Added a notebook I used to get acquainted with vectorization

02_Vectorization_Explained/.ipynb_checkpoints/02_01_Vectorization_Explained-checkpoint.ipynb

@@ -0,0 +1,169 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:79f6f8d38fa86628294d097bb12e0276d748f4c87e67bc8618a9f14678a01e02"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "import math\n",
+      "import numpy as np\n",
+      "from IPython.display import Image, display \n",
+      "random.seed(0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Lets generate a random array for illustration purposes!"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def generateNoise(width,height):\n",
+      "    noise = [[r for r in range(width)] for i in range(height)]\n",
+      "\n",
+      "    for i in range(0,height):\n",
+      "        for j in range(0,width):\n",
+      "            noise[i][j] = random.randint(0,9)\n",
+      "\n",
+      "    return noise\n",
+      "\n",
+      "A = generateNoise(30,10)\n",
+      "A = np.asarray(noise)\n",
+      "A[A > 1] = 0"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now we have an array with ones randomly distributed."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print A"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0]\n",
+        " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0]\n",
+        " [0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
+        " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0]\n",
+        " [0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1]\n",
+        " [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
+        " [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]\n",
+        " [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0]\n",
+        " [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]\n",
+        " [0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here's an illustration that made the vectorization \"click\" for me. Hopefully it makes sence to somebody else than me."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "display(Image(filename='vectorization.png'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<IPython.core.display.Image at 0x107372450>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In words what we are going to to is to sum all neighouring elements in the array. Let's do it in code!"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "B = np.zeros_like(A) # an array of A's size with only zeros\n",
+      "\n",
+      "B[1:-1,1:-1] = A[0:-2, 1:-1] + \\\n",
+      "               A[2:, 1:-1] + \\\n",
+      "               A[1:-1, 0:-2] + \\\n",
+      "               A[1:-1, 2:]\n",
+      "print B"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
+        " [0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 2 0 1 0 0]\n",
+        " [0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0]\n",
+        " [0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 2 1 1 1 0 1 0 0 0 0 0]\n",
+        " [0 0 0 0 0 0 1 1 1 2 0 0 0 1 1 1 0 0 1 1 2 1 0 2 0 1 0 0 1 0]\n",
+        " [0 0 0 0 0 0 0 2 2 0 1 0 0 0 1 0 0 0 0 1 0 0 2 0 1 0 0 0 0 0]\n",
+        " [0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0]\n",
+        " [0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 1 0 1 0 0]\n",
+        " [0 1 0 1 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0]\n",
+        " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Se how every element now is the sum of it's neighbours?"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}