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Commit 5bb9deb4 by Ari Whuandaniel Manurung
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Commitv6

parent 4bff5ec6
Showing with 1531 additions and 65 deletions
List_city.csv
ID_City,City,Biaya ID_City,City,Biaya
0,Pantai BUL BUL,10000 0,Pantai BUL BUL,0
1,BUKIT travel Gibeon,10001 1,BUKIT travel Gibeon,10000
2,Pakkodian,10002 2,Pakkodian,0
3,Taman Eden 100 Tobasa,10003 3,Taman Eden 100 Tobasa,10000
4,Water Park Tambunan,10004 4,Water Park Tambunan,15000
5,Bukit Pahoda,10005 5,Bukit Pahoda,0
6,Bukit Senyum,10006 6,Bukit Senyum,10000
7,Air Terjun Pandumaan,10007 7,Air Terjun Pandumaan,0
8,Long Beach,10008 8,Long Beach,5000
9,PANTAI AGADON,10009 9,PANTAI AGADON,0
10,Situmurun Waterfall,10010 10,Situmurun Waterfall,0
11,Simanimbo Waterfall,10011 11,Simanimbo Waterfall,0
12,Air Terjun Sigura Gura,10012 12,Air Terjun Sigura Gura,10000
13,The Kaldera,10013 13,The Kaldera,0
14,Museum T. B. Silalahi Center,10014 14,Museum T. B. Silalahi Center,10000
15,Bukit Tarabunga,10015 15,Bukit Tarabunga,0
16,Pantai Meat,10016 16,Pantai Meat,0
17,Air Terjun Sialogo,10017 17,Air Terjun Sialogo,0
18,Air Terjun Siboruon,10018 18,Air Terjun Siboruon,0
19,Gunung Pangulubao,10019 19,Gunung Pangulubao,5000
20,Lumban Silintong,10020 20,Lumban Silintong,10000
21,Siregar Aek Nalas,10021 21,Siregar Aek Nalas,5000
22,Pantai Janji Maria,10022 22,Pantai Janji Maria,0
23,Balerong Onan Balige,10023 23,Balerong Onan Balige,0
24,Dolok Tolong,10024 24,Dolok Tolong,0
25,Pantai Pasir Putih Parparean,10025 25,Pantai Pasir Putih Parparean,0
26,Dolok Surungan,10026 26,Dolok Surungan,0
27,Air Terjun Sampuran,10027 27,Air Terjun Sampuran,0
28,Air Terjun Morena,10028 28,Air Terjun Morena,0
29,Tornagodang,10029 29,Tornagodang,0
30,Hatulian Beach,10030 30,Hatulian Beach,5000
31,Monumen Raja Sonakmalela,10031 31,Monumen Raja Sonakmalela,0
32,Lumban Binanga Beach,10032 32,Lumban Binanga Beach,0
33,Pantai Pasifik Porsea,10033 33,Pantai Pasifik Porsea,2000
34,Desa Adat Ragi Hotang,10034 34,Desa Adat Ragi Hotang,0
35,Makam Raja Sisingamangaraja XII,10035 35,Makam Raja Sisingamangaraja XII,0
Sidang5.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kode Program TA 14"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Daftar isi\n",
"\n",
"1. Data Preprocessing\n",
"1.1 Data Cleaning\n",
"1.2 Data Integration\n",
"1.3 Data Transformation\n",
"\n",
"Adapun data yang di proses antara lain:\n",
" Kabupaten Dairi (kab1)\n",
" Kabupaten_Humbang_Hasundutan (kab2)\n",
" Kabupaten_karo (kab3)\n",
" Kabupaten_Samosir (kab4)\n",
" Kabupaten_Simalungun (kab5)\n",
" Kabupaten_Tapanuli_Utara (kab6)\n",
" Kabupaten_Toba_Samosir (kab7)\n",
" \n",
"2. Random Data\n",
"3. Encoding\n",
"4. Fitness Calculation\n",
"5. Prediksi Suhu\n",
"\n",
"PSO Implementation\n",
" Decoding PSO\n",
"ACO Implementation\n",
" Decoding ACO\n",
"ACO Implementation\n",
" Decoding ABC \n",
"\n",
"Evaluasi menggunakan VIKOR"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"# Library \n",
"import pandas as pd\n",
"from numpy import * \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import csv\n",
"import random\n",
"import time\n",
"import sys\n",
"import datetime\n",
"import timeit\n",
"from sklearn.neighbors import DistanceMetric\n",
"from math import radians,cos,sin\n",
"from haversine import haversine, Unit\n",
"from scipy.spatial import distance\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from keras.models import Sequential\n",
"from keras.layers import Bidirectional, GlobalMaxPool1D\n",
"from keras.layers import LSTM"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#Load dataset sebelum integrasi\n",
"Data1 = pd.read_csv('./tri/Data Toba Samosir - Sheet3.csv')\n",
"Data1.drop(Data1.filter(regex=\"Unname\"),axis=1, inplace=True)\n",
"Data2 = pd.read_csv('./tri/Data Toba Samosir - Sheet1.csv')\n",
"Data2.drop(Data2.filter(regex=\"Unname\"),axis=1, inplace=True)\n",
"Data3 = pd.read_csv('./tri/List_city.csv')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"#Data1\n",
"#Data2\n",
"#Data3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"start = datetime.datetime.strptime(\"21-07-2020\", \"%d-%m-%Y\")\n",
"end = datetime.datetime.strptime(\"22-07-2020\", \"%d-%m-%Y\")\n",
"date_generated = [start + datetime.timedelta(days=x) for x in range(0, (end-start).days)]\n",
"#print(len(date_generated))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#cost = input()\n",
"cost = 400000\n",
"Cost = int(cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[32, 2, 9, 10, 11, 4, 26]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id_city = list(Data3['ID_City'])\n",
"Data4 = random.sample(range(len(id_city)), 7)\n",
"Data4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fitness Calculation"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"class Fitness_value:\n",
" def getting_max_distance():\n",
" max_distance = 0 \n",
" max_distance += len(date_generated) * 720\n",
" return max_distance\n",
" def getting_max_cost():\n",
" max_cost = 0\n",
" max_cost +=Cost\n",
" return max_cost"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"720"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Fitness_value.getting_max_distance()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Initialization of PSO parameters,and velocity\n",
"#Number of Agents\n",
"PARTICLE_COUNT = 40;\n",
"#Acceleration Constant\n",
"Acceleration_constant = 2; # Maximum velocity change allowed. Range: 0 >= V_MAX < CITY_COUNT\n",
"#Iterasi\n",
"MAX_EPOCHS = 200\n",
"map = [];\n",
"particles = []\n",
"Maximum_distance = Fitness_value.getting_max_distance()\n",
"CITY_COUNT = len(Data4)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# define particle function\n",
"class Particle:\n",
" def __init__(self):\n",
" self.mData = [0] * CITY_COUNT\n",
" self.mpBest_distance = 0.9\n",
" self.mVelocity = 0.0\n",
" def get_data(self, index): \n",
" return self.mData[index]\n",
" def set_data(self, index, value):\n",
" self.mData[index] = value\n",
" def get_pBest_distance(self):\n",
" return self.mpBest_distance\n",
" def set_pBest_distance(self, value):\n",
" self.mpBest_distance = value\n",
" def get_velocity(self):\n",
" return self.mVelocity\n",
" def set_velocity(self, velocityScore):\n",
" self.mVelocity = velocityScore "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"Data4 = [32, 2, 9, 10, 11, 4, 26]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"# define city class\n",
"class City:\n",
" def __init__(self):\n",
" self.mX = 0\n",
" self.mY = 0\n",
"# calculating distance of city\n",
"def get_distance():\n",
" distance_route = []\n",
" last_distance = 0\n",
" distance = 0\n",
" for i in range(0,len(Data4)-1):\n",
" source = Data4[i]\n",
" target = Data4[i+1]\n",
" distance_route.append(Data2.iloc[source][target])\n",
" for i in range(0,len(Data4)-1):\n",
" source = Data4[len(Data4)-1]\n",
" target = Data4[len(Data4)-len(Data4)]\n",
" last_distance = Data2.iloc[source][target] \n",
" distance = sum(distance_route)+last_distance\n",
" return distance\n",
"def get_total_distance(index):\n",
" particles[index].set_pBest_distance(0)\n",
" for i in range(CITY_COUNT):\n",
" if i == CITY_COUNT - 1:\n",
" particles[index].set_pBest_distance(particles[index].get_pBest_distance() + get_distance(particles[index].get_data(CITY_COUNT - 1), particles[index].get_data(0))) # Complete trip.\n",
" else:\n",
" particles[index].set_pBest_distance(particles[index].get_pBest_distance() + get_distance(particles[index].get_data(i), particles[index].get_data(i + 1)))\n",
" return "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# mapping city \n",
"def initialize_map():\n",
" for i in range(CITY_COUNT):\n",
" newCity = City()\n",
" map.append(newCity)\n",
" return "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def randomly_arrange(index = 0):\n",
" cityA = random.randrange(0, CITY_COUNT)\n",
" cityB = 0\n",
" done = False\n",
" while not done:\n",
" cityB = random.randrange(0, CITY_COUNT)\n",
" if cityB != cityA:\n",
" done = \tTrue\n",
" \n",
" # swap cityA and cityB.\n",
" temp = particles[index].get_data(cityA)\n",
" particles[index].set_data(cityA, particles[index].get_data(cityB))\n",
" particles[index].set_data(cityB, temp)\n",
" return "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"def initialize_particles():\n",
" for i in range(PARTICLE_COUNT):\n",
" newParticle = Particle() \n",
" for j in range(CITY_COUNT):\n",
" newParticle.set_data(j, j) \n",
" particles.append(newParticle) \n",
" for j in range(10): # just any number of times to randomize them.\n",
" randomly_arrange(len(particles) - 1) \n",
" get_total_distance()\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"def quicksort(array, left, right):\n",
" pivot = quicksort_partition(array, left, right)\n",
" \n",
" if left < pivot:\n",
" quicksort(array, left, pivot - 1)\n",
" \n",
" if right > pivot:\n",
" quicksort(array, pivot + 1, right) \n",
" return array"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"def quicksort_partition(numbers, left, right):\n",
" # The comparison is on each particle's pBest value.\n",
" I_hold = left\n",
" r_hold = right\n",
" pivot = numbers[left]\n",
" \n",
" while left < right:\n",
" while (numbers[right].get_pBest_distance() >= pivot.get_pBest_distance()) and (left < right):\n",
" right -= 1\n",
"\n",
" if left != right:\n",
" numbers[left] = numbers[right]\n",
" left += 1\n",
" \n",
" while (numbers[left].get_pBest_distance() <= pivot.get_pBest_distance()) and (left < right):\n",
" left += 1 \n",
" if left != right:\n",
" numbers[right] = numbers[left]\n",
" right -= 1\n",
" \n",
" numbers[left] = pivot\n",
" pivot = left\n",
" left = I_hold\n",
" right = r_hold\n",
" \n",
" return pivot"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"def get_velocity():\n",
" worstResults_distance = 0.0\n",
" vValue_distance = 0.0 \n",
" # After sorting, worst will be last in list.\n",
" worstResults_distance = particles[PARTICLE_COUNT - 1].get_pBest_distance() \n",
" for i in range(PARTICLE_COUNT):\n",
" vValue_distance = (Acceleration_constant * particles[i].get_pBest_distance()) / worstResults_distance\n",
" \n",
" if (vValue_distance > Acceleration_constant): \n",
" particles[i].set_velocity(Acceleration_constant)\n",
" elif (vValue_distance < 0.0):\n",
" particles[i].set_velocity(0.0)\n",
" else:\n",
" particles[i].set_velocity(vValue_distance) \n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"def copy_from_particle(source, destination):\n",
" # push destination's data points closer to source's data points.\n",
" targetA = random.randrange(0, len(Data4)) # source's city to target.\n",
" targetB = 0\n",
" indexA = 0\n",
" indexB = 0\n",
" tempIndex = 0\n",
" \n",
" # targetB will be source's neighbor immediately succeeding targetA (circular).\n",
" for i in range(len(arr)):\n",
" if particles[source].get_data(i) == targetA:\n",
" if i == CITY_COUNT - 1:\n",
" targetB = particles[source].get_data(0) # if end of array, take from beginning.\n",
" else:\n",
" targetB = particles[source].get_data(i + 1)\n",
" \n",
" break\n",
" \n",
" # Move targetB next to targetA by switching values.\n",
" for j in range(CITY_COUNT):\n",
" if particles[destination].get_data(j) == targetA:\n",
" indexA = j\n",
" \n",
" if particles[destination].get_data(j) == targetB:\n",
" indexB = j\n",
" \n",
" # get temp index succeeding indexA.\n",
" if indexA == CITY_COUNT - 1:\n",
" tempIndex = 0\n",
" else:\n",
" tempIndex = indexA + 1\n",
" \n",
" # Switch indexB value with tempIndex value.\n",
" temp = particles[destination].get_data(tempIndex)\n",
" particles[destination].set_data(tempIndex, particles[destination].get_data(indexB))\n",
" particles[destination].set_data(indexB, temp)\n",
" \n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"# updating particles city\n",
"def update_particles():\n",
" # Best was previously sorted to index 0, so start from the second best.\n",
" for i in range(5):\n",
" if i > 0:\n",
" # The higher the velocity score, the more changes it will need.\n",
" changes = math.floor(math.fabs(particles[i].get_velocity()))\n",
" sys.stdout.write(\"Changes for particle \" + str(i) + \": \" + str(changes) + \"\\n\")\n",
" for j in range(changes):\n",
" # 50/50 chance.\n",
" if random.random() > 0.5:\n",
" randomly_arrange(i)\n",
" \n",
" # Push it closer to it's best neighbor.\n",
" copy_from_particle(i - 1, i)\n",
" \n",
" # Update pBest value.\n",
" get_total_distance()\n",
" \n",
" \n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"def PSO_algorithm():\n",
" epoch = 0\n",
" done = False\n",
" \n",
" initialize_particles()\n",
" \n",
" while not done:\n",
" # Two conditions can end this loop:\n",
" # if the maximum number of epochs allowed has been reached, or,\n",
" # if the Target value has been found.\n",
" if epoch < MAX_EPOCHS:\n",
" for i in range(5):\n",
" \n",
" sys.stdout.write(\"Route: \")\n",
" \n",
" for j in range(CITY_COUNT):\n",
" sys.stdout.write(str(particles[i].get_data(j)) + \", \")\n",
" get_total_distance()\n",
" \n",
" sys.stdout.write(\"Distance: \" + str(particles[i].get_pBest_distance()) + \"\\n\") \n",
" if (particles[i].get_pBest_distance() <= Maximum_distance):\n",
" done = True \n",
" quicksort(particles, 0, len(particles) - 1)\n",
" # list has to sorted in order for get_velocity() to work.\n",
" get_velocity() \n",
" update_particles()\n",
" \n",
" sys.stdout.write(\"epoch number: \" + str(epoch) + \"\\n\")\n",
" \n",
" epoch += 1 \n",
" else:\n",
" done = True\n",
" \n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"def print_best_solution():\n",
" if (particles[0].get_pBest_distance() <= Maximum_distance):\n",
" sys.stdout.write(\"Target reached.\\n\")\n",
" else:\n",
" sys.stdout.write(\"Target not reached.\\n\")\n",
" \n",
" sys.stdout.write(\"Best Route: \")\n",
" for j in range(CITY_COUNT):\n",
" sys.stdout.write(str(particles[0].get_data(j)) + \", \")\n",
" \n",
" sys.stdout.write(\"Distance: \" + str(particles[0].get_pBest_distance()) +\"\\n\")\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Route: 0, 1, 3, 2, 4, 5, 6, Distance: 0.9\n",
"Route: 2, 0, 6, 3, 5, 4, 1, Distance: 0.9\n",
"Route: 1, 6, 4, 5, 0, 2, 3, Distance: 0.9\n",
"Route: 3, 4, 5, 2, 6, 1, 0, Distance: 0.9\n",
"Route: 3, 1, 2, 0, 5, 4, 6, Distance: 0.9\n",
"Changes for particle 1: 2\n"
]
},
{
"ename": "NameError",
"evalue": "name 'arr' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-70-1a83413392a8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0minitialize_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mPSO_algorithm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mprint_best_solution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-68-7469a4923133>\u001b[0m in \u001b[0;36mPSO_algorithm\u001b[1;34m()\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;31m# list has to sorted in order for get_velocity() to work.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0mget_velocity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mupdate_particles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"epoch number: \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-61-58efe8e1ae0d>\u001b[0m in \u001b[0;36mupdate_particles\u001b[1;34m()\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;31m# Push it closer to it's best neighbor.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mcopy_from_particle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;31m# Update pBest value.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-56-3e6b95d5d0b4>\u001b[0m in \u001b[0;36mcopy_from_particle\u001b[1;34m(source, destination)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcopy_from_particle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdestination\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# push destination's data points closer to source's data points.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtargetA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# source's city to target.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mtargetB\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mindexA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'arr' is not defined"
]
}
],
"source": [
" if __name__ == '__main__':\n",
" initialize_map()\n",
" PSO_algorithm()\n",
" print_best_solution() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
TA14_Sidang.ipynb
...@@ -412,18 +412,18 @@ ...@@ -412,18 +412,18 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Route: 5, 1, 6, 4, 0, 3, 2, Distance: 238.00000000000003\n", "Route: 2, 5, 4, 1, 0, 3, 6, Distance: 248.0\n",
"Route: 1, 3, 6, 4, 5, 2, 0, Distance: 162.29999999999998\n", "Route: 6, 3, 4, 2, 5, 0, 1, Distance: 153.1\n",
"Route: 0, 2, 4, 1, 5, 3, 6, Distance: 251.5\n", "Route: 2, 4, 3, 6, 5, 0, 1, Distance: 251.5\n",
"Route: 1, 3, 5, 2, 0, 6, 4, Distance: 242.2\n", "Route: 2, 4, 3, 6, 5, 0, 1, Distance: 251.5\n",
"Route: 5, 4, 6, 1, 2, 3, 0, Distance: 305.3\n", "Route: 6, 1, 2, 0, 3, 4, 5, Distance: 241.39999999999998\n",
"Changes for particle 1: 0\n", "Changes for particle 1: 0\n",
"Changes for particle 2: 0\n", "Changes for particle 2: 0\n",
"Changes for particle 3: 0\n", "Changes for particle 3: 0\n",
"Changes for particle 4: 0\n", "Changes for particle 4: 1\n",
"epoch number: 0\n", "epoch number: 0\n",
"Target reached.\n", "Target reached.\n",
"Best Route: 4, 3, 6, 1, 5, 0, 2, Distance: 156.6\n" "Best Route: 6, 3, 4, 2, 5, 0, 1, Distance: 153.1\n"
] ]
} }
], ],
...@@ -442,7 +442,7 @@ ...@@ -442,7 +442,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[4, 3, 6, 1, 5, 0, 2]" "[6, 3, 4, 2, 5, 0, 1]"
] ]
}, },
"execution_count": 20, "execution_count": 20,
...@@ -487,13 +487,13 @@ ...@@ -487,13 +487,13 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Water Park Tambunan\n",
"Taman Eden 100 Tobasa\n",
"Bukit Senyum\n", "Bukit Senyum\n",
"BUKIT travel Gibeon\n", "Taman Eden 100 Tobasa\n",
"Water Park Tambunan\n",
"Pakkodian\n",
"Bukit Pahoda\n", "Bukit Pahoda\n",
"Pantai BUL BUL\n", "Pantai BUL BUL\n",
"Pakkodian\n" "BUKIT travel Gibeon\n"
] ]
} }
], ],
...@@ -686,24 +686,24 @@ ...@@ -686,24 +686,24 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Route : [2, 5, 35, 14, 29, 18, 21, 2]\n", "Route : [0, 14, 31, 23, 30, 27, 4, 0]\n",
"179.85 , 70124 , 26.897694\n", "117.53 , 30000 , 26.897694\n",
"\n", "\n",
"\n", "\n",
"Route : [0, 31, 23, 19, 3, 1, 13, 0]\n", "Route : [0, 18, 31, 23, 35, 20, 21, 0]\n",
"117.33 , 70090 , 26.897694\n", "94.23 , 15000 , 26.897694\n",
"\n", "\n",
"\n", "\n",
"Route : [5, 20, 14, 35, 31, 23, 4, 5]\n", "Route : [0, 23, 31, 33, 16, 27, 5, 0]\n",
"20.78 , 70132 , 26.897694\n", "134.83 , 2000 , 26.897694\n",
"\n", "\n",
"\n", "\n",
"Route : [4, 20, 23, 31, 14, 35, 24, 4]\n", "Route : [5, 12, 23, 31, 16, 34, 24, 5]\n",
"30.68 , 70151 , 26.897694\n", "132.22999999999996 , 10000 , 26.897694\n",
"\n", "\n",
"\n", "\n",
"Route : [0, 35, 14, 23, 31, 4, 32, 0]\n", "Route : [0, 16, 2, 31, 23, 34, 24, 0]\n",
"31.68 , 70139 , 26.897694\n", "50.43000000000001 , 0 , 26.897694\n",
"\n", "\n",
"\n" "\n"
] ]
...@@ -759,7 +759,7 @@ ...@@ -759,7 +759,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -774,7 +774,7 @@ ...@@ -774,7 +774,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 33,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -782,12 +782,12 @@ ...@@ -782,12 +782,12 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Pantai BUL BUL\n", "Pantai BUL BUL\n",
"Makam Raja Sisingamangaraja XII\n", "Pantai Meat\n",
"Museum T. B. Silalahi Center\n", "Pakkodian\n",
"Balerong Onan Balige\n",
"Monumen Raja Sonakmalela\n", "Monumen Raja Sonakmalela\n",
"Water Park Tambunan\n", "Balerong Onan Balige\n",
"Lumban Binanga Beach\n", "Desa Adat Ragi Hotang\n",
"Dolok Tolong\n",
"Pantai BUL BUL\n" "Pantai BUL BUL\n"
] ]
} }
...@@ -802,6 +802,133 @@ ...@@ -802,6 +802,133 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ABC"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"path = list(Dataz[\"ID_City\"])\n",
"Maximum_distance = Fitness_value.getting_max_distance()\n",
"Maximum_cost = Fitness_value.getting_max_cost()\n",
"def get_total_cost(path):\n",
" cost_route = []\n",
" cost = 0\n",
" for i in range(len(path)):\n",
" cost_route.append(Dataz.iloc[i][2])\n",
" cost = sum(cost_route)\n",
" return cost "
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"#Datax"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"path = [5, 12, 23, 31, 16, 34, 24, 5]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"def calculate_distance():\n",
" distance_route = []\n",
" last_distance = 0\n",
" distance = 0\n",
" for i in range(0,4):\n",
" source = path[i]\n",
" target = path[i+1]\n",
" distance_route.append(Datax.iloc[source][target])\n",
" for i in range(0,4):\n",
" source = path[len(path)-1]\n",
" target = path[len(path)-len(path)]\n",
" last_distance = Datax.iloc[source][target] \n",
" distance = sum(distance_route)+last_distance\n",
" return distance"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"116.62999999999998"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculate_distance()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"def get_last_distance():\n",
" last_distance = 0\n",
" for i in range(0,4):\n",
" source = path[len(path)-1]\n",
" target = path[len(path)-len(path)]\n",
" last_distance = Datax.iloc[source][target]\n",
" return last_distance "
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_last_distance()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
......
distance_calculation.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#read data\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"Data1 = pd.read_csv('./tri/Data Toba Samosir - Sheet2.csv')\n",
"Data1.drop(Data1.filter(regex=\"Unname\"),axis=1, inplace=True)\n",
"Data2 = pd.read_csv('./tri/Data Toba Samosir - Sheet1.csv')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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" <th>14</th>\n",
" <th>15</th>\n",
" <th>16</th>\n",
" <th>17</th>\n",
" <th>18</th>\n",
" <th>19</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>50.9</td>\n",
" <td>12.8</td>\n",
" <td>44.7</td>\n",
" <td>5.9</td>\n",
" <td>71.0</td>\n",
" <td>60.6</td>\n",
" <td>12.4</td>\n",
" <td>63.5</td>\n",
" <td>50.5</td>\n",
" <td>52.7</td>\n",
" <td>43.1</td>\n",
" <td>51.9</td>\n",
" <td>56.3</td>\n",
" <td>6.0</td>\n",
" <td>11.8</td>\n",
" <td>14.6</td>\n",
" <td>60.7</td>\n",
" <td>9.0</td>\n",
" <td>42.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.9</td>\n",
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" <td>47.3</td>\n",
" <td>53.5</td>\n",
" <td>12.0</td>\n",
" <td>51.4</td>\n",
" <td>17.6</td>\n",
" <td>18.6</td>\n",
" <td>20.7</td>\n",
" <td>50.3</td>\n",
" <td>59.0</td>\n",
" <td>7.7</td>\n",
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" <td>58.2</td>\n",
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" <td>67.9</td>\n",
" <td>55.5</td>\n",
" <td>8.7</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>12.8</td>\n",
" <td>59.2</td>\n",
" <td>0.0</td>\n",
" <td>53.0</td>\n",
" <td>14.2</td>\n",
" <td>11.9</td>\n",
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" <td>6.2</td>\n",
" <td>53.0</td>\n",
" <td>0.0</td>\n",
" <td>41.1</td>\n",
" <td>47.2</td>\n",
" <td>15.9</td>\n",
" <td>45.5</td>\n",
" <td>18.8</td>\n",
" <td>12.3</td>\n",
" <td>14.5</td>\n",
" <td>44.0</td>\n",
" <td>52.8</td>\n",
" <td>11.6</td>\n",
" <td>46.2</td>\n",
" <td>52.0</td>\n",
" <td>54.8</td>\n",
" <td>61.7</td>\n",
" <td>49.2</td>\n",
" <td>2.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.9</td>\n",
" <td>47.3</td>\n",
" <td>14.2</td>\n",
" <td>41.1</td>\n",
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" <td>8.4</td>\n",
" <td>57.0</td>\n",
" <td>13.8</td>\n",
" <td>59.8</td>\n",
" <td>46.9</td>\n",
" <td>49.0</td>\n",
" <td>39.4</td>\n",
" <td>48.2</td>\n",
" <td>52.7</td>\n",
" <td>7.4</td>\n",
" <td>13.1</td>\n",
" <td>16.0</td>\n",
" <td>57.0</td>\n",
" <td>10.4</td>\n",
" <td>38.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7.1</td>\n",
" <td>53.5</td>\n",
" <td>11.1</td>\n",
" <td>47.2</td>\n",
" <td>8.4</td>\n",
" <td>0.0</td>\n",
" <td>63.2</td>\n",
" <td>14.9</td>\n",
" <td>66.0</td>\n",
" <td>53.1</td>\n",
" <td>55.3</td>\n",
" <td>45.6</td>\n",
" <td>54.4</td>\n",
" <td>58.9</td>\n",
" <td>3.5</td>\n",
" <td>10.9</td>\n",
" <td>13.0</td>\n",
" <td>63.3</td>\n",
" <td>9.6</td>\n",
" <td>45.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>60.6</td>\n",
" <td>12.0</td>\n",
" <td>68.9</td>\n",
" <td>15.9</td>\n",
" <td>57.0</td>\n",
" <td>63.1</td>\n",
" <td>0.0</td>\n",
" <td>61.4</td>\n",
" <td>5.5</td>\n",
" <td>28.2</td>\n",
" <td>30.4</td>\n",
" <td>59.9</td>\n",
" <td>68.7</td>\n",
" <td>10.8</td>\n",
" <td>62.1</td>\n",
" <td>67.9</td>\n",
" <td>70.7</td>\n",
" <td>77.6</td>\n",
" <td>65.1</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>12.4</td>\n",
" <td>51.7</td>\n",
" <td>20.7</td>\n",
" <td>45.5</td>\n",
" <td>11.3</td>\n",
" <td>14.9</td>\n",
" <td>61.4</td>\n",
" <td>0.0</td>\n",
" <td>64.2</td>\n",
" <td>51.3</td>\n",
" <td>53.5</td>\n",
" <td>43.8</td>\n",
" <td>52.6</td>\n",
" <td>57.1</td>\n",
" <td>13.9</td>\n",
" <td>19.6</td>\n",
" <td>22.9</td>\n",
" <td>61.5</td>\n",
" <td>15.0</td>\n",
" <td>43.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>62.5</td>\n",
" <td>16.6</td>\n",
" <td>71.8</td>\n",
" <td>17.8</td>\n",
" <td>58.8</td>\n",
" <td>66.0</td>\n",
" <td>5.5</td>\n",
" <td>63.3</td>\n",
" <td>0.0</td>\n",
" <td>31.1</td>\n",
" <td>33.2</td>\n",
" <td>61.8</td>\n",
" <td>70.6</td>\n",
" <td>23.0</td>\n",
" <td>64.0</td>\n",
" <td>69.7</td>\n",
" <td>73.6</td>\n",
" <td>80.4</td>\n",
" <td>67.0</td>\n",
" <td>21.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>50.5</td>\n",
" <td>18.6</td>\n",
" <td>58.8</td>\n",
" <td>12.3</td>\n",
" <td>46.9</td>\n",
" <td>53.1</td>\n",
" <td>28.2</td>\n",
" <td>51.3</td>\n",
" <td>31.1</td>\n",
" <td>0.0</td>\n",
" <td>5.7</td>\n",
" <td>49.9</td>\n",
" <td>58.6</td>\n",
" <td>23.9</td>\n",
" <td>52.0</td>\n",
" <td>57.8</td>\n",
" <td>60.7</td>\n",
" <td>67.5</td>\n",
" <td>55.1</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>52.7</td>\n",
" <td>20.7</td>\n",
" <td>61.0</td>\n",
" <td>14.5</td>\n",
" <td>49.0</td>\n",
" <td>55.2</td>\n",
" <td>30.4</td>\n",
" <td>53.5</td>\n",
" <td>33.2</td>\n",
" <td>5.7</td>\n",
" <td>0.0</td>\n",
" <td>52.0</td>\n",
" <td>60.8</td>\n",
" <td>26.1</td>\n",
" <td>54.2</td>\n",
" <td>59.9</td>\n",
" <td>62.8</td>\n",
" <td>69.6</td>\n",
" <td>57.2</td>\n",
" <td>12.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>43.1</td>\n",
" <td>50.3</td>\n",
" <td>51.4</td>\n",
" <td>44.0</td>\n",
" <td>39.4</td>\n",
" <td>45.6</td>\n",
" <td>59.9</td>\n",
" <td>43.8</td>\n",
" <td>62.8</td>\n",
" <td>49.9</td>\n",
" <td>52.0</td>\n",
" <td>0.0</td>\n",
" <td>13.6</td>\n",
" <td>55.6</td>\n",
" <td>44.6</td>\n",
" <td>50.3</td>\n",
" <td>53.2</td>\n",
" <td>22.4</td>\n",
" <td>47.6</td>\n",
" <td>41.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>51.9</td>\n",
" <td>59.0</td>\n",
" <td>60.2</td>\n",
" <td>52.8</td>\n",
" <td>48.2</td>\n",
" <td>54.4</td>\n",
" <td>68.7</td>\n",
" <td>52.8</td>\n",
" <td>71.6</td>\n",
" <td>58.6</td>\n",
" <td>60.8</td>\n",
" <td>13.6</td>\n",
" <td>0.0</td>\n",
" <td>64.4</td>\n",
" <td>53.4</td>\n",
" <td>59.1</td>\n",
" <td>62.0</td>\n",
" <td>11.6</td>\n",
" <td>56.4</td>\n",
" <td>50.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>56.3</td>\n",
" <td>7.7</td>\n",
" <td>64.6</td>\n",
" <td>11.6</td>\n",
" <td>52.7</td>\n",
" <td>58.8</td>\n",
" <td>10.8</td>\n",
" <td>57.1</td>\n",
" <td>23.0</td>\n",
" <td>23.9</td>\n",
" <td>26.1</td>\n",
" <td>55.6</td>\n",
" <td>64.4</td>\n",
" <td>0.0</td>\n",
" <td>57.8</td>\n",
" <td>63.6</td>\n",
" <td>66.4</td>\n",
" <td>73.3</td>\n",
" <td>60.8</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>6.0</td>\n",
" <td>52.5</td>\n",
" <td>8.0</td>\n",
" <td>46.2</td>\n",
" <td>7.4</td>\n",
" <td>3.3</td>\n",
" <td>62.1</td>\n",
" <td>13.9</td>\n",
" <td>65.0</td>\n",
" <td>52.0</td>\n",
" <td>54.1</td>\n",
" <td>44.6</td>\n",
" <td>53.4</td>\n",
" <td>57.8</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>9.8</td>\n",
" <td>62.1</td>\n",
" <td>8.6</td>\n",
" <td>43.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>11.8</td>\n",
" <td>58.2</td>\n",
" <td>4.1</td>\n",
" <td>52.0</td>\n",
" <td>13.2</td>\n",
" <td>10.9</td>\n",
" <td>67.9</td>\n",
" <td>19.6</td>\n",
" <td>70.7</td>\n",
" <td>57.8</td>\n",
" <td>59.9</td>\n",
" <td>50.3</td>\n",
" <td>59.1</td>\n",
" <td>63.6</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>5.9</td>\n",
" <td>67.9</td>\n",
" <td>14.4</td>\n",
" <td>49.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>14.6</td>\n",
" <td>61.0</td>\n",
" <td>4.7</td>\n",
" <td>54.8</td>\n",
" <td>16.0</td>\n",
" <td>13.7</td>\n",
" <td>70.7</td>\n",
" <td>22.5</td>\n",
" <td>73.6</td>\n",
" <td>60.6</td>\n",
" <td>62.8</td>\n",
" <td>53.2</td>\n",
" <td>62.0</td>\n",
" <td>66.4</td>\n",
" <td>9.9</td>\n",
" <td>5.9</td>\n",
" <td>0.0</td>\n",
" <td>70.8</td>\n",
" <td>17.2</td>\n",
" <td>52.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>60.7</td>\n",
" <td>67.9</td>\n",
" <td>74.2</td>\n",
" <td>61.7</td>\n",
" <td>57.0</td>\n",
" <td>68.5</td>\n",
" <td>82.8</td>\n",
" <td>61.5</td>\n",
" <td>80.4</td>\n",
" <td>72.7</td>\n",
" <td>74.9</td>\n",
" <td>22.4</td>\n",
" <td>11.6</td>\n",
" <td>78.5</td>\n",
" <td>62.2</td>\n",
" <td>67.9</td>\n",
" <td>76.1</td>\n",
" <td>0.0</td>\n",
" <td>65.2</td>\n",
" <td>64.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>9.0</td>\n",
" <td>55.5</td>\n",
" <td>15.4</td>\n",
" <td>49.2</td>\n",
" <td>10.4</td>\n",
" <td>9.6</td>\n",
" <td>65.1</td>\n",
" <td>15.0</td>\n",
" <td>68.0</td>\n",
" <td>55.1</td>\n",
" <td>57.2</td>\n",
" <td>47.6</td>\n",
" <td>56.4</td>\n",
" <td>60.8</td>\n",
" <td>8.6</td>\n",
" <td>14.3</td>\n",
" <td>17.2</td>\n",
" <td>65.2</td>\n",
" <td>0.0</td>\n",
" <td>46.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>42.4</td>\n",
" <td>8.7</td>\n",
" <td>50.7</td>\n",
" <td>2.4</td>\n",
" <td>38.8</td>\n",
" <td>44.9</td>\n",
" <td>18.3</td>\n",
" <td>43.2</td>\n",
" <td>21.2</td>\n",
" <td>10.0</td>\n",
" <td>12.2</td>\n",
" <td>41.7</td>\n",
" <td>50.5</td>\n",
" <td>14.0</td>\n",
" <td>43.9</td>\n",
" <td>49.6</td>\n",
" <td>52.5</td>\n",
" <td>59.3</td>\n",
" <td>46.9</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 10 11 \\\n",
"0 0.0 50.9 12.8 44.7 5.9 71.0 60.6 12.4 63.5 50.5 52.7 43.1 \n",
"1 50.9 0.0 59.2 6.2 47.3 53.5 12.0 51.4 17.6 18.6 20.7 50.3 \n",
"2 12.8 59.2 0.0 53.0 14.2 11.9 68.9 20.7 71.8 58.8 61.0 51.4 \n",
"3 44.7 6.2 53.0 0.0 41.1 47.2 15.9 45.5 18.8 12.3 14.5 44.0 \n",
"4 5.9 47.3 14.2 41.1 0.0 8.4 57.0 13.8 59.8 46.9 49.0 39.4 \n",
"5 7.1 53.5 11.1 47.2 8.4 0.0 63.2 14.9 66.0 53.1 55.3 45.6 \n",
"6 60.6 12.0 68.9 15.9 57.0 63.1 0.0 61.4 5.5 28.2 30.4 59.9 \n",
"7 12.4 51.7 20.7 45.5 11.3 14.9 61.4 0.0 64.2 51.3 53.5 43.8 \n",
"8 62.5 16.6 71.8 17.8 58.8 66.0 5.5 63.3 0.0 31.1 33.2 61.8 \n",
"9 50.5 18.6 58.8 12.3 46.9 53.1 28.2 51.3 31.1 0.0 5.7 49.9 \n",
"10 52.7 20.7 61.0 14.5 49.0 55.2 30.4 53.5 33.2 5.7 0.0 52.0 \n",
"11 43.1 50.3 51.4 44.0 39.4 45.6 59.9 43.8 62.8 49.9 52.0 0.0 \n",
"12 51.9 59.0 60.2 52.8 48.2 54.4 68.7 52.8 71.6 58.6 60.8 13.6 \n",
"13 56.3 7.7 64.6 11.6 52.7 58.8 10.8 57.1 23.0 23.9 26.1 55.6 \n",
"14 6.0 52.5 8.0 46.2 7.4 3.3 62.1 13.9 65.0 52.0 54.1 44.6 \n",
"15 11.8 58.2 4.1 52.0 13.2 10.9 67.9 19.6 70.7 57.8 59.9 50.3 \n",
"16 14.6 61.0 4.7 54.8 16.0 13.7 70.7 22.5 73.6 60.6 62.8 53.2 \n",
"17 60.7 67.9 74.2 61.7 57.0 68.5 82.8 61.5 80.4 72.7 74.9 22.4 \n",
"18 9.0 55.5 15.4 49.2 10.4 9.6 65.1 15.0 68.0 55.1 57.2 47.6 \n",
"19 42.4 8.7 50.7 2.4 38.8 44.9 18.3 43.2 21.2 10.0 12.2 41.7 \n",
"\n",
" 12 13 14 15 16 17 18 19 \n",
"0 51.9 56.3 6.0 11.8 14.6 60.7 9.0 42.4 \n",
"1 59.0 7.7 52.4 58.2 61.0 67.9 55.5 8.7 \n",
"2 60.2 64.6 8.0 4.1 4.8 69.0 15.4 50.7 \n",
"3 52.8 11.6 46.2 52.0 54.8 61.7 49.2 2.4 \n",
"4 48.2 52.7 7.4 13.1 16.0 57.0 10.4 38.8 \n",
"5 54.4 58.9 3.5 10.9 13.0 63.3 9.6 45.0 \n",
"6 68.7 10.8 62.1 67.9 70.7 77.6 65.1 18.3 \n",
"7 52.6 57.1 13.9 19.6 22.9 61.5 15.0 43.2 \n",
"8 70.6 23.0 64.0 69.7 73.6 80.4 67.0 21.2 \n",
"9 58.6 23.9 52.0 57.8 60.7 67.5 55.1 10.0 \n",
"10 60.8 26.1 54.2 59.9 62.8 69.6 57.2 12.2 \n",
"11 13.6 55.6 44.6 50.3 53.2 22.4 47.6 41.7 \n",
"12 0.0 64.4 53.4 59.1 62.0 11.6 56.4 50.5 \n",
"13 64.4 0.0 57.8 63.6 66.4 73.3 60.8 14.0 \n",
"14 53.4 57.8 0.0 7.0 9.8 62.1 8.6 43.9 \n",
"15 59.1 63.6 7.0 0.0 5.9 67.9 14.4 49.6 \n",
"16 62.0 66.4 9.9 5.9 0.0 70.8 17.2 52.5 \n",
"17 11.6 78.5 62.2 67.9 76.1 0.0 65.2 64.6 \n",
"18 56.4 60.8 8.6 14.3 17.2 65.2 0.0 46.9 \n",
"19 50.5 14.0 43.9 49.6 52.5 59.3 46.9 0.0 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Data1"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"path = [2,3,4,5,1]"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"def calculate_distance():\n",
" distance_route = []\n",
" last_distance = 0\n",
" distance = 0\n",
" for i in range(0,4):\n",
" source = path[i]\n",
" target = path[i+1]\n",
" distance_route.append(Data1.iloc[source][target])\n",
" for i in range(0,4):\n",
" source = path[len(path)-1]\n",
" target = path[len(path)-len(path)]\n",
" last_distance = Data1.iloc[source][target] \n",
" distance = sum(distance_route)+last_distance\n",
" return distance"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"215.2"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculate_distance()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"def get_last_distance():\n",
" last_distance = 0\n",
" for i in range(0,4):\n",
" source = path[len(path)-1]\n",
" target = path[len(path)-len(path)]\n",
" last_distance = Data1.iloc[source][target]\n",
" return last_distance "
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"59.2"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_last_distance()"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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