Lab1_2_Numpy.ipynb

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      "name": "Lab1_2.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/JHKim-snu/AI_Expert/blob/main/Lab1_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_q8J-lixTOlz"
      },
      "source": [
        "# Numpy\n",
        "* Array 및 Tensor들과 관련된 연산을 편리하게 하기 위한 라이브러리이다.\n",
        "* numpy 모듈 설치 후 import numpy (as np)로 import하여 사용하면 된다.\n",
        " * colab에는 기본적으로 설치되어있다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rnsc6PexSyob"
      },
      "source": [
        "import numpy as np\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LEceU3VuUXnk"
      },
      "source": [
        "# np.array\n",
        " * python array와 같은 element를 가지는 numpy ndarray를 만드는 함수\n",
        " * np.array(object, dtype=None)\n",
        "  * object : 값을 가져올 python array\n",
        "  * dtype : 각 component가 가질 data type. int, float, bool 등이 있으며, 입력하지 않을 시 object에 맞춰서 자동으로 결정된다.\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCJxtR36UYEL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "c1444729-84e9-4c5d-d5b0-8dcb601fae23"
      },
      "source": [
        "a = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "print(a)\n",
        "\n",
        "b = np.array([[1, 2], [3, 4]], dtype=float)\n",
        "\n",
        "print(b)\n",
        "\n",
        "c = np.array([1, 0, 0, 1], dtype=bool)\n",
        "\n",
        "print(c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5]\n",
            "[[1. 2.]\n",
            " [3. 4.]]\n",
            "[ True False False  True]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "133crjxnTouM"
      },
      "source": [
        "# shape\n",
        "* numpy array는 **모양(shape)**와 **데이터 타입(dtype)**을 가진다.\n",
        "* 3차원 벡터는 shape가 (3), 5x3 행렬은 shape가 (5, 3), 4x3x2 텐서는 shape가 (4, 3, 2)...와 같은 식\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5KYYDVwTpH0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "c600bc9c-c107-4095-cd9d-18f5a6c22232"
      },
      "source": [
        "print(a.shape)\n",
        "print(b.shape)\n",
        "print(c.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5,)\n",
            "(2, 2)\n",
            "(4,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5wviHLMWKNF"
      },
      "source": [
        "# np.zeros, np.ones, np.full\n",
        "* 주어진 형태의 numpy array들을 만드는 함수\n",
        "* np.zeros(shape, dtype=None)\n",
        "* np.ones(shape, dtype=None)\n",
        "* np.full(shape, fill_value, dtype=None)\n",
        " * shape : 해당 numpy array가 가질 shape\n",
        " * fill_value : 해당 numpy array에 채울 상수\n",
        " * dtype : 해당 numpy array의 각 component가 가질 data type\n",
        " "
      ]
    },
    {
      "cell_type": "code",
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        "id": "k_aaaQdtWKgk",
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          "height": 175
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        "outputId": "fe5395b3-3165-4f12-8774-06a894df9361"
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      "source": [
        "zeros = np.zeros([3, 3])\n",
        "ones = np.ones([3, 3])\n",
        "twos = np.full([3, 3], 2)\n",
        "\n",
        "print(zeros, zeros.shape)\n",
        "print(ones, ones.shape)\n",
        "print(twos, twos.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0.]\n",
            " [0. 0. 0.]\n",
            " [0. 0. 0.]] (3, 3)\n",
            "[[1. 1. 1.]\n",
            " [1. 1. 1.]\n",
            " [1. 1. 1.]] (3, 3)\n",
            "[[2 2 2]\n",
            " [2 2 2]\n",
            " [2 2 2]] (3, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ut-Z9j2FXYgM"
      },
      "source": [
        "# Array 연산\n",
        "* 기본적으로 같은 크기의 array끼리 사칙연산이 가능하다.\n",
        "* 이 경우, 같은 위치의 component들끼리 연산된다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOUWN_1GXZBk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "c3ab399a-2508-4816-ab50-3e9eb521467f"
      },
      "source": [
        "print(zeros + ones)\n",
        "print(ones / twos)\n",
        "print(zeros + ones * twos)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 1. 1.]\n",
            " [1. 1. 1.]\n",
            " [1. 1. 1.]]\n",
            "[[0.5 0.5 0.5]\n",
            " [0.5 0.5 0.5]\n",
            " [0.5 0.5 0.5]]\n",
            "[[2. 2. 2.]\n",
            " [2. 2. 2.]\n",
            " [2. 2. 2.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVtwTKBmuH6q"
      },
      "source": [
        "* ==, <, > 등의 등호/부등호 연산자도 사용가능하며, 이 경우 결과값은 boolean array가 된다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khjXmSTXuIPR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "17e1772e-6f21-4b9c-c13f-5aa6d6eae550"
      },
      "source": [
        "print(zeros == [[0, 0, 0], [0, 0, 0], [0, 0, 0]])\n",
        "print(zeros > ones)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ True  True  True]\n",
            " [ True  True  True]\n",
            " [ True  True  True]]\n",
            "[[False False False]\n",
            " [False False False]\n",
            " [False False False]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-TmaKdjXyT8"
      },
      "source": [
        "# Brodacasting\n",
        "* 서로 다른 shape를 가진 array끼리도, 조건이 맞는다면 연산이 가능하다.\n",
        "* 예컨대 a가 n차원이고 b가 1차원이라면 b의 값들을 n차원으로 복제하여 계산하는 식\n",
        "* 차원의 비교는 뒤쪽에서부터 한다.\n",
        "* 정확한 룰은 다음과 같다 :\n",
        " * a의 shape가 [n1, n2, ...nk] 이고, b의 shape가 [m1, m2, ..., ml] 이라 하자.\n",
        " * (i, j) = (k, l)부터 시작하여 i와 j를 1씩 낮춰가며 ni와 mj를 비교한다.(i나 j가 0이 될 때까지)\n",
        " * ni = mj 이라면 차원을 그대로 놔둔다.\n",
        " * ni = 1 이라면, a의 i번째 차원(1차원)을 복제하여 mj차원으로 늘린다.\n",
        " * mj = 1 이라면, b의 j번째 차원(1차원)을 복제하여 ni차원으로 늘린다.\n",
        " * 세 경우 다 아니라면, Broadcasting이 안되는 것이므로 ValueError를 낸다.\n",
        " * i나 j가 0이 되었다면, shape의 길이가 더 작은 쪽(i나 j가 먼저 0이 된 쪽)의 나머지 차원이 모두 1이라고 간주하고 복제하여 더 큰 쪽의 차원에 맞춘다.\n",
        "\n",
        "  \n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDDojSVcuKyk"
      },
      "source": [
        "<img src=\"https://s3.amazonaws.com/cloudxlab/static/images/course/numpy_pandas_for_ml/Broacasting_2_rule.png\" alt=\"Alt text\" title=\"Optional title\">\n",
        "\n",
        "\n",
        "* 출처: https://s3.amazonaws.com/cloudxlab/static/images/course/numpy_pandas_for_ml/Broacasting_2_rule.png"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQ9Ysi1iXyqc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "c0851642-393e-4e82-9a21-8bdcb31911d2"
      },
      "source": [
        "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "print('a :')\n",
        "print(a)\n",
        "print(a.shape)\n",
        "b = np.array([-1, 0, 1])\n",
        "print('\\n\\nb :')\n",
        "print(b)\n",
        "print(b.shape)\n",
        "c = a + b                   # b is broadcasted into shape (2, 3), and become [[-1, 0, 1], [-1, 0, 1]]\n",
        "print('\\n\\na + b :')\n",
        "print(c)\n",
        "print(c.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a :\n",
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "(2, 3)\n",
            "\n",
            "\n",
            "b :\n",
            "[-1  0  1]\n",
            "(3,)\n",
            "\n",
            "\n",
            "a + b :\n",
            "[[0 2 4]\n",
            " [3 5 7]]\n",
            "(2, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WdI4voM1lWin"
      },
      "source": [
        "# Broadcasting : example\n",
        "* 예컨대 64x64 크기의 RGB 이미지 a의 모든 픽셀에 일정한 RGB값 b를 더해주고 싶다고 하자.\n",
        "* 이 때는 그냥 a + b를 해주면 된다.\n",
        "* a의 shape가 [64, 64, 3]이고 b의 shape가 [3]인 경우이므로, 뒤쪽 차원인 3이 같다. 이 경우 b쪽의 shape의 길이가 더 작으므로 b는 [1, 1, 3]의 shape를 가지고 있다고 간주하고, b를 복제하여 [64, 64, 3]의 shape를 갖게 만든다. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSLgPNQtlW63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "54e05594-1a68-4c3b-dbf7-279c7eccc530"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "url = 'https://picsum.photos/id/237/200/300'\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "\n",
        "from matplotlib.pyplot import imshow\n",
        "\n",
        "response = requests.get(url)\n",
        "img = Image.open(BytesIO(response.content))\n",
        "a = np.asarray(img)\n",
        "print('a.shape : ', a.shape)\n",
        "b = [64, 64, 64]\n",
        "\n",
        "imshow(a)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a.shape :  (300, 200, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3920dbd120>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kx2lSVbnoVWJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "outputId": "aabf4198-67d1-42ac-d464-60017ceea97f"
      },
      "source": [
        "print('(a+b).shape : ', (a+b).shape)\n",
        "imshow(a+b)                            # a의 각 픽셀에 [64, 64, 64]를 더한다."
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-35f199798aa9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'(a+b).shape : '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m                            \u001b[0;31m# a의 각 픽셀에 [64, 64, 64]를 더한다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (300,200,3) (2,) "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tkoy9l-gqRJB"
      },
      "source": [
        "# Array Indexing\n",
        "* 기본적으로 python의 indexing과 동일하다.\n",
        "* -1   &nbsp;&nbsp;&nbsp;&nbsp;&mdash; 맨 끝 원소\n",
        "* n:m  &nbsp;&mdash; n번째 원소부터 m-1번째 원소까지\n",
        "* :    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&mdash; 해당 차원에 있는 원소 전부"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPxcAcKfqRkw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "6a75e5d9-0282-4b4e-c8ea-830a67e9014d"
      },
      "source": [
        "a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
        "\n",
        "print(a[0], '\\n')           # a의 0번째 행\n",
        "print(a[1][-1], '\\n')       # a의 1번째 행의 마지막 원소\n",
        "print(a[0:2], '\\n')         # a의 0번째 행~1번째 행\n",
        "print(a[:, 1], '\\n')        # a의 1번째 열 전체\n",
        "print(a[1:-1, :], '\\n')      # a의 1번째 행 ~ 마지막에서 2번째 행 전체\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 2 3] \n",
            "\n",
            "6 \n",
            "\n",
            "[[1 2 3]\n",
            " [4 5 6]] \n",
            "\n",
            "[2 5 8] \n",
            "\n",
            "[[4 5 6]] \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8ZCRDXpj82N"
      },
      "source": [
        "#np.sum\n",
        "* 해당 array/tensor의 특정 축을 따라 모든 원소를 더한다. 축이 명시되지 않았다면, 모든 원소를 더한다.\n",
        "* np.sum(a, axis=None, keepdims=False)\n",
        " * a : 해당 array\n",
        " * axis : element를 따라 더할 축의 index\n",
        " * keepdims : True일 경우 해당 축에 1차원 벡터로 남고, False일 경우 해당 축에 (0차원) element로 남는다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO1H7-zoj-R0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "33b318ca-e6dc-42f6-a645-1d4ecde75e9c"
      },
      "source": [
        "a = np.array([[1, 2, 3], [3, 5, 7]])\n",
        "print(np.sum(a))\n",
        "\n",
        "# a의 shape가 [2, 3]이므로, axis=0으로 하면 첫 번째 축을 따라 더하므로 결과 array의 shape [1, 3]이 된다.\n",
        "# axis=1로 하면 두 번째 축을 따라 더하므로 결과 array의 shape가 [2, 1]이 된다.\n",
        "\n",
        "\n",
        "print(np.sum(a, axis=0, keepdims=True))    #   1    2     3\n",
        "                                           #   |    |     |\n",
        "                                           #   |    |     |\n",
        "                                           #   3    5     7\n",
        "                                           #   |    |     |\n",
        "                                           #   V    V     V\n",
        "                                           #   4    7     10\n",
        "                              \n",
        "print(np.sum(a, axis=1, keepdims=True))    #   -- 1 -- 2 -- 3 --> 6\n",
        "                                           #   -- 3 -- 5 -- 7 --> 15\n",
        "\n",
        "# keepdims=False로 하면 결과 array의 shape가 [2]가 된다.\n",
        "print(np.sum(a, axis=1, keepdims=False))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "21\n",
            "[[ 4  7 10]]\n",
            "[[ 6]\n",
            " [15]]\n",
            "[ 6 15]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMS6Il61n1VO"
      },
      "source": [
        "#np.mean, np.std, np.var\n",
        "* 해당 array/tensor의 특정 축을 따라 평균/표준편차/분산을 구한다. 축이 명시되지 않았다면, 모든 원소의 평균/표준편차/분산을 구한다.\n",
        "* np.mean(a, axis=None, keepdims=False)\n",
        "* np.std(a, axis=None, keepdims=False)\n",
        "* np.var(a, axis=None, keepdims=False)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQL2U0Fsn1p0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "a031ce86-7b43-4573-8414-873b7dbf0633"
      },
      "source": [
        "print(np.mean(a))\n",
        "print(np.std(a, axis=0))\n",
        "print(np.var(a, axis=1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.5\n",
            "[1.  1.5 2. ]\n",
            "[0.66666667 2.66666667]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjxFuBQIvFD5"
      },
      "source": [
        "#np.reshape\n",
        "* 해당 numpy array를 주어진 shape로 바꾼다. 이 때, 원래 array의 shape와 바꿀 shape의 총 크기는 같아야 한다.\n",
        "* np.reshape(a, newshape)\n",
        " * a : reshape시킬 array\n",
        " * newshape : 바꿀 shape\n",
        "* 최대 하나의 dimension의 크기를 -1로 입력할 수 있으며, 이 때 해당 dimension의 크기는 나머지 dimension들의 크기에 따라 자동으로 결정된다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6dTgPBsvFVx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "7a14a638-08f9-4d22-cc40-79b18d3396b9"
      },
      "source": [
        "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "print('a')\n",
        "print(a, a.shape)\n",
        "\n",
        "print('\\nb')\n",
        "b = np.reshape(a, [6, 1])\n",
        "print(b, b.shape)\n",
        "\n",
        "print('\\nc')\n",
        "c = np.reshape(a, [3, -1])    #[3, 2]로 자동으로 변환된다.\n",
        "print(c, c.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a\n",
            "[[1 2 3]\n",
            " [4 5 6]] (2, 3)\n",
            "\n",
            "b\n",
            "[[1]\n",
            " [2]\n",
            " [3]\n",
            " [4]\n",
            " [5]\n",
            " [6]] (6, 1)\n",
            "\n",
            "c\n",
            "[[1 2]\n",
            " [3 4]\n",
            " [5 6]] (3, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTunaYkrpMSv"
      },
      "source": [
        "# np.stack\n",
        "* **새로운 축을 만든 뒤** 해당 array들을 해당 축 방향으로 쌓아서 새로운 array를 만든다. 이 때 쌓을 array들의 shape는 완전히 같아야 한다.\n",
        "* np.stack(arrays, axis=0)\n",
        " * arrays : 쌓을 array들\n",
        " * axis : 쌓을 축의 index. axis=-1로 할 시 마지막 축을 따라 쌓는다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyWjZQaUpMmc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "outputId": "14178e89-cd28-42d3-c8b7-d2bd7b6ad60d"
      },
      "source": [
        "a = np.array([1, 2, 3])\n",
        "b = np.array([4, 5, 6])\n",
        "c = np.array([4, 5, 6, 7])\n",
        "\n",
        "# shape[3]과 shape[3]을 0번 축을 따라 쌓으므로 결과 array의 shape는 [2, 3]이 된다.\n",
        "print(np.stack((a, b)))\n",
        "\n",
        "# shape[3]과 shape[3]을 1번 축을 따라 쌓으므로 결과 array의 shape는 [3, 2]가 된다. \n",
        "print(np.stack((a, b), axis=1))\n",
        "\n",
        "\n",
        "#print(np.stack((a, c)))  #->  ValueError! 크기가 안 맞으므로 쌓을 수 없다.\n",
        "\n",
        "\n",
        "a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
        "b = np.array([[7, 8, 9], [10, 11, 12], [13, 14, 15]])\n",
        "\n",
        "# shape[3, 3]과 shape[3, 3]을 1번 축을 따라 쌓으므로 결과 array의 shape는 [3, 2, 3]이 된다.\n",
        "print()\n",
        "print(np.stack((a, b), axis=1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "[[1 4]\n",
            " [2 5]\n",
            " [3 6]]\n",
            "\n",
            "[[[ 1  2  3]\n",
            "  [ 7  8  9]]\n",
            "\n",
            " [[ 4  5  6]\n",
            "  [10 11 12]]\n",
            "\n",
            " [[ 7  8  9]\n",
            "  [13 14 15]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyr_TZHFrOAv"
      },
      "source": [
        "#np.concatenate\n",
        "* 해당 array들을 **기존의 주어진 축 방향으로** 연결한다. 이 때 array들의 shape는 연결할 축을 제외하고 shape가 완전히 같아야 한다.\n",
        "* np.concatenate(arrays, axis=0)\n",
        " * arrays : 연결할 array들\n",
        " * axis : 연결할 축의 index. axis=-1로 할 시 마지막 축을 따라 연결한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZPvkO3P9rOWV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "64f80814-4237-4b30-e93d-6384498b89b5"
      },
      "source": [
        "a = np.array([[1, 2], [3, 4]])\n",
        "b = np.array([[5, 6]])\n",
        "\n",
        "\n",
        "# shape[2, 2]와 shape[1, 2]를 0번 축 방향으로 연결하므로 결과 array의 shape는 [3, 2]가 된다.\n",
        "print(np.concatenate((a,b), axis=0))\n",
        "\n",
        "c = np.array([[7, 8, 9], [10, 11, 12]])\n",
        "\n",
        "# shape[2, 2]와 shape[2, 3]을 1번 축 방향으로 연결하므로 결과 array의 shape는 [2, 5]가 된다.\n",
        "print(np.concatenate((a, c), axis=1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2]\n",
            " [3 4]\n",
            " [5 6]]\n",
            "[[ 1  2  7  8  9]\n",
            " [ 3  4 10 11 12]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drKIZU2UtS0f"
      },
      "source": [
        "#np.random.random(size)\n",
        "* 각 element가 0에서 1사이의 랜덤한 값들을 가지는 size 크기의 array를 만든다.\n",
        "* 각 element는 서로 독립이며, 모두 uniform random distribution을 따른다.\n",
        "\n",
        "#np.random.randint(low, high=None, size=None)\n",
        "* high가 None일 때 : 각 element가 0 이상 low 이하의 랜덤한 정수인, size 크기의 array를 만든다.\n",
        "* high가 주어졌을 때 : 각 element가 low 이상 high 이하의 랜덤한 정수인, size 크기의 array를 만든다.\n",
        "* 각 element는 서로 독립이며, 범위 내의 각 정수가 나올 확률은 모두 같다.\n",
        "\n",
        "#np.random.normal(loc=0.0, scale=1.0, size=None)\n",
        "* 각 element가 평균이 loc이고 표준편차가 scale인 정규분포에서 추출된, size 크기의 array를 만든다.\n",
        "* 각 element는 서로 독립이며, N(loc, scale^2)의 분포를 가진다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AU7YNOlztTHd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "c65b88fd-1f05-4035-ed59-5d2c8f43be3b"
      },
      "source": [
        "print('np.random.random((2, 3)) :')\n",
        "print(np.random.random(size=(2, 3)))\n",
        "print('\\nnp.random.randint(3, size=(2, 3)) :')\n",
        "print(np.random.randint(3, size=(2, 3)))\n",
        "print('\\nnp.random.randint(3, 8, size=(2, 3)) :')\n",
        "print(np.random.randint(3, 8, size=(2, 3)))\n",
        "print('\\nnp.random.normal(2, 1, size=(2, 3)) :')\n",
        "print(np.random.normal(2, 1, size=(2, 3)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "np.random.random((2, 3)) :\n",
            "[[0.55380521 0.47484035 0.41128984]\n",
            " [0.23645287 0.05808476 0.77312212]]\n",
            "\n",
            "np.random.randint(3, size=(2, 3)) :\n",
            "[[2 2 0]\n",
            " [1 0 1]]\n",
            "\n",
            "np.random.randint(3, 8, size=(2, 3)) :\n",
            "[[3 3 5]\n",
            " [5 7 5]]\n",
            "\n",
            "np.random.normal(2, 1, size=(2, 3)) :\n",
            "[[1.85892876 3.03629825 1.30196629]\n",
            " [1.71492068 1.97904854 1.82491276]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7wAr5vNEu41"
      },
      "source": [
        "# Numpy array를 파일에 저장하고 불러오기\n",
        "* pickle 라이브러리를 이용하면 편하다.\n",
        "* pickle.dump(array, file) : 해당 array를 file에 저장한다.\n",
        "* pickle.load(f) : 해당 file에서 array를 로드하여 리턴한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72JleLaAEvOy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "04acc49a-c42b-46b6-b476-44dcfb91ad45"
      },
      "source": [
        "import pickle\n",
        "\n",
        "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "with open('./a.pkl', 'wb') as f:\n",
        "  pickle.dump(a, f)\n",
        "\n",
        "with open('./a.pkl', 'rb') as f:\n",
        "  b = pickle.load(f)\n",
        "\n",
        "print(b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KsSpJFmv5cw"
      },
      "source": [
        "# 그 외에 유용한 함수들\n",
        "* 각자 googling해서 정확한 용법을 찾아보세요! numpy를 사용하면서 한번쯤은 꼭 사용하게 될 편리한 함수들입니다.\n",
        "* np.max, np.min : 최대, 최소값을 리턴\n",
        "* np.argmax, np.argmin : 최대, 최소로 만드는 index(들)을 리턴\n",
        "* np.where : 특정 조건을 만족하는 곳에서 x의 값을, 아닌 곳에서 y의 값을 가지는 array를 리턴\n",
        "* np.argwhere : 특정 조건을 만족하는 index(들)을 리턴\n",
        "* np.take : 해당 array에서 해당 index들을 뽑아 만든 array를 리턴\n",
        "* np.sort : 해당 array를 주어진 기준에 대하여 정렬\n",
        "* np.transpose (또는 np.ndarray.T) : 해당 array를 transpose\n",
        "* np.expand_dims : 해당 array에 축을 추가\n",
        "* np.squeeze : 차원 크기가 1인 축을 제거\n",
        "\n",
        "* np.exp, np.log, np.sqrt, np.sin, np.cos, np.tan, ... : element-wise 함수들\n",
        "* np.matmul : 행렬 곱\n",
        "* 등등등... 필요한 게 있을 때는 구글링을 하거나 https://docs.scipy.org/doc/numpy/reference/index.html 를 참조\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWl9YZ03COFD"
      },
      "source": [
        "# pytorch 라이브러리\n",
        "* numpy에 있는 대부분의 함수들과 같은 역할을 pytorch 함수가 존재한다.\n",
        "* numpy의 함수들은 numpy array를 리턴하지만, pytorch의 함수들은 pytorch Tensor들을 리턴한다는 점이 다르다.\n",
        "* np.array -> torch.Tensor\n",
        "* np.sum -> torch.sum\n",
        "* np.reshape -> torch.view\n",
        "* 등등등... 역할은 거의 같으니 필요한 게 있다면 https://pytorch.org/docs/stable/torch.html 를 참조"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrxs51KXv-SB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "6b74f851-7dc0-47dd-ce5a-81047365291b"
      },
      "source": [
        "import torch\n",
        "a = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",
        "a.view([3, 2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 2],\n",
              "        [3, 4],\n",
              "        [5, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    }
  ]
}