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{
"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": {
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},
"file_extension": ".py",
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"pygments_lexer": "ipython3",
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"nbformat_minor": 2
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distance_calculation.ipynb deleted 100644 → 0
{
"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>18</th>\n",
" <th>19</th>\n",
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" <th>1</th>\n",
" <td>50.9</td>\n",
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" <td>47.3</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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" <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",
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" <td>15.4</td>\n",
" <td>50.7</td>\n",
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" <td>44.7</td>\n",
" <td>6.2</td>\n",
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" <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",
" <td>0.0</td>\n",
" <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",
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" <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",
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" <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",
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" <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",
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" <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",
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" <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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