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Add notebooks folder
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‎Deskripsi.txt‎

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Python File/.ipynb_checkpoints/Priority_Task_Selection_Using_Evolutionary_Programming-checkpoint.ipynb renamed to notebooks/.ipynb_checkpoints/Priority_Task_Selection_Using_Evolutionary_Programming-checkpoint.ipynb

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Python File/Priority_Task_Selection_Using_Evolutionary_Programming.ipynb renamed to notebooks/Priority_Task_Selection_Using_Evolutionary_Programming.ipynb

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‎notebooks/Untitled0.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "dyucO8hkBMJG",
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"outputId": "4d160b21-695c-4595-9dd0-585f4123ac2d"
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"item_number = np.arange(1,11)\n",
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"population_size = (10, item_number.shape[0])\n",
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"initial_population_x = np.random.randint(2, size = population_size)\n",
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"initial_population_sigma = np.random.randint(range(0,10), 10)\n",
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"initial_population = []\n",
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"initial_population.append(initial_population_x)\n",
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"initial_population.append(initial_population_sigma)\n",
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"print(initial_population[0])"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[[0 1 1 0 1 1 1 0 1 1]\n",
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" [1 1 1 1 1 0 0 0 1 1]\n",
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" [1 1 0 0 1 1 1 1 1 0]\n",
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" [1 0 0 0 1 1 1 0 0 0]\n",
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" [0 0 0 1 1 0 0 1 0 1]\n",
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" [1 0 0 1 1 1 0 1 1 0]\n",
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" [0 0 0 1 0 0 0 1 1 0]\n",
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" [1 1 0 0 1 1 0 0 0 1]\n",
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" [0 1 1 1 1 0 0 0 0 0]\n",
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" [1 0 0 0 1 0 1 0 0 1]]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "rvLr9PTuI04w",
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"outputId": "784dabe5-692c-4985-f2aa-b3fadd44faa0"
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},
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"source": [
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"j = 10\n",
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"for i in range(j):\n",
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" print(i)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"0\n",
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"1\n",
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"2\n",
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"3\n",
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"4\n",
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"5\n",
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"6\n",
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"7\n",
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"8\n",
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"9\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "rDT8QmcwLF9F"
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},
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import random as rd\n",
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"from random import randint\n",
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"import matplotlib.pyplot as plt"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "kDC40x5_Mipm"
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},
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"source": [
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"def cal_fitness(weight, value, population, threshold):\n",
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" fitness = np.empty(population.shape[0])\n",
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" for i in range(population.shape[0]):\n",
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" S1 = np.sum(population[i] * value)\n",
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" S2 = np.sum(population[i] * weight)\n",
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" if S2 <= threshold:\n",
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" fitness[i] = S1\n",
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" else :\n",
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" fitness[i] = 0\n",
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" return fitness.astype(int)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "nwmnOecxM7f9"
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},
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"source": [
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"def generate_offsprings_func(population, sigma, alpha):\n",
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" offsprings = np.empty((num_offsprings, parents.shape[1]))\n",
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" for i in range(offsprings.shape[0]):\n",
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" random_value = rd.randint(0,10)\n",
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"\t\tif (random_value > 5):\n",
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"\t\t\toffsprings[i, ]\n",
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" mutants[i,:] = offsprings[i,:]\n",
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" if random_value > mutation_rate:\n",
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" continue\n",
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" int_random_value = randint(0,offsprings.shape[1]-1)\n",
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" if mutants[i,int_random_value] == 0 :\n",
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" mutants[i,int_random_value] = 1\n",
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" else :\n",
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" mutants[i,int_random_value] = 0\n",
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" return offsprings"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Z6MLyKIXMow3"
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},
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"source": [
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"def selection(fitness, num_parents, population):\n",
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" fitness = list(fitness)\n",
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" parents = np.empty((num_parents, population.shape[1]))\n",
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" for i in range(num_parents):\n",
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" index=weighted_random_choice(fitness)\n",
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" parents[i,:] = population[index, :]\n",
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" return parents\n",
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"\n",
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"def crossover(parents, num_offsprings):\n",
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" offsprings = np.empty((num_offsprings, parents.shape[1]))\n",
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" crossover_point = int(parents.shape[1]/2)\n",
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" crossover_rate = 0.9\n",
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" i=0\n",
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" while (parents.shape[0] < num_offsprings):\n",
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" parent1_index = i%parents.shape[0]\n",
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" parent2_index = (i+1)%parents.shape[0]\n",
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" x = rd.random()\n",
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" if x > crossover_rate:\n",
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" continue\n",
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" parent1_index = i%parents.shape[0]\n",
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" parent2_index = (i+1)%parents.shape[0]\n",
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" offsprings[i,0:crossover_point] = parents[parent1_index,0:crossover_point]\n",
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" offsprings[i,crossover_point:] = parents[parent2_index,crossover_point:]\n",
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" i=+1\n",
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" return offsprings\n",
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"\n",
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"def mutation(offsprings):\n",
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" mutants = np.empty((offsprings.shape))\n",
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" mutation_rate = 0.2\n",
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" for i in range(mutants.shape[0]):\n",
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" random_value = rd.random()\n",
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" mutants[i,:] = offsprings[i,:]\n",
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" if random_value > mutation_rate:\n",
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" continue\n",
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" int_random_value = randint(0,offsprings.shape[1]-1)\n",
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" if mutants[i,int_random_value] == 0 :\n",
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" mutants[i,int_random_value] = 1\n",
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" else :\n",
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" mutants[i,int_random_value] = 0\n",
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" return mutants\n",
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"\n",
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"def elitism(mutants, population, fitness, weight, value, threshold):\n",
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" elitis = np.empty((mutants.shape))\n",
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" elitis = mutants\n",
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" parents_max_loc = np.where(fitness == np.max(fitness))[0]\n",
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" parents_max_loc_real = parents_max_loc[0]\n",
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" fitness_mutants = cal_fitness(weight, value, mutants, threshold)\n",
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" mutants_min_loc = np.where(fitness_mutants == np.min(fitness_mutants))\n",
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" mutants_min_loc_real = mutants_min_loc[0]\n",
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" elitis[mutants_min_loc_real] = population[parents_max_loc_real]\n",
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" return elitis"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "TxRkoHxBMvb1"
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},
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"source": [
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"def optimize(weight, value, population, pop_size, num_generations, threshold):\n",
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" parameters, fitness_history = [], []\n",
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" num_parents = int(pop_size[0])\n",
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" num_offsprings = pop_size[0]\n",
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" for i in range(num_generations-1):\n",
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" fitness = cal_fitness(weight, value, population, threshold)\n",
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" fitness_history.append(fitness)\n",
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" parents = selection(fitness, num_parents, population)\n",
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" offsprings = crossover(parents, num_offsprings)\n",
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" mutants = mutation(offsprings)\n",
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" population = elitism(mutants, population, fitness, weight, value, threshold)\n",
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"\n",
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" fitness_last_gen = cal_fitness(weight, value, population, threshold)\n",
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" max_fitness = np.where(fitness_last_gen == np.max(fitness_last_gen))\n",
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" parameters.append(population[max_fitness[0][0],:])\n",
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" print (parameters, fitness_history)\n",
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" return parameters, fitness_history"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "3s0k7zZzMxOM"
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},
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"source": [
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"def kalkulasi(nama_makanan, value_makanan, harga_makanan, uang) :\n",
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"\tnumpy_nama_makanan = np.array([i for i in nama_makanan.split(',')])\n",
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"\tnumpy_value_makanan = np.array([int(i) for i in value_makanan.split(',')])\n",
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"\tnumpy_harga_makanan = np.array([int(i) for i in harga_makanan.split(',')])\n",
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"\n",
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"\titem_number = np.arange(1,11)\n",
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"\tweight = numpy_harga_makanan\n",
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"\tvalue = numpy_value_makanan\n",
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"\tknapsack_threshold = int(uang)\n",
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"\n",
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"\tsolutions_per_pop = 10\n",
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"\tpop_size = (solutions_per_pop, item_number.shape[0])\n",
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"\tinitial_population = np.random.randint(2, size = pop_size)\n",
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"\tinitial_population = initial_population.astype(int)\n",
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"\tnum_generations = 50\n",
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"\n",
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"\tparameters, fitness_history = optimize(weight, value, initial_population, pop_size, num_generations, knapsack_threshold)\n",
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"\tselected_items = item_number * parameters\n",
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"\n",
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"\thasil_nama_makanan = []\n",
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"\tkalori = 0\n",
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"\tuang_yang_dibutuhkan = 0\n",
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"\tfor i in selected_items[0] :\n",
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"\t\tif (i!=0) :\n",
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"\t\t\thasil_nama_makanan.append(numpy_nama_makanan[int(i)-1])\n",
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"\t\t\tkalori = kalori + numpy_value_makanan[int(i)-1]\n",
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"\t\t\tuang_yang_dibutuhkan = uang_yang_dibutuhkan + numpy_harga_makanan[int(i)-1]\n",
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"\n",
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"\thasil_string = \"\"\n",
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"\tfor i in hasil_nama_makanan :\n",
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"\t\thasil_string = hasil_string + i + \",\"\n",
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"\ttotal = []\n",
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"\ttotal.append(hasil_string)\n",
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"\ttotal.append(kalori)\n",
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"\ttotal.append(uang_yang_dibutuhkan)\n",
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"\n",
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"\treturn total"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "raUezsc5LJGg",
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"outputId": "25a091c7-ed95-4c7f-b77e-42bf28703ef2"
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},
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"source": [
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"nama_makanan = \"Tugas Alevo, Tugas TBI, Menyuci baju, Mengepel lantai, Menyetrika baju, Memandikan kucing, Mengisi bak mandi, Bermain badminton, Mengurus anak, Mengisi galon\"\n",
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"value_makanan = \"4, 4, 3, 1, 2, 1, 1, 2, 3, 2\"\n",
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"harga_makanan = \"5, 4, 2, 1, 2, 1, 1, 3, 3, 1\"\n",
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"uang = 15\n",
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"kalkulasi(nama_makanan, value_makanan, harga_makanan, uang)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[array([0., 1., 1., 1., 1., 1., 1., 0., 1., 1.])] [array([ 0, 7, 11, 10, 14, 6, 8, 3, 10, 5]), array([14, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([10, 17, 10, 10, 10, 14, 10, 10, 14, 14]), array([17, 17, 17, 15, 17, 17, 17, 17, 17, 17]), array([10, 14, 14, 10, 10, 17, 17, 17, 10, 10]), array([17, 15, 17, 13, 15, 15, 15, 17, 17, 17]), array([10, 14, 10, 10, 10, 10, 17, 17, 10, 10]), array([17, 17, 14, 17, 17, 17, 15, 17, 17, 17]), array([13, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 15, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 16, 17, 17, 13, 17, 13, 17, 17]), array([14, 17, 17, 17, 17, 17, 15, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 15, 17, 16, 17, 17, 17, 17, 15]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([16, 17, 17, 14, 17, 17, 17, 17, 17, 14]), array([17, 17, 17, 17, 17, 16, 17, 17, 17, 17]), array([16, 17, 17, 17, 17, 15, 16, 17, 14, 14]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([16, 14, 13, 17, 17, 14, 17, 17, 17, 14]), array([17, 17, 17, 17, 17, 17, 17, 15, 17, 17]), array([11, 17, 17, 17, 14, 12, 17, 14, 17, 17]), array([17, 17, 17, 17, 15, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 12, 17, 17, 12, 13]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 12, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 15, 16, 17, 17, 17, 17, 17, 17, 16]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 16, 16, 17, 17, 17, 17, 17, 16, 16]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 16, 17, 17, 17, 16, 16]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 16, 17, 17, 17]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 16, 17, 17, 17, 17, 17, 17, 17]), array([17, 17, 17, 17, 15, 17, 17, 17, 17, 17]), array([17, 16, 16, 16, 14, 17, 17, 15, 17, 17]), array([17, 17, 15, 17, 17, 17, 17, 15, 17, 17]), array([17, 17, 17, 16, 17, 17, 17, 17, 17, 16])]\n"
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],
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"name": "stdout"
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"[' Tugas TBI, Menyuci baju, Mengepel lantai, Menyetrika baju, Memandikan kucing, Mengisi bak mandi, Mengurus anak, Mengisi galon,',\n",
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" 17,\n",
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" 15]"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 31
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}
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]
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}
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]
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}

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