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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": 39,
"metadata": {},
"outputs": [],
"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": 40,
"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": 41,
"metadata": {},
"outputs": [],
"source": [
"#Data1\n",
"#Data2\n",
"#Data3"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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": 43,
"metadata": {},
"outputs": [],
"source": [
"#cost = input()\n",
"cost = 400000\n",
"Cost = int(cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Data"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[13, 28, 35, 0, 7, 19, 26]"
]
},
"execution_count": 143,
"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": 45,
"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": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"720"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Fitness_value.getting_max_distance()"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[13, 28, 35, 0, 7, 19, 26]"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Data4"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [],
"source": [
"class Bee:\n",
" def __init__(self, node_set):\n",
" self.role = ''\n",
" self.path = list(node_set) # stores all nodes in each bee, will randomize foragers\n",
" self.distance = 0\n",
" self.temperature = 0\n",
" self.cycle = 0 \n",
" self.cost = 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Maximum_distance = Fitness_value.getting_max_distance()\n",
"Maximum_cost = Fitness_value.getting_max_cost()\n",
"path = list(Data[\"ID_City\"])\n",
"def get_total_cost(path):\n",
" cost_route = []\n",
" cost = 0\n",
" for i in range(len(path)):\n",
" cost_route.append(Data5.iloc[i][4])\n",
" cost = sum(cost_route)\n",
" return cost "
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"def getting_distance(Path):\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"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"404.6"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_distance()"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [],
"source": [
"def initialize_hive(population, data):\n",
" path = Data4\n",
" hive = [Bee(path) for i in range (0, population)]\n",
" return hive"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [],
"source": [
"def assign_roles(hive, role_percentiles):\n",
" forager_percent = 0.5\n",
" onlooker_percent = 0.5\n",
" role_percent = [onlooker_percent, forager_percent]\n",
" scout_percent = 0.2\n",
" population = len(hive)\n",
" onlooker_count = math.floor(population * role_percentiles[0])\n",
" forager_count = math.floor(population * role_percentiles[1])\n",
" for i in range(0, onlooker_count):\n",
" hive[i].role = 'O'\n",
" for i in range(onlooker_count, (onlooker_count + forager_count)):\n",
" hive[i].role = 'F'\n",
" random.shuffle(hive[i].path)\n",
" hive[i].distance = getting_distance(hive[i].path)\n",
" return hive"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [],
"source": [
"def mutate_path(path):\n",
" # - will go out of range if last element is chosen.\n",
" path = Data4\n",
" new_path = random.sample(path,len(path))\n",
" return new_path"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[13, 26, 0, 28, 35, 7, 19]"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mutate_path(path)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [],
"source": [
"def forage(bee,limit):\n",
" new_path = mutate_path(bee.path)\n",
" new_distance = getting_distance(new_path)\n",
" if new_distance < bee.distance:\n",
" bee.path = new_path\n",
" bee.distance = new_distance\n",
" bee.cycle = 0 # reset cycle so bee can continue to make progress\n",
" else:\n",
" bee.cycle += 1\n",
" if bee.cycle >= limit: # if bee is not making progress\n",
" bee.role = 'S'\n",
" return bee.distance, list(bee.path)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
"def scout(bee):\n",
" new_path = list(bee.path)\n",
" random.shuffle(new_path)\n",
" bee.path = new_path\n",
" bee.distance = getting_distance(bee.path)\n",
" # bee.temperature = Weather\n",
" bee.role = 'F'\n",
" bee.cycle = 0"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [],
"source": [
"def waggle(hive, best_distance,forager_limit, scout_count):\n",
" best_path = []\n",
" results = []\n",
" for i in range(0, len(hive)):\n",
" if hive[i].role == 'F':\n",
" distance, path = forage(hive[i], forager_limit)\n",
" if distance < best_distance:\n",
" best_distance = distance\n",
" best_path = list(hive[i].path)\n",
" results.append((i, distance))\n",
"\n",
" elif hive[i].role == 'S':\n",
" scout(hive[i])\n",
" # after processing all bees, set worst performers to scout\n",
" results.sort(reverse = True, key=lambda tup: tup[1])\n",
" scouts = [ tup[0] for tup in results[0:int(scout_count)] ]\n",
" for new_scout in scouts:\n",
" hive[new_scout].role = 'S'\n",
" return best_distance, best_path"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [],
"source": [
"def recruit(hive, best_distance, best_path):\n",
" for i in range(0, len(hive)):\n",
" if hive[i].role == 'O':\n",
" new_path = mutate_path(best_path)\n",
" new_distance = getting_distance(new_path)\n",
" if new_distance < best_distance:\n",
" best_distance = new_distance\n",
" best_path = new_path\n",
" return best_distance, best_path"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [],
"source": [
"def print_details(cycle, path, distance,bee):\n",
" \"\"\"\n",
" Prints cycle details to console.\n",
" \"\"\"\n",
" print(\"CYCLE: {}\".format(cycle))\n",
" print(\"PATH: {}\".format(path))\n",
" print(\"DISTANCE: {}\".format(distance))\n",
" # print(\"COST: {}\".format(cost))\n",
" # print(\"TEMPERATURE: {}\".format(temperature))\n",
" print(\"BEE: {}\".format(bee))\n",
" print(\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [],
"source": [
"def main():\n",
" # Control parameters\n",
" population = 40\n",
" forager_percent = 0.5\n",
" onlooker_percent = 0.4\n",
" role_percent = [onlooker_percent, forager_percent]\n",
" scout_percent = 0.01\n",
" scout_count = math.ceil(population * scout_percent)\n",
" forager_limit = 500\n",
" cycle_limit = 100\n",
" cycle = 1\n",
" # temperature = Weather\n",
" # Data source\n",
" # data = read_data_from_csv(\"data/data_10.csv\")\n",
" # data = read_data_from_csv(\"data/data_11.csv\")\n",
" data = Data4\n",
" # Global vars\n",
" best_distance = sys.maxsize\n",
" best_path = []\n",
" result = ()\n",
" # Initialization\n",
" hive = initialize_hive(population, data)\n",
" assign_roles(hive, role_percent)\n",
"# cost = get_total_cost(path)\n",
" while cycle < cycle_limit:\n",
" waggle_distance,waggle_path = waggle(hive, best_distance,forager_limit,scout_count)\n",
" if (waggle_distance < best_distance) and (waggle_distance <= Maximum_distance):\n",
" best_distance = waggle_distance\n",
" best_path = list(waggle_path)\n",
" # cost = get_total_cost(path)\n",
" # temperature = Weather\n",
" print_details(cycle, best_path, best_distance,'F')\n",
" result = (cycle, best_path, best_distance,'F')\n",
" recruit_distance,recruit_path = recruit(hive, best_distance,best_path)\n",
" if (recruit_distance < best_distance) and (recruit_distance <= Maximum_distance):\n",
" best_path = list(recruit_path)\n",
" best_distance = recruit_distance \n",
"# cost = get_total_cost(path)\n",
" print_details(cycle, best_path, best_distance,'R')\n",
" result = (cycle, best_path, best_distance,'R')\n",
" if cycle % 100 == 0:\n",
" print(\"CYCLE #: {}\\n\".format(cycle))\n",
" cycle += 1"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CYCLE: 1\n",
"PATH: [19, 35, 13, 7, 28, 26, 0]\n",
"DISTANCE: 404.6\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [7, 0, 35, 26, 13, 19, 28]\n",
"DISTANCE: 404.6\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [0, 26, 7, 13, 35, 19, 28]\n",
"DISTANCE: 404.6\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [0, 28, 7, 26, 35, 13, 19]\n",
"DISTANCE: 404.6\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [13, 28, 0, 26, 19, 7, 35]\n",
"DISTANCE: 404.6\n",
"BEE: F\n",
"\n",
"\n"
]
}
],
"source": [
"if __name__ == '__main__':\n",
" for i in range (0, 5):\n",
" \n",
" main()\n",
"\n",
" # main()"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [],
"source": [
"def getting_best_path():\n",
" # Control parameters\n",
" population = 40\n",
" forager_percent = 0.5\n",
" onlooker_percent = 0.4\n",
" role_percent = [onlooker_percent, forager_percent]\n",
" scout_percent = 0.01\n",
" scout_count = math.ceil(population * scout_percent)\n",
" forager_limit = 500\n",
" cycle_limit = 100\n",
" cycle = 1\n",
" best_distance = sys.maxsize\n",
" best_path = []\n",
" result = ()\n",
" data = Data4\n",
" # Initialization\n",
" hive = initialize_hive(population, data)\n",
" assign_roles(hive, role_percent)\n",
" #cost = get_total_cost(path)\n",
" waggle_distance,waggle_path = waggle(hive, best_distance,forager_limit,scout_count)\n",
" if (waggle_distance < best_distance) and (waggle_distance <= Maximum_distance):\n",
" best_distance = waggle_distance\n",
" best_path = list(waggle_path)\n",
" recruit_distance,recruit_path = recruit(hive, best_distance,best_path)\n",
" if (recruit_distance < best_distance) and (recruit_distance <= Maximum_distance):\n",
" best_path = list(recruit_path)\n",
" best_distance = recruit_distance \n",
" return best_path"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[19, 0, 13, 26, 7, 28, 35]"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_path = getting_best_path()\n",
"best_path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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