py2.실습Zero.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e011870",
   "metadata": {},
   "source": [
    "### 1. 파이썬 기초(Optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d941d3c",
   "metadata": {},
   "source": [
    "#### 프린트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f93ada69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, world\n",
      "정수: 3, 03, 003, 0003, 00003\n",
      "실수: 256, 256.1, 256.12\n",
      "문자열: [Hello, world]\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, world\")\n",
    "\n",
    "# integer\n",
    "x = 3\n",
    "print(\"정수: %01d, %02d, %03d, %04d, %05d\" % (x,x,x,x,x))\n",
    "\n",
    "# float\n",
    "x = 256.123\n",
    "print(\"실수: %.0f, %.1f, %.2f\" % (x,x,x))\n",
    "\n",
    "# string\n",
    "x = \"Hello, world\"\n",
    "print(\"문자열: [%s]\" % (x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890a08a1",
   "metadata": {},
   "source": [
    "#### 반복문, 조건문"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "10e5448f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression 은(는) 기계학습 내용입니다.\n",
      "Classifcation 은(는) 심층학습 내용입니다.\n",
      "SVM 은(는) 기계학습 내용입니다.\n",
      "Clustering 은(는) 기계학습 내용입니다.\n",
      "Demension reduction 은(는) 기계학습 내용입니다.\n",
      "NN 은(는) 심층학습 내용입니다.\n",
      "CNN 은(는) convolutional neural network입니다.\n",
      "AE 은(는) 심층학습 내용입니다.\n",
      "GAN 은(는) 심층학습 내용입니다.\n",
      "RNN 은(는) 심층학습 내용입니다.\n"
     ]
    }
   ],
   "source": [
    "contents = [\"Regression\", \"Classifcation\", \"SVM\", \"Clustering\", \"Demension reduction\", \"NN\", \"CNN\", \"AE\", \"GAN\", \"RNN\"]\n",
    "for con in contents:\n",
    "    if con in [\"Regression\", \"Classification\", \"SVM\", \"Clustering\", \"Demension reduction\"]:\n",
    "        print(\"%s 은(는) 기계학습 내용입니다.\" %con)\n",
    "    elif con in [\"CNN\"]:\n",
    "        print(\"%s 은(는) convolutional neural network입니다.\" %con)\n",
    "    else:\n",
    "        print(\"%s 은(는) 심층학습 내용입니다.\" %con)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faab3c1e",
   "metadata": {},
   "source": [
    "#### 반복문과 인덱스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff3a5664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0/10]: Regression\n",
      "[1/10]: Classifcation\n",
      "[2/10]: SVM\n",
      "[3/10]: Clustering\n",
      "[4/10]: Demension reduction\n",
      "[5/10]: NN\n",
      "[6/10]: CNN\n",
      "[7/10]: AE\n",
      "[8/10]: GAN\n",
      "[9/10]: RNN\n"
     ]
    }
   ],
   "source": [
    "for(i,con) in enumerate(contents):\n",
    "    print(\"[%d/%d]: %s\" %(i, len(contents), con))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d64bf5",
   "metadata": {},
   "source": [
    "#### 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f5a38a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0 + 20.0 = 30.0\n"
     ]
    }
   ],
   "source": [
    "def sum(a,b):\n",
    "    return a+b\n",
    "\n",
    "x = 10.0\n",
    "y = 20.0\n",
    "print(\"%.1f + %.1f = %.1f\" %(x,y,sum(x,y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba204d5",
   "metadata": {},
   "source": [
    "#### 리스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fb4a88d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "['a']\n",
      "['a', 123]\n",
      "['a', 123, ['a', 'b']]\n"
     ]
    }
   ],
   "source": [
    "a = []\n",
    "b = [1,2,3]\n",
    "c = [\"Hello\", \",\", \"world\"]\n",
    "d = [1,2,3,\"x\",\"y\",\"z\"]\n",
    "x = []\n",
    "print(x)\n",
    "\n",
    "x.append('a')\n",
    "print(x)\n",
    "\n",
    "x.append(123)\n",
    "print(x)\n",
    "\n",
    "x.append([\"a\",\"b\"])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d1436d",
   "metadata": {},
   "source": [
    "#### 딕셔너리(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa54720d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'town': 'Goyang city', 'job': 'Assistant professor', 'name': 'Heekyung'}\n"
     ]
    }
   ],
   "source": [
    "dic = dict()\n",
    "dic[\"name\"] = \"Heekyung\"\n",
    "dic[\"town\"] = \"Goyang city\"\n",
    "dic[\"job\"] = \"Assistant professor\"\n",
    "print dic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b8dcce",
   "metadata": {},
   "source": [
    "#### 클래스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f12b9f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heekyung 학생은 공부합니다.\n",
      "Heekyung 학생은 열심히 공부합니다.\n"
     ]
    }
   ],
   "source": [
    "class Student:\n",
    "    # 생성자\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        \n",
    "    # 메써드\n",
    "    def study(self, hard=False):\n",
    "        if hard:\n",
    "            print \"%s 학생은 열심히 공부합니다.\" %self.name\n",
    "        else:\n",
    "            print \"%s 학생은 공부합니다.\" %self.name\n",
    "\n",
    "s = Student('Heekyung')\n",
    "s.study()\n",
    "s.study(hard=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0146b0",
   "metadata": {},
   "source": [
    "### 2. 벡터, 행렬 연산, 그래프 그리기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149e8971",
   "metadata": {},
   "source": [
    "#### 라이브러리(패키지) 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a7cf61f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af2c257",
   "metadata": {},
   "source": [
    "#### 프린트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5446ea9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_val(x):\n",
    "    print \"Type:\", type(x)\n",
    "    print \"Shape:\", x.shape\n",
    "    print \"값:\\n\", x\n",
    "    print \" \""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f330341f",
   "metadata": {},
   "source": [
    "#### rank 1 np array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a676ca57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (3,)\n",
      "값:\n",
      "[1 2 3]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (3,)\n",
      "값:\n",
      "[5 2 3]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "x = np.array([1,2,3])\n",
    "print_val(x)\n",
    "\n",
    "x[0] = 5\n",
    "print_val(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf5861f",
   "metadata": {},
   "source": [
    "#### rank 2 np array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4884b601",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 3)\n",
      "값:\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "y = np.array([[1,2,3], [4,5,6]])\n",
    "print_val(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404642d9",
   "metadata": {},
   "source": [
    "#### rank 2 zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "925ba2bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[0. 0.]\n",
      " [0. 0.]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "a = np.zeros((2,2))\n",
    "print_val(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879dea1b",
   "metadata": {},
   "source": [
    "#### rank 2 ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f9da035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1.]\n",
      " [1. 1.]\n",
      " [1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.ones((3,2))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1acddb2b",
   "metadata": {},
   "source": [
    "#### rank 2 단위 행렬(identity matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b672035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (3, 3)\n",
      "값:\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "a = np.eye(3,3)\n",
    "print_val(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9119f713",
   "metadata": {},
   "source": [
    "#### 랜덤 행렬(uniform: 0~1 사이 모든 값들이 나올 확률이 같음)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6dff0392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (4, 4)\n",
      "값:\n",
      "[[0.02022848 0.82311298 0.83466355 0.2778049 ]\n",
      " [0.9159867  0.07976312 0.97526852 0.24625115]\n",
      " [0.84827355 0.84357048 0.89415927 0.93399583]\n",
      " [0.41291582 0.1320173  0.17604189 0.45768992]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "a = np.random.random((4,4))\n",
    "print_val(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cf251d",
   "metadata": {},
   "source": [
    "#### 랜덤 행렬(Gaussian: 0을 평균으로 하는 가우시안 분포를 따르는 랜덤값)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "76b69cc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (4, 4)\n",
      "값:\n",
      "[[ 0.86337681  0.0950026   0.95728091 -0.83948008]\n",
      " [-1.2300684  -0.32606102 -0.6113864   0.06851859]\n",
      " [ 0.77955031  0.46489836 -1.29109603  0.93764452]\n",
      " [ 0.04859251 -2.02474872 -1.84309228  1.46773276]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "a = np.random.randn(4,4)\n",
    "print_val(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8390a210",
   "metadata": {},
   "source": [
    "#### np array indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "879382a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (3, 4)\n",
      "값:\n",
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "print_val(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6dc4a70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[2 3]\n",
      " [6 7]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "b = a[:2, 1:3]  # 행 0~1, 열 1~2\n",
    "print_val(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0155843",
   "metadata": {},
   "source": [
    "#### 행렬의 n번째 행 얻기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7f75b5cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (4,)\n",
      "값:\n",
      "[5 6 7 8]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "row1 = a[1, :]  # 1번째 행\n",
    "print_val(row1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a226965",
   "metadata": {},
   "source": [
    "#### 행렬의 원소별 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3a4726e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = np.array([[1,2], [3,4]], dtype=np.float64)\n",
    "m2 = np.array([[5,6], [7,8]], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8276ae1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# elementwise sum\n",
    "print_val(m1+m2)\n",
    "print_val(np.add(m1,m2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f0d5e458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# elementwise difference\n",
    "print_val(m1 - m2)\n",
    "print_val(np.subtract(m1,m2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3e13d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# elementwise product\n",
    "print_val(m1*m2)\n",
    "print_val(np.multiply(m1,m2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "06f1099b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# elementwise division\n",
    "print_val(m1/m2)\n",
    "print_val(np.divide(m1,m2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b4a45b6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# elementwise square root\n",
    "print_val(np.sqrt(m1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411b79d5",
   "metadata": {},
   "source": [
    "#### 행렬 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "957e9b4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[1 2]\n",
      " [3 4]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[5 6]\n",
      " [7 8]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2,)\n",
      "값:\n",
      "[ 9 10]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2,)\n",
      "값:\n",
      "[11 12]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "m1 = np.array([[1,2],[3,4]])\n",
    "m2 = np.array([[5,6],[7,8]])\n",
    "v1 = np.array([9,10])\n",
    "v2 = np.array([11,12])\n",
    "\n",
    "print_val(m1)\n",
    "print_val(m2)\n",
    "print_val(v1)\n",
    "print_val(v2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f02fab2",
   "metadata": {},
   "source": [
    "#### 벡터-벡터 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "43c28c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.int64'>\n",
      "Shape: ()\n",
      "값:\n",
      "219\n",
      " \n",
      "Type: <type 'numpy.int64'>\n",
      "Shape: ()\n",
      "값:\n",
      "219\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(v1.dot(v2))\n",
    "print_val(np.dot(v1,v2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3d0021",
   "metadata": {},
   "source": [
    "#### 벡터-행렬 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ff2f81d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2,)\n",
      "값:\n",
      "[29 67]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2,)\n",
      "값:\n",
      "[29 67]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(m1.dot(v1))\n",
    "print_val(np.dot(m1,v1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b15c2c0",
   "metadata": {},
   "source": [
    "#### 행렬-행렬 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a047c14b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[19 22]\n",
      " [43 50]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[19 22]\n",
      " [43 50]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(m1.dot(m2))\n",
    "print_val(np.dot(m1,m2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5b0486",
   "metadata": {},
   "source": [
    "#### 전치 행렬(transpose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "576dcfcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[1 2]\n",
      " [3 4]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 2)\n",
      "값:\n",
      "[[1 3]\n",
      " [2 4]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(m1)\n",
    "print_val(m1.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6616fb3",
   "metadata": {},
   "source": [
    "#### 합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3f1d88a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.int64'>\n",
      "Shape: ()\n",
      "값:\n",
      "10\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2,)\n",
      "값:\n",
      "[4 6]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2,)\n",
      "값:\n",
      "[3 7]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(np.sum(m1))\n",
    "print_val(np.sum(m1, axis=0))  # shape[0] (행)을 압축시키자. (2,2) → (2,)\n",
    "print_val(np.sum(m1, axis=1))  # shape[1] (열)을 압축시키자. (2,2) → (2,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4395dce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "m1 = np.array([[1,2,3], [4,5,6]])\n",
    "print m1\n",
    "print m1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "abdc6da5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "[5 7 9]\n",
      "[ 6 15]\n"
     ]
    }
   ],
   "source": [
    "print np.sum(m1)\n",
    "print np.sum(m1, axis=0)\n",
    "print np.sum(m1, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9810def9",
   "metadata": {},
   "source": [
    "#### zeors-like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9e83d982",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (4, 3)\n",
      "값:\n",
      "[[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n",
      " \n",
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (4, 3)\n",
      "값:\n",
      "[[0 0 0]\n",
      " [0 0 0]\n",
      " [0 0 0]\n",
      " [0 0 0]]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "m1 = np.array([[1,2,3],\n",
    "              [4,5,6],\n",
    "              [7,8,9],\n",
    "              [10,11,12]])\n",
    "m2 = np.zeros_like(m1)  # m1과 같은 형태의 0으로 이루어진 np array\n",
    "print_val(m1)\n",
    "print_val(m2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc63a3e2",
   "metadata": {},
   "source": [
    "#### Matplot library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6c78f9cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "520119cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f745ce22e10>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sin 커브\n",
    "x = np.arange(0, 10, 0.1)  # 0~10까지의 0.1 간격의 숫자 배열\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ef2ce6",
   "metadata": {},
   "source": [
    "#### 한번에 두 개 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4556e521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CFdug+IuIGFjyGJzZ1a87tNaOLn722lFmjB/NSj9ltFyJb92cSUtHFz/bbt+hWYKmatjza8i9C8bPNVsNOIJgxQ+h8Qzs+uUVN1dVfrCpmJjIUP7hpil+EHhlPr9gCrGRoTy+qYjh1Aexv9gGxZ/MetCYG7HzP435E33wl92nqGxsDaiup1MTRvLgNRN5+v0zdgqlFXjrx0ZvuQXfNFvJh0y6AbLWGsWOl/pua7LzSC27T9Tx1cVpATMALio8hH9cksaeE/W8Xuy7fn1WxTYo/sQRBAu/BbUlcPiFXjdrbOngiR3HuTE9nuumBNZ40q8uTiMyLNhepQQ650/Bvj/DrE9AbGDc3X/Awu9AZyu8+/963aTLqfzn5mJSYyMCrvP1vfMmMDkukh9sLrbTiC/DNij+JnMtJOYaq5Reehz97q3jNLZ08LXl0/ws7sqMiQzl4fmT2FpYTXGlvUoJWHb+0LiBuelrZiv5OPHpRgX93t/DxdoeN3n5wFmOVl/kX5dn9Dl51AxCghx8bUUGJ2ovsT6/wmw5AUVg/U8NBxwOWPRto9As/+MTi+sutvHHd06xZkaKaWnCV+KR+ZOICgvmF68fM1uKTU/UFEP+M0ZWl1lpwlfipq8bq5RdH1+ldDmVJ3aWkpkczc25gRE/vJxlWYlkJEXxxI5Supx2LMWNqQZFRFaIyBERKRWRb/Tw+c9E5KDrcVREGrp91tXts/X+VT5I0lfA2Nnw5o+MuRTd+NO7p2jt7OKrS9JMEndlRkWE8Kn5qWw+XMWRqiaz5dhczps/gtCRcP0/ma2kd+KmQu7d8P7vPzY7aPPhSk7UXuKLC6cETPzwchwO4UuLpnK89hJbDtsFv25MMygiEgQ8AawEsoD7ROQjPRVU9Z9UdaaqzgT+B3ix28ct7s9UdY3fhHsDESONuPHMR1qyNLV28OTuU6zITmJKvG8b3w2WR66fRGRoEL94w16lBBT1J6DoZZj7aSOzMJC56WvQ1f6RWIqq8sSO40yOj2RlTrKJ4q7MypxkJsdH8j9vHLMzvlyYuUKZB5Sq6glVbQeeAdb2sf19wMd9RFZlyiKj4HHXLz9oHPnUe2doau3k8wsCLIjaA6MjQnnoulQ2FVRyrNpepQQMu34JjmBj1kmgEzsFpt8De/8Al+oAeKOkhuLKC3z+pil+bzM0UIIcwhcXTKWkqsnO+HJhpkEZC5R1+7nc9d7HEJGJwCTgjW5vh4tInojsEZFbezuJiHzWtV1ebW3PAUBTEIHrvgJ1x+DoFlo7uvj9Oye5fmoc08f5p5vwYPnMDZMZERLEr988brYUGzAC3Af/DjPuhajAjD18jOv/ETpbIO8PqCq/3FHK2NEjuHVWj18FAcfamSmMjxlhr1JcWCUofy+wTlW7F29MVNU5wP3Az0Wkx9t6Vf2dqs5R1Tnx8fH+0Np/sm6F0RNg1y94YX85tU1tlliduImJDOWu2ePYkF9BzYVWs+XYvPcb6GyD675qtpL+Ez8N0pbB+79jz7EKDpxp4HM3TSYkyBpfTcFBDr6wYCr55Y3sOl5nthzTMfN/7SwwvtvP41zv9cS9XObuUtWzrucTwE5glvcl+pigYLjmi3BmN2+/sZkZ40Zx3ZRYs1UNiIfnT6LTqfxl92mzpQxv2pqMXnGZq42At5W49otwqZaSrX8gNjKUu+aMv/I+AcRts8YSGxnKn961OxGbaVD2AmkiMklEQjGMxseytUQkAxgD7O723hgRCXO9jgPmA0V+Ue1tZj1Ie8go1jav43M3BW5WS2+kxkWyJDORv793mtYOuxOxaez/C7Q2wvwAzuzqjUk30R6XxfzaZ3hg3njCQ4LMVjQgwkOCeODqCbxeUsOpc5fMlmMqphkUVe0EvgRsBYqB51S1UES+LyLds7buBZ7RjzooM4E8EckHdgA/VFVrGpSwkWwMW8nyoDyWJl40W41HPHL9JM43d/Di/t4WmDY+xdkF7/0WJlwH42abrWbgiLBx5B2kO87y6SRrxuMevGYiwQ7hz8O8G7epjkpV3aSq6ao6RVUfd733qKqu77bNY6r6jcv226Wquao6w/X8B39r9xZHqpr4wbkbUQkieJ81f42rJ8WQMzaaP7xzAqdd5OV/jm2HhtNwtYnt6QfBxbZOvncyk4bgOEYf/K3ZcjwiITqc1dNTWLevnKbWDrPlmIY1Il9DmL/sPkVjcAydGWvg4FPQbr0ls4jwyPWTOF57iTePBVAm3XBh7/9CVDJkrDZbiUesyyujoQ2aZz4CJ9+Eams6Gx6en8rFtk6eyys3W4pp2AbFRBpbDDfR2hkphF37D9DWCAXrzJblEatyU0iICrMHcPmbuuPGNMbZDxsjfi2G06k8ufs0syaMJmXhZyEoFPb9yWxZHjF93GhmTxzDk7tODdt2LLZBMZHn88po6ejioetSjbnziTnG3aYF89lDgx3cO3c8bx6tpazeHpHqN/b+HhwhxvAqC/Lm0VpOnrvEw/MnQWSc0do+/xlos2Y88dPzJ3GmvpkdJcOz0NE2KCbhdCp/3XOaORPHGE0gRWDuI1BVAOV5ZsvziHvmTUCAZ/eWXXFbGy/QfgkO/N34Eo5KNFuNR/xtz2nio8I+HCI35xFou9DneIdAZll2InEjw3hm7xmzpZiCbVBM4u3Sc5yua+aT16V++Gbu3RAaZdx1WpCxo0ewcFoCz+aV0WHPifA9h54z3KTz/o/ZSjyisrGFHUdquHvOuA8LGSdcA/GZkPdHc8V5SEiQg7vnjOONkhoqG/s35ngoYRsUk3h27xliIkNZnt3tzjJsJMy8DwpfvOI0u0Dl/qsnUNvUxmtF1WZLGfrk/dGYrTP+arOVeMTzeeU4Fe6d222AlnulXnkQzu4zT9wguHfuBJwKz+0dfsF526CYwLmLbWwvqub2WWMJC76siGvOI0YH1gN/M0fcIFkwLYGUUeH8/b3hueT3GxUHoeoQzH7I+BK2GF1O5dm9ZdyQFsf4mIiPfjj9bgiJsOwqZUJsBDekxfHs3jPDLjhvGxQTeGn/WTq6lHvm9tBiIiEDxl9jGBQLBueDHMK98ybwTum5YV817FMO/BWCwiD3TrOVeMTbx2o529Dy0dWJm/BRkHsXFLwALQ0f/9wC3DdvAhWNrbx1dHil0dsGxc+oKs/sPcPsiWNIS4zqeaNZDxpdiMve9684L3HP3PEEOYSnh2lg0ud0tMCh5yFrDYwYY7Yaj3jm/TJiI0NZmtVLMsHsTxldiC0anF+SmUjcyFCeen94XQO2QfEzeafPc7z2Us+rEzfZt0JIpHEXakESo8NZlJHAi/vP0mkH571P8QYjGD/rE2Yr8YiaplZeK67mztnjep8XnzILErKMdvwWJDTYwV1zxvNGSQ1VjcOnE7dtUPzMM++XMTIsmNXT+5hGFxYF2bdB4UuWzce/c/Y4apvaePuYNZMLApr9f4ExqZB6g9lKPGLdvnI6nb24fN2IwMwHjMB8TYn/xHmRe+eOp8upvLB/+ATnbYPiRy60drCxoII1M1OICA3ue+NZD0L7RSh6xT/ivMzCaQnERIaybt/wuZj8Qv0JOPW28ffhsN7lq6qs21fOvNQYJl9pzPX0e4zpkxZdpUyMjWRu6hhe2F8+bIZvWe8v0sJsPFRJa4eTu/sz72HCNRAzxbLZXqHBDtbOTGF7UTUNze1myxk6HPgbiMO4e7cg+eWNnKi9xB2z+zGRcWQ8pC2HQ89CV6fvxfmAO64ax4naSxwss2ZywUCxDYofeWn/WabERzJj3KgrbywCsx6AM7vgXKnvxfmAO2ePo73LyYb8CrOlDA2cTqMtyZTFEJ1ithqPeGFfOWHBDlbm9uHy7c6sB+BitdGvzILcPD2ZsGDHsHF72QbFT5TVN/P+qXpuv2pc/4dozbjPuBvNf8q34nxEdsooMpOjbbeXtzj9Dlw4a8yMtyBtnV1sOFTB8uwkosP72cgybRlExFnW7RUdHsLy7CQ25FfS1jn0B9DZBsVPvHTAGD5166x+LPXdRKfA5IVGiqjTmtlSd84eR355I0erm8yWYn3ynzVa82SsMluJR+woqaGhuYPbrxrANRAUYsRSjmyGS9ac2X7H7HE0tnTwevHQbxhpqkERkRUickRESkXkGz18/ikRqRWRg67HZ7p99pCIHHM9HvKv8oGhqry4v5xrJscwdvSIge08/R5oPANle3wjzsesnZlCsEPsVcpgaW82EjSy1kLIAP+GAoQX9p8lPiqM66fGDWzHmfeDs8NoSWRBrp8aR2J0GC8OA7eXaQZFRIKAJ4CVQBZwn4hk9bDps6o60/X4vWvfGOC7wNXAPOC7IhKwFV4Hyho4VdfM7VeNG/jOGauMNhSHnvO+MD8QNzKMBdMSeOXg2WHXhsKrHN0M7U1GWxILUnexjR0lNdw2ayzBQQP82knKMWpSCp73jTgfE+QQbp01lp1Hajl3sc1sOT7FzBXKPKBUVU+oajvwDLC2n/suB7arar2qnge2Ayt8pHPQvLjfFYh0t+geCGEjDaNS+BJ0WvOP8dZZKVRfaOO9k9Z0WQQE+c9C9FjL1p5syK+g06kDc3d1J/cuKHsPzp/yqi5/cedV4+h0KusPDu0EFTMNylig++CMctd7l3OHiBwSkXUi4s637e++iMhnRSRPRPJqa/3fV6ets4tXD1WyPDuJqP4GIi9n+j3Q2mDMDrcgizMSiQwN4pUDQ/ti8hkXa40sp9y7LFl7AvDSwQoyk6PJSIr27ADunmUWXaWkJUaRlRzN+iGe8Rjof50bgFRVnY6xCnlyoAdQ1d+p6hxVnRMfH+91gVdi55FaGpo7uM3TOzMwAvMRcUY+vgUZERrE8pwkNh0eHpkuXufwC6Bdls3uOl13ifyyBtbOHESq8+gJMOE6I0HFokWCa2emcLCsgdN1Q7dpqpkG5SzQvcJvnOu9D1DVOlV1+3l+D8zu776Bwvr8CmIiQ7lhoIHI7gQFG3doR7dYtvvq2pljaWrtZOeR4dV91SscehaSpkNCptlKPMLt5rllxiBrZ6bfBeeOGG37LYj79x/Kbi8zDcpeIE1EJolIKHAvsL77BiLSvfppDVDser0VWCYiY1zB+GWu9wKKS22dvF5czc25SQMPRF7O9LuNOSnF66+8bQAyf0oscSNDeeVgQNr9wKX+BFTst2ybelVlfX4Fc1PHDDzD8XKybgVHiGUTVFJGj2Beagyv5FcM2VYsphkUVe0EvoRhCIqB51S1UES+LyJrXJt9RUQKRSQf+ArwKde+9cC/YxilvcD3Xe8FFK8VV9Pa4WTNjEG4u9ykXGW0YrGoDzk4yMHq6Sm8VlxDU2uH2XKsw2FXqmz27ebq8JCSqiaO1VxkzWBXJwARMZC2FArWgdOartM1M1MorblIceXQrMsyNYaiqptUNV1Vp6jq4673HlXV9a7X31TVbFWdoaoLVbWk275/VNWprsefzPod+mJDfgXJo8KZM9ELGc0ikHMHnHoHmqw5XnfNzBTaO51sOVxlthTrcPhFY+Da6H70fwtA1udXEOQQbu5vq5UrkXsXXKwyrgMLcnNuMsEOGbLB+UAPyluWhuZ23jxay+rpyTgcXhrRmnM7qNOyHYhnjR/NhJiIIXsxeZ2aYqgpNG4kLIiqsiG/guunxhE7Msw7B01fYcwKsmiRY0xkKDekxbEhvwLnEKzLsg2Kj9haWEVHlw4+ENmdhEyIz7TsxSQi3DIjmV3H66gb4gVeXuHwi0Yvt6z+lmcFFvvPNFB+vsU77i43oREwbQUUrYcua7pO18xM4WxDC/vPnDdbitexDYqPWJ9fQWpsBLlj+9FZeCDk3A5ndkOjNYPbq3JT6HIqWwptt1efqBrpwqk3QFQvY3IDnPUHzxIW7GBZtpf1Z98OLfVw8i3vHtdPLM1KIjzEMSRX6rZB8QE1Ta3sPl7HLTNS+t9ZuL+4g7OFL3n3uH4iMzmKyXGRbDxUabaUwKYyH+qPW9bd1eVUNh2uYuG0BM8Lentj6hKjSaZFV+ojw4JZOC2BzYerhlw7Itug+IDNBVU41Qt59z0RN9WoSbDoxSQirJ6ezJ4TddQ22W6vXjn8gjGtMPMWs5V4xN5T9dQ2tbGqr1HXnhISDhk3Q/Gr0GnN4W3kw6S2AAAgAElEQVQ35yZT29TG3lMBl5w6KGyD4gM2FlSSljCS9MQo35wg53Zj1rZF+xqtmp6CU7HdXr2haqxApywyUmUtyKaCSsKCHSzKSPDNCbJvN9oRndjpm+P7mEUZCYSHONhUMLRW6rZB8TI1F1rZe6ree2mSPZF9m/FsUbdXeuJIpiaMZOOhoedD9gpn90Nj2Yf/zxajy6lsPlzFoowEIsOCfXOSKYsgbJRlr4HIsGAWZSSwqWBoub1sg+JlthRWoYpvlvpuxqTC2NkfFr1ZDBFhVW4y752sp+ZCq9lyAo+il4yK8GkrzVbiEW53l09vqoJDIXM1lGy0bBfum3OTOXexjfdPDh23l21QvMymgkqm+tLd5SbrVqOnUf1J357HR6yanowqbLaLHD+KqlFnNHkBjAjYET99sqmgkvAQH7q73GTfDm2NUPq6b8/jI4ai28s2KF6ktsm42/DpnZmbLFd3Gov29kpPjCI9cSSv2m6vj1J5EBrOQPatZivxCLe7a+E0H7q73Ey+CcJHW/YaiAg13F5DKdvLNiheZEuhkd11c64Hg7QGyphUSJ5p2ap5MJb8eafPU9Nku70+oPBlI7tr2s1mK/EIv7i73ASFGP9OJZssm+21KjdlSLm9bIPiRTYdqmRyfCTTfO3ucpO11sj2aii78rYByMocw+21tdCavcm8jtvdNelGS2d3+cXd5SZrreH2smiR48KMeEaEBLGxYGis1G2D4iXOXTRG3K7KTfZ+MWNvuFtyFG/wz/m8THriSCbHRbLl8NDxIQ+KqgI4f9KIj1kQp1PZ4i93l5spC40ix6KX/XM+LxMRGsyCafFsLaweEr29bIPiJbZ+4O7yw1LfTewUSMy1rNtLRFiZm8SeE/XUX7Kmy8KrFL0MEgQZq81W4hH7z5ynpqmNFTl+cPm6CQ4zenuVbISuTv+d14usyEmitqltSPT2sg2Kl9hyuIrU2Agykvzk7nKTtRbK9sAFay6ZV+Yk0+VUthcN82yvD9xdN0BkrNlqPGLL4SpCg/zo7nKTucbo7XXami3tF2UkEBrkGBIZj6YaFBFZISJHRKRURL7Rw+f/LCJFInJIRF4XkYndPusSkYOuh6lpHo3NHew+XseKHD+6u9x84PZ61b/n9RLZKdGMGzNiSFxMg6KmGOpKjS9HC6JqZHddnxbn/d5dV2LqEgiJMDoQW5Co8BCuT4tjy+Eqy09yNM2giEgQ8ASwEsgC7hORrMs2OwDMUdXpwDrgR90+a1HVma6HqVfha8XVdDrVv0t9N/HpRkt7K7u9cpJ4t/QcjS3WbEfuFYo3AGJZd1dhxQXONrSwItuEayA0wpjkWLzBspMcV+QkcbahhYKzjWZLGRS9GhQR+R8R+UVvDy+cex5QqqonVLUdeAb4yOAHVd2hqs2uH/cA47xwXq+zpbCK5FHhzBjn5Vb1/SXzFjizCy6dM+f8g2RFTjIdXcobJcM426t4A0y4xrKt6jcfriTIISzJMkl/5hq4VANl75lz/kGyNDORIIdYfqXe1wolD9jXx2OwjAW657uWu97rjUeAzd1+DheRPBHZIyK9psWIyGdd2+XV1tYOTnEPXGrr5K2jtSzPTvK/u8tN5i3GJMcjm8w5/yCZNX40idFhbC6w9sXkMfUnoLrAsp2FwYifXD0phpjIUHMEpC+HoDDLur3GRIZy7eRYy7u9ejUoqvpk9wfw/GU/+w0ReRCYA/y429sTVXUOcD/wcxGZ0tO+qvo7VZ2jqnPi4+O9rm3nkVraOp3muLvcJOXC6ImWTR92OIQV2Um8ebSW5nZrZuoMCnf8y6LurmPVTRyvvcRKM6+BsCgjhbjkVSPBwYKsyEni5LlLHK2+aLYUj7liDEVErhWRIqDE9fMMEfmVF859Fhjf7edxrvcuP/8S4NvAGlX9oAucqp51PZ8AdgKzvKBpwGwprCI2MpS5qSYWookYd7cndkLrBfN0DILlOUm0dTp584j3V5EBT/EGSJ4BYyZeedsAZIvLTbPMjPhJdzJvMbo0Vx40V4eHLMtORMRwH1qV/gTlfw4sB+oAVDUfuNEL594LpInIJBEJBe4FPrJeFZFZwG8xjElNt/fHiEiY63UcMB8o8oKmAdHa0cUbxdUszTL8n6aSeQt0tcOxbebq8JB5qTGMiQhh63CbkXKhEsrft7S7a/PhKmZPHENidLi5QtJXgjgsm/GYEBXOnIljPjDQVqRfWV6qenlvj0GnUqhqJ/AlYCtQDDynqoUi8n0RcWdt/RgYCTx/WXpwJpAnIvnADuCHqup3g/Ju6TkutXeZ6+5yM24ejEy0rNsrOMjB4sxEXi+pob3TabYc/1Hi+vKzaLpwWX0zRZUXWO7tufGeEBkLE+d/+G9qQZZnJ1FS1cSZuuYrbxyA9MeglInIdYCKSIiI/F8MAzBoVHWTqqar6hRVfdz13qOqut71eomqJl6eHqyqu1Q1V1VnuJ7/4A09A2VrYRVRYcFcNyXOjNN/FIfDaJR3bDt0tJitxiOWZyfR1NrJnhN1ZkvxH8UbIC4d4qeZrcQj3CvK5Wa7u9xk3gK1JXDumNlKPGJZlvHvuM2ihb79MSifA76IkYFVAcx0/Tys6exy8lpxDYsyEwgNDpCGA5m3QMcly45FvSEtjojQoOHj9mquh1PvWDYYD7CtsJqMpCgmxkaaLcUgY5XxbNGV+gRXtw2rXgNX/CZU1XOq+oBrpRCvqg+q6jC6heyZfafPU3+p/YM7ioAg9QYIH2XZiyk8JIgF0+LZXjQ0GuVdkaNbQbssGz85d7GNvNP15gfjuzNqHKRcZXm3V97p89Q2WW8SZX+yvCaLyAYRqRWRGhF5RUQm+0NcILO1sJrQYAc3TfN+KrLHBIdC+gqjHsWijfKWZydR09TGgbIGs6X4npJXISoFUkxJUBw0rxdX41QCI37SnczVxliHxo8ljVqC5dlJqBodOKxGf3w1TwHPAclACvA88LQvRQU6qsrWwiqunxrHSH+16e4vGauh5Tyc2W22Eo9YmJFASJBYdsnfb9qbjdG1GauMtG8LsrWwmrGjR5CVHG22lI+S4VrxlWw0V4eHZCZHMT5mhCWvgf4YlAhV/auqdroefwNMzg80l6JKo29RwN2ZAUxdDMHhll3yR4eHcO2UOLYWWrti+IocfwM6W4y7aQtysa2Td46dM7dDRG/EpxuJDha9BkSE5VlJ7Cqto6nVWv3t+urlFSMiMcBmEfmGiKSKyEQR+RpgzR4fXmJrYTUOgSWZAWhQQiNh8kLj7syiX8jLsxM5Xdds6YrhK1Ky0ZiHPnG+2Uo84s0jtbR3OQPzpgqMlfqpd4zVugVZnpNEe5eTnRYr9O1rhbIPo5/X3cA/YNR77AQ+D9zjc2UBzLbCKuZMjCF2ZJjZUnomY5VRMVx1yGwlHrE0y6gY3mbBJX+/6OqEo5uNeFeQn1u9e4mtrg4Rc8zsENEXGauNhIej1iz0vWrCGOJGhlrO7dVXL69JqjrZ9Xz5Y9gG5c/UNVNS1cSyQL0zA5jmqhi2qA85ISqcWeNHs63IekHJfnFml3Hn7E5xtRjtnU52lNSwODPB/A4RvZEyC6KSLev2CnIIS7MSXb0CrdOSv18FFCKSIyJ3i8gn3Q9fCwtU3AVHAVPI1RORcTD+GssaFDD6QhWcbaSiwZpFmn1SstGIc01dbLYSj9hzoo6mts7Avgbchb6lr1m20HdZVhIX2zrZfdw6VRr9SRv+LvA/rsdCjCFX1uwT4QXchVzjYyLMltI3mauh+jDUnzRbiUcsc83V2D7UVimqhkGZssiId1mQbUVVRIQGMX9qAHSI6IuMVdDRbNlC32unxBIZGmSplXp/Vih3AouBKlV9GJgBmDRJylzchVwBfWfmZtrNxrNFVymT40cyNWGkZVtQ9EplvhHfsqi7y+lUthdVc1N6POEhQWbL6ZvUGyAs2rJur/CQIG6aFs9rFir07Y9BaVFVJ9ApItFADR9tOz9seKO4BqcS2PETNzGTIDHHshcTGKuUPSfqaWy2VupknxzZZMS30learcQjDp1tpPpCmzWugeBQSFsGRzZbdjTwsiyj0De/3BqFvv0xKHkiMhr4X4zMr/2ANavmBsm2ogAt5OqNjFXGSNSL1ko9dLMsO4kup/LGEess+a9IyUaYcK3RGdeCbCusIsghLJpmAYMCxjXQXGfZ0cALpyUQ7BDLuL3608vrC6raoKq/AZYCD7lcX8OK5vZO3j5W60ppDdDMlsvJWGWMBj66xWwlHjF97CgSo8PYVmiNi+mKnD9lxLUs6u4C46bq6kkxjIqwSLpz2lIICrXsjJRRESFcMznWMin0fRU2XnX5A4gBgl2vhxVvHT1HW6fTGkt9N0nTYdR4y86ad3RLnWztsKbL4iOUuP4f3PEti3G89iKlNRc/SJiwBGFRMOkmOGLdQt9l2Ykcr71EaU3gF/r2tUL5aR+Pn/heWmCxvaiaUSNCmBeohVw9IWJ8eR1/A9ovma3GI5ZlJdHS0cW7pefMljJ4SjZCQrYR37Ig7oy7pVZISulOxipjdVjj9xl8XsHdkcMKGY99FTYu7OOxyBsnF5EVInJEREpF5Bs9fB4mIs+6Pn9PRFK7ffZN1/tHRGS5N/T0RmeXk9dLqlmckUBwUIDMPukvGaugs9UwKhbkmsmxRIUFW9/tdanOKGi0srursIqcsdGMHT3CbCkDY9rNgHy4QrQYKaNHMH3cKEtkPJr27SgiQcATwEogC7hPRLIu2+wR4LyqTgV+BvyXa98sjBn02cAK4Feu4/mEvafO09DcYS13l5uJ1xkzUix6MYUGO1iQkcBrxdV0WSR1skeObjHiWRY1KDVNrRwoa2BppsVWJwBRiTBujqUzHpdmJnKwrIGaC61mS+kTM2+35wGlqnpCVduBZ4C1l22zFnjS9XodsFiMiPha4BlVbVPVk0Cp63g+YVtRFWHBDm5MD6DZJ/0lKMToGXV0s2VnpCzNSqTuUjsHzliz0R9gxLGix0HyDLOVeMTrxTWowvIcC95UgWHIKw9CY7nZSjxi2QczUmrMltInZhqUsUBZt5/LXe/1uI2qdgKNQGw/9wVARD4rInkikldb61n6bHunkyWZiUSEBtjsk/6SscrSM1IWTIsnJMg6qZMf44PZJzdbdvbJtsIqxseMYFpilNlSPGOaa2V4ZLO5OjwkPXEkE2MjAt7t1Z/WK/NFJNL1+kER+W8Rmeh7ad5BVX+nqnNUdU58vGcrjMdvy+WX91tzqh4AUxZDUJhls73cM1K2WXVGyokdxuwTi2Z3XWzr5N3SOpZlBeDsk/4Snw6xaZZ1e4kISzMT2VVax8W2wPU09GeF8mugWURmAP8CHAf+4oVzn+WjFffjXO/1uI2IBGO0fKnr575exbIXEkDYSJi8wLiYrPiFjOH2OlXXbInUyY9RssmIY6Veb7YSj3jrqDH7xFLpwj2Rsco1I8UaVeeXsyzbmJHyZgDPSOmPQelU47ZwLfBLVX0C8Ma6dy+QJiKTRCQUI8i+/rJt1gMPuV7fCbzh0rIeuNeVBTYJSAPe94KmoUvGKmg4YxTWWZClrtRJy7m9ujqNlWHacsvOPtlWWMWYiBBmTxxjtpTBkbEKnJ1wbLvZSjxi9sQxxESGBrTbqz8GpUlEvgk8CGwUEQcw6CvDFRP5ErAVKAaeU9VCEfm+iLi7Gf8BiBWRUuCfgW+49i3EmHNfBGwBvqiqQ6DyzYdMW4mROmnNZpFJo8KZMX60ZSqGP6DsPWipt2x2V0eXkzdKalicmWi9lPnLGTsHRiZCyQazlXhEkENYkpnAGyU1dHQ5zZbTI/35C7kHaAMeUdUqDPfSj71xclXdpKrpqjpFVR93vfeoqq53vW5V1btUdaqqzlPVE932fdy13zRVtWakzZ+MTIDxV1vWhwxGs8j88kaqGgM7dfIjlGw04lcWnX3y/sl6LrR2Wt/dBa4ZKSuNBIkOC/0NdWNpVhJNrZ28d6LebCk90p9eXlWq+t+q+rbr5zOq6o0Yio2/yVgFVQVw/rTZSjzigxkpxRZxe6kaBnzyAqMFiAXZVlhFeIiDG9IsmDLfExmrof0inHzLbCUecUNaHCNCggLW7dVXL693XM9NInKh26NJRC74T6KN18iwdurk1ISRTIqLtI7bq7oQGk5b1t2lqmwrquaGtHhGhAb47JP+MulGCB1p2ZV6eEgQN6bHsb2oOiAzHvtqvXK96zlKVaO7PaJU1SL9220+QuwUiM+07MUkIizLSmT38ToaWywwI+XIJkBc8SvrcfjsBSobW1k6FNxdboLDjA7ERzZZekZKZWMrBWcbzZbyMfpTh7Kkh/ce6mlbGwuQsQpO74LmwPTBXoll2Ul0OpWdRwK7YhgwDPf4eUb8yoJsK6rCIR82JxwyZKyGS7VQnme2Eo9YlJFAkEMCsr9df4Lyj4rIr0UkUkQSRWQDcIuvhdn4iIxVoF1wdKvZSjxi1vjRxI20wIyUhjJj3K9F3V0A2wqrmZsaQ0xkqNlSvEvaUnCEWHalPiYylHmpMQEZR+mPQbkJo5jxIPAO8JSq3ulTVTa+I2UWRKVY9mL6cEZKTWDPSHGnZ0+zpkE5de4SR6qbWG61VvX9IXwUTLrB0oW+y7MTOVp9kZPnAmssRX8MyhiMxovHMdKHJ4qly8aHOSLGXXPp60aPKQuyLDuRS+1d7D5eZ7aU3il5FeIzIG6q2Uo8wn33O6TiJ93JWAX1J6D2iNlKPMI9kybQElT6Y1D2AFtUdQUwF0gB3vWpKhvfkrHK6C11YofZSjziuimxRIYGbuokzfVGnCpjtdlKPGZbYTVZydGMj4kwW4pvcPdVs+hKfezoEeSMjQ64zhH9MShLVPWPAKraoqpfwVWxbmNRUq+H8NGWnbMdFhzEgowEthcF6IyUI5uNOFWmNQ1KbVMb+86cH5ruLjfRKTB2tmU7R4CR7bX/zHlqmgKnSLM/hY1nRGSMiMwTkRtF5EZ/CLPxIUNgRsqyrETOXQzQGSklr7pmn8w0W4lHvFZcjSrWHCg3EDJWQ8V+y85IWe6ekVIUOBmP/Ukb/gzwFkbPre+5nh/zrSwbn5O52piRctqa3suFGQmEBAlbA8yHTPslY9xyxirLzz7JSLJmdX+/yXQlq1p0lRKIM1L64/L6Kkbs5LSqLgRmAdbs/2zzIVMWQ/AIy/qQo8NDuG5KHFsCbUZK6evQ2WpZd5d79slyK88+6S9xaRA3DYqt2SzSXej7buk5LrQGRqFvfwxKq6q2AohImKqWANN8K8vG54RGGA0LSzZaNnVyRU4SZfUtFFc2mS3lQ0pehRFjYMJ1ZivxiB0lNcbsk6EcP+lO5i2WLvRdkZNER5eyoyQw3F79MSjlIjIaeBnYLiKvANbsLmjzUTJWwYWzhh/ZgizJTESEwHF7dXXA0S2QvhKCrDkuekthFXEjQ60/+6S/ZK42Eigs2t9u1vgxxEeFBcw10J+g/G2q2qCqjwH/hjGj5FZfC7PxA+krQIIsm+0VHxXG3IkxAXMxceodaG20rLurtaOLnSU1LM1KIsgxxN1dbpJnwqjxlnV7ORyG22vnkdqAKPQd0MQcVX1TVderaruvBNn4kYgYSJ1v2TgKGJlIJVVNnK4LgIrh4vUQEgGTF5qtxCPeLT3HpfYuVuQME3cXuAp9VxuJFG0WHC+N4fZqbu/i7WPnzJYyMIPiLUQkRkS2i8gx1/PH1tciMlNEdotIoYgcEpF7un32ZxE5KSIHXQ9r5mcGAhm3wLmjlq0YdtdKmL5KcXYZK720pUZ8yoJsOVxFVHgw106ONVuKf8lcDV1tUPqa2Uo84prJsUSHB7PlsPkrdbNmen4DeF1V04DX6blQshn4pKpmAyuAn7tiOW7+VVVnuh4HfS95iOJ2zxSvN1eHh4yPiSBnbLT5F1PZ+3CpBjLXXHnbAKSzy8n24mqWZCYSGmzxUb8DZcK1EBFrWbdXSJCDJZmJvFZcbfpo4P7UoXy5pxXEIFkLPOl6/SQ9xGRU9aiqHnO9rgBqgCEyNi6AiE6BcXOhyJoGBWB5VhL7zzRQc8HEiuHiDRAUCmnLzNMwCN4/WU9Dc8fQro7vDUeQ0Yrl2DbobDNbjUcsz0misaXD9NHA/bkVSQT2ishzIrLCS40hE1W10vW6ynWOXhGReUAoRoNKN4+7XGE/E5GwPvb9rIjkiUhebW3toIUPSTLXQNUhqD9pthKPcPv8TXN7qRoGZfJCCLfm7LktrlG/N6UP03u2rLXQdgFO7DRbiUfcmBZPeIjDdNdvf7K8vgOkYWR3fQo4JiI/EJEpfe0nIq+JyOEeHmsvO74CvRZCiEgy8FfgYVV1r+e+CWRgFFzGAF/vQ//vVHWOqs6Jjx+mF8uVyHK5aSy65J+aMJIp8ZFsNsvtVXkQGs98+O9oMZxOZWthFQvSE4bOqN+BMukmCBtl2ZX6iNAgFqQnsLWwCqeJ/e365Sx1felXuR6dGC3t14nIj/rYZ4mq5vTweAWodhkKt8HosSpHRKKBjcC3VXVPt2NXqkEb8CeM9vo2njImFZKmW9agiAgrc5J572Q9dRdNcFkUrTfSr90dbC3GgbIGqi+0sTxniPfu6ovgUJi2wsh47AqMqvOBsjI3iZqmNg6Umdffrj8xlK+KyD7gRxht63NV9fPAbOAOD8+7HnCPEX4IeKWH84YCLwF/UdV1l33mNkaCEX857KEOGzdZa6D8fbhQYbYSj1iZm0SXU9nu73beqkZCQ+r1Rhq2BdlcUElIkLB4qI36HShZa6G1AU69bbYSj1iUkUBokINNBea5vfqzQokBblfV5ar6vKp2ALjcT55WcP0QWCoix4Alrp8RkTki8nvXNncDNwKf6iE9+O8iUgAUAHHAf3iow8aNOzvJokWOWcnRTIyNYJO/3V61JVBXall3l6qy+XAVN6TFEx0eYrYcc5myCEIiLev2igoP4cb0ODYXVJrm9upPDOW7qtpjqxVVLfbkpKpap6qLVTXN5Rqrd72fp6qfcb3+m6qGdEsN/iA9WFUXqWquy4X2oKpasyIpkIif5mqUZ82LSURYkZPErtJzNDb70WVRtB4Qyw7TOlTeyNmGFm7OTTZbivmEjID0ZYbby2l+1bknrMxJpqKxlfxyc/r3DrOEc5s+yVpjtLO/aM1suJtzkul0KtuL/ej2KnrZqGOIsma67aaCSoIdwtLh7u5yk7kGLtXCmd1mK/GIJZmJhASJaQkqtkGx+ZCstaBOy7ZimT5uFGNHj2BzQeWVN/YGtUegpgiyrdnaTlXZdLiS+VPjGBUxzN1dbtKWQXC4Zd1eoyJCmD81jk0FlaaMdbANis2HJOZA7FQofMlsJR7hdnu9fewcTf6YD1H4MiCWrY4vrLhAWX0Lq2x314eEjYSpSwzXr9PcqnNPuTknmfLzLRw+e8Hv57YNis2HiEDWrUaWi0XdXitzkmjvcvJ6sR/mQxS9DBOugWhrfiFvLKgkyCEszbLdXR8h61ZoqoSy98xW4hHLshMJdgibDvtppd4N26DYfJTs2wy3l0WD81dNGENSdDivHvLxxVR71HB3ZVnX3bW5oJLrpsQyJjLUbDmBxbQVhtvLoiv10RGhXDslls0muL1sg2LzURKzITbNsheTwyHcnJvMW0draWzxodur6GXj2aLpwsWVTZyqa7azu3oiLMroGl30smWzvW7OTeZUXTOFFf51e9kGxeajiBirlNPvwsXAGCs6UFbPSKa9y8lrvixyLHwZxl9jNNe0IK8eqiDINZzJpgeyb4OL1ZbN9lqenUSwQ3y/Ur8M26DYfJzsWy3t9po1fjRjR4/g1UM+qvqvPQo1hZbO7tpwqIL5U+OIHdlrX9XhTfoKCB5h2ZV6TGQo86fG8eqhCr+6vWyDYvNxErIgLt2VxWQ9RIRV05N5+9g5Gpp9MFzU7e6yaHbXofJGyupbWD3ddnf1SmgkpC+Holegq9NsNR5xy4wUys+3cLDMf0WOtkGx+Thut9epd6DJ/ClwnrB6ulHk6PV23qpQsA4mXAejxnr32H5iQ34FIUEyPGefDITs24wix9Pvmq3EI5ZlJxIa5PCr28s2KDY9k30boMYdmgXJHTuKCTER3r+Yqg/DuSOQ62lfVHNxOpVXD1VyU3oCo0bYxYx9krbM6O1lUbdXdHgIN02LZ+Mh//X2sg2KTc8kZBqFjgXPm63EI0SE1dOT2XW8zrst7QvWGa3qLZounHf6PFUXWrllhu3uuiKhEUYKcfF6y7a0Xz09maoLreSd9k9Le9ug2PROzh1QvteykxxXT0+hy6ne62ukCodfhCkLITLOO8f0M68eqiA8xJhBbtMPcu6E5jo4vsNsJR6xJDOR8BAHG/L9M5bCNig2vZPjcuscfsFcHR6SmRzFlPhI1h/00sVU9r4xmTHnTu8cz890djnZVFDJ4oxEIsOCzZZjDaYugfDRll2pR4YFszgjkc2HK+ns8n0rGdug2PTOmIlGrUXB88bducUQEW6dOZb3T9VTfr558Ac8vM6ooM5YNfhjmcCeE/Wcu9huZ3cNhOBQIz28ZCO0XzJbjUfcMiOZ880dFFc2+fxcphgUEYkRke0icsz1PKaX7bq6Ddda3+39SSLynoiUisizrumONr4g905jiFR1odlKPGLtTCMTa/1gl/xdnUZwNm0ZhEd7QZn/efngWaLCglmYkWC2FGuRexd0XIIjm81W4hELMxJ4/1uLyR03yufnMmuF8g3gdVVNA153/dwTLd2Ga3VP+v8v4GeqOhU4DzziW7nDmOzbjCC0RZf8E2IjmD1xDC8fODu4Aq9TbxkppLnWdHe1tHexuaCSlblJhIcEmS3HWky4DqLHwqHnzFbiEWHBQX4rYDXLoKwFnnS9fhJjLny/cM2RXwS458wPaH+bARIZZwShD79o2Xbet85M4Wj1xcEt+QvWQWiUsUKxINuLq7nU3sWtszLuZCIAABydSURBVKxZO2MqDocRTzz+OlyqM1tNQGOWQUlUVXeBQBXQW8pJuIjkicgeEXEbjVigQVXd5avlgH2V+JLcu4xgtEXbea+ankKwQ3jl4FnPDtB+yajHyV5rjIm1IC/tLyd5VDjXTIo1W4o1mX43ODuhyJo1Kf7CZwZFRF4TkcM9PNZ2304NP0RvvoiJqjoHuB/4uYhM8UDHZ11GKa+21pozPkwnYxWERED+02Yr8YiYyFBuTI9nfX6FZwVeJRuh/SLMuM/74vzAuYttvHXsHGtnjsXhELPlWJPEHIjPgEPWdP36C58ZFFVdoqo5PTxeAapFJBnA9dxjW1tVPet6PgHsBGYBdcBoEXHnPY4Der31VNXfqeocVZ0THx/vtd9vWBEWBZm3GEHpjhaz1XjErbPGUtnYynsn6we+c/7TMGqC4Uu3IBvyK+hyKrdfZS/kPUbEWKmX7bFsXZY/MMvltR54yPX6IeBj/T1EZIyIhLlexwHzgSLXimYHcGdf+9t4mZn3Q9sF427dgizNTCQyNIgX95cPbMcLFXBiJ8y41/ClW5CXD5wlKzma9MQos6VYmxn3AgL5z5itJGAx6wr5IbBURI4BS1w/IyJzROT3rm0ygTwRyccwID9U1SLXZ18H/llESjFiKn/wq/rhSOqNED3Osm6vEaFBrJqezMaCSi61DaB7bMHzRiv/Gff6TpwPOV57kfzyRnt14g1GjYPJCyD/KcsmqPgaUwyKqtap6mJVTXO5xupd7+ep6mdcr3epaq6qznA9/6Hb/idUdZ6qTlXVu1TVi82abHrE4YAZ98DxN+CC/2dVe4O75oynub2LTQX91K8KB5+GcfMgdsDhu4Dgxf3lOATWzLDmILCAY+YD0HAGTr9jtpKAxJpreBtzmHGfcbdeYM18/DkTxzApLpLn9/XT7VWZD7XFll2ddHY5WbevnJvS40mIDjdbztAgYxWERcPBp8xWEpDYBsWm/8SlGXfrB5+2bCuWO2eP4/2T9Zyu60cbjfynISjU1crferx97BzVF9q4Z+54s6UMHUIjIOd2I428zfetTKyGbVBsBsbM+4y79sqDZivxiNuvGotDYN2VVikdrXDoWeOONCLGP+K8zLN7y4iNDGVRht1Z2KvMfAA6mi07K8iX2AbFZmBk3240SNz/V7OVeETyqBFcnxbPC/vK6eqrJqV4A7Sch6se6n2bAObcxTZeK67m9qvGEhpsX+ZeZdxciE2z3V49YP+l2QyMEaMNF9Ch5yzbffWu2eOoaGxl1/FzvW+0/0kYPREm3eQ/YV7kpf1n6XQqd8+x3V1eR8RIoz/9LtQdN1tNQGEbFJuBM/tT0N5k2TkpS7MSGTUihGf3lvW8Qd1xOPU2XPVJS9aeqCrP5pUxa8Jo0uzaE98w835wBMO+P5mtJKCw3tViYz7jrzbaUOz7s9lKPCI8JIg7rhrH1sIqapt6yDjf/6TRYXnWg/4X5wX2n2mgtOYi99irE98RlWTE1w783Yi32QC2QbHxBBGY/TCc3QeVh8xW4xEPXDOBji7lubzLVimd7YZvPH2F8aVhQZ55/wwjQoJYbdee+JbZD0NLvRFvswFsg2LjKTPuMYLzFl2lTIkfybWTY3nqvTMfDc4f3WzMPZltzWD8+UvtrM+v4LarxjLSHvPrWybdBDGTIe+PZisJGGyDYuMZI8ZYPjj/4DUTOdvQwptHu/Um3fdnY5jS1CWm6RoMz+8ro63TySevnWi2lKGPw2GsUs7sgppis9UEBLZBsfEcd3C+YN0VNw1ElmUnEh8Vxt/2nDHeqD1qtJaZ/TA4rDfVsMup/HXPaealxpCRZM0xxZZj5gNG8WueHZwH26DYDIbxV0NCNrz/O0tWzocEObh37nh2HKmhrL7Z+D2CQg1DaUHePFpDWX0Ln7zOXp34jchYyLrV6EBs0ZW6N7ENio3niMA1n4fqw0aarQW5b94EBHhhV6ERjM+5E0Zac27OX3afJiEqjOXZ1kwmsCxzH4G2RrutPbZBsRksuXdBRCzs+bXZSjwiZfQIlmQm0rnvr/z/9u48PIoyW+Dw75AEQthJIgJhiQhqRpAlLLJEFBUcUUZUFK+ICFdwdOR6AcUZGAV1RtQRXHGEAVEYFFzADRBXFBDZhbDILmEnRJCwheTcP77CGzIJCaS7K8t5nydPuqurq05h2ydffcshIx1a3+t3SOdkW2o63/y0j56t6hIRZv9bh1Sd1lCrOXz/aqlf1t4+eaZwIiIhsS+sn1VsZw3/d/t63Jo5i71Vm0KtZn6Hc07eWriNMBHuaF3X71BKHxG4/H5I3Qgb5/odja8soZjCa9nXzRr+4XW/IzkniScWU6/MXl49evWZ1/cqog4ezWDqDz9zfZOa1LBl6v2R0M0VoFv4st+R+MqXgeoiUh14B6gPbAV6qGpajn2uBEZn23QxcLuqzhCRN4ArgIPea3er6jktf5uRkUFKSgrHjpWM2a6RkZHExcURERERupNWOh8uvRmWT4Yr/wyRVUJ37gCQRWM5Wv58Jqc1oc2aPXS5tHj1QUz+fhvpJzLpn1Q8i4CVCGER7nbp3L+6yb41m/gdkS/8mvk0FPhCVZ8WkaHe80ey76CqXwFN4bcEtBH4LNsuQ1S10ONVU1JSqFSpEvXr10dECns4X6kqqamppKSkEB8fH9qTtxkAP77tViFu+0Boz10YKUthyzzKdnqc8xdUYvy3m4tVQjmWkcnE+VtJahRLQi0bKuyr5r3h61GuL+Wm1/yOxhd+3fLqBkzyHk8C/pDP/rcAs1T1SKADOXbsGNHR0cU+mYArIBUdHe1Pa6tWM6jfwTX5i9PaRt/+AyKrEtaqH/e0i2fJtjSW/ZyW//uKiPeX7WD/4eMMSLrA71BM+arQvJebl1VMy2QXll8JpYaqnvoX3w3kVwHodmBqjm1PiciPIjJaRMrl9UYRuVdElojIkn379uW1T0HjLvJ8vZakwfDrLlgx2b8YzsaeNbD+E2g9AMpVokfLOlSKDGfcvM1+R1YgmVnKuG830ySuCpc3iPY7HANuGL1mwYIX/Y7EF0FLKCLyuYiszuWnW/b9VFWBPHtCRaQm0BiYk23zo7g+lZZAdXLcLstx/NdVNVFVE2Nji+f8gmIj/gpXIvi7MW6RxaLuu9EQUQFa9wegYrlwel9en1mrd7Nu9yGfg8vfZ8m72bI/nf5JDUrUH0XFWrX6cFlPt77Xr3v8jibkgpZQVPVqVb00l5+ZwB4vUZxKGHvPcKgewAeqmpHt2LvUOQ5MBFoF6zr80K9fP9asWeN3GGdPBK54GA5ud+Vzi7IDm2H1u9DyntNK/PbrEE+lcuGMmbvBx+Dyl5WlvPzVRupFRxWrPp9SIWkQZGbA/Bf8jiTk/Lrl9SFwajnX3sCZijP3JMftrmzJSHD9L6uDEKNvxo8fT0JCgt9hnJsLr3b9Kd/+AzJP+h1N3ua/4IY6X376AIKqUWXp0z6e2cm7Sd55MI83+2/W6t0k7zzEwE4NCStjrZMipfoF0OQ2WPKvUtdK8WuU19PANBHpC2zDtUIQkURggKr2857XB+oA3+R4/xQRiQUEWAEMCERQIz5KZs3OwN7qSKhVmcdu+F2er6enp9OjRw9SUlLIzMxk+PDhjB07lueee47ExEQqVqzIwIED+fjjjylfvjwzZ86kRo38upx8JAJJQ+DtO1xFx8tu8zui/5S6yQ1xbt4715onfdvHM3H+Fl74fAOv35XoQ4BndjIzi+fnrqfheRXp1rS23+GY3CQNdq30BS9C56f8jiZkfGmhqGqqqnZS1YberbED3vYlp5KJ93yrqtZW1awc779KVRt7t9DuVNXDob6GQJk9eza1atVi5cqVrF69mi5dupz2enp6Om3atGHlypUkJSUxbtw4nyI9C42ugxqN4eu/wclcKiL67csn3CKQVzyc68tVykfQr/0FfLZmD6t3FL1WygfLd7BpXzqDrm1krZOiKroBNOkBi0tXK8Uq8GRzppZEsDRu3JhBgwbxyCOP0LVrVzp06HDa62XLlqVr164AtGjRgrlzi8HSDmXKwDUjYHJ3+GFc0ZqXkrIUkj+ApIfPWJGxT/v6TJi/hefn/sSEu1uGMMAzO34ykzGfb6Bx7Sq2CGRRlzQEVk2Hr/8ON4zxO5qQsKVXfNaoUSOWLVtG48aNGTZsGCNHjjzt9YiIiN9G8ISFhXHyZBHul8juwk6uP2XeM3DkgN/ROKpuJnNUDLR78Iy7Vo6M4L6ODfhy3V7m/ZT7cHM/vP3Ddnb8cpQhnS+ykV1FXXQDaNkPlk2C3SWqmzdPllB8tnPnTqKiorjzzjsZMmQIy5Yt8zukwLn2STj+K3zzjN+ROBs+g23fQcehUK5Svrv3aVef+tFRjPgomYxM/1eRTUs/wZjPf6J1fHU6NIzxOxxTEFc84pYimj20WNYMOluWUHy2atUqWrVqRdOmTRkxYgTDhg3zO6TAOe8SaH4XLB7n/0rEmRmudVK9QYELaJULD2N41wQ27Utn0oKtQQ2vIJ6Zs55Dx07y+I2/s9ZJcRFVHa78i6sXtO4Tv6MJOtFSkDVPSUxM1CVLlpy2be3atVxyySU+RRQcReqaDu+FF5tB/fbQ8203CswP855znfG3T4WLf1/gt6kqd09czLJtaXw1pCMxFfNclCGolv+cRvexC7inXTzDuxbTIeWlVeZJeK2dG6By/yII9+czVBgislRV8x3yaC0UE1wVz4OOj8JPs90wYj/s3+huuyV0O6tkAm4pm7/ekMDRjEyemb0uSAGeWWaWMmzGas6rVI7/ubqhLzGYQggLh85/g7QtJX6yoyUUE3xt7oO4lvDpENdiCaWsLPhooCsEdt2z53SIBrEV6ds+nmlLUnzpoJ/8/TaSdx5ieNcEKkWGsCyBCZwLO7ny0t+Mgl0rQ3/+EFWStIRigq9MGHR7BU4chk8Hh/bcy99yHfHXPAGVzn1C6EPXNKLheRUZPH0laemhW6ds877DjJq9jg4NY7i+cc2QndcEwe+fdeWyPxgQ2vlZKUvdLbf9G4N+KksoJjRiL3Kjq9bMhOQZoTnngS3w2XC3rH7zuwp1qMiIMEbf1pS0IycYNmM1oeh7PH4ykz9NXU7Z8DKMurmJdcQXd1HV4caXYO8aNzclFI6mwfS73WjLbGvWBYslFBM6bQdCzabuFlSwR32dOALv9HKL89z4UkAGA1xauwoPXdOIT1btYsaKHYWPMR9Pz1pH8s5DPHvLZdSqWj7o5zMh0KgzNOvl+lK2LQzuuVRhxv2upMStb1hCMSVMWLj7YIu4tb6O/xqc86jCxw/BntXQfTxUD1z1yv5JDWhZvxrDZyQHdYn7uWv2MHH+Vu5uW59rEorw2m3m7HX+G1StC9N6Qdq24J1n4Suu3s81IyEuNGvSWUIxoVU93iWV/Rvg/f7B6SxcPN6VI+44FBpdG9BDh5URXuzZjIrlwrl7wmJ2HTwa0OMDrNl5iEHTVvC7WpV59PcXB/z4xmeRleGOaa5m0L9vg2NBWC9u63z4/DG4uKsbFBMillBM6F3Q0f2Vtv4TNzckkNZ+7GYlN7zWrdcVBDWrlGdin5YcPn6SPhMXc+hYRv5vKqAt+9O5a8IiKpQL57U7W1AuPCxgxzZFSOxFcNubkLoBpvcJbKmHlCUuUVWLd4NhQtj3ZotDZjdrKOxeFdhjnt8Yrnv6jLu8+eabPPfcc4gITZo04YknnuCee+5h//79xMbGMnHiROrWrcv06dMZMWIEYWFhVKlShXnz5gU21lBq3d91Tn73PGSddM3ywn7wkz+A9/q5fpru49wilUFySc3KjL2zOX0mLua+yUsZd1ciUWUL97/Tzl+Ocuf4RWQpvNW3NXWqRwUoWlMkXdARrv+H61OcMQC6vQrhZQt3zJ3L4a3uUCEGen/o6tyHkCUUnyUnJ/Pkk0+yYMECYmJiOHDgAL179/7tZ8KECTz44IPMmDGDkSNHMmfOHGrXrs0vv/zid+iFIwJdR0NYhKsZcTQNuo5x/Szn4sdp8EF/V4L4v6a72wpB1qFhLM/c0oTB01dyy9iFjO+deM6d5z/t+ZV731zCoaMZTL23DReeVzHA0ZoiqcXdbvHUL0bAr7vhtregfLVzO1bKUrfCd2QV6P0RVK4V0FALwpZe8XmZkpdeeondu3fz1FP/X4QnJiaGXbt2ERERQUZGBjVr1mT//v0MGDCATZs20aNHD7p37050dHSux/T7ms6KqhtC+c0oaNQFbnjx7OaLnDgCXz3lOiDrtYM73oFyof0y/mr9Xh7893LKRYQx7q4WNKt7dl8I7y5NYdiMVVQsF8E/ezWnRb3gj8YxRcyP02DGH121xzveObuBJFlZ7o+yL5+ASjVdMgngQBQo4kuviMitIpIsIllelca89usiIutFZKOIDM22PV5EFnnb3xGRQrYTi4fXXnuNJ598ku3bt9OiRQtSU1P9DqnwRODKP7tZ7Ju+hJcTYdHrkJWZ/3t//h5eaw8LX4bEPq5lEuJkAnDlRefx/h/bElU2jB7/XMjjHyaz99dj+b5v+4EjDJq2ksHTV9K0TlU+Hdjekklp1aQH9HrftVJebQOfj4BjBRhFeGAzvPUHrwP+eug/L+DJ5Gz41Sm/GugO5NkJICJhwCvAdUAC0FNETq2KNwoYraoXAmlA3+CGGzxXXXUV06dP/y05HDhwgLZt2/L2228DMGXKlN+Kbm3atInWrVszcuRIYmNj2b59u29xB1zre+G+hVC7OcwaAmPbufW3dq44fdnvw/tc0a5/dYYJnd0qwnfNdLfPyvrX59CwRiVm3t+Om5vH8db327jima/5+6drmb9xPwePuE57VSX18HEWbNzPH6cs5Ypnv2LGih08cOWFTO7bmvMqRfoWvykC4pPgvvlwyY2ub/Gl5vD107DlW8jwRhOqwtFf3ATht25yC6+mLHZzrW6dFJK5Jmfi6y0vEfkaGKyqS3J57XLgcVXt7D1/1HvpaWAfcL6qnsy535kUxVteAJMmTeLZZ58lLCyMZs2aMWLECPr06fMfnfLdu3dnw4YNqCqdOnVizJgxuc6eLgrXdM5UIfl9dwtrxzJAoaxXuyTzOGR6y57EXgKNb3Gd+wWobRJKW/anM+bzn/hw5c7fcuH5lSM5eDSDoxmu5VWlfAQ9W9Wld9t61KxikxZNDjuWwtzHYOt3gEKZCLdsy5FUyPJGFVaOcytANO8V9P6Sgt7yKsoJ5Ragy6ka8yLSC2gNPA5877VOEJE6wCxVvTSPc9wL3AtQt27dFtu2nT6RqFh/+eahxFzT4X2wca4buVIm3NWBj6zs+lpqhL5c89lKSz/Bqh0HWbXjIJv2HqZahbLUrlqeuGrlad8wptCjwkwpcDQNfl4EPy9wnfcVYlzF0diLocGVbp28EChoQgnaJ1pEPgdyK3r9F1WdGazz5qSqrwOvg2uhhOq8JgAqxkLTO9xPMVStQlmSGsWS1CjW71BMcVW+GlzUxf0UA0FLKKp6dSEPsQOok+15nLctFagqIuGqejLbdmOMMT4qyjPlFwMNvRFdZYHbgQ/V3aP7CrjF2683UKgWT0kaOl2SrsUYU7z4NWz4JhFJAS4HPhGROd72WiLyKYDX+ngAmAOsBaaparJ3iEeA/xWRjUA08K9zjSUyMpLU1NQS8UWsqqSmphIZaaOFjDGhV+onNmZkZJCSksKxY/nPGygOIiMjiYuLIyLCKvsZYwLD90754iIiIoL4eP8mAhljTElRlPtQjDHGFCOWUIwxxgSEJRRjjDEBUao65UVkH3CuNTdjgP0BDKe4KI3XXRqvGUrndds1F0w9Vc13hm6pSiiFISJLCjLKoaQpjdddGq8ZSud12zUHlt3yMsYYExCWUIwxxgSEJZSCe93vAHxSGq+7NF4zlM7rtmsOIOtDMcYYExDWQjHGGBMQllCMMcYEhCWUAhCRLiKyXkQ2ishQv+MJNhGpIyJficgaEUkWkYF+xxQqIhImIstF5GO/YwkVEakqIu+KyDoRWeuV1S7RROQh77O9WkSmikiJXKJbRCaIyF4RWZ1tW3URmSsiG7zf1QJ1Pkso+RCRMOAV4DogAegpIgn+RhV0J4FBqpoAtAHuLwXXfMpAXLmE0uQFYLaqXgxcRgm/fhGpDTwIJHqlw8Nw9ZZKojeAnOUehwJfqGpD4AvveUBYQslfK2Cjqm5W1RPA20A3n2MKKlXdparLvMe/4r5gavsbVfCJSBxwPTDe71hCRUSqAEl4NYVU9YSq/uJvVCERDpQXkXAgCtjpczxBoarzgAM5NncDJnmPJwF/CNT5LKHkrzawPdvzFErBl+spIlIfaAYs8jeSkBgDPAxk+R1ICMUD+4CJ3q2+8SJSwe+ggklVdwDPAT8Du4CDqvqZv1GFVA1V3eU93g3UCNSBLaGYPIlIReA94H9U9ZDf8QSTiHQF9qrqUr9jCbFwoDkwVlWbAekE8BZIUeT1GXTDJdNaQAURudPfqPzhlVQP2NwRSyj52wHUyfY8zttWoolIBC6ZTFHV9/2OJwTaATeKyFbcbc2rRGSyvyGFRAqQoqqnWqDv4hJMSXY1sEVV96lqBvA+0NbnmEJpj4jUBPB+7w3UgS2h5G8x0FBE4kWkLK7z7kOfYwoqERHcPfW1qvq83/GEgqo+qqpxqlof99/4S1Ut8X+1qupuYLuIXORt6gSs8TGkUPgZaCMiUd5nvRMlfCBCDh8Cvb3HvYGZgTpwqS8BnB9VPSkiDwBzcKNBJqhqss9hBVs7oBewSkRWeNv+rKqf+hiTCZ4/AVO8P5g2A318jieoVHWRiLwLLMONaFxOCV2CRUSmAh2BGBFJAR4DngamiUhfXDmPHgE7ny29YowxJhDslpcxxpiAsIRijDEmICyhGGOMCQhLKMYYYwLCEooxxpiAsIRiTAiJyIKz2PdrEUnMZ5+tIhJzFse8W0ReLuj+xpwNSyjGhJCqlqYZ2aaUsYRiTC5EpKWI/CgikSJSwaudcWku+80QkaXe6/d62+p5tSZiRKSMiHwrItd6rx32ftcUkXkissKrydEhn3jGisgS7zwjcrz8sIisEpEfRORCb/9YEXlPRBZ7P+0C8g9jzBnYTHljcqGqi0XkQ+BJoDwwWVVX57LrPap6QETKA4tF5D1V3SYio4CxwA/AmlxWs70DmKOqT3k1d6LyCekv3nnCgC9EpImq/ui9dlBVG4vIXbgVk7viapyMVtXvRKQubqWHS87+X8KYgrOEYkzeRuLWcjuGK8iUmwdF5CbvcR2gIZCqquNF5FZgANA0l/ctBiZ4i3DOUNUVueyTXQ+vBRQO1MQVezuVUKZm+z3ae3w1kOCWqgKgsrd6tDFBY7e8jMlbNFARqAT8R4lYEemI++K+XFUvw60JFem9FoVbmRrvGKfxCh8l4VaufsNrXeRKROKBwUAnVW0CfJIjHs3lcRmgjao29X5qq+rhfK/YmEKwhGJM3v4JDAemAKNyeb0KkKaqR0TkYly55FNGee/7KzAu5xtFpB6wR1XH4SpEnmnJ+Mq4OiUHRaQGrhx1drdl+73Qe/wZbtHHU+fLrZVkTEDZLS9jcuG1GDJU9d9ev8UCEblKVb/MtttsYICIrAXWA997770CaAm0U9VMEblZRPqo6sRs7+0IDBGRDOAwkGcLRVVXishyYB2ueuj8HLtUE5EfgeNAT2/bg8Ar3vZwYB7u9psxQWOrDRtjjAkIu+VljDEmICyhGGOMCQhLKMYYYwLCEooxxpiAsIRijDEmICyhGGOMCQhLKMYYYwLi/wCPZGXLKXK6igAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "\n",
    "plt.plot(x, y_sin)\n",
    "plt.plot(x, y_cos)\n",
    "plt.xlabel('x axis label')\n",
    "plt.ylabel('y axis label')\n",
    "plt.title('sin and cos')\n",
    "plt.legend(['sin','cos'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be40f2dc",
   "metadata": {},
   "source": [
    "#### Subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4a75609d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)  # (2,1) 형태 플랏의 첫번째 자리에 그리겠다\n",
    "plt.plot(x, y_sin)\n",
    "plt.title('sin')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(x,y_cos)\n",
    "plt.title('cos')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d40cada",
   "metadata": {},
   "source": [
    "### 3. 파이토치 기초"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4275eff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "22c40aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2, 3, 2])\n",
      "값:\n",
      "tensor([[[0.7133, 0.2135],\n",
      "         [0.2139, 0.9812],\n",
      "         [0.9667, 0.1235]],\n",
      "\n",
      "        [[0.2139, 0.6618],\n",
      "         [0.5396, 0.5573],\n",
      "         [0.1547, 0.8464]]])\n",
      " \n",
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2, 3])\n",
      "값:\n",
      "tensor([[-7.5727e-01, -1.8851e+00,  4.2798e-01],\n",
      "        [-8.3425e-01,  7.1040e-04, -5.1942e-01]])\n",
      " \n"
     ]
    }
   ],
   "source": [
    "x = torch.rand(2,3,2)  # uniform: 0~1 사이 모든 값들이 나올 확률이 같음\n",
    "print_val(x)\n",
    "\n",
    "x = torch.randn(2,3)  # Gaussian: 0을 평균으로 하는 가우시안 분포를 따르는 랜덤값\n",
    "print_val(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "97d06e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([4])\n",
      "값:\n",
      "tensor([3, 2, 1, 0])\n",
      " \n"
     ]
    }
   ],
   "source": [
    "x = torch.randperm(4)  # 0~n-1 까지 값을 원소로 갖는 배열 → 랜덤 배열\n",
    "print_val(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b67e150f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([6])\n",
      "값:\n",
      "tensor([0.0000, 0.5000, 1.0000, 1.5000, 2.0000, 2.5000])\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# [start, end) 구간을 step 단위로 나눠서 배열로 생성\n",
    "x = torch.arange(0, 3, step=0.5)\n",
    "print_val(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f3d4589a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([3, 4])\n",
      "값:\n",
      "tensor([[1., 1., 1., 1.],\n",
      "        [1., 1., 1., 1.],\n",
      "        [1., 1., 1., 1.]])\n",
      " \n",
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([3, 4])\n",
      "값:\n",
      "tensor([[0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.]])\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(torch.ones(3,4))\n",
    "print_val(torch.zeros(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f5df1c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2, 4])\n",
      "값:\n",
      "tensor([[-3.8919e-28,  4.5722e-41, -3.8919e-28,  4.5722e-41],\n",
      "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00]])\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(torch.Tensor(2,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "468fd572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2])\n",
      "값:\n",
      "tensor([2., 3.])\n",
      " \n",
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2, 2])\n",
      "값:\n",
      "tensor([[2., 3.],\n",
      "        [3., 4.]])\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print_val(torch.Tensor([2,3]))\n",
    "\n",
    "print_val(torch.Tensor([[2,3], [3,4]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3ab5c3ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2, 3])\n",
      "값:\n",
      "tensor([[ 9.2754e-39,  4.9720e-40, -3.7616e-37],\n",
      "        [ 1.6016e-37,  3.1174e-40, -3.7619e-37]])\n",
      " \n",
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2, 3])\n",
      "값:\n",
      "tensor([[0, 0, 0],\n",
      "        [0, 0, 0]], dtype=torch.int32)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# 텐서 타입 변환\n",
    "x = torch.FloatTensor(2,3)\n",
    "print_val(x)\n",
    "\n",
    "x = x.type_as(torch.IntTensor())\n",
    "print_val(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6508a5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 4, 5])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 텐서 크기 반환\n",
    "torch.FloatTensor(3,4,5).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1dc946ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type: <type 'numpy.ndarray'>\n",
      "Shape: (2, 3)\n",
      "값:\n",
      "[[4625534587281552180 4625534587281552180 4625534587281552180]\n",
      " [4625534587281552180 4625534587281552180 4625534587281552180]]\n",
      " \n",
      "Type: <class 'torch.Tensor'>\n",
      "Shape: torch.Size([2, 3])\n",
      "값:\n",
      "tensor([[4625534587281552180, 4625534587281552180, 4625534587281552180],\n",
      "        [4625534587281552180, 4625534587281552180, 4625534587281552180]])\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# numpy array → torch tensor\n",
    "import numpy as np\n",
    "np_x = np.ndarray((2,3), dtype=int)\n",
    "print_val(np_x)\n",
    "\n",
    "x = torch.from_numpy(np_x)\n",
    "print_val(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36701d9",
   "metadata": {},
   "source": [
    "### 4. MNIST 데이터로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fa717b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "371cf3eb",
   "metadata": {},
   "source": [
    "#### 0) MNIST 는 어떻게 생겼나?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a3fbf77c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6edb4d677ba840ef89e0c892b5323381",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style=u'info', max=1), HTML(value=u'')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST/raw/train-images-idx3-ubyte.gz to MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c617cfac48744539bf0b05d73da4a28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style=u'info', max=1), HTML(value=u'')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST/raw/train-labels-idx1-ubyte.gz to MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61e0e178954341f6be32952706a65af3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style=u'info', max=1), HTML(value=u'')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST/raw/t10k-images-idx3-ubyte.gz to MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0684b77f4bf4599bbe1a629a0feb15a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style=u'info', max=1), HTML(value=u'')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST/raw/t10k-labels-idx1-ubyte.gz to MNIST/raw\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# 1. MNIST 로드\n",
    "mnist_train = dset.MNIST(\"\", train=True, transform=transforms.ToTensor(), target_transform=None, download=True)  # train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4c9d2ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28]) 5\n",
      "torch.Size([1, 28, 28]) 0\n",
      "60000\n",
      "mnist_train 길이: 60000\n",
      "5\n",
      "torch.Size([1, 28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/mZWn7D2/mZXE4TfLVCnhl3SOpJ9L2iTpmjJ6qEbSZkmvFtOOlzq/YDEH4nZJG3otO1rSk5LeKH73OUdiSb11xLTtiWnlS33vOm26+7af80s6FPgF8HVgC/ACMCMiXm9rI1VI2gxMiojSLwiR9AfAXmDpganQJN0M7IyIfyj+xzkqIv6mQ3pbwACnbW9Rb9Wmlf82Jb53zZzuvhnK2PNPBjZFxJsR8QmwHJheQh8dLyKeBXYetHg6sKR4vITKP562q9JbR4iInohYVzzeAxyYVr7U9y7RVynKCP/xwDu9/ryFEt+APgTwE0kvSppddjN9GN1rWrT3gNFlNtOHmtO2t9NB08p3zHtXz3T3zeYP/D7vjIg4Dfgm8J3i8LYjReWcrZPGahcCX6Yyh2MPcGuZzRTTyq8EvhsRu3vXynzv+uirlPetjPBvBU7o9eexxbKOEBFbi9/bgUepnKZ0km0HZkgufm8vuZ//FxHbImJfROwH7qHE966YVn4l8GBEPFIsLv2966uvst63MsL/AjBe0pckHQZcCqwuoY/PkXRk8UEMko4EvkHnTT2+GphVPJ4FrCqxl8/olGnbq00rT8nvXcdNdx8Rbf8BplH5xP+XwN+W0UOVvn4HeLn4ea3s3oBlVA4D/5fKZyN/AhwDPAW8AfwncHQH9favVKZyf4VK0MaU1NsZVA7pXwHWFz/Tyn7vEn2V8r758l6zTPkDP7NMOfxmmXL4zTLl8JtlyuE3y5TDb5Yph98sU/8HgEwuJV+2f/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rsbv4uR9YReM0pZ/sOzxCcvFzf831fCgi9kXE+xHxAXAvNe67Ylj5lcCKiHismF37vhuvrrr2Wx3hXwfMkTRb0jHAV4HVNdTxCZKOKy7EIOk44CL6b+jx1cCyYnoZ8ESNtXxMvwzb3mxYeWred3033H1E9PwFLKZxxf9/gL+so4Ymdf0a8N/Fa1PdtQGP0DgM/AWNayPXACcBTwPbgH8HZvRRbf9KYyj3F2kEbbCm2s6jcUj/IrCheC2ue9+V1FXLfvPjvWZJ+YKfWVIOv1lSDr9ZUg6/WVIOv1lSDr9ZUg6/WVL/Dzr6jNe7Wpo9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. 그려보기\n",
    "# MNIST data 하나 형태 출력\n",
    "image, label = mnist_train.__getitem__(0)\n",
    "print image.size(), label\n",
    "\n",
    "image, label = mnist_train[1]\n",
    "print image.size(), label\n",
    "\n",
    "print mnist_train.__len__()\n",
    "print \"mnist_train 길이:\", len(mnist_train)\n",
    "\n",
    "# 그리기\n",
    "print mnist_train[0][1]  # label\n",
    "print mnist_train[0][0].size()  # image\n",
    "\n",
    "for i in range(3):\n",
    "    img = mnist_train[i][0].numpy()  # image 타입을 numpy로 변환 (1,28,28)\n",
    "    plt.title(\"label: %d\" %mnist_train[i][1])\n",
    "    plt.imshow(img[0], cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda544ba",
   "metadata": {},
   "source": [
    "#### 1) MNIST train, test dataset 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "519ba9e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"\": 현재 폴더에 MNIST 있음\n",
    "mnist_train = dset.MNIST(\"\", train=True, transform=transforms.ToTensor(), target_transform=None, download=True)  # train\n",
    "mnist_test = dset.MNIST(\"\", train=False, transform=transforms.ToTensor(), target_transform=None, download=True)  # test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27347ef0",
   "metadata": {},
   "source": [
    "#### 2) 대략적인 데이터 형태"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d93e8fa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mnist_train 길이: 60000\n",
      "mnist_test 길이: 10000\n",
      "image data 형태: torch.Size([1, 28, 28])\n",
      "label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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L90v97hJ9lfK9tX2fX9IQ4BfAVGAL8BowIyI2trWRKiRtAiZGROkXhEj6Q2A3sPTgUGiSfgTsiIg7i/9xnhgRf90hvd3GYQ7b3qLeqg0r/6eU+N01c7j7ZihjzT8J+CAiPoyIvcDjwPQS+uh4EbEG2HHI29OBJcXzJVT+42m7Kr11hIjoiYh1xfNdwMFh5Uv97hJ9laKM8I8GPurzegslfgH9COA5Sa9Lml12M/0Y2WdYtI+BkWU204+aw7a30yHDynfMd1fPcPfN5gN+3zQ5Is4FpgE3F5u3HSkq+2yddK72x8BpVMZw7AHuKbOZYlj5J4FbImJn31qZ310/fZXyvZUR/q3AmD6vv1281xEiYmvxdzvwNJXdlE6y7eAIycXf7SX380sRsS0i9kfEAeBBSvzuimHlnwQei4inirdL/+7666us762M8L8GnC7pO5KOAb4PrCihj2+QNLQ4EIOkocDFdN7Q4yuAmcXzmcDyEnv5mk4Ztr3asPKU/N113HD3EdH2B3AZlSP+/wX8bRk9VOlrHPBm8Xin7N6AZVQ2A/+PyrGR64FfB14A3gd+BozooN7+icpQ7m9RCdqoknqbTGWT/i1gffG4rOzvLtFXKd+bL+81y5QP+JllyuE3y5TDb5Yph98sUw6/WaYcfrNMOfxmmfp/HsM1PbBIdecAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"mnist_train 길이:\", len(mnist_train)\n",
    "print \"mnist_test 길이:\", len(mnist_test)\n",
    "\n",
    "# 데이터 하나 형태\n",
    "image, label = mnist_train.__getitem__(0)  # 0번째 데이터\n",
    "print \"image data 형태:\", image.size()\n",
    "print \"label:\", label\n",
    "\n",
    "# 그리기\n",
    "img = image.numpy()\n",
    "plt.title(\"label: %d\" %label)\n",
    "plt.imshow(img[0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a15fd31",
   "metadata": {},
   "source": [
    "#### 3) 데이터 로드함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "87e902f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9332c40d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(list(mnist_train)[:batch_size*100], batch_size=batch_size,\n",
    "                                           # mnist_train을 트레인 시키자.\n",
    "                                           shuffle=True, num_workers=2,\n",
    "                                           drop_last=True)  # batch_size 만큼 나눌 때 나머지는 버려라.\n",
    "test_loader = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size,\n",
    "                                          shuffle=False, num_workers=2,\n",
    "                                          drop_last=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2040f60",
   "metadata": {},
   "source": [
    "#### 4) 데이터 로드함수 이해하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1eef8109",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "한번에 로드되는 데이터 크기: 16\n",
      "torch.Size([16, 28, 28])\n",
      "torch.Size([16])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "한번에 로드되는 데이터 크기: 16\n",
      "torch.Size([16, 28, 28])\n",
      "torch.Size([16])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n",
      "한번에 로드되는 데이터 크기: 16\n",
      "torch.Size([16, 28, 28])\n",
      "torch.Size([16])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n",
      "한번에 로드되는 데이터 크기: 16\n",
      "torch.Size([16, 28, 28])\n",
      "torch.Size([16])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4]\n",
      "한번에 로드되는 데이터 크기: 16\n",
      "torch.Size([16, 28, 28])\n",
      "torch.Size([16])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5]\n",
      "한번에 로드되는 데이터 크기: 16\n",
      "torch.Size([16, 28, 28])\n",
      "torch.Size([16])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 3  # 샘플로 그려볼 데이터 개수\n",
    "for i, [imgs, labels] in enumerate(test_loader):  # batch_size 만큼\n",
    "    if i>5:\n",
    "        break\n",
    "    print \"[%d]\" %i\n",
    "    print \"한번에 로드되는 데이터 크기:\", len(imgs)\n",
    "    \n",
    "    # GPU에 로드되기 위함. 만약 CPU로 설정되어 있다면 자동으로(?) CPU로 로드됨\n",
    "    x = Variable(imgs)\n",
    "    x = x.reshape((x.shape[0], x.shape[2], x.shape[3]))\n",
    "    y_ = Variable(labels)\n",
    "    \n",
    "    print x.shape\n",
    "    print y_.shape\n",
    "    \n",
    "    for j in range(n):\n",
    "        img = imgs[j].numpy()\n",
    "        img = img.reshape((img.shape[1],img.shape[2]))\n",
    "        \n",
    "        plt.subplot(1, n, j+1)\n",
    "        plt.imshow(img, cmap='gray')\n",
    "        plt.title(\"label: %d\" %labels[j])\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_pytorch_p27",
   "language": "python",
   "name": "conda_pytorch_p27"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}