{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Manipulation with Ndarray\n",
    "\n",
    "Importing `np` (numpy-like) module and `npx` (numpy extensions) module from MXNet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.205412Z",
     "start_time": "2019-07-03T21:48:36.544703Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "1"
    }
   },
   "outputs": [],
   "source": [
    "from mxnet import np, npx\n",
    "# Invoke the experimental numpy-compatible feature in MXNet \n",
    "npx.set_np()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Create a vector and query its attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.232852Z",
     "start_time": "2019-07-03T21:48:38.208019Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(12)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.239446Z",
     "start_time": "2019-07-03T21:48:38.235098Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.246552Z",
     "start_time": "2019-07-03T21:48:38.242380Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "More ways to construct arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.254509Z",
     "start_time": "2019-07-03T21:48:38.248757Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "4"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.263742Z",
     "start_time": "2019-07-03T21:48:38.256770Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "6"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 1., 4., 3.],\n",
       "       [1., 2., 3., 4.],\n",
       "       [4., 3., 2., 1.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.283115Z",
     "start_time": "2019-07-03T21:48:38.266029Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.2122064 ,  0.7740038 ,  1.0434405 ,  1.1839255 ],\n",
       "       [ 1.8917114 , -1.2347414 , -1.771029  , -0.45138445],\n",
       "       [ 0.57938355, -1.856082  , -1.9768796 , -0.20801921]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(0, 1, size=(3, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Elemental-wise operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.298150Z",
     "start_time": "2019-07-03T21:48:38.286183Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [1. 2. 4. 8.]\n",
      "x + y [ 3.  4.  6. 10.]\n",
      "x - y [-1.  0.  2.  6.]\n",
      "x * y [ 2.  4.  8. 16.]\n",
      "x ** y [ 1.  4. 16. 64.]\n",
      "x / y [0.5 1.  2.  4. ]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2, 4, 8])\n",
    "y = np.ones_like(x) * 2\n",
    "print('x =', x)\n",
    "print('x + y', x + y)\n",
    "print('x - y', x - y)\n",
    "print('x * y', x * y)\n",
    "print('x ** y', x ** y)\n",
    "print('x / y', x / y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Matrix multiplication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.320311Z",
     "start_time": "2019-07-03T21:48:38.300609Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "13"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 18.,  20.,  10.],\n",
       "       [ 58.,  60.,  50.],\n",
       "       [ 98., 100.,  90.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(12).reshape((3,4))\n",
    "y = np.array([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])\n",
    "np.dot(x, y.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Concatenate arrays along a particular axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.353948Z",
     "start_time": "2019-07-03T21:48:38.322469Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.,  1.,  2.,  3.],\n",
       "        [ 4.,  5.,  6.,  7.],\n",
       "        [ 8.,  9., 10., 11.],\n",
       "        [ 2.,  1.,  4.,  3.],\n",
       "        [ 1.,  2.,  3.,  4.],\n",
       "        [ 4.,  3.,  2.,  1.]]),\n",
       " array([[ 0.,  1.,  2.,  3.,  2.,  1.,  4.,  3.],\n",
       "        [ 4.,  5.,  6.,  7.,  1.,  2.,  3.,  4.],\n",
       "        [ 8.,  9., 10., 11.,  4.,  3.,  2.,  1.]]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([x, y], axis=0), np.concatenate([x, y], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Broadcast Mechanism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.382407Z",
     "start_time": "2019-07-03T21:48:38.355839Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "14"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:\n",
      " [[0.]\n",
      " [1.]\n",
      " [2.]]\n",
      "b:\n",
      " [[0. 1.]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [1., 2.],\n",
       "       [2., 3.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(3).reshape((3, 1))\n",
    "b = np.arange(2).reshape((1, 2))\n",
    "print('a:\\n', a)\n",
    "print('b:\\n', b)\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Indexing and Slicing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.400208Z",
     "start_time": "2019-07-03T21:48:38.384624Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "19"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[-1] =\n",
      " [ 8.  9. 10. 11.]\n",
      "x[1:3] =\n",
      " [[ 4.  5.  6.  7.]\n",
      " [ 8.  9. 10. 11.]]\n",
      "x[1:3, 2:4] =\n",
      " [[ 6.  7.]\n",
      " [10. 11.]]\n",
      "x[1,2] = 6.0\n"
     ]
    }
   ],
   "source": [
    "print('x[-1] =\\n', x[-1])\n",
    "print('x[1:3] =\\n', x[1:3])\n",
    "print('x[1:3, 2:4] =\\n', x[1:3, 2:4])\n",
    "print('x[1,2] =', x[1,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "`mxnet.numpy.ndarray` and `numpy.ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T21:48:38.406593Z",
     "start_time": "2019-07-03T21:48:38.402022Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "22"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'mxnet.numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "a = x.asnumpy()\n",
    "print(type(a))\n",
    "b = np.array(a)\n",
    "print(type(b))"
   ]
  }
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