knn_examples.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/knn_examples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "text/plain": [
              "8.717797887081348"
            ]
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          "metadata": {},
          "execution_count": 2
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      ],
      "source": [
        "((2-1)**2+(-4-3)**2+5**2+1)**(1/2)\n",
        "# Вычислить евклидово расстояние"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Вычислить манхэттенское расстояние\n",
        "\n",
        "(abs(1-2)+abs(3+4)+5+1)"
      ],
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              "14"
            ]
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        "import pandas as pd\n",
        "import numpy as np\n",
        "data = pd.DataFrame(np.array([\n",
        "    [1, 28, 10,1],\n",
        "    [2,\t49,\t49,\t1],\n",
        "    [3,\t48,\t35,\t0],\n",
        "    [4,\t36,\t33,\t1],\n",
        "    [5,\t45,\t54,\t0]\n",
        "]), columns=['id','X','Y','Class'])\n",
        "data"
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              "   id   X   Y  Class\n",
              "0   1  28  10      1\n",
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              "2   3  48  35      0\n",
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      "cell_type": "code",
      "source": [
        "# Введите идентификатор (id) ближайшего соседа для объекта (33,47) , используя евклидову метрику.\n",
        "from math import sqrt\n",
        "x, y = 33,47\n",
        "data['evkl'] = np.sqrt((data['X'] - x)**2 + (data['Y'] - y)**2)\n",
        "#  манх\n",
        "data['manh'] = abs(data.X-x)+abs(data.Y-y)\n",
        "# Чебышев\n",
        "data['che'] = abs(data.X-x)\n",
        "data"
      ],
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              "   id   X   Y  Class       evkl  manh  che\n",
              "0   1  28  10      1  37.336309    42    5\n",
              "1   2  49  49      1  16.124515    18   16\n",
              "2   3  48  35      0  19.209373    27   15\n",
              "3   4  36  33      1  14.317821    17    3\n",
              "4   5  45  54      0  13.892444    19   12"
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-39c85844-9db7-4171-b1df-ff4ee1879f51 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRZPZV3iOzAN",
        "outputId": "ee141e89-8c97-44eb-dd34-def72d536e88"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}