20211014_이재관.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "20211014_이재관.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNG7DKjOOJlT6DpSyxbIlXK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JamesLeeeeeee/machineLearning_practice/blob/master/20211014_%EC%9D%B4%EC%9E%AC%EA%B4%80.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNU6pRNS7IIQ"
      },
      "source": [
        "bream_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, 31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, 35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0]\n",
        "bream_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, 500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, 700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0]"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "yJ0E9aL47lZM",
        "outputId": "89b6b3a8-6f69-4ee0-9ed9-171a19d3b200"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(bream_length, bream_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kx8yHceL77kk"
      },
      "source": [
        "smelt_length = [9.8, 10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
        "smelt_weight = [6.7, 7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Vi5kFGpq8BA9",
        "outputId": "49653701-2f4a-44e3-f6d7-962c9998031f"
      },
      "source": [
        "plt.scatter(smelt_length, smelt_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "aXxZQtQQ8b2y",
        "outputId": "e96073c2-4d3f-47e6-f242-64146761c7f2"
      },
      "source": [
        "plt.scatter(bream_length, bream_weight)\n",
        "plt.scatter(smelt_length, smelt_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVw37nus9cC2"
      },
      "source": [
        "length=bream_length + smelt_length\n",
        "weight=bream_weight + smelt_weight"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CP06ylOU9nSe",
        "outputId": "2c4276e9-8353-4dac-da8b-072c0b3f1fd1"
      },
      "source": [
        "fish_data=[[l,w] for l, w in zip(length,weight) ]\n",
        "print(fish_data)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0], [29.7, 450.0], [29.7, 500.0], [30.0, 390.0], [30.0, 450.0], [30.7, 500.0], [31.0, 475.0], [31.0, 500.0], [31.5, 500.0], [32.0, 340.0], [32.0, 600.0], [32.0, 600.0], [33.0, 700.0], [33.0, 700.0], [33.5, 610.0], [33.5, 650.0], [34.0, 575.0], [34.0, 685.0], [34.5, 620.0], [35.0, 680.0], [35.0, 700.0], [35.0, 725.0], [35.0, 720.0], [36.0, 714.0], [36.0, 850.0], [37.0, 1000.0], [38.5, 920.0], [38.5, 955.0], [39.5, 925.0], [41.0, 975.0], [41.0, 950.0], [9.8, 6.7], [10.5, 7.5], [10.6, 7.0], [11.0, 9.7], [11.2, 9.8], [11.3, 8.7], [11.8, 10.0], [11.8, 9.9], [12.0, 9.8], [12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJGHtmww-SSH"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TRvk751-ea2"
      },
      "source": [
        "kn= KNeighborsClassifier()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OE_Smjll-mh9",
        "outputId": "80936dd1-06a8-48c2-a911-386d9b8d180a"
      },
      "source": [
        "fish_target= [1]*35 + [0]*14\n",
        "print(fish_target)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9k6CKsBk-uxn",
        "outputId": "fb234ace-9ef2-4923-a360-fc52b2a1f822"
      },
      "source": [
        "kn.fit(fish_data, fish_target)\n",
        "#학습시키기\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hU8jbhBh-98y",
        "outputId": "64b79c6a-6f01-4da4-8033-76dc6ea92be1"
      },
      "source": [
        "kn.score(fish_data, fish_target)\n",
        "\n",
        "#0.5는 정확도 절반"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bABxh_Gj_oBZ",
        "outputId": "bcf0490e-9785-483e-d125-e001960513f3"
      },
      "source": [
        "kn.predict([[30,600]])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnTGTKwUA6LG"
      },
      "source": [
        "#@title 타이틀\n",
        "#@markdown fff\n",
        "variable_name = \"machine learnig\" #@param {type:\"string\"}"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsccnqkTGwK2"
      },
      "source": [
        "*연습*\n",
        "\n",
        "**연습**\n",
        "\n",
        "-연습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5w_-B4irG5HX",
        "outputId": "acc44fc4-2d22-4fe9-de3e-86e19e633f59"
      },
      "source": [
        "print(kn._fit_X)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  25.4  242. ]\n",
            " [  26.3  290. ]\n",
            " [  26.5  340. ]\n",
            " [  29.   363. ]\n",
            " [  29.   430. ]\n",
            " [  29.7  450. ]\n",
            " [  29.7  500. ]\n",
            " [  30.   390. ]\n",
            " [  30.   450. ]\n",
            " [  30.7  500. ]\n",
            " [  31.   475. ]\n",
            " [  31.   500. ]\n",
            " [  31.5  500. ]\n",
            " [  32.   340. ]\n",
            " [  32.   600. ]\n",
            " [  32.   600. ]\n",
            " [  33.   700. ]\n",
            " [  33.   700. ]\n",
            " [  33.5  610. ]\n",
            " [  33.5  650. ]\n",
            " [  34.   575. ]\n",
            " [  34.   685. ]\n",
            " [  34.5  620. ]\n",
            " [  35.   680. ]\n",
            " [  35.   700. ]\n",
            " [  35.   725. ]\n",
            " [  35.   720. ]\n",
            " [  36.   714. ]\n",
            " [  36.   850. ]\n",
            " [  37.  1000. ]\n",
            " [  38.5  920. ]\n",
            " [  38.5  955. ]\n",
            " [  39.5  925. ]\n",
            " [  41.   975. ]\n",
            " [  41.   950. ]\n",
            " [   9.8    6.7]\n",
            " [  10.5    7.5]\n",
            " [  10.6    7. ]\n",
            " [  11.     9.7]\n",
            " [  11.2    9.8]\n",
            " [  11.3    8.7]\n",
            " [  11.8   10. ]\n",
            " [  11.8    9.9]\n",
            " [  12.     9.8]\n",
            " [  12.2   12.2]\n",
            " [  12.4   13.4]\n",
            " [  13.    12.2]\n",
            " [  14.3   19.7]\n",
            " [  15.    19.9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9o4N49aHLve",
        "outputId": "7f8bf4ae-368a-4c3c-846d-6be30ff387c7"
      },
      "source": [
        "kn._y"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2buuhGPHT14"
      },
      "source": [
        "kn49= KNeighborsClassifier(n_neighbors=49)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4asKYWg4IYnD",
        "outputId": "fdfb87c9-d786-4493-c0af-e1952ac69d34"
      },
      "source": [
        "kn49.fit(fish_data, fish_target)\n",
        "kn49.score(fish_data, fish_target)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7142857142857143"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bmXpY5rIiFl",
        "outputId": "7b51d0c6-aa66-4972-e25e-326e26130011"
      },
      "source": [
        "print(35/49)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7142857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W-s8CaaDIznv",
        "outputId": "dae99acb-1722-44fa-c567-e980e7b6aab5"
      },
      "source": [
        "kn= KNeighborsClassifier()\n",
        "kn.fit(fish_data , fish_target)\n",
        "\n",
        "for n in range(5,50):\n",
        "  kn.n_neighbors= n\n",
        "\n",
        "  score =kn.score(fish_data, fish_target)\n",
        "  if score <1:\n",
        "    print(n, score)\n",
        "    break"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18 0.9795918367346939\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Lb4jS7xK82F",
        "outputId": "aa0234b6-df00-48cd-dd08-99c2775e3c0e"
      },
      "source": [
        "fish_length=bream_length + smelt_length\n",
        "fish_weight=bream_weight + smelt_weight\n",
        "print(fish_length)\n",
        "print(fish_weight)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, 31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, 35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8, 10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
            "[242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, 500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, 700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7, 7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xdmb4Eq8NdwM",
        "outputId": "13d8feb2-8610-47ea-a5ff-848785596b13"
      },
      "source": [
        "fish_data=[[l,w] for l,w in zip(fish_length, fish_weight)]\n",
        "fish_target= [1]*35 + [0]*14\n",
        "print(fish_data)\n",
        "print(fish_target)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0], [29.7, 450.0], [29.7, 500.0], [30.0, 390.0], [30.0, 450.0], [30.7, 500.0], [31.0, 475.0], [31.0, 500.0], [31.5, 500.0], [32.0, 340.0], [32.0, 600.0], [32.0, 600.0], [33.0, 700.0], [33.0, 700.0], [33.5, 610.0], [33.5, 650.0], [34.0, 575.0], [34.0, 685.0], [34.5, 620.0], [35.0, 680.0], [35.0, 700.0], [35.0, 725.0], [35.0, 720.0], [36.0, 714.0], [36.0, 850.0], [37.0, 1000.0], [38.5, 920.0], [38.5, 955.0], [39.5, 925.0], [41.0, 975.0], [41.0, 950.0], [9.8, 6.7], [10.5, 7.5], [10.6, 7.0], [11.0, 9.7], [11.2, 9.8], [11.3, 8.7], [11.8, 10.0], [11.8, 9.9], [12.0, 9.8], [12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]\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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFruBD6FN1np"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "kn = KNeighborsClassifier()"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxadOK5eOFqi",
        "outputId": "2b37135f-2250-4b7c-f80c-81c65c6c5ff0"
      },
      "source": [
        "print(fish_data[4])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[29.0, 430.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmLzBu7MOIsU"
      },
      "source": [
        "train_input= fish_data[0:35]\n",
        "train_target=fish_target[0:35]\n",
        "\n",
        "test_input= fish_data[35:]\n",
        "test_target= fish_target[35:]"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stLjg9HkOt00",
        "outputId": "1c5e6b70-bafb-4620-c8cc-37c97007c3d1"
      },
      "source": [
        "kn.fit(train_input , train_target)\n",
        "kn.score(test_input, test_target)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxZZqF4rPWW9"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhH60Sv1QQcj"
      },
      "source": [
        "input_arr= np.array(fish_data)\n",
        "target_arr = np.array(fish_target)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKK-emYOQwgg",
        "outputId": "9a57ac9d-1575-4eb7-f933-f06d47312885"
      },
      "source": [
        "print(input_arr)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  25.4  242. ]\n",
            " [  26.3  290. ]\n",
            " [  26.5  340. ]\n",
            " [  29.   363. ]\n",
            " [  29.   430. ]\n",
            " [  29.7  450. ]\n",
            " [  29.7  500. ]\n",
            " [  30.   390. ]\n",
            " [  30.   450. ]\n",
            " [  30.7  500. ]\n",
            " [  31.   475. ]\n",
            " [  31.   500. ]\n",
            " [  31.5  500. ]\n",
            " [  32.   340. ]\n",
            " [  32.   600. ]\n",
            " [  32.   600. ]\n",
            " [  33.   700. ]\n",
            " [  33.   700. ]\n",
            " [  33.5  610. ]\n",
            " [  33.5  650. ]\n",
            " [  34.   575. ]\n",
            " [  34.   685. ]\n",
            " [  34.5  620. ]\n",
            " [  35.   680. ]\n",
            " [  35.   700. ]\n",
            " [  35.   725. ]\n",
            " [  35.   720. ]\n",
            " [  36.   714. ]\n",
            " [  36.   850. ]\n",
            " [  37.  1000. ]\n",
            " [  38.5  920. ]\n",
            " [  38.5  955. ]\n",
            " [  39.5  925. ]\n",
            " [  41.   975. ]\n",
            " [  41.   950. ]\n",
            " [   9.8    6.7]\n",
            " [  10.5    7.5]\n",
            " [  10.6    7. ]\n",
            " [  11.     9.7]\n",
            " [  11.2    9.8]\n",
            " [  11.3    8.7]\n",
            " [  11.8   10. ]\n",
            " [  11.8    9.9]\n",
            " [  12.     9.8]\n",
            " [  12.2   12.2]\n",
            " [  12.4   13.4]\n",
            " [  13.    12.2]\n",
            " [  14.3   19.7]\n",
            " [  15.    19.9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pFlKAfsQxjA",
        "outputId": "747c13fb-ce30-4458-aa80-79820c6cc7bf"
      },
      "source": [
        "print(target_arr)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[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 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXZau3T7Q6yY",
        "outputId": "d8ab1e1a-755e-4ecc-f4e7-9fbc3636f441"
      },
      "source": [
        "print(input_arr.shape)\n",
        "print(target_arr.shape)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(49, 2)\n",
            "(49,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqWsMvM5RH_3"
      },
      "source": [
        "np.random.seed(42)\n",
        "index=np.arange(49)\n",
        "np.random.shuffle(index)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ophUqsogSRWX",
        "outputId": "f617a988-7586-4aa5-f3e4-772bdd8ea07c"
      },
      "source": [
        "print(index)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13 45 47 44 17 27 26 25 31 19 12  4 34  8  3  6 40 41 46 15  9 16 24 33\n",
            " 30  0 43 32  5 29 11 36  1 21  2 37 35 23 39 10 22 18 48 20  7 42 14 28\n",
            " 38]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6T61AqRSWHz",
        "outputId": "87c48144-5513-466c-ae39-6a6238bc6a24"
      },
      "source": [
        "print(input_arr[[0,4,3]])"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 25.4 242. ]\n",
            " [ 29.  430. ]\n",
            " [ 29.  363. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLnxYvPqSz9y"
      },
      "source": [
        "train_input = input_arr[index[:35]]\n",
        "train_target = target_arr[index[:35]]"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHp3Qgq8Tjpk"
      },
      "source": [
        "test_input = input_arr[index[35:]]\n",
        "test_target= target_arr[index[35:]]"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jf8NIADCUtbl",
        "outputId": "9442a9fb-5d75-4667-e236-6bbf6d2a1bb8"
      },
      "source": [
        "print(test_input[:])\n",
        "\n",
        "print(test_input[:,1])\n",
        "\n",
        "print(test_input[:,0])"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 10.6   7. ]\n",
            " [  9.8   6.7]\n",
            " [ 35.  680. ]\n",
            " [ 11.2   9.8]\n",
            " [ 31.  475. ]\n",
            " [ 34.5 620. ]\n",
            " [ 33.5 610. ]\n",
            " [ 15.   19.9]\n",
            " [ 34.  575. ]\n",
            " [ 30.  390. ]\n",
            " [ 11.8   9.9]\n",
            " [ 32.  600. ]\n",
            " [ 36.  850. ]\n",
            " [ 11.    9.7]]\n",
            "[  7.    6.7 680.    9.8 475.  620.  610.   19.9 575.  390.    9.9 600.\n",
            " 850.    9.7]\n",
            "[10.6  9.8 35.  11.2 31.  34.5 33.5 15.  34.  30.  11.8 32.  36.  11. ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "-ZQNp2hlT248",
        "outputId": "16be4bd4-2c28-429b-fe31-1650730ae572"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(train_input[:,0], train_input[:,1])\n",
        "plt.scatter(test_input[:,0],test_input[:,1],marker=\"s\",s=200,c='k')\n",
        "plt.xlabel('lenth')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mS2VBJGViIc"
      },
      "source": [
        "kn = kn.fit(train_input, train_target)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2lVL-R1V43V",
        "outputId": "84bc4afc-9b39-457a-8da7-c9ecde444a16"
      },
      "source": [
        "kn.score(test_input, test_target)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2YAOp9eV8W_",
        "outputId": "01e693e3-af3a-4cb1-9a63-bcc52cf0fe87"
      },
      "source": [
        "kn.predict(test_input)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfwSgveDWCNd",
        "outputId": "ce91eac2-a88a-4764-df81-deaecaa3da34"
      },
      "source": [
        "test_target"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87hHOL5PWF4c"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}