code/numpy/6_ndarray_operations.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arrary operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_a = np.array([[1,2,3],[4,5,6]], float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.,   4.,   6.],\n",
       "       [  8.,  10.,  12.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a + test_a # Matrix + Matrix 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a - test_a # Matrix - Matrix 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   4.,   9.],\n",
       "       [ 16.,  25.,  36.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a * test_a # Matrix내 element들 간 같은 위치에 있는 값들끼리 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   4,   9,  16],\n",
       "       [ 25,  36,  49,  64],\n",
       "       [ 81, 100, 121, 144]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_a = np.arange(1,13).reshape(3,4)\n",
    "matrix_a * matrix_a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### dot product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_a = np.arange(1,7).reshape(2,3)\n",
    "test_b = np.arange(7,13).reshape(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 58,  64],\n",
       "       [139, 154]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a.dot(test_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a = np.arange(1,7).reshape(2,3)\n",
    "test_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [2, 5],\n",
       "       [3, 6]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a.transpose() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4],\n",
       "       [2, 5],\n",
       "       [3, 6]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17, 22, 27],\n",
       "       [22, 29, 36],\n",
       "       [27, 36, 45]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_a.T.dot(test_a) # Matrix 간 곱셈"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_matrix = np.array([[1,2,3],[4,5,6]], float)\n",
    "scalar = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.,  5.,  6.],\n",
       "       [ 7.,  8.,  9.]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_matrix + scalar # Matrix - Scalar 덧셈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4., -3., -2.],\n",
       "       [-1.,  0.,  1.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_matrix - scalar # Matrix - Scalar 뺄셈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.,  10.,  15.],\n",
       "       [ 20.,  25.,  30.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_matrix * 5 # Matrix - Scalar 곱셈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.2,  0.4,  0.6],\n",
       "       [ 0.8,  1. ,  1.2]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_matrix / 5 # Matrix - Scalar 나눗셈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.,   9.,  14.],\n",
       "       [ 19.,  24.,  29.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_matrix // 0.2 # Matrix - Scalar 몫"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   4.,   9.],\n",
       "       [ 16.,  25.,  36.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_matrix ** 2 # Matrix - Scalar 제곱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 22, 33],\n",
       "       [14, 25, 36],\n",
       "       [17, 28, 39],\n",
       "       [20, 31, 42]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_matrix = np.arange(1,13).reshape(4,3)\n",
    "test_vector = np.arange(10,40,10)\n",
    "test_matrix+ test_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### numpy performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-09-26T07:37:50.311Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sclar_vector_product(scalar, vector):\n",
    "    result = []\n",
    "    for value in vector:\n",
    "        result.append(scalar * value)\n",
    "    return result \n",
    "\n",
    "iternation_max = 100000000\n",
    "\n",
    "vector = list(range(iternation_max))\n",
    "scalar = 2\n",
    "\n",
    "%timeit sclar_vector_product(scalar, vector) # for loop을 이용한 성능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-09-26T07:37:39.170Z"
    }
   },
   "outputs": [],
   "source": [
    "%timeit [scalar * value for value in range(iternation_max)] # list comprehension을 이용한 성능\n",
    "%timeit np.arange(iternation_max) * scalar # numpy를 이용한 성능"
   ]
  }
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