/NA7u4/2/TjZZJ6Ofvq7l9x99PnfnxQ0pVdtqcp7v64u8d/x+NqXifpKXf/rrv/WtJnJb294zYF5+5fk/R81+1omrv/0N2/ee7/fy7pcUlJbmgfxY0lzOxjkv5W0k8lXd9xc9rwHkmf67oRKG1R0g82/fyMpNd31BYEZGa7JF0n6esdN6WSVgK5mX1V0isy/ulWd/+iu98q6VYzu0XS+yTd1ka7Qpv1Os/9zq06O6Q70mbbQiryOoFUmNlLJN0l6QMTGYJktBLI3f1NBX/1iKR7lGggn/U6zezdkt4q6Y2ecAF/ifezb9YkXbXp5yvPPYZEmdmczgbxI+5+d9ftqarzHLmZvWrTj2+X9ERXbWmSmd0o6UOS/tzdT3bdHlTyDUmvMrPdZnaJpHdJ+lLHbUJFZmaS7pD0uLt/vOv21NH5yk4zu0vSHkkv6uzWtu919971cszsKUmXSvq/cw896O7v7bBJjTCzd0j6V0k7JK1Lesjd93baqIDM7M8k/YukbZI+5e4f67ZF4ZnZZyS9QWe3d/1fSbe5+x2dNqoBZvYnkv5b0iM6G38k6cPufk93raqm80AOAKin89QKAKAeAjkAJI5ADgCJI5ADQOII5ACQOAI5ACSOQA4Aift/PYeM7dtlew0AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "noise = np.random.randn(120, 2) # 120 точек, 2 координаты\n",
        "plt.scatter(noise[:,0], noise[:,1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRxwjXKOpuWQ"
      },
      "source": [
        "### Сдвижка наборов данных"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "id": "mV5tvSRXpuWQ"
      },
      "outputs": [],
      "source": [
        "# cloud1 = noise + np.array([1, 1])\n",
        "# cloud2 = noise + np.array([-1, -1])\n",
        "cloud1 = noise + np.array([1, 1])\n",
        "cloud2 = noise + np.array([-2, -1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OqejhuhipuWQ",
        "outputId": "f98a4c69-ebd1-4fc5-fafc-ea6d9772d395"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((120, 2), (120, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ],
      "source": [
        "cloud1.shape, cloud2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "id3BYul7puWQ",
        "outputId": "f93fd138-e0e2-40b1-ad91-c6712fbb6ca1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(240, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ],
      "source": [
        "XY = np.vstack([cloud1, cloud2])\n",
        "XY.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlcMXQzQpuWQ",
        "outputId": "f85e07f2-346a-496c-9fbe-aa61ed777be3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ],
      "source": [
        "annotation1 = [0]*120 # Создание списка с 120 нулями (метки первого класса)\n",
        "print(annotation1)\n",
        "annotation2 = [1]*120 # Создание списка с 120 единицами (метки второго класса)\n",
        "print(annotation2)\n",
        "annotations = annotation1 + annotation2 # объединения списков\n",
        "print(annotations)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "1ndyg24epuWR",
        "outputId": "baea7386-deab-4df9-d6e2-56054d6b6160"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fccc971f3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 161
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(XY[:,0], XY[:,1], c=annotations) # отрисовка точек с заданными метками (каждая метка своего цвета)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkD5snSKpuWR"
      },
      "source": [
        "### Вычисление среднего и СКО для каждого облака точек"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zM2oTizlpuWR",
        "outputId": "af62f037-4234-4ae9-c633-d16aa0bdd0df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.082693811685371\n",
            "1.0045020996438574\n",
            "[1.11583628 1.04955135]\n"
          ]
        }
      ],
      "source": [
        "print(cloud1.mean())\n",
        "print(cloud1.std())\n",
        "print(cloud1.mean(axis=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMk2Q-RTpuWR",
        "outputId": "1ed7476b-94bd-40d2-b649-0f777b31a789"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.417306188314629\n",
            "1.107195557524688\n",
            "[-1.88416372 -0.95044865]\n"
          ]
        }
      ],
      "source": [
        "print(cloud2.mean())\n",
        "print(cloud2.std())\n",
        "print(cloud2.mean(axis=0))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cfdj7kK5puWR"
      },
      "source": [
        "# Библиотека sklearn. Логистическая регрессия"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "3UMtJLucpuWR"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnNTirezpuWR"
      },
      "source": [
        "#### В роли набора данных будем использовать облака точек, которые сгененировали ранее"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "ewTBIVMkpuWR",
        "outputId": "b45670a5-bbc1-4318-b942-f40d6fb67ca5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fccc9673e50>"
            ]
          },
          "metadata": {},
          "execution_count": 165
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(XY[:,0], XY[:,1], c=annotations) # mark each point with a certain color"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRapSoQqpuWR"
      },
      "source": [
        "#### Перемешаем данные"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "aqyTFBJ1puWR"
      },
      "outputs": [],
      "source": [
        "data, labels = shuffle(XY, annotations)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rTd13P8QpuWR"
      },
      "source": [
        "#### Разделим данные на обучающие и тестовые"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "id": "Id1mWipZpuWR"
      },
      "outputs": [],
      "source": [
        "test_size = int(len(data) * 0.4)\n",
        "Xtrain = data[:test_size]\n",
        "Ytrain = labels[:test_size]\n",
        "Xtest = data[test_size:]\n",
        "Ytest = labels[test_size:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "0BxvgpjypuWR",
        "outputId": "e88d1ec9-52c8-4c93-e8ce-1023b53e57d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([80.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., 64.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 168
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.hist(Ytest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0EEhur2puWS"
      },
      "source": [
        "#### Создадим и обучим классификатор на обучающем наборе данных"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "nuca_rLGpuWS",
        "outputId": "4386fea2-9bfd-4f67-9454-d3240b64b4de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ],
      "source": [
        "clf = LogisticRegression()\n",
        "clf.fit(Xtrain, Ytrain)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GI5bn3MhpuWS"
      },
      "source": [
        "#### Оценим точность на тестовом наборе данных"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "id": "Z566LXZppuWS"
      },
      "outputs": [],
      "source": [
        "predictions = clf.predict(Xtest) #Определим метки классов для тестовых данных"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLejB0k0puWS",
        "outputId": "b2672c52-37a7-4d80-b789-2519404bbe4c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9583333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ],
      "source": [
        "accuracy_score(Ytest, predictions) #Оценим точность определения меток"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIxbl4P9puWS",
        "outputId": "73027730-0546-4f4d-f47b-7a389c3e8818"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[77,  3],\n",
              "       [ 3, 61]])"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ],
      "source": [
        "confusion_matrix(Ytest, predictions) #Оценим точность определения меток"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3ZTJiJ1puWS"
      },
      "source": [
        "### Подсказки для задания №2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ri_ze1ROpuWS",
        "outputId": "92258a7e-e015-40e0-e9b1-7c6d2c95e32d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.22230123, -2.10704223]])"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ],
      "source": [
        "clf.coef_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZAdEoYApuWS",
        "outputId": "0c7f8d93-c2fe-416a-a8ac-0f908deec503"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.22484262])"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ],
      "source": [
        "clf.intercept_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wMGMewUpuWS",
        "outputId": "8a12ce3f-162a-4fd7-c6b6-020a7e01327e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9933071490757152"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ],
      "source": [
        "x=5\n",
        "np.exp(x)/(np.exp(x)+1)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.4"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}