1020 lines
26 KiB
Plaintext
1020 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Make sure you run this at the begining**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"import math\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Append template path to sys path\n",
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"sys.path.append(os.getcwd() + \"/template\") "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils.load_data import load_data\n",
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"from utils.visualize_tsp import plotTSP\n",
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"\n",
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"from tsp import TSP_Bench_ONE\n",
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"from tsp import TSP_Bench_PATH\n",
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"from tsp import TSP_Bench_ALL"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Workshop Starts Here"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/tsp.jpg\" alt=\"TSP\" style=\"width: 900px;\"/>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/solutions.png\" alt=\"solutions\" style=\"width: 900px;\"/>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Get familiar with your dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"There are problems at different levels. **3 simple, 2 medium, 1 hard**."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"for root, _, files in os.walk('./template/data'):\n",
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" if(files):\n",
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" for f in files:\n",
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" print(str(root) + \"/\" + f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ulysses16 = np.array(load_data(\"./template/data/simple/ulysses16.tsp\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"ulysses16[:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"plt.scatter(ulysses16[:, 0], ulysses16[:, 1])\n",
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"for i in range(0, 16):\n",
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" plt.annotate(i, (ulysses16[i, 0], ulysses16[i, 1]+0.5))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Naive Solution: In Order"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"simple_sequence = list(range(0, 16))\n",
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"print(simple_sequence)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plotTSP([simple_sequence], ulysses16, num_iters=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Naive Solution: Random Permutation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"random_permutation = np.random.permutation(16).tolist()\n",
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"print(random_permutation)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plotTSP([random_permutation], ulysses16, num_iters=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Best Solution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"best_ulysses16 = [0, 13, 12, 11, 6, 5, 14, 4, 10, 8, 9, 15, 2, 1, 3, 7]\n",
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"plotTSP([best_ulysses16], ulysses16, num_iters=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Calculate Fitness (Sum of all Distances)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def dist(node_0, node_1, coords):\n",
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" \"\"\"\n",
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" Euclidean distance between two nodes.\n",
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" \"\"\"\n",
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" coord_0, coord_1 = coords[node_0], coords[node_1]\n",
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" return math.sqrt((coord_0[0] - coord_1[0]) ** 2 + (coord_0[1] - coord_1[1]) ** 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Coordinate of City 0:\", ulysses16[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Coordinate of City 1:\", ulysses16[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"print(\"Distance Between\", dist(0, 1, ulysses16))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def fitness(solution, coords):\n",
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" N = len(coords)\n",
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" cur_fit = 0\n",
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" for i in range(len(solution)):\n",
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" cur_fit += dist(solution[i % N], solution[(i + 1) % N], coords)\n",
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" return cur_fit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print (\"Order Fitness:\\t\", fitness(simple_sequence, ulysses16))\n",
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"print (\"Random Fitness:\\t\", fitness(random_permutation, ulysses16))\n",
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"print (\"Best Fitness:\\t\", fitness(best_ulysses16, ulysses16))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Naive Random Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import random\n",
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"from model.base_model import Model\n",
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"import numpy as np\n",
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"\n",
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"class MyRandomModel(Model):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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"\n",
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" def init(self, nodes):\n",
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" \"\"\"\n",
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" Put your initialization here.\n",
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" \"\"\"\n",
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" super().init(nodes)\n",
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"\n",
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" def fit(self, max_it=1000):\n",
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" \"\"\"\n",
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" Put your iteration process here.\n",
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" \"\"\"\n",
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" random_solutions = []\n",
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" for i in range(0, max_it):\n",
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" solution = np.random.permutation(self.N).tolist()\n",
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" random_solutions.append(solution)\n",
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" self.fitness_list.append(self.fitness(solution))\n",
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"\n",
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" self.best_solution = random_solutions[self.fitness_list.index(min(self.fitness_list))]\n",
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" return self.best_solution, self.fitness_list"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"tsp_file = './template/data/simple/ulysses16.tsp'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyRandomModel)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot(fitness_list, 'o-')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Minimum Spanning Tree (Depth First)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import random\n",
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"from model.base_model import Model\n",
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"\n",
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"class MyDFSModel(Model):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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"\n",
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" def init(self, nodes):\n",
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" \"\"\"\n",
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" Put your initialization here.\n",
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" \"\"\"\n",
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" super().init(nodes)\n",
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"\n",
|
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" def fit(self, max_it=1000):\n",
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" \"\"\"\n",
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" Put your iteration process here.\n",
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" \"\"\"\n",
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"\n",
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" MST_solutions = []\n",
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" # Depth First: Set one city as starting point, iterate to the end, then select next city as starting point.\n",
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" for i in range(0, self.N):\n",
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" solution = []\n",
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" solution.append(i)\n",
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" unvisited_list = list(range(0, self.N))\n",
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" cur_city = i\n",
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" # print(\"[starting]\", i)\n",
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" for steps in range(self.N - 1):\n",
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" # print(unvisited_list)\n",
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" unvisited_list.remove(cur_city)\n",
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" closest_neighbour = -1\n",
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" shortest_distance = math.inf\n",
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" for j in unvisited_list:\n",
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" if(self.dist(cur_city, j) < shortest_distance):\n",
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" closest_neighbour = j\n",
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" shortest_distance = self.dist(cur_city, j)\n",
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" solution.append(closest_neighbour)\n",
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" cur_city = closest_neighbour\n",
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" MST_solutions.append(solution)\n",
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" self.fitness_list.append(self.fitness(solution))\n",
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"\n",
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" self.best_solution = MST_solutions[ self.fitness_list.index(min(self.fitness_list)) ]\n",
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"\n",
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" return self.best_solution, self.fitness_list\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tsp_file = './template/data/simple/ulysses16.tsp'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyDFSModel)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot(fitness_list, 'o-')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Minimum Spanning Tree (Breadth First)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import random\n",
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"from model.base_model import Model\n",
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"\n",
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"class MyBFSModel(Model):\n",
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" def __init__(self):\n",
|
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" super().__init__()\n",
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"\n",
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" def init(self, nodes):\n",
|
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" \"\"\"\n",
|
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" Put your initialization here.\n",
|
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" \"\"\"\n",
|
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" super().init(nodes)\n",
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"\n",
|
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" def fit(self, max_it=1000):\n",
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" \"\"\"\n",
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" Put your iteration process here.\n",
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" \"\"\"\n",
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"\n",
|
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" UCS_solutions = []\n",
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" \n",
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" for i in range(0, self.N):\n",
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" solution = [i]\n",
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" UCS_solutions.append(solution)\n",
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" \n",
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" # Breadth First: Set each city as starting point, then go to next city simultaneously\n",
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" for step in range(0, self.N - 1):\n",
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" # print(\"[step]\", step)\n",
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" unvisited_list = list(range(0, self.N))\n",
|
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" # For each search path\n",
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" for i in range(0, self.N):\n",
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" cur_city = UCS_solutions[i][-1]\n",
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" unvisited_list = list( set(range(0, self.N)) - set(UCS_solutions[i]) )\n",
|
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" # print(unvisited_list)\n",
|
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" closest_neighbour = -1\n",
|
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" shortest_distance = math.inf\n",
|
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" for j in unvisited_list:\n",
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" if(self.dist(cur_city, j) < shortest_distance):\n",
|
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" closest_neighbour = j\n",
|
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" shortest_distance = self.dist(cur_city, j)\n",
|
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" UCS_solutions[i].append(closest_neighbour)\n",
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"\n",
|
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" for i in range(0, self.N):\n",
|
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" self.fitness_list.append(self.fitness(UCS_solutions[i]))\n",
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" \n",
|
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" self.best_solution = UCS_solutions[ self.fitness_list.index(min(self.fitness_list)) ]\n",
|
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" self.fitness_list.append(self.fitness(self.best_solution))\n",
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"\n",
|
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" return self.best_solution, self.fitness_list\n"
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]
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},
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{
|
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"cell_type": "code",
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|
"execution_count": null,
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|
"metadata": {
|
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"scrolled": true
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|
},
|
|
"outputs": [],
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"source": [
|
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"tsp_file = './template/data/simple/ulysses16.tsp'"
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]
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},
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{
|
|
"cell_type": "code",
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|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
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|
},
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|
"outputs": [],
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|
"source": [
|
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyBFSModel)"
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
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|
},
|
|
"outputs": [],
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"source": [
|
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"plt.plot(fitness_list, 'o-')"
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]
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},
|
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{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
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"## Dynamic Programming (DFS)"
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]
|
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},
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{
|
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"cell_type": "markdown",
|
|
"metadata": {},
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"source": [
|
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"Costs a lot of memory"
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import math\n",
|
|
"import random\n",
|
|
"from model.base_model import Model\n",
|
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"\n",
|
|
"class MyDPDModel(Model):\n",
|
|
" def __init__(self):\n",
|
|
" super().__init__()\n",
|
|
"\n",
|
|
" def init(self, nodes):\n",
|
|
" \"\"\"\n",
|
|
" Put your initialization here.\n",
|
|
" \"\"\"\n",
|
|
" super().init(nodes)\n",
|
|
"\n",
|
|
" def getMST(self, node):\n",
|
|
" MST = []\n",
|
|
" distances = []\n",
|
|
" for i in range(0, self.N):\n",
|
|
" if i != node:\n",
|
|
" MST.append(i)\n",
|
|
" distances.append(self.dist(node, i))\n",
|
|
" return [x for _,x in sorted(zip(distances, MST))]\n",
|
|
"\n",
|
|
" def fit(self, max_it=1000):\n",
|
|
" \"\"\"\n",
|
|
" Put your iteration process here.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" MST_solutions = []\n",
|
|
"\n",
|
|
" # Depth First: Set one city as starting point, iterate to the end, then select next city as starting point.\n",
|
|
" MSTs = []\n",
|
|
" for i in range(0, self.N):\n",
|
|
" MSTs.append([-1] * self.N)\n",
|
|
" for i in range(0, self.N):\n",
|
|
" solution = []\n",
|
|
" solution.append(i)\n",
|
|
" unvisited_list = list(range(0, self.N))\n",
|
|
" cur_city = i\n",
|
|
" # print(\"[starting]\", i)\n",
|
|
" for steps in range(self.N - 1):\n",
|
|
" # print(unvisited_list)\n",
|
|
" unvisited_list.remove(cur_city)\n",
|
|
" if MSTs[cur_city][0] == -1:\n",
|
|
" MST = self.getMST(cur_city)\n",
|
|
" MSTs[cur_city] = MST\n",
|
|
" \n",
|
|
" for j in MSTs[cur_city]:\n",
|
|
" if(j in unvisited_list):\n",
|
|
" solution.append(j)\n",
|
|
" cur_city = j\n",
|
|
" break\n",
|
|
" # print(solution)\n",
|
|
" MST_solutions.append(solution)\n",
|
|
" self.fitness_list.append(self.fitness(solution))\n",
|
|
"\n",
|
|
" self.best_solution = MST_solutions[ self.fitness_list.index(min(self.fitness_list)) ]\n",
|
|
"\n",
|
|
" return self.best_solution, self.fitness_list\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tsp_file = './template/data/simple/ulysses16.tsp'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyDPDModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.plot(fitness_list, 'o-')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Dynamic Programming (BFS)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import math\n",
|
|
"import random\n",
|
|
"from model.base_model import Model\n",
|
|
"\n",
|
|
"class MyDPBModel(Model):\n",
|
|
" def __init__(self):\n",
|
|
" super().__init__()\n",
|
|
"\n",
|
|
" def init(self, nodes):\n",
|
|
" \"\"\"\n",
|
|
" Put your initialization here.\n",
|
|
" \"\"\"\n",
|
|
" super().init(nodes)\n",
|
|
"\n",
|
|
" def getMST(self, node):\n",
|
|
" MST = []\n",
|
|
" distances = []\n",
|
|
" for i in range(0, self.N):\n",
|
|
" if i != node:\n",
|
|
" MST.append(i)\n",
|
|
" distances.append(self.dist(node, i))\n",
|
|
" return [x for _,x in sorted(zip(distances, MST))]\n",
|
|
"\n",
|
|
" def fit(self, max_it=1000):\n",
|
|
" \"\"\"\n",
|
|
" Put your iteration process here.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" MST_solutions = []\n",
|
|
" \n",
|
|
" for i in range(0, self.N):\n",
|
|
" solution = [i]\n",
|
|
" MST_solutions.append(solution)\n",
|
|
" \n",
|
|
" MSTs = []\n",
|
|
" for i in range(0, self.N):\n",
|
|
" MSTs.append([-1] * self.N)\n",
|
|
"\n",
|
|
" # Breadth First: Set each city as starting point, then go to next city simultaneously\n",
|
|
" for step in range(0, self.N - 1):\n",
|
|
" # print(\"[step]\", step)\n",
|
|
" unvisited_list = list(range(0, self.N))\n",
|
|
" # For each search path\n",
|
|
" for i in range(0, self.N):\n",
|
|
" cur_city = MST_solutions[i][-1]\n",
|
|
" unvisited_list = list( set(range(0, self.N)) - set(MST_solutions[i]) )\n",
|
|
"\n",
|
|
" if MSTs[cur_city][0] == -1:\n",
|
|
" MST = self.getMST(cur_city)\n",
|
|
" MSTs[cur_city] = MST\n",
|
|
"\n",
|
|
" for j in MSTs[cur_city]:\n",
|
|
" if(j in unvisited_list):\n",
|
|
" MST_solutions[i].append(j)\n",
|
|
" break\n",
|
|
"\n",
|
|
" for i in range(0, self.N):\n",
|
|
" self.fitness_list.append(self.fitness(MST_solutions[i]))\n",
|
|
" \n",
|
|
" self.best_solution = MST_solutions[ self.fitness_list.index(min(self.fitness_list)) ]\n",
|
|
" self.fitness_list.append(self.fitness(self.best_solution))\n",
|
|
"\n",
|
|
" return self.best_solution, self.fitness_list\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tsp_file = './template/data/simple/ulysses16.tsp'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyDPBModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.plot(fitness_list, 'o-')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Your Smart Model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import math\n",
|
|
"import random\n",
|
|
"from model.base_model import Model\n",
|
|
"\n",
|
|
"class MyModel(Model):\n",
|
|
" def __init__(self):\n",
|
|
" super().__init__()\n",
|
|
"\n",
|
|
" def init(self, nodes):\n",
|
|
" \"\"\"\n",
|
|
" Put your initialization here.\n",
|
|
" \"\"\"\n",
|
|
" super().init(nodes)\n",
|
|
"\n",
|
|
" self.log(\"Nothing to initialize in your model now\")\n",
|
|
"\n",
|
|
" def fit(self, max_it=1000):\n",
|
|
" \"\"\"\n",
|
|
" Put your iteration process here.\n",
|
|
" \"\"\"\n",
|
|
" self.best_solution = np.random.permutation(self.N).tolist()\n",
|
|
" self.fitness_list.append(self.fitness(self.best_solution))\n",
|
|
"\n",
|
|
" return self.best_solution, self.fitness_list"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Test your Model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tsp_problem = './template/data/simple/ulysses16.tsp'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Test All Dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"tsp_path = './template'\n",
|
|
"for root, _, files in os.walk(tsp_path + '/data'):\n",
|
|
" if(files):\n",
|
|
" for f in files:\n",
|
|
" print(str(root) + \"/\" + f)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def plot_results(best_solutions, times, title):\n",
|
|
" fig = plt.figure()\n",
|
|
" nodes = [len(s) for s in best_solutions]\n",
|
|
" data = np.array([[node, time] for node, time in sorted(zip(nodes, times))])\n",
|
|
" plt.plot(data[:, 0], data[:, 1], 'o-')\n",
|
|
" fig.suptitle(title, fontsize=20)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(\"Random Search\")\n",
|
|
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyRandomModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plot_results(best_solutions, times, \"Random Model\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(\"Depth First Search\")\n",
|
|
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyDFSModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plot_results(best_solutions, times, \"Depth First Search\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(\"Breadth First Search\")\n",
|
|
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyBFSModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plot_results(best_solutions, times, \"Breadth First Search\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(\"Dynamic Programming (Depth First)\")\n",
|
|
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyDPDModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plot_results(best_solutions, times, \"Dynamic Programming (Depth First)\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(\"Dynamic Progrmaming (Breadth First)\")\n",
|
|
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyDPBModel)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plot_results(best_solutions, times, \"Dynamic Programming (Breadth First)\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Conclusions (Random, BFS, DFS, DP)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Simple\n",
|
|
"# ulysses16: 77 (BFS), 84 (DFS)\n",
|
|
"# att48: 39236 (BFS), 40763 (DFS)\n",
|
|
"# st70: 761 (BFS), 901 (DFS)\n",
|
|
"\n",
|
|
"# Medium\n",
|
|
"# a280: 3088 (BFS), 3558 (DFS)\n",
|
|
"# pcb442: 58952 (BFS), 61984 (DFS)\n",
|
|
"\n",
|
|
"# Hard\n",
|
|
"# dsj1000: time-out (DP-BFS) 23,552,227 (DP-DFS)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<p style=\"font-size: 18px\"> 1. All different models get <strong>the same results</strong> every time (except random). </p>\n",
|
|
"<p style=\"font-size: 18px\"> 2. All different models have an <strong>exponential time complexity</strong> (except random). </p>\n",
|
|
"<p style=\"font-size: 18px\"> 3. Depth First Seach is a little faster than Breadth First Search, but Breadth First get better results. </p>\n",
|
|
"<p style=\"font-size: 18px\"> 4. Only <strong>dynamic programming</strong> solves the problem with 1000 cities (Fast). </p>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# In the next workshop \n",
|
|
"# Will try to solve the problem with 1000 cities faster, and get better results"
|
|
]
|
|
},
|
|
{
|
|
"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": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|