numpy/manipulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thay đổi - Manipulation\n",
    "----\n",
    "\n",
    "Xem thêm về [Manipulation](https://www.tutorialspoint.com/numpy/numpy_array_manipulation.htm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Thay đổi kích thước mảng"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. reshape()\n",
    "Trả về mảng mới với kích cỡ được thay đổi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_2D = np.array([\n",
    "    [1, 2, 3, 4],\n",
    "    [5, 6, 7, 8]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_2D.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trả về mảng mới 3 chiều (2, 2, 2) từ mảng 2 chiều (2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_3D = numbers_2D.reshape(2, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2],\n",
       "        [3, 4]],\n",
       "\n",
       "       [[5, 6],\n",
       "        [7, 8]]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_3D.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. flatten()\n",
    "Trả về mảng được duỗi còn 1 chiều"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers = np.random.random((3, 3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[9.50066461e-01, 9.01003882e-01, 7.84720479e-01],\n",
       "        [9.47879738e-01, 1.35717696e-04, 5.07770033e-01],\n",
       "        [3.44713769e-02, 2.59649993e-01, 1.91719024e-01]],\n",
       "\n",
       "       [[7.58448374e-01, 5.60712235e-02, 4.01477814e-01],\n",
       "        [1.45508565e-01, 3.05456872e-01, 9.78449095e-01],\n",
       "        [4.51628284e-01, 1.67485426e-01, 9.31215167e-01]],\n",
       "\n",
       "       [[2.62117961e-01, 5.17461074e-01, 2.50675234e-01],\n",
       "        [9.57124891e-01, 9.73925297e-01, 1.86436315e-01],\n",
       "        [8.37553296e-01, 6.90010658e-01, 4.94900385e-01]]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "flattened_numbers = numbers.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flattened_numbers.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. expand_dims()\n",
    "Trả về mảng mới được thêm chiều"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_2D = np.array(([1,2],[3,4])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_2D.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mở rộng theo dòng axis=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_3D_axis_0 = np.expand_dims(numbers_2D, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 2)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_3D_axis_0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mở rộng theo cột axis=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_3D_axis_1 = np.expand_dims(numbers_2D, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1, 2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_3D.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. squeeze()\n",
    "Trả về mảng mới được giảm chiều"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_3D = np.random.random((1, 20, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_2D_axis_0 = np.squeeze(numbers_3D, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_2D_axis_0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II. Chuyển vị"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Xem lại chuyển vị ở phần [kiến thức toán học](https://github.com/bangoc123/learn-machine-learning-in-two-months/#1-ki%E1%BA%BFn-th%E1%BB%A9c-to%C3%A1n-h%E1%BB%8Dc-c%E1%BA%A7n-thi%E1%BA%BFt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![transpose](https://lh3.googleusercontent.com/9a1m0a1kozsZtPgffI2fAsgm2nnRymGYVgp3EXjXnHTZqBe-O5IT-WLT4N1r_2HsK_vP16ssazkAQvmh4ccv1-rMCZDA5f1llpvkqZFC7_ZFmNpVXXNkKHhenen0DXfzS1NqauBRiThtxxrnqvs6DjiHNv3o6z9QPOTuq19cWz9TBU2mkJKEMihglnKdKlNJa2l05WeBmJwAcpjwSQft5jlrn6kr9X782n9jPuC32pE7SOtIEzVSdV_DoRZMonrOnRRGsiz1bJFSumC9gaAaTx-kqGQbf6sKU3avjzNe6Elyg36MkwEfWAbCR9kMGXZ9shkgh41ymTLCNfqsqWuX2DrcfXbwNCjwj17IHeAeITYJoIP-W1lEk0yC7faQUmUh_OSpxG2Km5GFVQQxEj1SeNOW-k0jLyzjYuoyhLX3AGEYaHuDk81a4_HFRpg9fAD6aOGkQHNPghQrA2BQW9FgFK3pL6JDqtbuGQd9mz-KIjHSDTBm1OpQwxXMAnfYr6V0Jq4fULBKCCd96IZC_eJRxIFY_Za_cegK7FYIla9s7ggBg_n7zlAF7V9-BFJcZ9eVl6-_8UhUT20xdSkWERnqwtGwiikEDf5mbzg9iNjktPswaqqd3Y-JupOCH8KdLF5MgXAhexeb6SlWAuu55gy4ucR62iRZj7tan5fjyBlcT-ZqHOhZEXSxGoqEcQ=w800-h400-no)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers_2D = np.array([\n",
    "    [1, 2, 3, 4],\n",
    "    [5, 6, 7, 8]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers_2D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "transposed_numbers_1 = numbers_2D.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 5],\n",
       "       [2, 6],\n",
       "       [3, 7],\n",
       "       [4, 8]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transposed_numbers_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transposed_numbers_1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cách tương tự"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tương đương với\n",
    "transposed_numbers_2 = np.transpose(numbers_2D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transposed_numbers_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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