20221111(3)_numpy_matplotlib.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/heejininku/class2022Fall/blob/main/20221111(3)_numpy_matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "numpy는 팬더스와 비슷함.\n",
        "팬더스는 숫자일 필요가 없음. 텍스트도 들어감.\n",
        "엑셀같이 생겼는데 숫자만 있는 것. 그걸 array라고 하는 것. 또는 nd array라고도 함. array는 2차원일수도, 3,4차원일수도 있음.\n",
        "즉, 숫자 팬더스\n",
        "넘파이는 3차원, 4차원 모두 가능\n",
        "숫자가 2차원이면 행렬이라고 부름 (matrices)\n",
        "2차원 이상은, 즉 3,4,차원 등은 tensor라고 부름. tensor flow-> 파이썬 패키지 중 하나, ai 패키지 중 하나"
      ],
      "metadata": {
        "id": "XLHZ6EACQfn2"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcCdLoe_4bY7"
      },
      "source": [
        "### create nd arrays (≈matrices)\n",
        "\n",
        "3차원도 되고 4차원도 되는 nd arrays \n",
        "물론 matrices라고 불러도 된다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ap7xyofShBB"
      },
      "source": [
        "import numpy as np #간단히 np로 줄임."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rqpSPZCShBC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b41b32c-0467-48b2-cf2f-7c519f7e38e4"
      },
      "source": [
        "x = np.array([1,2,3]) \n",
        "#array라는 함수(내부적으로list(_얘는 숫자, 글자 모두 들어있음) 넣어주면 nd array로 바꿔주는 것) \n",
        "#입력이 list임.\n",
        "#array는 찐숫자, list는 찐 숫자가 아닐 수 있음.\n",
        "#생긴 것은 똑같지만 아래의 type보면 ndarray임을 알 수 있음.\n",
        "#print(x.shape)하면 (3,)로 나오는데 이건 3개짜리 1d라는 뜻.\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape)\n",
        "print(x.ndim) #이건 몇차원인지 나오는 것 #꺽세가 3개면 3차원"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3]\n",
            "<class 'numpy.ndarray'>\n",
            "(3,)\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[1,2,3], [4,5,6]])  #이렇게 하면 Shape이 (2,3) 즉, 행 2개 열 3개\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uimQlUpSRn4-",
        "outputId": "c6df7f84-4f9e-4c14-fc93-0345f5c95ee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "<class 'numpy.ndarray'>\n",
            "(2, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjC88C75ShBC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7143e4dd-2864-4b57-855e-56eb92a6bd75"
      },
      "source": [
        "x = np.arange(0,10,2) #하나의 range를 만들어주는 것. #넘파이가 만들어지는 것\n",
        "#0에서 10까지 2만큼씩 띄어서 만들어달라 (단, 10 불포함)\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape) #1차원 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 2 4 6 8]\n",
            "<class 'numpy.ndarray'>\n",
            "(5,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MwkCEjTShBC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6aad3fb-1306-43e5-c5ae-d6d5b39eddd6"
      },
      "source": [
        "x = np.linspace(0,10,6)\n",
        "#linspace는 liner space라는 뜻인데 0에서 10까지 (10 포함) 6개로 등분(liner space)해서 6개의 숫자를 만들어라\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.  2.  4.  6.  8. 10.]\n",
            "<class 'numpy.ndarray'>\n",
            "(6,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjk79SQ27fNv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34c5fd66-fcae-4d91-d988-53e99b4f1a1e"
      },
      "source": [
        "x = np.array([[1,2,3], [4,5,6]])\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape) #행2개열3개"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "<class 'numpy.ndarray'>\n",
            "(2, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1ogg6iZ7yir",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cad3fb9-d5bd-4773-f7f3-88e71b5db075"
      },
      "source": [
        "x = np.array([[[1,2,3], [4,5,6]], [[7,8,9], [10,11,12]]]) #아 3개겠구나 추측 가능\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 1  2  3]\n",
            "  [ 4  5  6]]\n",
            "\n",
            " [[ 7  8  9]\n",
            "  [10 11 12]]]\n",
            "<class 'numpy.ndarray'>\n",
            "(2, 2, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efgeNHtD8ZlT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b073fb4b-baf1-4f1d-bd27-2cc527cb3030"
      },
      "source": [
        "x = np.ones([2, 2, 3]) \n",
        "#2,2,3에 1이 들어가게 만들어라\n",
        "#3은 세개\n",
        "#첫번째 2는 크게 두덩어리\n",
        "#두번째 2는 작은 두덩어리\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[1. 1. 1.]\n",
            "  [1. 1. 1.]]\n",
            "\n",
            " [[1. 1. 1.]\n",
            "  [1. 1. 1.]]]\n",
            "<class 'numpy.ndarray'>\n",
            "(2, 2, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_F-ccY08zzl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b109dfb4-64f4-441f-9819-a4c622c507fc"
      },
      "source": [
        "x = np.zeros([2, 2, 3]) #3차원\n",
        "#2,2,3에 1이 들어가게 만들어라\n",
        "print(x)\n",
        "print(type(x))\n",
        "print(x.shape)\n",
        "x.reshape(3,2,2) #(6,2)를 (4,3)로 바꿀 수 있듯이 #reshape개념 중요 #개수 맞으면 구조 변경 가능\n",
        "#3차원 말고 2차원 (3,4)도 가능\n",
        "#numpy도 class라고 생각하면 , reshape도 --?(못 들음)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]]]\n",
            "<class 'numpy.ndarray'>\n",
            "(2, 2, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0., 0.],\n",
              "        [0., 0.]],\n",
              "\n",
              "       [[0., 0.],\n",
              "        [0., 0.]],\n",
              "\n",
              "       [[0., 0.],\n",
              "        [0., 0.]]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hQuDJ5g9gpE"
      },
      "source": [
        "### create random numbers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.normal(0, 1, [2,3,4]) \n",
        "#넘파이의 패키지가 있는데 이 안에 여러 방이 있을 것, \n",
        "#하나의 방 안에 array가 바로 있으면 바로 numpy.array라고 하지만, \n",
        "#random 방안에 normal있으면 점이 두개-> np.random.normal\n",
        "#normal distribution을 꾸며주는 아이\n",
        "#첫번째 숫자 0은 평균\n",
        "#두번째 숫자 1은 표준편차\n",
        "#그렇게 해서 [2,3,4] array로 만들어라 (24개의 숫자-> )"
      ],
      "metadata": {
        "id": "pCVnElBgan70",
        "outputId": "73a1be0b-c055-4951-e704-287f8a822609",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[-0.16948446, -2.55041561,  0.12177138,  0.22205159],\n",
              "        [ 1.03710071,  0.71764148,  0.15071453, -0.45390027],\n",
              "        [-0.37514637, -0.26634169,  0.4236602 , -2.04190571]],\n",
              "\n",
              "       [[ 1.16464137,  0.06341401, -0.15303246,  0.07244766],\n",
              "        [-0.31148056, -1.59519727,  0.93028972,  0.7623865 ],\n",
              "        [-0.53108148,  0.87677634, -1.66084323,  0.36062975]]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRatmkx79otL"
      },
      "source": [
        "import matplotlib.pyplot as plt #그래프해주는 아이 즉, plot 그려주는 아이\n",
        "#matplotlib.pyplot matplotlib 안에 있는 pyplot 폴더까지를 plt로 부르겠다"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1oOAB6dShBD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "9a4d208b-ad66-4a98-b49a-11173558f557"
      },
      "source": [
        "x = np.random.normal(0, 1, 100) \n",
        "#평균0, 표준편차 1, 100은 100개짜리 1d array를 만들어라\n",
        "#-1.30410375e+00은 이 앞에 있는 숫자의 10의 0승\n",
        "#-8.55730740e-02는 이 숫자의 10의 -2승\n",
        "#+01승 하면 10을 곱하는 것\n",
        "#컴퓨터를 위해 아래같은 표기법 사용 (자리수 맞추려고)\n",
        "print(x)\n",
        "plt.hist(x, bins=10) #hist라는 함수 (x는 위의 1d array, bins (막대기 개수) 설정)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.6695835  -0.78356765  0.105909   -0.11344162  0.37639137  0.94634496\n",
            " -0.92334613  1.18490682 -0.49140323  0.21306917 -0.210384    0.83860743\n",
            "  0.66200439 -0.42017639 -1.2330022  -0.51320299  0.7816754   0.06749792\n",
            "  0.56661049  0.12921821 -0.21779304  2.93512567  0.57510899  1.24002643\n",
            " -0.87242811  1.01878975  0.59587419  1.43021827  0.25120122  1.94376407\n",
            "  0.28169401  0.51099636  1.26369207 -0.02916964 -0.09605858 -0.06161059\n",
            "  0.74145514  1.81946314 -1.15056392 -0.77217776  0.75291106  0.58317158\n",
            "  0.31517667 -0.03440521 -1.17871031  0.77286078  0.48223818  1.12227382\n",
            "  1.40324019  0.17315162 -1.33676958 -0.41035066 -0.98971368 -1.08073194\n",
            "  1.26647569 -0.19069018  0.6586206  -0.05948031 -0.24717733  0.38675048\n",
            " -0.61388729 -0.27297912  0.1545425  -0.49200607  0.46876849  1.48626796\n",
            "  0.90554824 -2.23215258  0.62146867 -1.62984399  0.24229016  0.94673591\n",
            "  0.91396797 -0.92025413 -0.29055748 -0.1869726  -1.68922816  0.45831568\n",
            "  1.05317302 -0.79739408 -1.10379713 -0.06399663  0.1551572  -0.67099221\n",
            " -0.11172887 -1.41627507  0.4203987  -0.20103424  0.79148123  0.86141821\n",
            " -0.99687991 -0.66352244 -0.55138383 -0.4032265   0.63261897 -0.33562136\n",
            " -0.18711885 -1.30376876  0.70405689  0.52287798]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 1.,  6., 12., 21., 19., 24., 11.,  4.,  1.,  1.]),\n",
              " array([-2.23215258, -1.71542475, -1.19869693, -0.6819691 , -0.16524128,\n",
              "         0.35148655,  0.86821437,  1.38494219,  1.90167002,  2.41839784,\n",
              "         2.93512567]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x, bins=100) #bins의 크기를 다르게 하니까 이런 느낌\n",
        "#하지만 숫자가 100개인 상황에서 100개의 bin하니까 약간 이상"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "id": "ftEkqUyKV7T4",
        "outputId": "df1971ac-20c2-4a22-813a-98424e92d6fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1., 0.,\n",
              "        2., 0., 1., 2., 1., 1., 1., 1., 2., 1., 1., 2., 0., 3., 1., 1., 3.,\n",
              "        0., 3., 1., 2., 2., 5., 0., 4., 4., 0., 1., 2., 3., 2., 2., 1., 2.,\n",
              "        1., 3., 2., 4., 3., 2., 2., 3., 2., 2., 2., 1., 1., 1., 0., 1., 3.,\n",
              "        0., 0., 2., 1., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n",
              " array([-2.23215258, -2.18047979, -2.12880701, -2.07713423, -2.02546145,\n",
              "        -1.97378866, -1.92211588, -1.8704431 , -1.81877032, -1.76709754,\n",
              "        -1.71542475, -1.66375197, -1.61207919, -1.56040641, -1.50873362,\n",
              "        -1.45706084, -1.40538806, -1.35371528, -1.30204249, -1.25036971,\n",
              "        -1.19869693, -1.14702415, -1.09535136, -1.04367858, -0.9920058 ,\n",
              "        -0.94033302, -0.88866023, -0.83698745, -0.78531467, -0.73364189,\n",
              "        -0.6819691 , -0.63029632, -0.57862354, -0.52695076, -0.47527797,\n",
              "        -0.42360519, -0.37193241, -0.32025963, -0.26858684, -0.21691406,\n",
              "        -0.16524128, -0.1135685 , -0.06189571, -0.01022293,  0.04144985,\n",
              "         0.09312263,  0.14479542,  0.1964682 ,  0.24814098,  0.29981376,\n",
              "         0.35148655,  0.40315933,  0.45483211,  0.50650489,  0.55817768,\n",
              "         0.60985046,  0.66152324,  0.71319602,  0.76486881,  0.81654159,\n",
              "         0.86821437,  0.91988715,  0.97155993,  1.02323272,  1.0749055 ,\n",
              "         1.12657828,  1.17825106,  1.22992385,  1.28159663,  1.33326941,\n",
              "         1.38494219,  1.43661498,  1.48828776,  1.53996054,  1.59163332,\n",
              "         1.64330611,  1.69497889,  1.74665167,  1.79832445,  1.84999724,\n",
              "         1.90167002,  1.9533428 ,  2.00501558,  2.05668837,  2.10836115,\n",
              "         2.16003393,  2.21170671,  2.2633795 ,  2.31505228,  2.36672506,\n",
              "         2.41839784,  2.47007063,  2.52174341,  2.57341619,  2.62508897,\n",
              "         2.67676176,  2.72843454,  2.78010732,  2.8317801 ,  2.88345289,\n",
              "         2.93512567]),\n",
              " <a list of 100 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.random.normal(0, 1, 10000)  #숫자가 많아진 상태에서 bins가 100개 되니까 의미O\n",
        "plt.hist(x, bins=100)\n",
        "#y축은 N (Number of --- 못들음)\n",
        "#count를 나타내는 것이기에 음수 안되고, 소수점도 안됨. 0이상의 정수만 가능\n",
        "#y축의 숫자들을 다 합치면 위의 총 개수 (여기서는 10000이겠지)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "id": "_xtBTjxOWLCR",
        "outputId": "7ec6c93f-311b-40f4-f095-0b6e5b47a545"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  1.,   0.,   1.,   1.,   3.,   0.,   2.,   2.,   1.,   4.,   6.,\n",
              "          5.,   8.,  10.,  12.,   7.,  12.,  18.,  17.,  26.,  29.,  27.,\n",
              "         36.,  56.,  46.,  72.,  65.,  69.,  91., 114., 125., 126., 132.,\n",
              "        147., 147., 168., 200., 202., 222., 244., 274., 230., 277., 263.,\n",
              "        259., 308., 314., 325., 292., 314., 277., 322., 344., 267., 251.,\n",
              "        279., 260., 238., 238., 215., 185., 164., 197., 146., 158., 130.,\n",
              "        124., 127.,  86.,  94.,  82.,  68.,  66.,  69.,  46.,  44.,  39.,\n",
              "         28.,  26.,  21.,  25.,   9.,   9.,  15.,  10.,   4.,   6.,   3.,\n",
              "          5.,   1.,   6.,   2.,   1.,   1.,   1.,   0.,   0.,   0.,   0.,\n",
              "          1.]),\n",
              " array([-3.72173297, -3.64588236, -3.57003175, -3.49418114, -3.41833053,\n",
              "        -3.34247992, -3.26662931, -3.1907787 , -3.11492809, -3.03907748,\n",
              "        -2.96322686, -2.88737625, -2.81152564, -2.73567503, -2.65982442,\n",
              "        -2.58397381, -2.5081232 , -2.43227259, -2.35642198, -2.28057137,\n",
              "        -2.20472076, -2.12887015, -2.05301954, -1.97716893, -1.90131832,\n",
              "        -1.82546771, -1.7496171 , -1.67376649, -1.59791588, -1.52206527,\n",
              "        -1.44621466, -1.37036405, -1.29451344, -1.21866283, -1.14281222,\n",
              "        -1.06696161, -0.991111  , -0.91526039, -0.83940978, -0.76355917,\n",
              "        -0.68770856, -0.61185795, -0.53600734, -0.46015673, -0.38430612,\n",
              "        -0.30845551, -0.2326049 , -0.15675429, -0.08090368, -0.00505307,\n",
              "         0.07079754,  0.14664815,  0.22249876,  0.29834937,  0.37419998,\n",
              "         0.45005059,  0.5259012 ,  0.60175181,  0.67760242,  0.75345303,\n",
              "         0.82930364,  0.90515425,  0.98100486,  1.05685548,  1.13270609,\n",
              "         1.2085567 ,  1.28440731,  1.36025792,  1.43610853,  1.51195914,\n",
              "         1.58780975,  1.66366036,  1.73951097,  1.81536158,  1.89121219,\n",
              "         1.9670628 ,  2.04291341,  2.11876402,  2.19461463,  2.27046524,\n",
              "         2.34631585,  2.42216646,  2.49801707,  2.57386768,  2.64971829,\n",
              "         2.7255689 ,  2.80141951,  2.87727012,  2.95312073,  3.02897134,\n",
              "         3.10482195,  3.18067256,  3.25652317,  3.33237378,  3.40822439,\n",
              "         3.484075  ,  3.55992561,  3.63577622,  3.71162683,  3.78747744,\n",
              "         3.86332805]),\n",
              " <a list of 100 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0exvwBbShBE"
      },
      "source": [
        "### generate a sine curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgQmRXzMShBE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a2ea107a-d599-48e3-df40-a0fcc93aaf48"
      },
      "source": [
        "plt.figure()\n",
        "\n",
        "theta1 = np.linspace(0, 2*np.pi, 100) #.linspace는 주로 nd array 만드는 것\n",
        "#np.pi가 여기서는 3,14--- pie임.\n",
        "#0부터 360도(radian값)까지를 100개의 숫자를 등간격으로 만들어라 그걸 theta1으로 저장\n",
        "#theta1은 100개의 숫자가 들어가 있는 1d array (100,)\n",
        "s1 = np.sin(theta1) #np안의 sin 즉, sin함수에 넣은 것 (sin함수는 radian을 받음)\n",
        "#s1은 총 100개의 값이 받아져있는 것. 그럼 출력값이 나오겠지 (사인 2분의 1파이는 1 이런식으로)\n",
        "theta2 = np.linspace(0, 2*np.pi, 100) + np.pi/2 \n",
        "#2분의 파이를 더한 것.(90도만큼 이동) \n",
        "#첫번째가 사인 2분의 파이라는 값이 나오겠지(?)\n",
        "s2 = np.sin(theta2)\n",
        "\n",
        "plt.subplot(2, 1, 1) # (rows, columns, panel number) #2,1로 만든 것(2행으로 (위, 아래로))\n",
        "#2,3을 처음에 쓴다면 유리창이 6개인 느낌\n",
        "plt.plot(theta1, s1, '.')\n",
        "plt.subplot(2, 1, 2) # (rows, columns, panel number)\n",
        "plt.plot(theta2, s2, '.')\n",
        "\n",
        "\n",
        "#inner product할 때 각도가 90도 차이나면, 코사인 90도가 되어서 그냥 0과 같음 \n",
        "#따라서 r값이 0이 된다.(correlation)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f240ab96650>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuH489v9BvQ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4d4550c-3820-414f-c2b6-1b106847b512"
      },
      "source": [
        "np.corrcoef(s1,s2) #correlation을 하면 r값을 뱉어줌. \n",
        "#4개가 나오는데 첫번째는 1.000000 10의 00승 즉 그냥 1\n",
        "#2.41915449의 -17승이니 사실상 0임.\n",
        "#결국\n",
        "1.0\n",
        "0.1\n",
        "의 값인 것.\n",
        "#이것과 자기자신(s1,s1), 이것과 다른 것(s1,s2)\n",
        "이것과 다른 것(s2,s1), 자기자신 (s2,s2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.00000000e+00, 2.41915449e-17],\n",
              "       [2.41915449e-17, 1.00000000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}