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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from math import sqrt, pow\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "def euclidean_distance(x,y):\n",
    "    return sqrt(sum(pow(a-b,2) for a, b in zip(x, y)))\n",
    "\n",
    "def manhattan_distance(x,y):\n",
    "    return sum(abs(a-b) for a,b in zip(x,y))\n",
    "\n",
    "data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw Graph\n",
    "x = data[data['Gender']=='Male'].plot(kind='scatter', x= 'Height',y = 'Weight',color='blue',figsize=(10,7))\n",
    "data[data['Gender']=='Female'].plot(kind='scatter',x= 'Height',y = 'Weight',color='pink',figsize=(10,7) ,ax=x)\n",
    "\n",
    "plt.xlabel('Height')\n",
    "plt.ylabel('weight')\n",
    "plt.title('Analyze Height and Weight of men and women')\n",
    "plt.legend(labels=['Males','Females'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is no similarity using Euclidean Distance between heights and weights for this dataset with similarity value = 0.00010071759753209523\n",
      "------------------------------------------------------\n",
      "This is no similarity using Manhattan Distance between heights and weights for this dataset with similarity value = 1.0518244744130195e-06\n"
     ]
    }
   ],
   "source": [
    "# Retrieving all heights and weights\n",
    "heights = data['Height']\n",
    "weights = data['Weight']\n",
    "\n",
    "# Calculating Euclidean Distance \n",
    "euclidean_result = euclidean_distance(heights, weights)\n",
    "\n",
    "# Calculating Manhattan Distance \n",
    "manhattan_result = manhattan_distance(heights, weights)\n",
    "\n",
    "# To get value between 0 and 1 \n",
    "euclidean_result = 1 / (1 + euclidean_result)\n",
    "manhattan_result = 1 / (1 + manhattan_result)\n",
    "\n",
    "# Checking if the similarity value is nearest to 0 or 1\n",
    "# Eucliean Distance\n",
    "if round(euclidean_result) == 0:\n",
    "    print(f\"This is no similarity using Euclidean Distance between heights and weights for this dataset with similarity value = {euclidean_result}\")\n",
    "else:\n",
    "    print(f\"This is similarity using Euclidean Distance between heights and weights for this dataset with similarity value = {euclidean_result}\")\n",
    "\n",
    "print('------------------------------------------------------')\n",
    "\n",
    "# Manhattan Distance\n",
    "if round(manhattan_result) == 0:\n",
    "    print(f\"This is no similarity using Manhattan Distance between heights and weights for this dataset with similarity value = {manhattan_result}\")\n",
    "else:\n",
    "    print(f\"This is similarity using Manhattan Distance between heights and weights for this dataset with similarity value = {manhattan_result}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
À propos de cet Algorithme
import pandas as pd
from math import sqrt, pow
import matplotlib.pyplot as plt
 
def euclidean_distance(x,y):
    return sqrt(sum(pow(a-b,2) for a, b in zip(x, y)))

def manhattan_distance(x,y):
    return sum(abs(a-b) for a,b in zip(x,y))

data = pd.read_csv('data.csv')
# Draw Graph
x = data[data['Gender']=='Male'].plot(kind='scatter', x= 'Height',y = 'Weight',color='blue',figsize=(10,7))
data[data['Gender']=='Female'].plot(kind='scatter',x= 'Height',y = 'Weight',color='pink',figsize=(10,7) ,ax=x)

plt.xlabel('Height')
plt.ylabel('weight')
plt.title('Analyze Height and Weight of men and women')
plt.legend(labels=['Males','Females'])
plt.show()
# Retrieving all heights and weights
heights = data['Height']
weights = data['Weight']

# Calculating Euclidean Distance 
euclidean_result = euclidean_distance(heights, weights)

# Calculating Manhattan Distance 
manhattan_result = manhattan_distance(heights, weights)

# To get value between 0 and 1 
euclidean_result = 1 / (1 + euclidean_result)
manhattan_result = 1 / (1 + manhattan_result)

# Checking if the similarity value is nearest to 0 or 1
# Eucliean Distance
if round(euclidean_result) == 0:
    print(f"This is no similarity using Euclidean Distance between heights and weights for this dataset with similarity value = {euclidean_result}")
else:
    print(f"This is similarity using Euclidean Distance between heights and weights for this dataset with similarity value = {euclidean_result}")

print('------------------------------------------------------')

# Manhattan Distance
if round(manhattan_result) == 0:
    print(f"This is no similarity using Manhattan Distance between heights and weights for this dataset with similarity value = {manhattan_result}")
else:
    print(f"This is similarity using Manhattan Distance between heights and weights for this dataset with similarity value = {manhattan_result}")

This is no similarity using Euclidean Distance between heights and weights for this dataset with similarity value = 0.00010071759753209523
------------------------------------------------------
This is no similarity using Manhattan Distance between heights and weights for this dataset with similarity value = 1.0518244744130195e-06