{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 1: Introduction to Clustering\n",
    "\n",
    "## Exercise 1.02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "\n",
    "def dist(a, b):\n",
    "    return math.sqrt(math.pow(a[0]-b[0],2) + math.pow(a[1]-b[1],2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = [ (2, 5), (8, 3), (4,5) ]\n",
    "x = (0, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean Distance between x (0, 8) and centroid (2, 5) is 3.605551275463989\n",
      "Euclidean Distance between x (0, 8) and centroid (8, 3) is 9.433981132056603\n",
      "Euclidean Distance between x (0, 8) and centroid (4, 5) is 5.0\n"
     ]
    }
   ],
   "source": [
    "# Calculating Euclidean Distance between x and centroid\n",
    "centroid_distances =[]\n",
    "for centroid in centroids:\n",
    "    print(\"Euclidean Distance between x {} and centroid {} is {}\".format(x ,centroid, dist(x,centroid)))\n",
    "    centroid_distances.append(dist(x,centroid))"
   ]
  }
 ],
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