2020-10-01 16:03:04 +00:00
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{
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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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"collapsed": true,
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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2020-11-01 18:24:26 +00:00
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"## Second Lab\n",
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"\n",
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"What we are going to do today:\n",
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"- Recap of the previous Lab\n",
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"- Define the nearest neighbor method and use it\n",
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"- Define the Best Nearest Neighbors and use it \n",
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"- Define the Multi Fragment and use it\n",
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"- Finally compare these Constructive Methods"
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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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"This cell below is simply importing some useful stuff for later"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import glob\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"from time import time as t"
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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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"## Recap\n",
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"### The problems\n",
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"As we saw last time, we have 12 problems and two have an optimal 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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"- ch130.tsp\n",
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"- d198.tsp\n",
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"- eil76.tsp\n",
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"- fl1577.tsp\n",
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"- kroA100.tsp\n",
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"- lin318.tsp\n",
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"- pcb442.tsp\n",
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"- pr439.tsp\n",
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"- rat783.tsp\n",
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"- u1060.tsp\n"
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]
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}
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],
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"source": [
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"problems = glob.glob('../problems/*.tsp')\n",
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"for prob in problems:\n",
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" if prob in [\"../problems/eil76.tsp\", \"../problems/kroA100.tsp\"]:\n",
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" print(f\"- {prob[12:]} with opt\", sep='\\t')\n",
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" else:\n",
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" print(f\"- {prob[12:]}\", sep='\\t')"
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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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"#### ProblemInstance Class\n",
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"Last time we implemented an ProblemInstance class that we can use to load a problem. "
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.io_tsp import ProblemInstance"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"#############################\n",
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"name: eil76\n",
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"nPoints: 76\n",
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"best_sol: 538.0\n",
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"exist optimal: True\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 576x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0. 15. 23. ... 18. 23. 25.]\n",
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" [15. 0. 24. ... 7. 12. 15.]\n",
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" [23. 24. 0. ... 31. 21. 20.]\n",
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" ...\n",
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" [18. 7. 31. ... 0. 17. 20.]\n",
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" [23. 12. 21. ... 17. 0. 3.]\n",
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" [25. 15. 20. ... 20. 3. 0.]]\n"
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]
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}
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],
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"source": [
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"example_problem = \"../problems/eil76.tsp\"\n",
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"p_inst = ProblemInstance(example_problem)\n",
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"p_inst.print_info()\n",
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"p_inst.plot_data()\n",
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"print(p_inst.dist_matrix)"
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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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"### TSPSolver Class\n",
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"Even here, instead of reimplementing the class everytime, we can import the one I prepared before.\n",
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"This implementation is ready to receive a lot of different types of problems"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.TSP_solver import TSPSolver\n",
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"from src.constructive_algorithms import random_method"
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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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"The class implements the following methods:\n",
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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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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"### solving with ['random'] ####\n"
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]
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},
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{
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"data": {
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"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 576x576 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
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|
|
},
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"best solution with len 2484.0 \n",
|
|
|
|
|
"with a total time of 0.0 seconds\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"available_solvers = {\"random\": random_method}\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"solver = TSPSolver(\"random\", p_inst, available_solvers)\n",
|
|
|
|
|
"start = t()\n",
|
|
|
|
|
"solver.compute_solution(return_value=False, verbose=True)\n",
|
|
|
|
|
"end = t()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"solver.plot_solution()\n",
|
|
|
|
|
"print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"### Let's analyse the performances of the random Method\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 7,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"scrolled": true
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 800x600 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"best solution 2146.0 on 1000 trials,\n",
|
|
|
|
|
"with a total time of 0.6 seconds\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"\n",
|
|
|
|
|
"number_of_trials = 1000\n",
|
|
|
|
|
"found_lens = []\n",
|
|
|
|
|
"start = t()\n",
|
|
|
|
|
"for _ in range(number_of_trials):\n",
|
|
|
|
|
" solver.compute_solution(return_value=False, verbose=False)\n",
|
|
|
|
|
" found_lens.append(solver.found_length)\n",
|
|
|
|
|
"end = t()\n",
|
|
|
|
|
"fig=plt.figure(figsize=(8,6), dpi= 100, facecolor='w', edgecolor='k')\n",
|
|
|
|
|
"plt.ylabel(\"Solution Length\")\n",
|
|
|
|
|
"plt.xlabel(\"Tries\")\n",
|
|
|
|
|
"plt.plot(found_lens, \"*\",label=\"Random\")\n",
|
|
|
|
|
"plt.plot(np.ones(number_of_trials)*p_inst.best_sol, \"r-\",label=\"Optimal\")\n",
|
|
|
|
|
"plt.legend(loc=\"center right\")\n",
|
|
|
|
|
"plt.show()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"print(f\"best solution {min(found_lens)} on {number_of_trials} trials,\\nwith a total time of {np.round(end - start, 2)} seconds\")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"-------------\n",
|
|
|
|
|
"## Constructive Algorithm\n",
|
|
|
|
|
"### Nearest Neighbor Method"
|
|
|
|
|
]
|
|
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|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 8,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"def nn( instance_, starting_node=0): \n",
|
|
|
|
|
" # TODO\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" # [...]\n",
|
|
|
|
|
" tour.append(starting_node)\n",
|
|
|
|
|
" return np.array(tour)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 9,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"### solving with ['nn'] ####\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 576x576 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"best solution with len 680.0 \n",
|
|
|
|
|
"with a total time of 0.00199 seconds\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"available_solvers = {\"random\": random_method,\n",
|
|
|
|
|
" \"nn\":nn}\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"solver = TSPSolver(\"nn\", p_inst, available_solvers)\n",
|
|
|
|
|
"start = t()\n",
|
|
|
|
|
"solver.compute_solution(return_value=False, verbose=True)\n",
|
|
|
|
|
"end = t()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"solver.plot_solution()\n",
|
|
|
|
|
"print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {
|
|
|
|
|
"id": "A25UdIeTJOp8"
|
|
|
|
|
},
|
|
|
|
|
"source": [
|
|
|
|
|
"### Best Nearest Neighbors"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 10,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"id": "i_cC5eA-XT0B"
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"def best_nn(instance):\n",
|
|
|
|
|
" # TODO\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" # [...]\n",
|
|
|
|
|
" return solution\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 11,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"colab": {
|
|
|
|
|
"base_uri": "https://localhost:8080/",
|
|
|
|
|
"height": 534
|
|
|
|
|
},
|
|
|
|
|
"id": "lRAGk5zEXW0U",
|
|
|
|
|
"outputId": "493f692e-ef0c-4a94-dfc9-6726e9a5ac43"
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"### solving with ['best_nn'] ####\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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"text/plain": [
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"<Figure size 576x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best solution with len 601.0 \n",
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"with a total time of 0.05389 seconds\n"
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]
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}
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],
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"source": [
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"available_solvers = {\"random\": random_method,\n",
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" \"nn\": nn,\n",
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" \"best_nn\": best_nn}\n",
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"\n",
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"solver = TSPSolver(\"best_nn\", p_inst, available_solvers)\n",
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"start = t()\n",
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"solver.compute_solution(return_value=False, verbose=True)\n",
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"end = t()\n",
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"\n",
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"solver.plot_solution()\n",
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"print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")"
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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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"id": "uXtOOk25XXpR"
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},
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"source": [
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"### Multi Fragment"
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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": 12,
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"metadata": {
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"id": "1oo-Zv7vXdTq"
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},
|
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"outputs": [],
|
|
|
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"source": [
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|
|
"def check_if_edge_available(n1, n2, sol):\n",
|
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" return (len(sol[str(n1)]) < 2) and (len(sol[str(n2)]) < 2)\n",
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"\n",
|
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|
|
|
"def check_if_not_close(edge_to_append, sol):\n",
|
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|
|
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" n1, n2 = edge_to_append\n",
|
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|
|
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" from_city = n2\n",
|
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|
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" if len(sol[str(from_city)]) == 0:\n",
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" return True\n",
|
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|
|
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" partial_tour = [from_city]\n",
|
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|
|
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" iter_num = 0\n",
|
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|
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" while True:\n",
|
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|
|
" if len(sol[str(from_city)]) == 1:\n",
|
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|
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" if from_city == n1:\n",
|
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|
|
|
" return_value = False\n",
|
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|
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|
" break\n",
|
|
|
|
|
" elif iter_num > 1:\n",
|
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|
|
|
" return_value = True\n",
|
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|
|
|
" break\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" from_city = sol[str(from_city)][0]\n",
|
|
|
|
|
" partial_tour.append(from_city)\n",
|
|
|
|
|
" iter_num += 1\n",
|
|
|
|
|
" else:\n",
|
|
|
|
|
" for node_connected in sol[str(from_city)]:\n",
|
|
|
|
|
" if node_connected not in partial_tour:\n",
|
|
|
|
|
" from_city = node_connected\n",
|
|
|
|
|
" partial_tour.append(node_connected)\n",
|
|
|
|
|
" iter_num +=1\n",
|
|
|
|
|
" return return_value\n",
|
|
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|
|
"\n",
|
|
|
|
|
"def create_solution(start_sol, sol):\n",
|
|
|
|
|
" assert len(start_sol)==2, \"too many cities with just one link\"\n",
|
|
|
|
|
" n1, n2 = start_sol\n",
|
|
|
|
|
" from_city = n2\n",
|
|
|
|
|
" sol_list = [n1, n2]\n",
|
|
|
|
|
" iter_num = 0\n",
|
|
|
|
|
" while iter_num <300:\n",
|
|
|
|
|
" for node_connected in sol[str(from_city)]:\n",
|
|
|
|
|
" iter_num += 1\n",
|
|
|
|
|
" if node_connected not in sol_list:\n",
|
|
|
|
|
" from_city = node_connected\n",
|
|
|
|
|
" sol_list.append(node_connected)\n",
|
|
|
|
|
" sol_list.append(n1)\n",
|
|
|
|
|
" return sol_list\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"def mf(instance):\n",
|
|
|
|
|
" mat = np.copy(instance.dist_matrix)\n",
|
|
|
|
|
" mat = np.triu(mat)\n",
|
|
|
|
|
" mat[mat == 0] = 100000\n",
|
|
|
|
|
" # TODO\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" #[...]\n",
|
|
|
|
|
" return create_solution(start_list, solution)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 13,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"colab": {
|
|
|
|
|
"base_uri": "https://localhost:8080/",
|
|
|
|
|
"height": 516
|
|
|
|
|
},
|
|
|
|
|
"id": "-iTyocncXg0d",
|
|
|
|
|
"outputId": "9a48f544-d4ef-4f57-f786-789006ca5432"
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"### solving with ['multi_fragment'] ####\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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"text/plain": [
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"<Figure size 576x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"best solution with len 600.0 \n",
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"with a total time of 0.00597 seconds\n"
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]
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}
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],
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"source": [
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"available_solvers = {\"random\": random_method,\n",
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" \"nn\":nn,\n",
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" \"best_nn\":best_nn,\n",
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" \"multi_fragment\": mf\n",
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" }\n",
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"\n",
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"solver = TSPSolver(\"multi_fragment\", p_inst, available_solvers)\n",
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"start = t()\n",
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"solver.compute_solution(return_value=False, verbose=True)\n",
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"end = t()\n",
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"\n",
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"solver.plot_solution()\n",
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|
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"print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")"
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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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"id": "lp9RXLX9Ye1M"
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},
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"source": [
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"### Comparison Constructive methods"
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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": 23,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 239
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},
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"id": "jZ4R8UZgYed7",
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"outputId": "28475799-f823-42e9-91ad-b9c3ed029bdd",
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 4/4 [00:01<00:00, 2.94it/s, using multi_fragment for d198] \n"
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]
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}
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],
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"source": [
|
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|
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"from time import time as t\n",
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"import pandas as pd\n",
|
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"from tqdm import tqdm\n",
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"\n",
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|
"available_solvers = {\"random\": random_method,\n",
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" \"nn\":nn,\n",
|
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" \"best_nn\":best_nn,\n",
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" \"multi_fragment\": mf\n",
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" }\n",
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"\n",
|
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|
|
|
"show_plots = False\n",
|
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|
"verbose = False\n",
|
|
|
|
|
"problems = [\"../problems/eil76.tsp\", \"../problems/kroA100.tsp\", \"../problems/ch130.tsp\", \"../problems/d198.tsp\"]\n",
|
|
|
|
|
"methods = available_solvers.keys()\n",
|
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|
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|
"results = []\n",
|
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|
|
|
"index = []\n",
|
|
|
|
|
"with tqdm(problems) as tq:\n",
|
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|
|
" for filename in tq:\n",
|
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|
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" p_inst = ProblemInstance(filename)\n",
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|
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" if verbose:\n",
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|
" print(\"\\n\\n#############################\")\n",
|
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" p_inst.print_info()\n",
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|
|
" if show_plots:\n",
|
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" p_inst.plot_data()\n",
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"\n",
|
|
|
|
|
" for method in methods:\n",
|
|
|
|
|
" tq.set_postfix_str(f\"using {method} for {p_inst.name}\")\n",
|
|
|
|
|
" solver = TSPSolver(method, p_inst, available_solvers)\n",
|
|
|
|
|
" start = t()\n",
|
|
|
|
|
" solver.compute_solution(return_value=False, verbose=verbose)\n",
|
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|
|
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" end = t()\n",
|
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|
|
|
" if verbose: \n",
|
|
|
|
|
" print(f\"the total length for the solution found is {solver.found_length}\",\n",
|
|
|
|
|
" f\"while the optimal length is {p_inst.best_sol}\",\n",
|
|
|
|
|
" f\"the gap is {solver.gap} %\",\n",
|
|
|
|
|
" f\"the solution is found in {np.round(end - start, 5)} seconds\",sep=\"\\n\")\n",
|
|
|
|
|
" index.append((filename[12:], method))\n",
|
|
|
|
|
" results.append([solver.found_length, p_inst.best_sol, solver.gap, end - start])\n",
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"\n",
|
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|
|
|
" if show_plots:\n",
|
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" solver.plot_solution()\n",
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"\n",
|
|
|
|
|
" if p_inst.exist_opt and show_plots:\n",
|
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|
|
|
" solver = TSPSolver(\"optimal\", p_inst)\n",
|
|
|
|
|
" solver.solved = True\n",
|
|
|
|
|
" solver.solution = np.concatenate([p_inst.optimal_tour, [p_inst.optimal_tour[0]]])\n",
|
|
|
|
|
" solver.plot_solution()\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": 24,
|
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|
|
"metadata": {
|
|
|
|
|
"id": "v8iJZ1QtY6WM"
|
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|
|
},
|
|
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|
"outputs": [
|
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{
|
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"data": {
|
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"text/html": [
|
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|
"<div>\n",
|
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|
|
|
"<style scoped>\n",
|
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|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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|
" vertical-align: middle;\n",
|
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|
" }\n",
|
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"\n",
|
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|
|
" .dataframe tbody tr th {\n",
|
|
|
|
|
" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
|
|
|
|
" .dataframe thead th {\n",
|
|
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|
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" text-align: right;\n",
|
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|
" }\n",
|
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|
"</style>\n",
|
|
|
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
|
|
|
" <thead>\n",
|
|
|
|
|
" <tr style=\"text-align: right;\">\n",
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
" <th>tour length</th>\n",
|
|
|
|
|
" <th>optimal solution</th>\n",
|
|
|
|
|
" <th>gap</th>\n",
|
|
|
|
|
" <th>time to solve</th>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>problem</th>\n",
|
|
|
|
|
" <th>method</th>\n",
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th rowspan=\"4\" valign=\"top\">eil76.tsp</th>\n",
|
|
|
|
|
" <th>random</th>\n",
|
|
|
|
|
" <td>2616.0</td>\n",
|
|
|
|
|
" <td>538.0</td>\n",
|
|
|
|
|
" <td>386.25</td>\n",
|
|
|
|
|
" <td>0.000970</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>nn</th>\n",
|
|
|
|
|
" <td>680.0</td>\n",
|
|
|
|
|
" <td>538.0</td>\n",
|
|
|
|
|
" <td>26.39</td>\n",
|
|
|
|
|
" <td>0.000997</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>best_nn</th>\n",
|
|
|
|
|
" <td>601.0</td>\n",
|
|
|
|
|
" <td>538.0</td>\n",
|
|
|
|
|
" <td>11.71</td>\n",
|
|
|
|
|
" <td>0.049906</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>multi_fragment</th>\n",
|
|
|
|
|
" <td>600.0</td>\n",
|
|
|
|
|
" <td>538.0</td>\n",
|
|
|
|
|
" <td>11.52</td>\n",
|
|
|
|
|
" <td>0.005983</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th rowspan=\"4\" valign=\"top\">kroA100.tsp</th>\n",
|
|
|
|
|
" <th>random</th>\n",
|
|
|
|
|
" <td>163891.0</td>\n",
|
|
|
|
|
" <td>21282.0</td>\n",
|
|
|
|
|
" <td>670.09</td>\n",
|
|
|
|
|
" <td>0.001030</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>nn</th>\n",
|
|
|
|
|
" <td>27807.0</td>\n",
|
|
|
|
|
" <td>21282.0</td>\n",
|
|
|
|
|
" <td>30.66</td>\n",
|
|
|
|
|
" <td>0.001961</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>best_nn</th>\n",
|
|
|
|
|
" <td>24815.0</td>\n",
|
|
|
|
|
" <td>21282.0</td>\n",
|
|
|
|
|
" <td>16.60</td>\n",
|
|
|
|
|
" <td>0.101760</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>multi_fragment</th>\n",
|
|
|
|
|
" <td>24287.0</td>\n",
|
|
|
|
|
" <td>21282.0</td>\n",
|
|
|
|
|
" <td>14.12</td>\n",
|
|
|
|
|
" <td>0.007005</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th rowspan=\"4\" valign=\"top\">ch130.tsp</th>\n",
|
|
|
|
|
" <th>random</th>\n",
|
|
|
|
|
" <td>44209.0</td>\n",
|
|
|
|
|
" <td>6110.0</td>\n",
|
|
|
|
|
" <td>623.55</td>\n",
|
|
|
|
|
" <td>0.001029</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>nn</th>\n",
|
|
|
|
|
" <td>7578.0</td>\n",
|
|
|
|
|
" <td>6110.0</td>\n",
|
|
|
|
|
" <td>24.03</td>\n",
|
|
|
|
|
" <td>0.002980</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>best_nn</th>\n",
|
|
|
|
|
" <td>6880.0</td>\n",
|
|
|
|
|
" <td>6110.0</td>\n",
|
|
|
|
|
" <td>12.60</td>\n",
|
|
|
|
|
" <td>0.206479</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>multi_fragment</th>\n",
|
|
|
|
|
" <td>7849.0</td>\n",
|
|
|
|
|
" <td>6110.0</td>\n",
|
|
|
|
|
" <td>28.46</td>\n",
|
|
|
|
|
" <td>0.017952</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th rowspan=\"4\" valign=\"top\">d198.tsp</th>\n",
|
|
|
|
|
" <th>random</th>\n",
|
|
|
|
|
" <td>188837.0</td>\n",
|
|
|
|
|
" <td>15780.0</td>\n",
|
|
|
|
|
" <td>1096.69</td>\n",
|
|
|
|
|
" <td>0.001047</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>nn</th>\n",
|
|
|
|
|
" <td>18975.0</td>\n",
|
|
|
|
|
" <td>15780.0</td>\n",
|
|
|
|
|
" <td>20.25</td>\n",
|
|
|
|
|
" <td>0.005039</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>best_nn</th>\n",
|
|
|
|
|
" <td>17565.0</td>\n",
|
|
|
|
|
" <td>15780.0</td>\n",
|
|
|
|
|
" <td>11.31</td>\n",
|
|
|
|
|
" <td>0.694035</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>multi_fragment</th>\n",
|
|
|
|
|
" <td>17111.0</td>\n",
|
|
|
|
|
" <td>15780.0</td>\n",
|
|
|
|
|
" <td>8.43</td>\n",
|
|
|
|
|
" <td>0.065004</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
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" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
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],
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"text/plain": [
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" tour length optimal solution gap \\\n",
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"problem method \n",
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"eil76.tsp random 2616.0 538.0 386.25 \n",
|
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" nn 680.0 538.0 26.39 \n",
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" best_nn 601.0 538.0 11.71 \n",
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" multi_fragment 600.0 538.0 11.52 \n",
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"kroA100.tsp random 163891.0 21282.0 670.09 \n",
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" nn 27807.0 21282.0 30.66 \n",
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" best_nn 24815.0 21282.0 16.60 \n",
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" multi_fragment 24287.0 21282.0 14.12 \n",
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"ch130.tsp random 44209.0 6110.0 623.55 \n",
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" nn 7578.0 6110.0 24.03 \n",
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" best_nn 6880.0 6110.0 12.60 \n",
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" multi_fragment 7849.0 6110.0 28.46 \n",
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"d198.tsp random 188837.0 15780.0 1096.69 \n",
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" nn 18975.0 15780.0 20.25 \n",
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" best_nn 17565.0 15780.0 11.31 \n",
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" multi_fragment 17111.0 15780.0 8.43 \n",
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"\n",
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" time to solve \n",
|
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"problem method \n",
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"eil76.tsp random 0.000970 \n",
|
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" nn 0.000997 \n",
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" best_nn 0.049906 \n",
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" multi_fragment 0.005983 \n",
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"kroA100.tsp random 0.001030 \n",
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" nn 0.001961 \n",
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" best_nn 0.101760 \n",
|
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" multi_fragment 0.007005 \n",
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"ch130.tsp random 0.001029 \n",
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" nn 0.002980 \n",
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" best_nn 0.206479 \n",
|
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" multi_fragment 0.017952 \n",
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"d198.tsp random 0.001047 \n",
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" nn 0.005039 \n",
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" best_nn 0.694035 \n",
|
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" multi_fragment 0.065004 "
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"index = pd.MultiIndex.from_tuples(index, names=['problem', 'method'])\n",
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"\n",
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"df = pd.DataFrame(results, index=index, columns=[\"tour length\", \"optimal solution\", \"gap\", \"time to solve\"])\n",
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"df"
|
2020-10-01 16:03:04 +00:00
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]
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}
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],
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"metadata": {
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"kernelspec": {
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2020-11-01 18:24:26 +00:00
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"display_name": "PyCharm (AI2020BsC)",
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2020-10-01 16:03:04 +00:00
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"language": "python",
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2020-11-01 18:24:26 +00:00
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"name": "pycharm-61970693"
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2020-10-01 16:03:04 +00:00
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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2020-11-01 18:24:26 +00:00
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}
|
