From b67e685168f12100c746da2a2cf98124c7e1c730 Mon Sep 17 00:00:00 2001
From: ratna kasmala <iss16009@students.del.ac.id>
Date: Fri, 24 Jul 2020 11:49:49 +0700
Subject: [PATCH] Delete ABC_getting_distance.ipynb
---
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1 file changed, 652 deletions(-)
delete mode 100644 ABC_getting_distance.ipynb
diff --git a/ABC_getting_distance.ipynb b/ABC_getting_distance.ipynb
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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": []
- }
- ],
- "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
-}
--
libgit2 0.27.1