2_KNeighborsClassifier_Scaling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQEfg_XBHbS9"
      },
      "source": [
        "## 훈련 세트와 테스트 세트"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N1oAXHg2Y6DJ"
      },
      "outputs": [],
      "source": [
        "fish_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,\n",
        "                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0,\n",
        "                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8,\n",
        "                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",
        "fish_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,\n",
        "                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0,\n",
        "                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7,\n",
        "                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]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4mQxk1Vx7dL5"
      },
      "outputs": [],
      "source": [
        "fish_data = [[l, w] for l, w in zip(fish_length, fish_weight)]\n",
        "fish_target = [1]*35+[0]*14"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train set의 input (35개)\n",
        "train_input = fish_data[:35]\n",
        "# train set의 target\n",
        "train_target = fish_target[:35]\n",
        "\n",
        "# test set의 input (14개)\n",
        "test_input = fish_data[35:]\n",
        "# test set의 target\n",
        "test_target = fish_target[35:]"
      ],
      "metadata": {
        "id": "J4nzF1LSiCV9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "URr_0wBTYsZV"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "kn = KNeighborsClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxiunutA8yMQ",
        "outputId": "fc574c18-04a7-4d30-aa5a-ed1d36ac2851"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "kn.fit(train_input, train_target)\n",
        "kn.score(test_input, test_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOyftJmqHbTB"
      },
      "source": [
        "## 넘파이"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kdoec4RBcrZU"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# python list to numpy array\n",
        "input_arr = np.array(fish_data)\n",
        "target_arr = np.array(fish_target)"
      ],
      "metadata": {
        "id": "pjdLmg8CkrYV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8t38rejifbv",
        "outputId": "54008844-bf2d-4e4e-a8cc-ed856d9b476a"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(input_arr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPMqX_1qluYe",
        "outputId": "a1a79514-cf3d-4c63-fcbe-232661304a20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(49, 2)\n"
          ]
        }
      ],
      "source": [
        "print(input_arr.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8ZKi6avTeKaB"
      },
      "outputs": [],
      "source": [
        "np.random.seed(42)  # seed 설정하면 일정한 결과를 줌 - 실행할 때마다 다르게 하려면 삭제\n",
        "index = np.arange(49)\n",
        "np.random.shuffle(index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ndz46Y7aoS9h",
        "outputId": "138388d7-54e4-4c75-aef4-4a3fc7ddf516"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_OYZlI1yicM",
        "outputId": "8ac47021-9e5f-4310-f2ab-55b1c30ad6e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 26.3 290. ]\n",
            " [ 29.  363. ]]\n"
          ]
        }
      ],
      "source": [
        "print(input_arr[[1,3]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o_MTsZW3eg96"
      },
      "outputs": [],
      "source": [
        "# array 인덱싱 (0~34 까지에 저장된 인덱스 값의 샘플들을 순서대로 리스트에 저장)\n",
        "train_input = input_arr[index[:35]]\n",
        "train_target = target_arr[index[:35]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmO5KDSlBhtt",
        "outputId": "682eb937-b190-437f-d577-2dda01c4bc2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 32. 340.] [ 32. 340.]\n"
          ]
        }
      ],
      "source": [
        "print(input_arr[13], train_input[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NUNTa8G9gqw2"
      },
      "outputs": [],
      "source": [
        "test_input = input_arr[index[35:]]\n",
        "test_target = target_arr[index[35:]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "gbnl1bpoiR0x",
        "outputId": "fac41259-6bbd-4d37-a8c1-491e27deaf2d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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pWL/cEhiGJMlrSL/cUqLFfyoO6+/F2G26+/rGJz7ffX16vRWrUxMTgvqNYNuh5RGIAACmd/6iVy/8taTRNi/8tUTnL3rD9pser6F1HzQ+8XndB6X1zgOqGV1q6OEeNjU8uoTIIBABAEzvt0Wf1BkZqs1r+NqFy+XUIYqx27RojO8WXu1QVPN+0ZgsnodmIgQiAIDpHa48F5Z2zSmSeLnzgPL6pWvFAwPldATeFnM6ErTigYHUITIZJlUDAEyvZ3KHy27X3CKJ4ZgHlNcvXbdnObW9pFIVp6uUmui7TcbIkPkwQgQAML1v5FylpjKE3eZrV59QiiReOg/ILq9uthfrbvtW3Wwvll3eoOcBxdhtyumVontu6KacXimEIZNihAgAYHpxsXZNuTVTv9zS8MTqKbdm1luPqKkiiTb5iiTenuUMCCs184BeW7tSC9v9Rhm2L+YKHTeS9cSFiRo75lsEnDaCESIAQFSYf1eW/mt4Zp2RIrtN+q/hDdchak6RxNry7Du0Iu4pOW2B+5y2Sq2Ie0p59h3NPg6YEyNEAICoMf+uLD12R59mVaoOeXK01yMVzJVNRp2VYv5fK5gn9Rkt2WOCPgaYE4EIABBV4mLtmnzrl4JuH/Lk6MNbJffxRj5hSO5jvnaZtwbdH5gTt8wAAG1ayEUSz5QH9wPBtoOpEYgAAG1ayEUSO6UF9wPBtoOpEYgAAG1eSEUSew6VkjJUN0bVsElJ3XztEPWYQwQAsIRmF0m0x0h5S6U/TJQvFF26cP/zz+QtYUJ1G0EgAgBYRk2RxKBl3S3d9xupYG7gBOukDF8Yyro7/J1ERBCIAABthsdrhP8xGVl3+5bWH97qm0DdKc13m4yRoTaFQAQAaBOa+6yyZrHHsLS+jWNSNQAg6oXyrDLgUgQiAEBUa+pZZZLvWWUeb30tAB8CEQAgql3Os8qAGgQiAEBUC/lZZcAlCEQAgKgW8rPKgEsQiAAAUS3kZ5UBlyAQAQCiWsjPKgMuQSACAES9kJ5VBlyCwowAgDah2c8qq83roRq1hRGIAABtRrOfVVajeF0DzytbyvPKLIJbZgAAayte53ui/aVhSJLcpb7txesi0y+0KgIRAMC6vB7fyFBjda4L5vnaoU0jEAEA2g6vRyr5q7T3j75/NhVkDm+tOzIUwJDcx3zt0KYxhwgA0DaEMg/oTHlw3x1sO0QtRogAANEv1HlAndKC+/5g2yFqEYgAABHhuXhR+//2ht7b8Lz2/+0NeS5eDO2LLmceUM+hvlGkxupcJ3XztUObxi0zAECre//N1cooytd1OunfVr4xRcdzFunGUZOa92XNmQeUeWvgLnuM75baHybKF4ouDVWfh6S8JdQjsgDTjxAdO3ZMDzzwgFJSUtS+fXv1799f7733nn+/YRhauHCh0tPT1b59e+Xm5urgwYMB31FZWakJEyYoKSlJnTt31uTJk3XmzJnWPhQAgHxh6PqtD6urcTJge1fjpK7f+rDef3N1877wcucBZd0t3fcbKalWNeukDN926hBZgqlHiD777DPdcsst+vKXv6w///nP6tq1qw4ePKguXbr42yxbtkzLly/X6tWrlZmZqQULFmjUqFEqLi5WQoKvhPuECRNUWlqqjRs36sKFC/rmN7+pqVOnau3atZE6NACwJM/Fi8ooypck1S4gbbdJXkNKL8qXZ+QExcQG+ScqHPOAsu6W+oymUrWF2QzDqO+mqynMmzdPf/vb3/TXv/613v2GYSgjI0OPPfaY5syZI0lyuVxKS0vTqlWrNH78eH344YfKysrSjh07NHjwYElSQUGB7rrrLn366afKyMio873V1dWqrq72v3e73erevbtcLpeSkpJa4EgBwBr2/+0NXbfx/qbb3b5W190yOrgv9XqkJ/v5JlDXO4/I5hvtmbWXgGMxbrdbDocjqL/fpr5ltm7dOg0ePFhf//rXlZqaqhtvvFEvvPCCf39JSYnKysqUm5vr3+ZwOJSdna2ioiJJUlFRkTp37uwPQ5KUm5sru92ubdu21fu7ixcvlsPh8L+6d+/eQkcIANbyr8+OhbWdpC/mAUlq8Hn3zANCE0wdiP7xj39oxYoVuvrqq/Xmm29q2rRpevjhh7V6te/+cllZmSQpLS1wGDQtLc2/r6ysTKmpqQH7Y2NjlZyc7G9T2/z58+Vyufyvo0ePhvvQAMCS2nfpFtZ2fswDwmUy9Rwir9erwYMH68c//rEk6cYbb9S+ffu0cuVKTZrUzFUIzRAfH6/4+PgW+34AsKo+2aNUvjFFXY2TdeYQSb45RBW2FPXJHtX8L2ceEC6DqUeI0tPTlZWVFbCtb9++OnLkiCTJ6XRKksrLA1cOlJeX+/c5nU5VVFQE7L948aIqKyv9bQAArSMmNlbHcxZJ8oWfS9W8L81ZFPyE6trsMb6l9f3/zfdPwhCCZOpAdMstt+jAgQMB2z7++GP17NlTkpSZmSmn06lNmzb597vdbm3btk05OTmSpJycHJ06dUo7d+70t3n77bfl9XqVnZ3dCkcBALjUjaMm6YOhy3XClhKwvcKWog+GLm9+HaLmaO6zzmAZpl5ltmPHDg0dOlT5+fm67777tH37dk2ZMkXPP/+8JkyYIElaunSplixZErDsfs+ePQHL7u+8806Vl5dr5cqV/mX3gwcPDnrZfXNmqQMAguO5eFEfbXtT//rsmNp36aY+2aNCHxkKRijPOkNUa87fb1MHIknasGGD5s+fr4MHDyozM1OzZ8/WlClT/PsNw9CiRYv0/PPP69SpUxo2bJiee+45XXPNNf42lZWVmjFjhtavXy+73a5x48Zp+fLl6tSpU1B9IBABQJSredZZnWX5n09kYuJ1m9SmApEZEIgAIIr56xQ19HgP6hS1VW2mDhEAAJetOc86g2URiAAAbdvlPusMlkAgAgC0beF41hnaPAIRAKBt6znUN0eozmM9atikpG6+drCskALRE088oXPnztXZ/q9//UtPPPHEZXcKAICw4VlnCEJIq8xiYmJUWlpa5xlhJ0+eVGpqqjyetlXoilVmANAG1FuHqJsvDLHkvk1qzt/vkCpgGYYhm63u0OMHH3yg5OTkUL4SAICWxbPO0IhmBaIuXbrIZrPJZrPpmmuuCQhFHo9HZ86c0be+9a2wdxIAgLCoedYZUEuzAtGTTz4pwzD00EMPKT8/Xw6Hw78vLi5OV111lf8ZYgAAANGiWYFo0iTfA/cyMzM1dOhQtWvXrkU6BQAA0JpCmkM0YsQIeb1effzxx6qoqJDX6w3YP3z48LB0DgAAoDWEFIjeffdd3X///Tp8+LBqL1Kz2WxtbpUZAABo20IKRN/61rc0ePBgvfHGG0pPT693xRkAAEC0CCkQHTx4UH/84x/Vu3fvcPcHAACg1YVUqTo7O1t///vfw90XAACAiAh6hGjPnj3+f585c6Yee+wxlZWVqX///nVWmw0YMCB8PQQAAGhhQT+6w263y2az1ZlE7f+iz/e1xUnVPLoDAIDo0yKP7igpKbnsjgEAAJhR0IGoZ8+eLdkPAACAiAlpldm6devq3W6z2ZSQkKDevXsrMzPzsjoGAADQWkIKRGPHjq13PtGl84iGDRum1157TV26dAlLRwEAAFpKSMvuN27cqJtuukkbN26Uy+WSy+XSxo0blZ2drQ0bNmjLli06efKk5syZE+7+AgDCwOM1VHTopF7ffUxFh07K4w1qfU2dz52/6G30e0L9HaC1hTRC9Mgjj+j555/X0KFD/dtGjhyphIQETZ06Vfv379eTTz6phx56KGwdBQCER8G+UuWvL1apq8q/Ld2RoEVjspTXL71Zn7PbpEszzqXfE+rvAJEQ0gjRoUOH6l2+lpSUpH/84x+SpKuvvlr//Oc/L693AICwKthXqmlrdgWEFEkqc1Vp2ppdKthX2qzP1R7wqfmexX8qDul3gEgJKRANGjRIjz/+uE6cOOHfduLECX3nO9/RTTfdJMn3eI/u3buHp5cAgMvm8RrKX1+s+m5a1WzLX19c722vhj7X0Pe88NeSZv8OEEkhBaJf//rXKikp0ZVXXqnevXurd+/euvLKK/XJJ5/oV7/6lSTpzJkz+v73vx/WzgIAQre9pLLOiM2lDEmlriptL6ls1ufq+57Gsk5DvwNEUkhziK699loVFxfrrbfe0scff+zfdvvtt8tu92WssWPHhq2TAIDLV3E6uFBTu12wn2uulvpeIBQhBSLJ9yiPvLw85eXlhbM/AIAWkpqYEFK7YD/XXC31vUAogg5Ey5cv19SpU5WQkKDly5c32vbhhx++7I4BAMJrSGay0h0JKnNV1Tu/xybJ6UjQkMzkZn2uvu+x2Rq+bdbQ7wCRFPTDXTMzM/Xee+8pJSWl0SrUNpvNv9KsreDhrgDaiprVYpICwo3t83+ueGBgvUviG/pcbTXfM3V4pp7fUtLs3wHCqTl/v4MORFZGIALQloSzDlGszavBto+UqlOqUGcd7XS9FtzdnzpEMIVWC0Tnz59XSUmJevXqpdjYkKcjmR6BCEBb4/Ea2l5SqYrTVUpN9N2+irHbmvW5Pp9t1jW7fijb6eP+/UZShmx5S6Wsuy/rd4BwaPFAdO7cOc2cOVOrV6+WJH388cf60pe+pJkzZ6pbt26aN29eaD03KQIRANRSvE76w0TVvYH2edi57zf+UARESnP+fodUh2j+/Pn64IMPtHnzZiUkfLFKIDc3V7///e9D+UoAQLTweqSCuap/NtHn2wrm+doBUSKkQPTaa6/pmWee0bBhw2SzfTH0ed111+nQoUNh6xwAwIQOb5XcxxtpYEjuY752QJQIKRCdOHFCqampdbafPXs2ICABANqgM+XhbQeYQEiBaPDgwXrjjTf872tC0K9+9Svl5OSEp2cAAHPqlBbedoAJhLQ07Mc//rHuvPNOFRcX6+LFi3rqqadUXFysrVu3qrCwMNx9BACYSc+hUlKG5C5V/fOIbL79PYe2ds+AkIU0QjRs2DB98MEHunjxovr376+33npLqampKioq0qBBg8LdRwBAmHm8hooOndTru4+p6NBJnb/oDXjf6JPo7TFS3tLP39SeJvH5+7wlvnZAlAhp2f3EiRP15S9/WcOHD1evXr1aol+mwrJ7AG1JfQUT7bUetRFUAcXidb7VZpdOsE7q5gtDLLmHCbR4HaL//M//1JYtW3To0CFlZGRoxIgRuu222zRixAhdffXVIXfcrAhEANqKmkdwNPUf/qAfseH1+FaTnSn3zRnqOZSRIZhGq1WqPnbsmLZs2aLCwkIVFhbq448/Vnp6uj799NNQv9KUCEQA2gKP19CwpW8HjAw1puYhrO/M/QrVpRGVWrwwY40uXbooJSVFXbp0UefOnRUbG6uuXbtezlcCAFrI9pLKoMOQ5JsuXeqq0vaSypbrFGASIQWi7373uxo6dKhSUlI0b948VVVVad68eSorK9P7778f7j4CAMKg4nTwYSgcnwOiSUjL7pcsWaKuXbtq0aJFuvfee3XNNdeEu18AgDBLTUxoulEYPwdEk5AC0fvvv6/CwkJt3rxZP/vZzxQXF+efWH3bbbcRkAAgApp6svyQzGSlOxJU5qpqclK19MUcoiGZyS3WZ8AsLmtSdY0PPvhAv/jFL/TSSy/J6/XK42lbD/RjUjUAs6tvKX19S+drVplJ9ZdUrBH0KjPAxJrz9zukESLDMPT+++9r8+bN2rx5s9555x253W4NGDBAI0aMCKnTAIDQNLSUvsxVpWlrdgWEmrx+6VrxwMAm6xA5g6lDBLQhIY0QdenSRWfOnNH111/vv1V26623qnPnzi3QxchjhAiAWTW1lL6hpfO1b68N6tlFOw9/1uDtNiAatfgI0Zo1a3TrrbcSDgAgwppaSn/p0vmcXin+7TF2W8B7SXXeA1YSUiAaPXp0uPsBAAhBsEviWToPNO6yCjMCACIr2CXxLJ0HGkcgAoAoVrOUvqHZPjb5VpuxdB5oHIEIAKJYjN2mRWOyJKlOKKp5v2hMFhOkgSYQiAAgytUspXc6Am+LOR0J1BECghTSpGoAgLnk9UvX7VnORitVA2hYVI0QLVmyRDabTbNmzfJvq6qq0vTp05WSkqJOnTpp3LhxKi8vD/jckSNHNHr0aHXo0EGpqal6/PHHdfHixVbuPQC0rJql9Pfc0E05vVIIQ0AzRE0g2rFjh375y19qwIABAdsfffRRrV+/Xq+88ooKCwt1/Phx3Xvvvf79Ho9Ho0eP1vnz57V161atXr1aq1at0sKFC1v7EAAAgElFRSA6c+aMJkyYoBdeeEFdunTxb3e5XPr1r3+tn//85/rKV76iQYMG6cUXX9TWrVv17rvvSpLeeustFRcXa82aNbrhhht055136gc/+IGeffZZnT9/PlKHBADm4vVIJX+V9v7R909v23omJdCUqAhE06dP1+jRo5WbmxuwfefOnbpw4ULA9j59+qhHjx4qKiqSJBUVFal///5KS0vztxk1apTcbrf2799f7+9VV1fL7XYHvACgzSpeJz3ZT1r9Ven/Tfb988l+vu2ARZg+EL388svatWuXFi9eXGdfWVmZ4uLi6jxDLS0tTWVlZf42l4ahmv01++qzePFiORwO/6t79+5hOBIAMKHiddIfJkru44Hb3aW+7YQiWISpA9HRo0f1yCOP6KWXXlJCQutVWZ0/f75cLpf/dfTo0Vb7bQBoNV6PVDBXviee1fb5toJ53D6DJZg6EO3cuVMVFRUaOHCgYmNjFRsbq8LCQi1fvlyxsbFKS0vT+fPnderUqYDPlZeXy+l0SpKcTmedVWc172va1BYfH6+kpKSAFwC0OYe31h0ZCmBI7mO+dkAbZ+pANHLkSO3du1e7d+/2vwYPHqwJEyb4/71du3batGmT/zMHDhzQkSNHlJOTI0nKycnR3r17VVFR4W+zceNGJSUlKSsrq9WPCQBM40x5022a0w6IYqYuzJiYmKh+/foFbOvYsaNSUlL82ydPnqzZs2crOTlZSUlJmjlzpnJycnTzzTdLku644w5lZWXpG9/4hpYtW6aysjJ9//vf1/Tp0xUfH9/qxwQAptEprek2zWkHRDFTB6Jg/OIXv5Ddbte4ceNUXV2tUaNG6bnnnvPvj4mJ0YYNGzRt2jTl5OSoY8eOmjRpkp544okI9hoATKDnUCkpwzeBut55RDbf/p5DW7tnQKuzGYZR3/8KcAm32y2HwyGXy8V8IgBtS80qM0mBoejzKtf3/UbKuru1ewWERXP+fpt6DhEAIHger6GiQyf1+u5jKjp0Uh5vEP9/N+tuX+hJqvUA2KQMwhAsJepvmQEApIJ9pcpfX6xSV5V/W7ojQYvGZDX9tPusu6U+o32ryc6U++YM9Rwq2WNauNeAeXDLLAjcMgNgZgX7SjVtza46s4BqHu264oGBTYcioA3ilhkAWITHayh/fXFjpRWVv744uNtngIURiAAgim0vqQy4TVabIanUVaXtJZWt1ykgChGIACCKVZxuOAyF0g6wKgIRAESx1MTgnvMYbDvAqghEABDFhmQmK92R4J9AXZtNvtVmQzKTW7NbQNQhEAFAiEKq+xNmMXabFo3xPZexdiiqeb9oTJZi7A1Fpsgxw/kDalCHCABCcFl1f8Isr1+6VjwwsE5/nBHqTzDMdP4AiTpEQaEOEYBLmbXuj8draHtJpSpOVyk10XebzIwjQ2Y9f2h7qEMEAC3EzHV/Yuw25fRK0T03dFNOrxRThiEznz9YG4EIAJqBuj+Xh/MHsyIQAUAzUPfn8nD+YFYEIgBoBur+XB7OH8yKQAQAzUDdn8vD+YNZEYgAoBmiue6PGXD+YFYEIgBoppq6P05H4G0dpyOBJeNB4PzBjKhDFATqEAGoT7TU/TErzh9aWnP+flOpGgBCVFP3B6Hh/MFMuGUGAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj1VmANAMLBUH2iYCEQAEqWBfqfLXFwc8rT3dkaBFY7IoJghEOW6ZAUAQCvaVatqaXQFhSJLKXFWatmaXCvaVRqhnAMKBQAQATfB4DeWvL1Z9Zf1rtuWvL5bHS+F/IFoRiACgCdtLKuuMDF3KkFTqqtL2ksrW6xSAsCIQAUATKk43HIZCaQfAfAhEANCE1MSEphs1ox0A8yEQAUAThmQmK92RoIYW19vkW202JDO5NbsFIIwIRADQhBi7TYvGZElSnVBU837RmCzqEQFRjEAEAEHI65euFQ8MlNMReFvM6UjQigcGUocIiHIUZgSAIOX1S9ftWU4qVQNtEIEIAJohxm5TTq+USHcDQJhxywwAAFgegQgAAFgegQgAAFgegQgAAFgek6oBWJbHa7BiDIAkAhEAiyrYV6r89cUBD21NdyRo0ZgsagoBFsQtMwCWU7CvVNPW7KrzBPsyV5Wmrdmlgn2lEeoZgEghEAGwFI/XUP76Yhn17KvZlr++WB5vfS0AtFUEIgCWsr2kss7I0KUMSaWuKm0vqWy9TgGIOAIRAEupON1wGAqlHYC2gUAEwFJSExOabtSMdgDaBgIRAEsZkpmsdEeCGlpcb5NvtdmQzOTW7BaACCMQAbCUGLtNi8ZkSVKdUFTzftGYLOoRARZDIAJgOXn90rXigYFyOgJvizkdCVrxwEDqEAEWRGFGAJaU1y9dt2c5qVQNQBKBCICFxdhtyumVEuluADABbpkBAADLIxABAADLIxABAADLIxABAADLM3UgWrx4sW666SYlJiYqNTVVY8eO1YEDBwLaVFVVafr06UpJSVGnTp00btw4lZeXB7Q5cuSIRo8erQ4dOig1NVWPP/64Ll682JqHAgAATMzUgaiwsFDTp0/Xu+++q40bN+rChQu64447dPbsWX+bRx99VOvXr9crr7yiwsJCHT9+XPfee69/v8fj0ejRo3X+/Hlt3bpVq1ev1qpVq7Rw4cJIHBIAADAhm2EYRqQ7EawTJ04oNTVVhYWFGj58uFwul7p27aq1a9fq3/7t3yRJH330kfr27auioiLdfPPN+vOf/6yvfvWrOn78uNLS0iRJK1eu1Ny5c3XixAnFxcU1+btut1sOh0Mul0tJSUkteowAACA8mvP329QjRLW5XC5JUnKy7xlDO3fu1IULF5Sbm+tv06dPH/Xo0UNFRUWSpKKiIvXv398fhiRp1KhRcrvd2r9/f72/U11dLbfbHfACAABtV9QEIq/Xq1mzZumWW25Rv379JEllZWWKi4tT586dA9qmpaWprKzM3+bSMFSzv2ZffRYvXiyHw+F/de/ePcxHAwAAzCRqAtH06dO1b98+vfzyyy3+W/Pnz5fL5fK/jh492uK/CQAAIicqHt0xY8YMbdiwQVu2bNGVV17p3+50OnX+/HmdOnUqYJSovLxcTqfT32b79u0B31ezCq2mTW3x8fGKj48P81EAAACzMvUIkWEYmjFjhl599VW9/fbbyszMDNg/aNAgtWvXTps2bfJvO3DggI4cOaKcnBxJUk5Ojvbu3auKigp/m40bNyopKUlZWVmtcyAAAMDUTD1CNH36dK1du1avv/66EhMT/XN+HA6H2rdvL4fDocmTJ2v27NlKTk5WUlKSZs6cqZycHN18882SpDvuuENZWVn6xje+oWXLlqmsrEzf//73NX36dEaBAACAJJMvu7fZbPVuf/HFF/Xggw9K8hVmfOyxx/S73/1O1dXVGjVqlJ577rmA22GHDx/WtGnTtHnzZnXs2FGTJk3SkiVLFBsbXB5k2T0AANGnOX+/TR2IzIJABABA9GmzdYgAAABaAoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYXmykOwAExeuRDm+VzpRLndKknkMle0ykewUAaCMIRIgoj9fQ9pJKVZyuUmpigoZkJivGbgtsVLxOKpgruY9/sS0pQ8pbKmXd3bodBgC0SQQiREzBvlLlry9WqavKvy3dkaBFY7KU1y/dt6F4nfSHiZKMwA+7S33b7/sNoQgAcNlshmEYTTezNrfbLYfDIZfLpaSkpEh3p0VcOlJzRad4yZD+eba63lGboEZ1mlCwr1TT1uyqHXNU8y0rHhiovKxU6cl+gSNDtVsnZUiz9nL7DABQR3P+fjNCFO0unVvTsatkGPKePaEPT3fQ3zv0V2pSx4DA4rl4UR9te1P/+uyY4jtnyJ16k94+8E+9uvuYKs9eqPcnLh21CWpUpwker6H89cV1wpDkGweyScpfX6zbOxxUTINh6PPW7mO+48+8NajfBgCgPgSiSApyorB/RMZ9Vr3P7VXfxHOyJzqlcyelN+fXGUGxS7pOUhcjWfkXJmp24nAtGpOltGNvKaMoX9fppL/tcSNZn16YqErvkAa7Weaq0rQ1uzR1eKae31JSJ8jU7F/xwMCgQtH2ksqAQFWbIanUVaVD/ziia5r8NvnOHwAAl4FAFClBThSuGZEZcHqLFrX7jTJslUH/hFOVWtHuSX37tPTq2i1a0e5J3w5b3TbTLszSmw2EopoA9MJf64ahmv3+UZ0sZ5O3zypONxyGAtoZnYMLRJ3Sgvo+AAAaQh2iSKiZKFz7dlDNROHidZK+mGcz4LQvzDgVGIaamvxVk0sWtvuN/rvdbwK21W6zqN1vZZe30e/zNvKDNaM620uaDmypiQlNtpGkmKtu8YVENRSwbFJSN9/IGgAAl4FA1Nq8Ht/IUINjLZIK5slz8aLy1xfLJq8WNRBmgpnGbLdJGbZKpdsq63w+sM1JDbF/FOxRNCiY0Z8hmclKdyQ0FnOU7kjQkF5dfSNm/q21W0nKW8KEagDAZbNUIHr22Wd11VVXKSEhQdnZ2dq+fXvrd+Lw1kZWTUk1E4U/2vamSl1VGmL/SBmNhJlwStWpy/+OIEZ/Yuw2LRqTJanBmKNFY7J8t96y7vYtrU+qNTcpKYMl9wCAsLHMHKLf//73mj17tlauXKns7Gw9+eSTGjVqlA4cOKDU1NTW60iQE4D/9dkxSd3CElKCVaHOje632yTDqH9syybJ6fAtwQ9GXr90rXhgYJ0Va876Vqxl3S31GU2lagBAi7FMIPr5z3+uKVOm6Jvf/KYkaeXKlXrjjTf0P//zP5o3b15A2+rqalVXV/vfu93u8HUkyAnA7bt0k9R0SGmK15DKlCybpDTVP9Lka5Oi7d4+9X5HzUem3OpbZWZTYCiqM6oTpLx+6bo9yxlcTSN7DEvrAQAtxhK3zM6fP6+dO3cqNzfXv81utys3N1dFRUV12i9evFgOh8P/6t69e/g603NoUBOF+2SPUrojQTu8fXTcSG5wQnNjE6trPvPEhYn67wsTA7bVbpN/4RvyNnA5OB0JWvHAQM2/K0srHhgopyOh3v3B1iG6VIzdppxeKbrnhm7K6ZXS7AKPAACEgyVGiP75z3/K4/EoLS1wdCYtLU0ffVR3IvH8+fM1e/Zs/3u32x2+UGSP8U0U/sNEqaGxlrwliomN1aIxWZq2ZpeeuDBRz7V7Ul4jcGK1ocYnVpcpRfkXvqE9n9ch+uDYl5RRlK+0S+oQ1bR50ztEKR3jdM8NGRrZN63BStXNGtUBACBKWCIQNVd8fLzi4+Nb7gdqJgrXW4doiX+i8BfzbBI07bR8dYguWXpvS+om3fFjqWNKg5WqH7y0UnW/SfKMnKD9tSpV33Xuoh5sRrCpGdUBAKCtsEQguuKKKxQTE6Py8sAJzeXl5XI6nZHpVJAThb8YkblBO9yTAytVNzCxuKZS9XX1/GxMbKyuu2V0ixwSAADRyhKBKC4uToMGDdKmTZs0duxYSZLX69WmTZs0Y8aMyHUsyInCX4zIpEjq0eLdAgDAaiwRiCRp9uzZmjRpkgYPHqwhQ4boySef1NmzZ/2rzgAAgHVZJhD9+7//u06cOKGFCxeqrKxMN9xwgwoKCupMtAYAANZjMwyjqUdiWZ7b7ZbD4ZDL5VJSUlKkuwMAAILQnL/flqhDBAAA0BgCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDzL1CG6HDWVCdxud4R7AgAAglXzdzuYCkMEoiCcPn1aksL3xHsAANBqTp8+LYfD0WgbCjMGwev16vjx40pMTJTN1vTT4EPldrvVvXt3HT16lAKQ9eD8NI7z0zTOUeM4P43j/DTOjOfHMAydPn1aGRkZstsbnyXECFEQ7Ha7rrzyylb7vaSkJNNcTGbE+Wkc56dpnKPGcX4ax/lpnNnOT1MjQzWYVA0AACyPQAQAACyPQGQi8fHxWrRokeLj4yPdFVPi/DSO89M0zlHjOD+N4/w0LtrPD5OqAQCA5TFCBAAALI9ABAAALI9ABAAALI9ABAAALI9AFAFbtmzRmDFjlJGRIZvNptdeey1gv2EYWrhwodLT09W+fXvl5ubq4MGDkelsBDR1fh588EHZbLaAV15eXmQ6GwGLFy/WTTfdpMTERKWmpmrs2LE6cOBAQJuqqipNnz5dKSkp6tSpk8aNG6fy8vII9bh1BXN+brvttjrX0Le+9a0I9bh1rVixQgMGDPAXz8vJydGf//xn/34rXztS0+fHytdOfZYsWSKbzaZZs2b5t0XrNUQgioCzZ8/q+uuv17PPPlvv/mXLlmn58uVauXKltm3bpo4dO2rUqFGqqqpq5Z5GRlPnR5Ly8vJUWlrqf/3ud79rxR5GVmFhoaZPn653331XGzdu1IULF3THHXfo7Nmz/jaPPvqo1q9fr1deeUWFhYU6fvy47r333gj2uvUEc34kacqUKQHX0LJlyyLU49Z15ZVXasmSJdq5c6fee+89feUrX9E999yj/fv3S7L2tSM1fX4k6147te3YsUO//OUvNWDAgIDtUXsNGYgoScarr77qf+/1eg2n02n85Cc/8W87deqUER8fb/zud7+LQA8jq/b5MQzDmDRpknHPPfdEpD9mVFFRYUgyCgsLDcPwXS/t2rUzXnnlFX+bDz/80JBkFBUVRaqbEVP7/BiGYYwYMcJ45JFHItcpk+nSpYvxq1/9imunATXnxzC4dmqcPn3auPrqq42NGzcGnJNovoYYITKZkpISlZWVKTc317/N4XAoOztbRUVFEeyZuWzevFmpqam69tprNW3aNJ08eTLSXYoYl8slSUpOTpYk7dy5UxcuXAi4hvr06aMePXpY8hqqfX5qvPTSS7riiivUr18/zZ8/X+fOnYtE9yLK4/Ho5Zdf1tmzZ5WTk8O1U0vt81ODa0eaPn26Ro8eHXCtSNH93x8e7moyZWVlkqS0tLSA7Wlpaf59VpeXl6d7771XmZmZOnTokL773e/qzjvvVFFRkWJiYiLdvVbl9Xo1a9Ys3XLLLerXr58k3zUUFxenzp07B7S14jVU3/mRpPvvv189e/ZURkaG9uzZo7lz5+rAgQP63//93wj2tvXs3btXOTk5qqqqUqdOnfTqq68qKytLu3fv5tpRw+dH4tqRpJdfflm7du3Sjh076uyL5v/+EIgQdcaPH+//9/79+2vAgAHq1auXNm/erJEjR0awZ61v+vTp2rdvn955551Id8WUGjo/U6dO9f97//79lZ6erpEjR+rQoUPq1atXa3ez1V177bXavXu3XC6X/vjHP2rSpEkqLCyMdLdMo6Hzk5WVZflr5+jRo3rkkUe0ceNGJSQkRLo7YcUtM5NxOp2SVGdGfnl5uX8fAn3pS1/SFVdcob///e+R7kqrmjFjhjZs2KC//OUvuvLKK/3bnU6nzp8/r1OnTgW0t9o11ND5qU92drYkWeYaiouLU+/evTVo0CAtXrxY119/vZ566imunc81dH7qY7VrZ+fOnaqoqNDAgQMVGxur2NhYFRYWavny5YqNjVVaWlrUXkMEIpPJzMyU0+nUpk2b/Nvcbre2bdsWcA8bX/j000918uRJpaenR7orrcIwDM2YMUOvvvqq3n77bWVmZgbsHzRokNq1axdwDR04cEBHjhyxxDXU1Pmpz+7duyXJMtdQbV6vV9XV1Za/dhpSc37qY7VrZ+TIkdq7d692797tfw0ePFgTJkzw/3u0XkPcMouAM2fOBPy/iZKSEu3evVvJycnq0aOHZs2apR/+8Ie6+uqrlZmZqQULFigjI0Njx46NXKdbUWPnJzk5Wfn5+Ro3bpycTqcOHTqk73znO+rdu7dGjRoVwV63nunTp2vt2rV6/fXXlZiY6L8v73A41L59ezkcDk2ePFmzZ89WcnKykpKSNHPmTOXk5Ojmm2+OcO9bXlPn59ChQ1q7dq3uuusupaSkaM+ePXr00Uc1fPjwOsuH26L58+frzjvvVI8ePXT69GmtXbtWmzdv1ptvvmn5a0dq/PxY/dqRpMTExID5eJLUsWNHpaSk+LdH7TUU6WVuVvSXv/zFkFTnNWnSJMMwfEvvFyxYYKSlpRnx8fHGyJEjjQMHDkS2062osfNz7tw544477jC6du1qtGvXzujZs6cxZcoUo6ysLNLdbjX1nRtJxosvvuhv869//cv49re/bXTp0sXo0KGD8bWvfc0oLS2NXKdbUVPn58iRI8bw4cON5ORkIz4+3ujdu7fx+OOPGy6XK7IdbyUPPfSQ0bNnTyMuLs7o2rWrMXLkSOOtt97y77fytWMYjZ8fq187DaldiiBaryGbYRhGawYwAAAAs2EOEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEYCoc9ttt2nWrFmR7oY2b94sm81W50GWAKIPgQgAgmCWEAagZRCIAACA5RGIAES16upqzZkzR926dVPHjh2VnZ2tzZs3+/evWrVKnTt31ptvvqm+ffuqU6dOysvLU2lpqb/NxYsX9fDDD6tz585KSUnR3LlzNWnSJI0dO1aS9OCDD6qwsFBPPfWUbDabbDabPvnkE//nd+7cqcGDB6tDhw4aOnSoDhw40EpHDyBcCEQAotqMGTNUVFSkl19+WXv27NHXv/515eXl6eDBg/42586d009/+lP99re/1ZYtW3TkyBHNmTPHv3/p0qV66aWX9OKLL+pvf/ub3G63XnvtNf/+p556Sjk5OZoyZYpKS0tVWlqq7t27+/d/73vf089+9jO99957io2N1UMPPdQqxw4gfGIj3QEACNWRI0f04osv6siRI8rIyJAkzZkzRwUFBXrxxRf14x//WJJ04cIFrVy5Ur169ZLkC1FPPPGE/3uefvppzZ8/X1/72tckSc8884z+9Kc/+fc7HA7FxcWpQ4cOcjqddfrxox/9SCNGjJAkzZs3T6NHj1ZVVZUSEhJa5sABhB2BCEDU2rt3rzwej6655pqA7dXV1UpJSfG/79Chgz8MSVJ6eroqKiokSS6XS+Xl5RoyZIh/f0xMjAYNGiSv1xtUPwYMGBDw3ZJUUVGhHj16NP+gAEQEgQhA1Dpz5oxiYmK0c+dOxcTEBOzr1KmT/9/btWsXsM9ms8kwjLD149Lvt9lskhR0mAJgDswhAhC1brzxRnk8HlVUVKh3794Br/pubdXH4XAoLS1NO3bs8G/zeDzatWtXQLu4uDh5PJ6w9h+AeTBCBCBqXXPNNZowYYImTpyon/3sZ7rxxht14sQJbdq0SQMGDNDo0aOD+p6ZM2dq8eLF6t27t/r06aOnn35an332mX+0R5Kuuuoqbdu2TZ988ok6deqk5OTkljosABHACBGAqPbiiy9q4sSJeuyxx3Tttddq7Nix2rFjR7Pm78ydO1f/8R//oYkTJyonJ0edOnXSqFGjAiZFz5kzRzExMcrKylLXrl115MiRljgcABFiM8J5Ix0A2gCv16u+ffvqvvvu0w9+8INIdwdAK+CWGQDLO3z4sN566y2NGDFC1dXVeuaZZ1RSUqL7778/0l0D0Eq4ZQbA8ux2u1atWqWbbrpJt9xyi/bu3av/+7//U9++fSPdNQCthFtmAADA8hghAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlvf/AXLEJWA+BZFuAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# x축: length, y축: weight\n",
        "plt.scatter(train_input[:, 0], train_input[:, 1])  # 파란색: train\n",
        "plt.scatter(test_input[:, 0], test_input[:, 1])  # 주황색: test\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pphep3SEHbTD"
      },
      "source": [
        "## k-최근접 이웃 모델"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "RpiXncTRfY7i",
        "outputId": "949b9f52-ac6f-4fa7-ec78-949e4ed7c834"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ],
      "source": [
        "kn.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Stdtp3RvhImd",
        "outputId": "596f44bb-83cc-4f4f-f6ee-69004db0d2c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "kn.score(test_input, test_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Od3gLmcJihAe",
        "outputId": "5a195314-e017-45d2-ec26-7b0da27c21c2"
      },
      "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": 115
        }
      ],
      "source": [
        "kn.predict(test_input)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-iCt9zHilaa",
        "outputId": "fbddc3ff-94bf-4bdd-affc-50c27e6d30e8",
        "scrolled": true
      },
      "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": 116
        }
      ],
      "source": [
        "test_target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5PO-C32HsBx"
      },
      "source": [
        "## 넘파이로 데이터 준비하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SLUDuHl8oJZX"
      },
      "outputs": [],
      "source": [
        "fish_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,\n",
        "                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0,\n",
        "                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8,\n",
        "                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",
        "fish_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,\n",
        "                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0,\n",
        "                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7,\n",
        "                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]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0-OTt0n1LMjf"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fish_data = np.column_stack((fish_length, fish_weight))\n",
        "print(fish_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8hxxuDgeEdW",
        "outputId": "43bb7e2a-8e1a-43b7-ac17-74a9a3ba8e19"
      },
      "execution_count": null,
      "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",
      "execution_count": null,
      "metadata": {
        "id": "ILMRbWnkLZVF"
      },
      "outputs": [],
      "source": [
        "fish_data = np.column_stack((fish_length, fish_weight))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GgyJLb06yzEy"
      },
      "outputs": [],
      "source": [
        "fish_target = np.concatenate((np.ones(35), np.zeros(14)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZepGdT9uL7tA",
        "outputId": "6a7428b5-9ca1-41aa-a23f-3850be4fb7bb"
      },
      "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.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0.]\n"
          ]
        }
      ],
      "source": [
        "print(fish_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShnaZsAAHsB1"
      },
      "source": [
        "## 사이킷런으로 훈련 세트와 테스트 세트 나누기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vpkI83t3KBsF"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l-gm9r-jKMJY"
      },
      "outputs": [],
      "source": [
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    fish_data, fish_target, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CS696_mxKZ38",
        "outputId": "b2a84bf8-929e-4af6-f743-120043cff34e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(36, 2) (13, 2)\n"
          ]
        }
      ],
      "source": [
        "print(train_input.shape, test_input.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtNqwNjbKnI-",
        "outputId": "ef80d259-49ae-42f0-9d4d-16c4d42002f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(36,) (13,)\n"
          ]
        }
      ],
      "source": [
        "print(train_target.shape, test_target.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mR9_bB3COHaA",
        "outputId": "c0b872ca-2e41-4769-d9fc-109aa362dabd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ],
      "source": [
        "print(test_target)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    fish_data, fish_target, random_state=42, stratify=fish_target)"
      ],
      "metadata": {
        "id": "vnRSFInZlX60"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDkLM6oDPdO9",
        "outputId": "ed2cb6d8-23bc-4fe1-e8d3-ec0a05ff5b58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ],
      "source": [
        "print(test_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFO96jGvHsB2"
      },
      "source": [
        "## 스케일 맞추기 - 전처리\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhB-Alz--6U4",
        "outputId": "53cfb71f-4486-4a2e-aa7b-9f26bcdf3e74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "kn = KNeighborsClassifier()\n",
        "kn.fit(train_input, train_target)\n",
        "kn.score(test_input, test_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRKoCZFeT9xE",
        "outputId": "bc778671-6e5b-4ef6-d9a6-bd8c27c73549"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.]\n"
          ]
        }
      ],
      "source": [
        "print(kn.predict([[25, 150]]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jSEc853pQHSu"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "1-GE9ic9_XRU",
        "outputId": "921fde66-87b1-487b-eaee-70cc3b9ea9de"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train_input[:,0], train_input[:,1])\n",
        "plt.scatter(25, 150, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G_jwm_ouQKQ7"
      },
      "outputs": [],
      "source": [
        "distances, indexes = kn.kneighbors([[25, 150]])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(distances)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SO7ae3kKRm2V",
        "outputId": "da1d168d-0937-46e7-bd1f-008ac0d8882b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 92.00086956 130.48375378 130.73859415 138.32150953 138.39320793]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "tVVz-wJoQ2ps",
        "outputId": "002c6a8e-ed53-411e-bcac-a9b6343c60a7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train_input[:,0], train_input[:,1])\n",
        "plt.scatter(25, 150, marker='^')\n",
        "plt.scatter(train_input[indexes,0], train_input[indexes,1], marker='D')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IvKvCx1RyhY",
        "outputId": "d7bd6feb-a43c-498f-bd2b-9bcf428c3930"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 25.4 242. ]\n",
            "  [ 15.   19.9]\n",
            "  [ 14.3  19.7]\n",
            "  [ 13.   12.2]\n",
            "  [ 12.2  12.2]]]\n"
          ]
        }
      ],
      "source": [
        "print(train_input[indexes])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tEGpNPT00ut",
        "outputId": "f4c69fa4-e02c-462b-e79d-bc1b60147235"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0. 0. 0.]]\n"
          ]
        }
      ],
      "source": [
        "print(train_target[indexes])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ry0R_gEqRVsQ",
        "outputId": "5293aa67-f947-4f42-c4ca-c5a486b42ee4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 92.00086956 130.48375378 130.73859415 138.32150953 138.39320793]]\n"
          ]
        }
      ],
      "source": [
        "print(distances)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "P6PXDboiTx_j",
        "outputId": "780fcb2b-3d15-4c9e-ad33-f79a4db88519",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train_input[:,0], train_input[:,1])\n",
        "plt.scatter(25, 150, marker='^')\n",
        "plt.scatter(train_input[indexes,0], train_input[indexes,1], marker='D')\n",
        "plt.xlim((0, 1000))\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yi9xncqkUGIZ"
      },
      "outputs": [],
      "source": [
        "mean = np.mean(train_input, axis=0)\n",
        "std = np.std(train_input, axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7CuQC8IUsty",
        "outputId": "f6ad6c82-1d56-4aa6-e833-c8e72884ba68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 27.29722222 454.09722222] [  9.98244253 323.29893931]\n"
          ]
        }
      ],
      "source": [
        "print(mean, std)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jmSFa3LzUteI"
      },
      "outputs": [],
      "source": [
        "train_scaled = (train_input - mean) / std"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQEcM4FoHsB5"
      },
      "source": [
        "## 전처리 데이터로 모델 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "P4dx2WDOVDMD",
        "outputId": "35524ee2-a830-40fe-b962-1e7be58f25b5",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train_scaled[:,0], train_scaled[:,1])\n",
        "plt.scatter(25, 150, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평균\n",
        "mean = np.mean(train_input, axis=0)\n",
        "# 표준편차\n",
        "std = np.std(train_input, axis=0)"
      ],
      "metadata": {
        "id": "oaRLa4CqVQLF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kjd2W6ZbVlgG"
      },
      "outputs": [],
      "source": [
        "new = ([25, 150] - mean) / std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "5-Zq-jIMVbV-",
        "outputId": "7e2d338f-5aa2-4f25-909a-6dc362390195"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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/ey1ZssR0H5vNJqfTGckyAQARFuzaIjp1IxCWmUk6efKktm7dqvz8fN+2uLg45efnq7S0tM39jh07pkGDBikrK0tXXnmldu/e3e5xGhsb5Xa7/R4AgNgVytoiOnUjEJYJSZ9//rk8Ho/S09P9tqenp6u2ttZ0n2HDhun3v/+9XnjhBa1bt05er1eTJk3Sp59+2uZxioqK5HA4fI+srKywfg4A6OlOnvLqd3//WMte2KXf/f1jnTzlDfm9Ql1bRKduBMIyISkUeXl5mjNnjsaMGaMpU6boueeeU2pqqp544ok291m6dKlcLpfvceDAgQhWDADdW9ErFRr+87/q7pf36A+l+3X3y3s0/Od/VdErFSG9X6hri+jUjUBYJiQNHDhQ8fHxqqur89teV1cX8Jqj3r17a+zYsdq3b1+bYxITE5WUlOT3AAB0XtErFXpic5Varqf2GtITm6tCCkqdWVtEp250xDILtxMSEjR+/Hht2rRJs2fPliR5vV5t2rRJt9xyS0Dv4fF4tHPnTl1xxRVdWCkAoKWTp7z67d+r2h3z279XadHlw5XQK/D/fu/s2qLOdOpG92eZkCRJt99+u+bOnasJEyYoNzdXDz/8sI4fP+67223OnDk666yzVFRUJEm666679LWvfU1Dhw7VkSNH9Mtf/lL79+/XTTfdFM2PAQA9zlOln7SaQWrJazSNu/GSwQG/b/PaolpXg+m6JJuaZobaW1sUSqdu9AyWCklXX321PvvsMy1btky1tbUaM2aMiouLfYu5q6urFRf37/8C+eKLLzR//nzV1taqf//+Gj9+vN555x3l5ORE6yMAQI+0v/5EWMc1a15btHDdNtkkv6DE2iJ0ls0wDNqJtsPtdsvhcMjlcrE+CQBC9Lu/f6y7X97T4bifz7wgqJmkZvwGG1oKx/c3IakDhCQA6LyTp7wa/vO/tnvJLc4mfXD3jKDWJJ3O4zVYWwSfcHx/W+pyGwDAmhJ6xWn+Jdl6YnPbi7fnX5IdckCSWFuE8CMkAQAiYukVTetBf/t3/zYAcbamgNT8OhAruNzWAS63AUB4nTzl1VOln2h//QkNSumj7+ad26kZJMAMl9sAAJaT0CsupMXZQKQR3QEAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEyEFJLuuusunTjR+vd1vvzyS911112dLgoAACDaQuqTFB8fr5qaGqWlpfltP3z4sNLS0uTxeMJWYLTRJwkAAOsJx/d3SDNJhmHIZmv9ezjvv/++UlJSQioEAAAglgTVTLJ///6y2Wyy2Ww6//zz/YKSx+PRsWPHdPPNN4e9SAAAgEgLKiQ9/PDDMgxD3//+91VYWCiHw+F7LSEhQeeee67y8vLCXiQAAECkBRWS5s6dK0nKzs7WpEmT1Lt37y4pCgAAINpC+u22KVOmyOv16sMPP9ShQ4fk9Xr9Xp88eXJYigMAAIiWkELSP/7xD1133XXav3+/Wt4cZ7PZutXdbQAAoGcKKSTdfPPNmjBhgl5++WVlZGSY3ukGAABgZSGFpI8++kj/93//p6FDh4a7HgAAgJgQUp+kiRMnat++feGuBQAAIGYEPJO0Y8cO3z/feuutWrRokWprazVy5MhWd7mNGjUqfBUCAABEQcA/SxIXFyebzdZqobbvjf71WndbuM3PkgAAYD3h+P4OeCapqqoqpAMAAABYUcAhadCgQV1ZBwAAQEwJ6e62F1980XS7zWaT3W7X0KFDlZ2d3anCAAAAoimkkDR79mzT9Umnr0u6+OKLtX79evXv3z8shQIAAERSSC0ANm7cqIsuukgbN26Uy+WSy+XSxo0bNXHiRL300kvavHmzDh8+rMWLF4e7XgBAJ3i8hkorD+uF8oMqrTwsjzege3eC3i/U4wCxJKSZpB/96Ef6zW9+o0mTJvm2TZs2TXa7XQsWLNDu3bv18MMP6/vf/37YCgUAdE7xrhoVbqhQjavBty3DYdfyWTkqGJERtv1CPQ4Qa0KaSaqsrDS9nS4pKUkff/yxJOm8887T559/3rnqAABhUbyrRgvXbfMLLpJU62rQwnXbVLyrJiz7hXocIBaFFJLGjx+vH//4x/rss8982z777DPdcccduuiiiyQ1/XRJVlZWeKoEAITM4zVUuKFCZhe8mrcVbqhodUks2P1CPQ4Qq0IKSb/73e9UVVWls88+W0OHDtXQoUN19tln65NPPtHq1aslSceOHdPPfvazsBYLAAheWVV9q5md0xmSalwNKquq79R+oR4HiFUhrUkaNmyYKioq9Nprr+nDDz/0bbvssssUF9eUu2bPnh22IgEAoTt0tO3g0t64YPcL9ThArAopJElNP1NSUFCggoKCcNYDAAiztH72kMYFu1+oxwFiVcAh6ZFHHtGCBQtkt9v1yCOPtDv2hz/8YacLAwCER252ijIcdtW6GkzXC9kkOR125WandGq/UI8DxKqAf+A2Oztb7733ngYMGNBuN22bzea7w6074AduAXQHzXedSfILMLZ//e/jN4xr83b+YPYL9ThAuIXj+zvgkNRTEZIAdBfh7pP085k56n9mgg4dbVBav6YZovg4G32SEBOiHpJOnjypqqoqDRkyRL16hby8KaYRkgB0Jx6vobKq+lbBJtj9vjjeqLtf3tNmEAr1OEC4RC0knThxQrfeequefPJJSdKHH36owYMH69Zbb9VZZ52lJUuWhFRMLCIkAYC/5ktqLb88uKSGWBKO7++Q+iQtXbpU77//vkpKSmS3//suhfz8fD3zzDMhFQIAiH00jERPElJIWr9+vR577DFdfPHFstn+PX164YUXqrKyMmzFAQBiCw0j0ZOEFJI+++wzpaWltdp+/Phxv9AEAOheaBiJniSkkDRhwgS9/PLLvufNwWj16tXKy8sLT2UAgJhDw0j0JCHdknbvvfdqxowZqqio0KlTp/TrX/9aFRUVeuedd/S3v/0t3DUCAGIEDSPRk4Q0k3TxxRfr/fff16lTpzRy5Ei99tprSktLU2lpqcaPHx/uGv2sXLlS5557rux2uyZOnKiysrJ2x//lL3/R8OHDZbfbNXLkSL3yyitdWh8AWInHa6i08rBeKD+o0srDHS64jo+zafmsHEn/vputWfPz5bNyuN0f3UJILQDmzJmjSy+9VJMnT9aQIUO6oi5TzzzzjObMmaNVq1Zp4sSJevjhh/WXv/xFe/fuNV0j9c4772jy5MkqKirSf/7nf+qPf/yjVqxYoW3btmnEiBEBHZMWAAC6q840faRhJGJd1Pok3XTTTdq8ebMqKyuVmZmpKVOmaOrUqZoyZYrOO++8kAoJxMSJE3XRRRfpsccekyR5vV5lZWXp1ltvNe3NdPXVV+v48eN66aWXfNu+9rWvacyYMVq1alVAxyQkAeiOwtHriIaRiGVR65O0evVqffjhh6qurtb999+vvn376sEHH9Tw4cN19tlnh1RIR06ePKmtW7cqPz/fty0uLk75+fkqLS013ae0tNRvvCRNnz69zfGS1NjYKLfb7fcAgO4kXL2O4uNsyhsyQFeOOUt5QwYQkNDthBSSmvXv318DBgxQ//79lZycrF69eik1NTVctfn5/PPP5fF4lJ6e7rc9PT1dtbW1pvvU1tYGNV6SioqK5HA4fI+srKzOFw8AMYReR0BgQgpJP/nJTzRp0iQNGDBAS5YsUUNDg5YsWaLa2lpt37493DVG1NKlS+VyuXyPAwcORLskAAgreh0BgQmpBcB9992n1NRULV++XN/61rd0/vnnh7uuVgYOHKj4+HjV1dX5ba+rq5PT6TTdx+l0BjVekhITE5WYmNj5ggEgRtHrCAhMSDNJ27dv109/+lOVlZXp61//us466yxdd911+s1vfqMPP/ww3DVKkhISEjR+/Hht2rTJt83r9WrTpk1tNrDMy8vzGy9JGzdupOElgG4nmFv5m3sdtbWCyKamO9XodYSeLqS721p6//339dBDD+npp5+W1+uVx+MJR22tPPPMM5o7d66eeOIJ5ebm6uGHH9azzz6rDz74QOnp6ZozZ47OOussFRUVSWpqATBlyhTdd999mjlzpv785z/r3nvvpQUAgG4llNvxm+9uk+S3gDuYu9uAWBaO7++QLrcZhqHt27erpKREJSUleuutt+R2uzVq1ChNmTIlpEICcfXVV+uzzz7TsmXLVFtbqzFjxqi4uNi3OLu6ulpxcf+eHJs0aZL++Mc/6mc/+5l+8pOf6LzzztP69esDDkgAEOvaupW/1tWgheu2tRl2CkZk6PEbxrUKV056HQE+Ic0k9e/fX8eOHdPo0aN9PZIuueQSJScnd0GJ0cVMEoBY5fEaunjFG23eqdb8EyFv3fkfbd6eT68jdFdRm0lat26dLrnkEkIDAERRMLfy5w0ZYDqmudcRgNZCCkkzZ84Mdx0AgCBxKz/QtTrVTBIAED3cyg90LUISAFgUt/IDXYuQBAAWFR9n0/JZOZLUKig1P18+K4eF2ECICEkAYGHNt/I7Hf6X1JwOO72OgE4KaeE2ACB2FIzI0GU5Tm7lB8KMkAQA3QC38gPhx+U2AAAAE8wkAUAPRsdtoG2EJADooUL5YVygJ+FyGwD0QM0/jNvyZ02afxi3eFdNlCoDYgchCQB6GI/XUOGGCpn9unnztsINFfJ4g/79c6BbISQBQA8TzA/jAj0ZIQkAehh+GBcIDCEJAHoYfhgXCAwhCQB6GH4YFwgMIQkAehh+GBcIDCEJALoBj9dQaeVhvVB+UKWVhzu8M40fxgU6RjNJALC4UJtC8sO4QPtshmHQCKMdbrdbDodDLpdLSUlJ0S4HAPw0N4Vs+S/y5pjDrBB6qnB8f3O5DQAsiqaQQNciJAGARdEUEuhahCQAsCiaQgJdi5AEABZFU0igaxGSAMCiaAoJdC1CEgBYlFWbQgbb0wmIFvokAYCFNTeFbNknyRlAn6RoCLWnExAN9EnqAH2SAFiBx2vEfFNIejohksLx/c1MEgB0A/FxNuUNGRDtMtrUUU8nm5p6Ol2W44y5cIeeizVJAIAuR08nWBEhCQDQ5ejpBCsiJAEAuhw9nWBFhCQAQJejpxOsiJAEAOhyVu3phJ6NkAQAiIjmnk5Oh/8lNafDzu3/iEm0AAAAREzBiAxdluOM+Z5OgERIAgBEWKz3dAKacbkNAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADAhGVCUn19va6//nolJSUpOTlZN954o44dO9buPlOnTpXNZvN73HzzzRGqGAAAWJll+iRdf/31qqmp0caNG/XVV1/pe9/7nhYsWKA//vGP7e43f/583XXXXb7nffr06epSAQBAN2CJkLRnzx4VFxfr3Xff1YQJEyRJjz76qK644go98MADyszMbHPfPn36yOl0BnysxsZGNTY2+p673e7QCwcAAJZlicttpaWlSk5O9gUkScrPz1dcXJy2bNnS7r5PP/20Bg4cqBEjRmjp0qU6ceJEu+OLiorkcDh8j6ysrLB8BgAAYC2WmEmqra1VWlqa37ZevXopJSVFtbW1be533XXXadCgQcrMzNSOHTt05513au/evXruuefa3Gfp0qW6/fbbfc/dbjdBCQCAHiiqIWnJkiVasWJFu2P27NkT8vsvWLDA988jR45URkaGpk2bpsrKSg0ZMsR0n8TERCUmJoZ8TAAA0D1ENSQtWrRI8+bNa3fM4MGD5XQ6dejQIb/tp06dUn19fVDrjSZOnChJ2rdvX5shCQAAQIpySEpNTVVqamqH4/Ly8nTkyBFt3bpV48ePlyS98cYb8nq9vuATiPLycklSRkZGSPUCAICewxILty+44AIVFBRo/vz5Kisr09tvv61bbrlF11xzje/OtoMHD2r48OEqKyuTJFVWVuruu+/W1q1b9cknn+jFF1/UnDlzNHnyZI0aNSqaHwcAAFiAJUKS1HSX2vDhwzVt2jRdccUVuvjii/Wb3/zG9/pXX32lvXv3+u5eS0hI0Ouvv67LL79cw4cP16JFi/Ttb39bGzZsiNZHAAAAFmIzDMOIdhGxzO12y+FwyOVyKSkpKdrlAACAAITj+9syM0kAAACRREgCAAAwQUgCAAAwQUgCAAAwYYmfJQGAcPN4DZVV1evQ0Qal9bMrNztF8XG2aJcFIIYQkgD0OMW7alS4oUI1rgbftgyHXctn5ahgBM1mATThchuAHqV4V40WrtvmF5AkqdbVoIXrtql4V02UKgMQawhJAHoMj9dQ4YYKmTWHa95WuKFCHi/t4wAQkgD0IGVV9a1mkE5nSKpxNaisqj5yRQGIWYQkAD3GoaNtB6RQxgHo3ghJAHqMtH72sI4D0L0RkgD0GLnZKcpw2NXWjf42Nd3llpudEsmyAMQoQhKAHiM+zqbls3IkqVVQan6+fFYO/ZIASCIkAehhCkZk6PEbxsnp8L+k5nTY9fgN4+iTBMCHZpIAepyCERm6LMdJx20A7SIkAeiR4uNsyhsyINplAIhhXG4DAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgC0P0ZhnRwa9P/AkCACEkAur8dz0i//Q9px7PRrgSAhRCSAHRvnlPSm/c2/XPJvU3PASAAhCQA3duu/5OO7G/65y8+kXb9v6iWA8A6CEkAui/fLJLtXxvimE0CEDBCEoDuyzeL1Lxg28tsEoCAEZIAdE+tZpGaMZsEIDCEJADdU6tZpGbMJgEIjGVC0j333KNJkyapT58+Sk5ODmgfwzC0bNkyZWRk6IwzzlB+fr4++uijri0UQPS1OYvUjNkkAB2zTEg6efKkrrrqKi1cuDDgfe6//3498sgjWrVqlbZs2aIzzzxT06dPV0NDQxdWCiDqqkvbmEVq9q/ZpOrSCBYFwGpshmGtFrRr167VbbfdpiNHjrQ7zjAMZWZmatGiRVq8eLEkyeVyKT09XWvXrtU111wT0PHcbrccDodcLpeSkpI6Wz6ASDjVKO19RTp1su0xvRKkYVdIvRIjVxeAiAnH93evMNcUM6qqqlRbW6v8/HzfNofDoYkTJ6q0tLTNkNTY2KjGxkbfc7fb3eW1AgizXonShd+MdhUALM4yl9uCVVtbK0lKT0/3256enu57zUxRUZEcDofvkZWV1aV1AgCA2BTVkLRkyRLZbLZ2Hx988EFEa1q6dKlcLpfvceDAgYgeHwAAxIaoXm5btGiR5s2b1+6YwYMHh/TeTqdTklRXV6eMjAzf9rq6Oo0ZM6bN/RITE5WYyBoFAAB6uqiGpNTUVKWmpnbJe2dnZ8vpdGrTpk2+UOR2u7Vly5ag7pADAAA9k2XWJFVXV6u8vFzV1dXyeDwqLy9XeXm5jh075hszfPhwPf/885Ikm82m2267Tf/zP/+jF198UTt37tScOXOUmZmp2bNnR+lTAAAAq7DM3W3Lli3Tk08+6Xs+duxYSdKbb76pqVOnSpL27t0rl8vlG3PHHXfo+PHjWrBggY4cOaKLL75YxcXFstvtEa0dAABYj+X6JEUafZIAALCecHx/W+ZyGwAAQCQRkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEz0inYBQEc8XkNlVfU6dLRBaf3sys1OUXycLdplAQC6OUISoiLQ4FO8q0aFGypU42rwbctw2LV8Vo4KRmREsmQAQA9DSELEBRp8infVaOG6bTJa7F/ratDCddv0+A3jCEoAgC7DmqQezuM1VFp5WC+UH1Rp5WF5vC0jSefGt9QcfE4PSNK/g0/xrhrfcQo3VLQKSJJ82wo3VAR9fAAAAsVMUjfh8Rr6x8eHVVp5WJKhvMED9bUhA0wvYTVf6nq9olbPlx9U/fGvfK+1dymrs5e+Ogo+NjUFn8tynCqrqm8VpFqOr3E1qKyqXnlDBnR4bAAAgkVIioJgFiJ7vIb+UXlYpR9/LsmmvCED9LXB/uGneFeNljy3U0dO/DvsPPZmpZL79NZ93xrZ6hJWy6BzurYuZYXj0lcwwefQ0bbHnS7QcQAABIuQFGHBzMaYh599fuGneFeNbl63zfRYR058pZvXbdOqfwWYtoLO6VrO6MTH2YKaAWrvrrNggk9aP3tAYwMdBwBAsFiTFEGBrsdpHnvzum1+AalZc/h5ZUeNfvFiRYfH/cWLu3XylLfNoNPS6TM6UnAzQO0JJvjkZqcow2FXW5HLpqZwmZudEtB7AgAQLEJShASzENnjNfSLF3d3+J4/e2GXat0dz87Uuhv1VOkn7QYdM80zP+G69BVM8ImPs2n5rBzf9pbjJGn5rBz6JQEAugwhKUKCmY0pq6pXrbuxw/esP34y4OPvrz8R8NhmzTM/4br0FWzwKRiRocdvGCenw/99nQ47t/8DALoca5IiJNoLkQel9Al4rE1NQaT5UlbzDFCtq8F0Jqzl+PY0B5+W67KcbazLKhiR4bvbjY7bAIBIIiRFSFctRO7fp7e+MFm3dDpnUqK+m3euVr9V1WbQaen0GZ3mGaCF67bJJvntH8qlr2CDT3ycjdv8AQARx+W2CAlmPU5udoqcSYkdvmeGw657Zo/ocNwvvnGhEnrFtXmpq+V7ml3KCvelr+bgc+WYs5TXRj8nAACiyWYYBi2L2+F2u+VwOORyuZSUlNSp92q+u00yn405PWy0d2t/s9Nv7W/ZKkBSwH2SBpyZoCvHZOqyHGeHl7L4sVkAgBWE4/ubkNSBcIYkqfN9kqSmS2xFLcJPKB23CToAgO6KkBQB4Q5JUvg7bgMAAH+EpAjoipAEAAC6Vji+v1m4DQAAYIKQBAAAYMIyIemee+7RpEmT1KdPHyUnJwe0z7x582Sz2fweBQUFXVsoAADoFizTTPLkyZO66qqrlJeXp9/97ncB71dQUKA1a9b4nicmdtx/CAAAwDIhqbCwUJK0du3aoPZLTEyU0+nsgooAAEB3ZpnLbaEqKSlRWlqahg0bpoULF+rw4cPtjm9sbJTb7fZ7AACAnqdbh6SCggL94Q9/0KZNm7RixQr97W9/04wZM+TxeNrcp6ioSA6Hw/fIysqKYMUAACBWRDUkLVmypNXC6paPDz74IOT3v+aaa/SNb3xDI0eO1OzZs/XSSy/p3XffVUlJSZv7LF26VC6Xy/c4cOBAyMcHAADWFdU1SYsWLdK8efPaHTN48OCwHW/w4MEaOHCg9u3bp2nTppmOSUxMZHE3AACIbkhKTU1VampqxI736aef6vDhw8rICPwX65sbkrM2CQAA62j+3u7MD4tY5u626upq1dfXq7q6Wh6PR+Xl5ZKkoUOHqm/fvpKk4cOHq6ioSN/85jd17NgxFRYW6tvf/racTqcqKyt1xx13aOjQoZo+fXrAxz169KgksTYJAAALOnr0qBwOR0j7WiYkLVu2TE8++aTv+dixYyVJb775pqZOnSpJ2rt3r1wulyQpPj5eO3bs0JNPPqkjR44oMzNTl19+ue6+++6gLqdlZmbqwIED6tevn2y27v2jsm63W1lZWTpw4AC/U2eC89M+zk/7OD/t4/x0jHPUvpbnxzAMHT16VJmZmSG/Jz9wCx9+zLd9nJ/2cX7ax/lpH+enY5yj9nXF+enWLQAAAABCRUgCAAAwQUiCT2JiopYvX04LhDZwftrH+Wkf56d9nJ+OcY7a1xXnhzVJAAAAJphJAgAAMEFIAgAAMEFIAgAAMEFIAgAAMEFI6uHuueceTZo0SX369FFycnJA+8ybN082m83vUVBQ0LWFRkko58cwDC1btkwZGRk644wzlJ+fr48++qhrC42S+vp6XX/99UpKSlJycrJuvPFGHTt2rN19pk6d2urv5+abb45QxV1r5cqVOvfcc2W32zVx4kSVlZW1O/4vf/mLhg8fLrvdrpEjR+qVV16JUKXREcz5Wbt2bau/E7vdHsFqI2vz5s2aNWuWMjMzZbPZtH79+g73KSkp0bhx45SYmKihQ4dq7dq1XV5ntAR7fkpKSlr9/dhsNtXW1gZ1XEJSD3fy5EldddVVWrhwYVD7FRQUqKamxvf405/+1EUVRlco5+f+++/XI488olWrVmnLli0688wzNX36dDU0NHRhpdFx/fXXa/fu3dq4caNeeuklbd68WQsWLOhwv/nz5/v9/dx///0RqLZrPfPMM7r99tu1fPlybdu2TaNHj9b06dN16NAh0/HvvPOOrr32Wt14443avn27Zs+erdmzZ2vXrl0Rrjwygj0/kpSUlOT3d7J///4IVhxZx48f1+jRo7Vy5cqAxldVVWnmzJm69NJLVV5erttuu0033XSTXn311S6uNDqCPT/N9u7d6/c3lJaWFtyBDcAwjDVr1hgOhyOgsXPnzjWuvPLKLq0n1gR6frxer+F0Oo1f/vKXvm1HjhwxEhMTjT/96U9dWGHkVVRUGJKMd99917ftr3/9q2Gz2YyDBw+2ud+UKVOMH/3oRxGoMLJyc3ON//qv//I993g8RmZmplFUVGQ6/jvf+Y4xc+ZMv20TJ040fvCDH3RpndES7PkJ5t9J3Y0k4/nnn293zB133GFceOGFftuuvvpqY/r06V1YWWwI5Py8+eabhiTjiy++6NSxmElCSEpKSpSWlqZhw4Zp4cKFOnz4cLRLiglVVVWqra1Vfn6+b5vD4dDEiRNVWloaxcrCr7S0VMnJyZowYYJvW35+vuLi4rRly5Z293366ac1cOBAjRgxQkuXLtWJEye6utwudfLkSW3dutXv//e4uDjl5+e3+f97aWmp33hJmj59erf7O5FCOz+SdOzYMQ0aNEhZWVm68sortXv37kiUawk96e+nM8aMGaOMjAxddtllevvtt4Pev1cX1IRurqCgQN/61reUnZ2tyspK/eQnP9GMGTNUWlqq+Pj4aJcXVc3Xu9PT0/22p6enB30tPNbV1ta2mrru1auXUlJS2v2s1113nQYNGqTMzEzt2LFDd955p/bu3avnnnuuq0vuMp9//rk8Ho/p/+8ffPCB6T61tbU94u9ECu38DBs2TL///e81atQouVwuPfDAA5o0aZJ2796ts88+OxJlx7S2/n7cbre+/PJLnXHGGVGqLDZkZGRo1apVmjBhghobG7V69WpNnTpVW7Zs0bhx4wJ+H0JSN7RkyRKtWLGi3TF79uzR8OHDQ3r/a665xvfPI0eO1KhRozRkyBCVlJRo2rRpIb1nJHX1+bG6QM9PqE5fszRy5EhlZGRo2rRpqqys1JAhQ0J+X3QveXl5ysvL8z2fNGmSLrjgAj3xxBO6++67o1gZrGDYsGEaNmyY7/mkSZNUWVmphx56SE899VTA70NI6oYWLVqkefPmtTtm8ODBYTve4MGDNXDgQO3bt88SIakrz4/T6ZQk1dXVKSMjw7e9rq5OY8aMCek9Iy3Q8+N0Olstuj116pTq6+t95yEQEydOlCTt27fPsiFp4MCBio+PV11dnd/2urq6Ns+F0+kMaryVhXJ+Wurdu7fGjh2rffv2dUWJltPW309SUlKPn0VqS25urt56662g9iEkdUOpqalKTU2N2PE+/fRTHT582C8UxLKuPD/Z2dlyOp3atGmTLxS53W5t2bIl6DsIoyXQ85OXl6cjR45o69atGj9+vCTpjTfekNfr9QWfQJSXl0uSZf5+zCQkJGj8+PHatGmTZs+eLUnyer3atGmTbrnlFtN98vLytGnTJt12222+bRs3bvSbPekuQjk/LXk8Hu3cuVNXXHFFF1ZqHXl5ea1aRnTXv59wKS8vD/7fM51a9g3L279/v7F9+3ajsLDQ6Nu3r7F9+3Zj+/btxtGjR31jhg0bZjz33HOGYRjG0aNHjcWLFxulpaVGVVWV8frrrxvjxo0zzjvvPKOhoSFaH6PLBHt+DMMw7rvvPiM5Odl44YUXjB07dhhXXnmlkZ2dbXz55ZfR+AhdqqCgwBg7dqyxZcsW46233jLOO+8849prr/W9/umnnxrDhg0ztmzZYhiGYezbt8+46667jPfee8+oqqoyXnjhBWPw4MHG5MmTo/URwubPf/6zkZiYaKxdu9aoqKgwFixYYCQnJxu1tbWGYRjGd7/7XWPJkiW+8W+//bbRq1cv44EHHjD27NljLF++3Ojdu7exc+fOaH2ELhXs+SksLDReffVVo7Ky0ti6datxzTXXGHa73di9e3e0PkKXOnr0qO/fL5KMX/3qV8b27duN/fv3G4ZhGEuWLDG++93v+sZ//PHHRp8+fYwf//jHxp49e4yVK1ca8fHxRnFxcbQ+QpcK9vw89NBDxvr1642PPvrI2Llzp/GjH/3IiIuLM15//fWgjktI6uHmzp1rSGr1ePPNN31jJBlr1qwxDMMwTpw4YVx++eVGamqq0bt3b2PQoEHG/Pnzff+i626CPT+G0dQG4Oc//7mRnp5uJCYmGtOmTTP27t0b+eIj4PDhw8a1115r9O3b10hKSjK+973v+QXIqqoqv/NVXV1tTJ482UhJSTESExONoUOHGj/+8Y8Nl8sVpU8QXo8++qhxzjnnGAkJCUZubq7xj3/8w/falClTjLlz5/qNf/bZZ43zzz/fSEhIMC688ELj5ZdfjnDFkRXM+bntttt8Y9PT040rrrjC2LZtWxSqjozmW9ZbPprPydy5c40pU6a02mfMmDFGQkKCMXjwYL9/D3U3wZ6fFStWGEOGDDHsdruRkpJiTJ061XjjjTeCPq7NMAyjU/NXAAAA3RB9kgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgAAAEwQkgBYxtSpU/1+6yxaSkpKZLPZdOTIkWiXAqALEZIAoB2xEswARB4hCQAAwAQhCYAlNTY2avHixTrrrLN05plnauLEiSopKfG9vnbtWiUnJ+vVV1/VBRdcoL59+6qgoEA1NTW+MadOndIPf/hDJScna8CAAbrzzjs1d+5czZ49W5I0b948/e1vf9Ovf/1r2Ww22Ww2ffLJJ779t27dqgkTJqhPnz6aNGmS9u7dG6FPDyASCEkALOmWW25RaWmp/vznP2vHjh266qqrVFBQoI8++sg35sSJE3rggQf01FNPafPmzaqurtbixYt9r69YsUJPP/201qxZo7fffltut1vr16/3vf7rX/9aeXl5mj9/vmpqalRTU6OsrCzf6z/96U/14IMP6r333lOvXr30/e9/PyKfHUBk9Ip2AQAQrOrqaq1Zs0bV1dXKzMyUJC1evFjFxcVas2aN7r33XknSV199pVWrVmnIkCGSmoLVXXfd5XufRx99VEuXLtU3v/lNSdJjjz2mV155xfe6w+FQQkKC+vTpI6fT2aqOe+65R1OmTJEkLVmyRDNnzlRDQ4PsdnvXfHAAEUVIAmA5O3fulMfj0fnnn++3vbGxUQMGDPA979Onjy8gSVJGRoYOHTokSXK5XKqrq1Nubq7v9fj4eI0fP15erzegOkaNGuX33pJ06NAhnXPOOcF/KAAxh5AEwHKOHTum+Ph4bd26VfHx8X6v9e3b1/fPvXv39nvNZrPJMIyw1XH6+9tsNkkKOGABiH2sSQJgOWPHjpXH49GhQ4c0dOhQv4fZZTEzDodD6enpevfdd33bPB6Ptm3b5jcuISFBHo8nrPUDsAZmkgBYzvnnn6/rr79ec+bM0YMPPqixY8fqs88+06ZNmzRq1CjNnDkzoPe59dZbVVRUpKFDh2r48OF69NFH9cUXX/hmhSTp3HPP1ZYtW/TJJ5+ob9++SklJ6aqPBSDGMJMEwJLWrFmjOXPmaNGiRRo2bJhmz56td999N6j1QHfeeaeuvfZazZkzR3l5eerbt6+mT5/ut/B68eLFio+PV05OjlJTU1VdXd0VHwdADLIZ4bxADwAW5vV6dcEFF+g73/mO7r777miXAyDKuNwGoMfav3+/XnvtNU2ZMkWNjY167LHHVFVVpeuuuy7apQGIAVxuA9BjxcXFae3atbrooov09a9/XTt37tTrr7+uCy64INqlAYgBXG4DAAAwwUwSAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACAif8P8O67S5mqltcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train_scaled[:,0], train_scaled[:,1])\n",
        "plt.scatter(new[0], new[1], marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "NbsFki8MVtrn",
        "outputId": "2eb0de8e-c505-4864-cbdc-e1a8b70df636"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "kn.fit(train_scaled, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5uc_k1nlWnNN"
      },
      "outputs": [],
      "source": [
        "test_scaled = (test_input - mean) / std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PfEwPLxWvdz",
        "outputId": "ad09c1db-6f60-4a20-a1e1-22c7b1e1a6db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "kn.score(test_scaled, test_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mox3oWCLWzGW",
        "outputId": "234b1e11-fb4c-485d-d994-2c5ba7667853"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.]\n"
          ]
        }
      ],
      "source": [
        "print(kn.predict([new]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bRU4r44-W1t3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c452a1b4-5f87-4a01-9886-9eaafcc9c0cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.2873737  0.7711188  0.89552179 0.91493515 0.95427626]]\n"
          ]
        }
      ],
      "source": [
        "distances, indexes = kn.kneighbors([new])\n",
        "\n",
        "print(distances)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "bHWAbdefW9W6",
        "outputId": "3e005843-e4bd-4356-e84c-2c567d58674b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train_scaled[:,0], train_scaled[:,1])\n",
        "plt.scatter(new[0], new[1], marker='^')\n",
        "plt.scatter(train_scaled[indexes,0], train_scaled[indexes,1], marker='D')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "LQEfg_XBHbS9",
        "IOyftJmqHbTB",
        "l5PO-C32HsBx",
        "ShnaZsAAHsB1",
        "KFO96jGvHsB2",
        "fQEcM4FoHsB5"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}