{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/python_with_Birds.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center"
   },
   "source": [
    "# Numpy เบื้องต้น - 2 D\n",
    "\n",
    "**20** minutes\n",
    "\n",
    "<font size=\"4\"> **วัตถุประสงค์**\n",
    "</font>\n",
    "\n",
    "<B> หลังจากทำทำแล็บ นศ.จะสามารถ </B>\n",
    "\n",
    "\n",
    "*  สร้างและดำเนินการทางคณิตศาสตร์กับข้อมูลชนิด `numpy` อาร์เรย์ 2 มิติได้\n",
    "\n",
    "Ref:\n",
    "- https://docs.scipy.org/doc/numpy/user/quickstart.html\n",
    "- https://docs.scipy.org/doc/numpy/user/basics.html\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ความสำคัญของข้อมูลอาร์เรย์ สถิติ หลายมิติ RBG ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## การสร้างอาร์เรย์ 2 มิติ (2D Numpy Array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the libraries\n",
    "\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สร้างลิสต์ (List) <code>a</code> ขึ้นมา ซึ่งเป็น nested list 3 ลิสต์**ที่มีขนาดเท่ากัน**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[11, 12, 13], [21, 22, 23], [31, 32, 33]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list\n",
    "\n",
    "#a = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]\n",
    "a = [[11, 12, 13], \n",
    "     [21, 22, 23], \n",
    "     [31, 32, 33]]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถสร้าง Numpy Array จากลิสต์ได้ ดังต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 12, 13],\n",
       "       [21, 22, 23],\n",
       "       [31, 32, 33]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert list to Numpy Array\n",
    "# Every element is the same type\n",
    "\n",
    "A = np.array(a)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถเรียกแอตทริบิวต์ (attribute) <code>ndim</code> (number of dimensions) เพื่อดูจำนวนแกน (axes)  หรือ จำนวนมิติ (dimensions) ของอาเรย์ได้\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the numpy array dimensions\n",
    "\n",
    "A.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "แอตทริบิวต์ <code>shape</code> ส่งค่ากลับเป็นข้อมูลชนิด tuple ที่สอดคล้องกับขนาด (size) หรือจำนวนสมาชิกในแต่ละมิติ (รูปร่างของอาเรย์) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the numpy array shape\n",
    "\n",
    "A.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จำนวนสมาชิกทั้งหมดที่มีอยู่ในอาร์เรย์สามารถดูได้จากแอตทริบิวต์ <code>size</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the numpy array size\n",
    "\n",
    "A.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## การเข้าถึงสมาชิกในอาร์เรย์\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เช่นเดียวกันกับอาร์เรย์ 1 มิติ เราสามารถเข้าถึงข้อมูลแต่ละตัวภายในอาร์เรย์ได้โดยใช้เครื่องหมายวงเล็บเหลี่ยม (ก้ามปู) **[ ]** (square brackets)\n",
    "\n",
    "ความสัมพันธ์ระหว่างเลขดัชนีในวงเล็บเหลี่ยมกับสมาชิกแต่ละตัวแสดงได้ในรูปต่อไปนี้ (อาร์เรย์ 3x3)\n",
    "\n",
    "**อาร์เรย์ 2 มิติ คือ Array of Array**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/NumTwoEg.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถเข้าถึงสมาชิกที่อยู่ในแถวที่ 2 (2nd-row) และคอลัมน์ที่ 3 (3rd column) ดังแสดงในรูป\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/NumTwoFT.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "โดยใช้เครื่องหมายวงเล็บเหลี่ยม **[ ]** และดัชนีที่สอดคล้องกับตำแน่งที่เราต้องการ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access the element on the second row and third column\n",
    "\n",
    "A[1, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "หรือจะเขียนรูปแบบแบบนี้ก็ได้ ให้ผลลัพธ์เหมือนกัน **(มีสองรูปแบบ!)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access the element on the second row and third column\n",
    "\n",
    "A[1][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ลองเข้าถึงสมาชิกที่กำหนดในรูปภาพต่อไปนี้"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/NumTwoFF.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "แถวที่ 0 คอลัมน์ที่ 0 ดังนั้น"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access the element on the first row and first column\n",
    "\n",
    "A[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถตัดเฉพาะส่วน (slicing) ได้เช่นเดียวกัยอาร์เรย์ 1 มิติ\n",
    "\n",
    "รูปด้านล่าง: เราจะตัดเอาเฉพาะสองคอลัมน์แรกที่อยู่ในแถวแรกเท่านั้น\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/NumTwoFSF.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ตัดได้โดยใช้คำสั่งต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access the element on the first row and first and second columns\n",
    "\n",
    "A[0][0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เช่นเดียวกัน เราสามารถตัดเอาเฉพาะสองแถวแรกที่อยู่ในคอลัมน์ที่ 3 เท่านั้น"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 23])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access the element on the first and second rows and third column\n",
    "\n",
    "A[0:2, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ดังแสดงในรูป\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/2D_numpy.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สมาชิกที่อยู่ในแถวที่ 0, แถวที่ 1, แถวที่ 2 ตามลำดับ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12, 13])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22, 23])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31, 32, 33])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "หรือแบบนี้ก็ได้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31, 32, 33])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สมาชิกที่อยู่ในคอลัมน์ที่ 0, คอลัมน์ที่ 1, คอลัมน์ที่ 2 ตามลำดับ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12, 13])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[:][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22, 23])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[:][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31, 32, 33])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[:][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <B>Note:</B>\n",
    "ใน Numpy axis 0 คือแกนของแถว (row) ส่วน axis 1 คือแกนของคอลัมน์ (column)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## การดำเนินการสำหรับ Numpy Array 2 มิติขั้นพื้นฐาน"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เรายังสามารถบวกอาร์เรย์ได้อีกด้วย การบวกนี้เหมือนกับการบวกเมทริกซ์ กล่าวคือเป็นการนำสมาชิกแต่ละตัวมาบวกกันเป็นคู่ๆ\n",
    "\n",
    "รูปต่อไปนี้แสดงการบวกเมทริกซ์ <code>X</code> และ <code>Y</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/NumTwoAdd.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สร้างอาร์เรย์ <code>X</code> และ <code>Y</code> ขึ้นมา\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array X\n",
    "\n",
    "X = np.array([[1, 0], [0, 1]]) \n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1],\n",
       "       [1, 2]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array Y\n",
    "\n",
    "Y = np.array([[2, 1], [1, 2]]) \n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ทำการบวกอาร์เรย์ <code>X</code> และ <code>Y</code> ด้วยคำสั่งต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1],\n",
       "       [1, 3]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add X and Y\n",
    "\n",
    "Z = X + Y\n",
    "Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ทำการลบอาร์เรย์ <code>X</code> และ <code>Y</code> ด้วยคำสั่งต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1, -1],\n",
       "       [-1, -1]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X - Y\n",
    "\n",
    "Z = X - Y\n",
    "Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "อาร์เรย์จะบวกลบกันได้ ก็ต่อเมื่อมีรูปร่าง (shape) เหมือนกัน!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1]\n",
      " [0 1 1]]\n",
      "[[2 1]\n",
      " [1 2]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[1, 0, 1], [0, 1, 1]]) \n",
    "Y = np.array([[2, 1], [1, 2], [1, 1]]) \n",
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ValueError: operands could not be broadcast together with shapes (2,3) (3,2) \n",
    "#Z = X+Y\n",
    "#print(Z)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "การคูณ Numpy array ด้วยสเกลาร์นั้นเหมือนกับการคูณเมทริกซ์ด้วยสเกลาร์ ถ้าเราคูณเมทริกซ์ <code>Y</code> ด้วยสเกล 2 เราก็คูณทุกองค์ประกอบในเมทริกซ์ด้วย 2 ดังที่แสดงในรูป"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/NumTwoDb.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "คูณอาร์เรย์ <code>Y</code> ด้วย 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1],\n",
       "       [1, 2]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array Y\n",
    "\n",
    "Y = np.array([[2, 1], [1, 2]]) \n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 2],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiply Y with 2\n",
    "\n",
    "Z = 2 * Y\n",
    "Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "การคูณอาร์เรย์สองอาร์เรย์เป็น <em>การคูณฮาดามาร์ด (Hadamard product/Element-wise multiplication)</em> <sup>*</sup>\n",
    "\n",
    "การคูณ Hadamard เป็นการคูณแต่ละสมาชิกที่อยู่ในตำแหน่งเดียวกันเป็นคู่ๆ ผลลัพธ์ของการคูณจะมีขนาดเดียวกับ <code>X</code> หรือ <code>Y</code> ดังแสดงในรูปต่อไปนี้\n",
    "\n",
    "<sup>*</sup> ผลคูณฮาดามาร์ดใช้ในการวิเคราะห์เชิงสถิติ โดยเฉพาะอย่างยิ่งใช้ในแบบจำลองเชิงเส้นทั่วไป (general linear models) และแบบจำลองการวิเคราะห์ตัวประกอบในรูปทั่วไป (generalized factor analysis model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/NumTwoMul.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถคูณแต่ละสมาชิกของอาร์เรย์ <code>X</code> และ <code>Y</code> ได้โดยการเขียนโค้ดต่อไปนี้\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array X\n",
    "\n",
    "X = np.array([[1, 0], [0, 1]]) \n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1],\n",
       "       [1, 2]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array \n",
    "\n",
    "Y = np.array([[2, 1], [1, 2]]) \n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [0, 2]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiply X with Y\n",
    "\n",
    "Z = X * Y\n",
    "Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "นอกจากการคูณแล้ว การหาร การยกกำลัง และหารเอาเศษ ก็เป็นการคำนวณเป็นคู่ๆ ทีละตัว ดังตัวอย่างต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5 0. ]\n",
      " [0.  0.5]] \n",
      "\n",
      "[[1 0]\n",
      " [0 1]] \n",
      "\n",
      "[[1 0]\n",
      " [0 1]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(X/Y, '\\n')\n",
    "print(X**Y, '\\n')\n",
    "print(X%Y, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**คูณแบบเมทริกซ์**\n",
    "\n",
    "นอกจากนี้ เรายังสามารถคูณ  Numpy arrays <code>A</code> และ <code>B</code> แบบเมทริกซ์ ได้อีกด้วย"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ก่อนอื่น กำหนดเมทริกซ์ <code>A</code> (2x3) และ <code>B</code> (3x2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 1],\n",
       "       [1, 0, 1]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a matrix A\n",
    "\n",
    "A = np.array([[0, 1, 1], [1, 0, 1]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  1],\n",
       "       [ 1,  1],\n",
       "       [-1,  1]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a matrix B\n",
    "\n",
    "B = np.array([[1, 1], [1, 1], [-1, 1]])\n",
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ใช้ฟังก์ชั่น <code>dot</code> คูณอาร์เรย์ทั้งสอง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2],\n",
       "       [0, 2]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the dot product\n",
    "\n",
    "Z = np.dot(A,B)\n",
    "Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.90929743],\n",
       "       [0.        , 0.90929743]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the sine of Z\n",
    "\n",
    "np.sin(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "เราสามารถเรียกแอตทริบิวต์ <code>T</code> (Transpose) เพื่อคำนวณเมทริกซ์สลับเปลี่ยน (Transpose) **สลับแถวและคอลัมน์**\n",
    "\n",
    "*Note:* เมทริกซ์สลับเปลี่ยน (transpose of a matrix) คือเมทริกซ์ที่ได้จากการสลับสมาชิกจากแถวเป็นคอลัมน์ และจากคอลัมน์เป็นแถวของเมทริกซ์ต้นแบบ เมตริกซ์ทรานสโพสของ A เขียนแทนด้วย A<sup>T</sup>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 2],\n",
       "       [3, 3]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a matrix C\n",
    "\n",
    "C = np.array([[1,1],[2,2],[3,3]])\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [1, 2, 3]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the transposed of C\n",
    "\n",
    "C.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 2],\n",
       "       [3, 3]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.T.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Exercise]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 จงเขียนโค้ดเปลี่ยนลิสต์ <code>a</code> ต่อไปนี้ให้เป็น Numpy Array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "\n",
    "a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "A = np.array(a)\n",
    "A\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 จงคำนวณหาจำนวนสมาชิกหรือขนาด (size) ของอาร์เรย์\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "A.size\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 จงเข้าถึงสมาชิกที่อยู่ในแถวแรกคอลัมน์แรก และ แถวแรกคอลัมน์ที่สอง\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "A[0][0:2]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4 จงหาผลคูณแบบเมทริกซ์ของ numpy arrays <code>A</code> และ <code>B</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "\n",
    "B = np.array([[0, 1], [1, 0], [1, 1], [-1, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "X = np.dot(A,B)\n",
    "X\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Author\n",
    "\n",
    "S.C.\n",
    "\n",
    "### Change Log\n",
    "\n",
    "    \n",
    "|  Date  |  Version | Change Description |\n",
    "|---|---|---|\n",
    "| 08-08-2021 | 0.1  | First edition |\n",
    "|   |   |   |\n",
    "|   |   |   |\n"
   ]
  }
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