{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Make sure you run this at the begining**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import math\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Append template path to sys path\n",
"sys.path.append(os.getcwd() + \"/template\") "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from utils.load_data import load_data\n",
"from utils.load_data import log\n",
"from utils.visualize_tsp import plotTSP\n",
"from utils.tsp import TSP"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Workshop Starts Here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![\"TSP\"](\"images/tsp.jpg\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![\"solutions\"](\"images/solutions.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get familiar with your dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are problems at different levels. **3 simple, 2 medium, 1 hard**."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"for root, _, files in os.walk('./template/data'):\n",
" if(files):\n",
" for f in files:\n",
" print(str(root) + \"/\" + f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ulysses16 = np.array(load_data(\"./template/data/simple/ulysses16.tsp\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ulysses16[:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"plt.scatter(ulysses16[:, 0], ulysses16[:, 1])\n",
"for i in range(0, 16):\n",
" plt.annotate(i, (ulysses16[i, 0], ulysses16[i, 1]+0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Naive Solution: In Order"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"simple_sequence = list(range(0, 16))\n",
"print(simple_sequence)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotTSP([simple_sequence], ulysses16, num_iters=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Naive Solution: Random Permutation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"random_permutation = np.random.permutation(16).tolist()\n",
"print(random_permutation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotTSP([random_permutation], ulysses16, num_iters=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Best Solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_ulysses16 = [0, 13, 12, 11, 6, 5, 14, 4, 10, 8, 9, 15, 2, 1, 3, 7]\n",
"plotTSP([best_ulysses16], ulysses16, num_iters=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate Fitness (Sum of all Distances)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def dist(node_0, node_1, coords):\n",
" \"\"\"\n",
" Euclidean distance between two nodes.\n",
" \"\"\"\n",
" coord_0, coord_1 = coords[node_0], coords[node_1]\n",
" return math.sqrt((coord_0[0] - coord_1[0]) ** 2 + (coord_0[1] - coord_1[1]) ** 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Coordinate of City 0:\", ulysses16[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Coordinate of City 1:\", ulysses16[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print(\"Distance Between\", dist(0, 1, ulysses16))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def fitness(solution, coords):\n",
" N = len(coords)\n",
" cur_fit = 0\n",
" for i in range(len(solution)):\n",
" cur_fit += dist(solution[i % N], solution[(i + 1) % N], coords)\n",
" return cur_fit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print (\"Order Fitness:\\t\", fitness(simple_sequence, ulysses16))\n",
"print (\"Random Fitness:\\t\", fitness(random_permutation, ulysses16))\n",
"print (\"Best Fitness:\\t\", fitness(best_ulysses16, ulysses16))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Naive Random 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, coords):\n",
" \"\"\"\n",
" Put your initialization here.\n",
" \"\"\"\n",
" super().init(coords)\n",
" self.log(\"Nothing to initialize in your model now\")\n",
"\n",
" def fit(self, max_it=1000, visualize=False):\n",
" \"\"\"\n",
" Put your iteration process here.\n",
" \"\"\"\n",
" self.log(\"Naive Random Solution\")\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": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"tsp_problem = './template/data/simple/ulysses16.tsp'\n",
"tsp_coords = np.array(load_data(tsp_problem))\n",
"\n",
"import timeit\n",
"start = timeit.default_timer()\n",
"\n",
"# Your Model Running\n",
"\n",
"model = MyModel()\n",
"best_solution, fitness_list = TSP(tsp_problem, model)\n",
"\n",
"# Your Model End\n",
"\n",
"stop = timeit.default_timer()\n",
"print()\n",
"print('[*] Running for: {time:.1f} seconds'.format(time=(stop - start)))\n",
"\n",
"print()\n",
"print (\"Best Fitness:\\t\", fitness(best_solution, tsp_coords))\n",
"print (\"Best Solution:\\t\", best_solution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Minimum Spanning Tree (Depth First)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"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, coords):\n",
" \"\"\"\n",
" Put your initialization here.\n",
" \"\"\"\n",
" super().init(coords)\n",
" self.log(\"Nothing to initialize in your model now\")\n",
"\n",
" def fit(self, max_it=1000, visualize=False):\n",
" \"\"\"\n",
" Put your iteration process here.\n",
" \"\"\"\n",
" self.log(\"Uniform Cost Search Solution\")\n",
"\n",
" UCS_solutions = []\n",
" UCS_losses = []\n",
" # Depth First: Set one city as starting point, iterate to the end, then select next city as starting point.\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",
" closest_neighbour = -1\n",
" shortest_distance = math.inf\n",
" for j in unvisited_list:\n",
" if(self.dist(cur_city, j) < shortest_distance):\n",
" closest_neighbour = j\n",
" shortest_distance = self.dist(cur_city, j)\n",
" solution.append(closest_neighbour)\n",
" cur_city = closest_neighbour\n",
" UCS_solutions.append(solution)\n",
" UCS_losses.append(self.fitness(solution))\n",
"\n",
" self.best_solution = UCS_solutions[ UCS_losses.index(min(UCS_losses)) ]\n",
" self.fitness_list.append(self.fitness(self.best_solution))\n",
"\n",
" return self.best_solution, self.fitness_list"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MyModel] Nothing to initialize in your model now\n",
"[MyModel] Uniform Cost Search Solution\n",
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"[*] Best: 58952.967129705365\n",
"[*] Writing the best solution to file\n",
"\n",
"[*] Running for: 18.7 seconds\n",
"\n",
"Best Fitness:\t 58952.967129705365\n",
"Best Solution:\t [395, 173, 172, 161, 149, 136, 126, 385, 114, 103, 440, 101, 102, 113, 125, 135, 148, 160, 171, 184, 399, 404, 226, 233, 237, 238, 265, 268, 272, 275, 278, 280, 281, 427, 341, 340, 345, 346, 347, 432, 348, 349, 350, 351, 342, 352, 353, 354, 433, 355, 356, 357, 434, 358, 359, 360, 343, 361, 362, 363, 364, 365, 366, 344, 367, 368, 369, 370, 371, 372, 373, 374, 337, 336, 426, 335, 334, 333, 306, 332, 331, 330, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 292, 291, 290, 289, 288, 287, 286, 285, 284, 283, 282, 279, 425, 439, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 428, 324, 325, 326, 327, 328, 329, 430, 429, 277, 416, 417, 252, 251, 250, 249, 414, 248, 247, 246, 245, 244, 243, 242, 241, 240, 239, 234, 227, 405, 400, 185, 398, 186, 174, 391, 137, 115, 388, 386, 150, 151, 392, 152, 138, 127, 116, 104, 105, 106, 117, 128, 140, 153, 164, 163, 176, 188, 200, 401, 201, 189, 190, 397, 177, 165, 154, 141, 129, 118, 107, 438, 82, 50, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 441, 98, 99, 83, 51, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 375, 376, 32, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 84, 85, 86, 377, 87, 88, 89, 90, 91, 92, 93, 94, 378, 95, 379, 96, 97, 382, 383, 112, 124, 134, 147, 159, 170, 183, 198, 209, 219, 225, 410, 409, 413, 236, 264, 419, 267, 415, 263, 262, 235, 261, 260, 259, 258, 257, 256, 255, 254, 253, 231, 223, 214, 202, 178, 394, 393, 166, 155, 142, 130, 119, 108, 384, 120, 121, 109, 131, 144, 143, 390, 156, 167, 180, 193, 204, 205, 206, 194, 195, 196, 181, 168, 157, 145, 132, 122, 110, 100, 435, 111, 123, 133, 146, 158, 169, 182, 197, 208, 218, 217, 207, 216, 403, 408, 407, 411, 412, 418, 421, 437, 422, 271, 274, 436, 431, 232, 224, 215, 203, 191, 179, 192, 389, 387, 380, 381, 139, 162, 175, 187, 199, 212, 221, 229, 228, 220, 210, 402, 211, 396, 213, 222, 230, 424, 420, 423, 339, 338, 276, 273, 270, 266, 269, 406]\n"
]
}
],
"source": [
"tsp_problem = './template/data/medium/pcb442.tsp'\n",
"tsp_coords = np.array(load_data(tsp_problem))\n",
"\n",
"import timeit\n",
"start = timeit.default_timer()\n",
"\n",
"# Your Model Running\n",
"\n",
"model = MyModel()\n",
"best_solution, fitness_list = TSP(tsp_problem, model)\n",
"\n",
"# Your Model End\n",
"\n",
"stop = timeit.default_timer()\n",
"print()\n",
"print('[*] Running for: {time:.1f} seconds'.format(time=(stop - start)))\n",
"\n",
"print()\n",
"print (\"Best Fitness:\\t\", fitness(best_solution, tsp_coords))\n",
"print (\"Best Solution:\\t\", best_solution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Minimum Spanning Tree (Breadth First)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"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, coords):\n",
" \"\"\"\n",
" Put your initialization here.\n",
" \"\"\"\n",
" super().init(coords)\n",
" self.log(\"Nothing to initialize in your model now\")\n",
"\n",
" def fit(self, max_it=1000, visualize=False):\n",
" \"\"\"\n",
" Put your iteration process here.\n",
" \"\"\"\n",
" self.log(\"Uniform Cost Search Solution\")\n",
"\n",
" UCS_solutions = []\n",
" UCS_losses = []\n",
" \n",
" for i in range(0, self.N):\n",
" solution = [i]\n",
" UCS_solutions.append(solution)\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",
" UCS_solutions[i]\n",
" solution = []\n",
" solution.append(i)\n",
" unvisited_list = list(range(0, self.N))\n",
" cur_city = i\n",
" # For each search path\n",
" for i in range(0, self.N):\n",
" cur_city = UCS_solutions[i][step]\n",
" unvisited_list = list( set(range(0, self.N)) - set(UCS_solutions[i]) )\n",
" # print(unvisited_list)\n",
" closest_neighbour = -1\n",
" shortest_distance = math.inf\n",
" for j in unvisited_list:\n",
" if(self.dist(cur_city, j) < shortest_distance):\n",
" closest_neighbour = j\n",
" shortest_distance = self.dist(cur_city, j)\n",
" UCS_solutions[i].append(closest_neighbour)\n",
"\n",
" for i in range(0, self.N):\n",
" UCS_losses.append(self.fitness(UCS_solutions[i]))\n",
" \n",
" self.best_solution = UCS_solutions[ UCS_losses.index(min(UCS_losses)) ]\n",
" self.fitness_list.append(self.fitness(self.best_solution))\n",
"\n",
" return self.best_solution, self.fitness_list"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MyModel] Nothing to initialize in your model now\n",
"[MyModel] Uniform Cost Search Solution\n",
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"[*] Best: 58952.967129705365\n",
"[*] Writing the best solution to file\n",
"\n",
"[*] Running for: 21.8 seconds\n",
"\n",
"Best Fitness:\t 58952.967129705365\n",
"Best Solution:\t [395, 173, 172, 161, 149, 136, 126, 385, 114, 103, 440, 101, 102, 113, 125, 135, 148, 160, 171, 184, 399, 404, 226, 233, 237, 238, 265, 268, 272, 275, 278, 280, 281, 427, 341, 340, 345, 346, 347, 432, 348, 349, 350, 351, 342, 352, 353, 354, 433, 355, 356, 357, 434, 358, 359, 360, 343, 361, 362, 363, 364, 365, 366, 344, 367, 368, 369, 370, 371, 372, 373, 374, 337, 336, 426, 335, 334, 333, 306, 332, 331, 330, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 292, 291, 290, 289, 288, 287, 286, 285, 284, 283, 282, 279, 425, 439, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 428, 324, 325, 326, 327, 328, 329, 430, 429, 277, 416, 417, 252, 251, 250, 249, 414, 248, 247, 246, 245, 244, 243, 242, 241, 240, 239, 234, 227, 405, 400, 185, 398, 186, 174, 391, 137, 115, 388, 386, 150, 151, 392, 152, 138, 127, 116, 104, 105, 106, 117, 128, 140, 153, 164, 163, 176, 188, 200, 401, 201, 189, 190, 397, 177, 165, 154, 141, 129, 118, 107, 438, 82, 50, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 441, 98, 99, 83, 51, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 375, 376, 32, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 84, 85, 86, 377, 87, 88, 89, 90, 91, 92, 93, 94, 378, 95, 379, 96, 97, 382, 383, 112, 124, 134, 147, 159, 170, 183, 198, 209, 219, 225, 410, 409, 413, 236, 264, 419, 267, 415, 263, 262, 235, 261, 260, 259, 258, 257, 256, 255, 254, 253, 231, 223, 214, 202, 178, 394, 393, 166, 155, 142, 130, 119, 108, 384, 120, 121, 109, 131, 144, 143, 390, 156, 167, 180, 193, 204, 205, 206, 194, 195, 196, 181, 168, 157, 145, 132, 122, 110, 100, 435, 111, 123, 133, 146, 158, 169, 182, 197, 208, 218, 217, 207, 216, 403, 408, 407, 411, 412, 418, 421, 437, 422, 271, 274, 436, 431, 232, 224, 215, 203, 191, 179, 192, 389, 387, 380, 381, 139, 162, 175, 187, 199, 212, 221, 229, 228, 220, 210, 402, 211, 396, 213, 222, 230, 424, 420, 423, 339, 338, 276, 273, 270, 266, 269, 406]\n"
]
}
],
"source": [
"tsp_problem = './template/data/medium/pcb442.tsp'\n",
"tsp_coords = np.array(load_data(tsp_problem))\n",
"\n",
"import timeit\n",
"start = timeit.default_timer()\n",
"\n",
"# Your Model Running\n",
"\n",
"model = MyModel()\n",
"best_solution, fitness_list = TSP(tsp_problem, model)\n",
"\n",
"# Your Model End\n",
"\n",
"stop = timeit.default_timer()\n",
"print()\n",
"print('[*] Running for: {time:.1f} seconds'.format(time=(stop - start)))\n",
"\n",
"print()\n",
"print (\"Best Fitness:\\t\", fitness(best_solution, tsp_coords))\n",
"print (\"Best Solution:\\t\", best_solution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dynamic Programming"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Costs a lot of memory"
]
},
{
"cell_type": "code",
"execution_count": 90,
"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, coords):\n",
" \"\"\"\n",
" Put your initialization here.\n",
" \"\"\"\n",
" super().init(coords)\n",
" self.log(\"Nothing to initialize in your model now\")\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, visualize=False):\n",
" \"\"\"\n",
" Put your iteration process here.\n",
" \"\"\"\n",
" self.log(\"Uniform Cost Search Solution\")\n",
"\n",
" UCS_solutions = []\n",
" UCS_losses = []\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",
" UCS_solutions.append(solution)\n",
" UCS_losses.append(self.fitness(solution))\n",
"\n",
" self.best_solution = UCS_solutions[ UCS_losses.index(min(UCS_losses)) ]\n",
" self.fitness_list.append(self.fitness(self.best_solution))\n",
"\n",
" return self.best_solution, self.fitness_list"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[MyModel] Nothing to initialize in your model now\n",
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"[*] Best: 58952.967129705365\n",
"[*] Writing the best solution to file\n",
"\n",
"[*] Running for: 1.3 seconds\n",
"\n",
"Best Fitness:\t 58952.967129705365\n",
"Best Solution:\t [395, 173, 172, 161, 149, 136, 126, 385, 114, 103, 440, 101, 102, 113, 125, 135, 148, 160, 171, 184, 399, 404, 226, 233, 237, 238, 265, 268, 272, 275, 278, 280, 281, 427, 341, 340, 345, 346, 347, 432, 348, 349, 350, 351, 342, 352, 353, 354, 433, 355, 356, 357, 434, 358, 359, 360, 343, 361, 362, 363, 364, 365, 366, 344, 367, 368, 369, 370, 371, 372, 373, 374, 337, 336, 426, 335, 334, 333, 306, 332, 331, 330, 305, 304, 303, 302, 301, 300, 299, 298, 297, 296, 295, 294, 293, 292, 291, 290, 289, 288, 287, 286, 285, 284, 283, 282, 279, 425, 439, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 428, 324, 325, 326, 327, 328, 329, 430, 429, 277, 416, 417, 252, 251, 250, 249, 414, 248, 247, 246, 245, 244, 243, 242, 241, 240, 239, 234, 227, 405, 400, 185, 398, 186, 174, 391, 137, 115, 388, 386, 150, 151, 392, 152, 138, 127, 116, 104, 105, 106, 117, 128, 140, 153, 164, 163, 176, 188, 200, 401, 201, 189, 190, 397, 177, 165, 154, 141, 129, 118, 107, 438, 82, 50, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 441, 98, 99, 83, 51, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 375, 376, 32, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 84, 85, 86, 377, 87, 88, 89, 90, 91, 92, 93, 94, 378, 95, 379, 96, 97, 382, 383, 112, 124, 134, 147, 159, 170, 183, 198, 209, 219, 225, 410, 409, 413, 236, 264, 419, 267, 415, 263, 262, 235, 261, 260, 259, 258, 257, 256, 255, 254, 253, 231, 223, 214, 202, 178, 394, 393, 166, 155, 142, 130, 119, 108, 384, 120, 121, 109, 131, 144, 143, 390, 156, 167, 180, 193, 204, 205, 206, 194, 195, 196, 181, 168, 157, 145, 132, 122, 110, 100, 435, 111, 123, 133, 146, 158, 169, 182, 197, 208, 218, 217, 207, 216, 403, 408, 407, 411, 412, 418, 421, 437, 422, 271, 274, 436, 431, 232, 224, 215, 203, 191, 179, 192, 389, 387, 380, 381, 139, 162, 175, 187, 199, 212, 221, 229, 228, 220, 210, 402, 211, 396, 213, 222, 230, 424, 420, 423, 339, 338, 276, 273, 270, 266, 269, 406]\n"
]
}
],
"source": [
"tsp_problem = './template/data/medium/pcb442.tsp'\n",
"tsp_coords = np.array(load_data(tsp_problem))\n",
"\n",
"import timeit\n",
"start = timeit.default_timer()\n",
"\n",
"# Your Model Running\n",
"\n",
"model = MyModel()\n",
"best_solution, fitness_list = TSP(tsp_problem, model)\n",
"\n",
"# Your Model End\n",
"\n",
"stop = timeit.default_timer()\n",
"print()\n",
"print('[*] Running for: {time:.1f} seconds'.format(time=(stop - start)))\n",
"\n",
"print()\n",
"print (\"Best Fitness:\\t\", fitness(best_solution, tsp_coords))\n",
"print (\"Best Solution:\\t\", best_solution)"
]
},
{
"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, coords):\n",
" \"\"\"\n",
" Put your initialization here.\n",
" \"\"\"\n",
" super().init(coords)\n",
" self.log(\"Nothing to initialize in your model now\")\n",
"\n",
" def fit(self, max_it=1000, visualize=False):\n",
" \"\"\"\n",
" Put your iteration process here.\n",
" \"\"\"\n",
" self.log(\"Naive Random Solution\")\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": [
"## Simulated Annealing"
]
},
{
"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, T=-1, alpha=-1, stopping_T=-1):\n",
" super().__init__()\n",
"\n",
" self.iteration = 0\n",
"\n",
" self.T = T\n",
" self.alpha = 0.995 if alpha == -1 else alpha\n",
" self.stopping_temperature = 1e-8 if stopping_T == -1 else stopping_T\n",
"\n",
" def init(self, coords):\n",
" super().init(coords)\n",
"\n",
" if (self.T == -1):\n",
" self.T = math.sqrt(self.N) \n",
" self.T_save = self.T # save inital T to reset if batch annealing is used\n",
"\n",
" def initial_solution(self):\n",
" \"\"\"\n",
" Greedy algorithm to get an initial solution (closest-neighbour).\n",
" \"\"\"\n",
" cur_node = random.choice(self.nodes) # start from a random node\n",
" solution = [cur_node]\n",
"\n",
" free_nodes = set(self.nodes)\n",
" free_nodes.remove(cur_node)\n",
" while free_nodes:\n",
" next_node = min(free_nodes, key=lambda x: self.dist(cur_node, x)) # nearest neighbour\n",
" free_nodes.remove(next_node)\n",
" solution.append(next_node)\n",
" cur_node = next_node\n",
"\n",
" cur_fit = self.fitness(solution)\n",
" if cur_fit < self.best_fitness: # If best found so far, update best fitness\n",
" self.best_fitness = cur_fit\n",
" self.best_solution = solution\n",
" self.fitness_list.append(cur_fit)\n",
" return solution, cur_fit\n",
"\n",
" def p_accept(self, candidate_fitness):\n",
" \"\"\"\n",
" Probability of accepting if the candidate is worse than current.\n",
" Depends on the current temperature and difference between candidate and current.\n",
" \"\"\"\n",
" return math.exp(-abs(candidate_fitness - self.cur_fitness) / self.T)\n",
"\n",
" def accept(self, candidate):\n",
" \"\"\"\n",
" Accept with probability 1 if candidate is better than current.\n",
" Accept with probabilty p_accept(..) if candidate is worse.\n",
" \"\"\"\n",
" candidate_fitness = self.fitness(candidate)\n",
" if candidate_fitness < self.cur_fitness:\n",
" self.cur_fitness, self.cur_solution = candidate_fitness, candidate\n",
" if candidate_fitness < self.best_fitness:\n",
" self.best_fitness, self.best_solution = candidate_fitness, candidate\n",
" else:\n",
" if random.random() < self.p_accept(candidate_fitness):\n",
" self.cur_fitness, self.cur_solution = candidate_fitness, candidate\n",
"\n",
" def fit(self, max_it=1000):\n",
" \"\"\"\n",
" Execute simulated annealing algorithm.\n",
" \"\"\"\n",
" # Initialize with the greedy solution.\n",
" self.cur_solution, self.cur_fitness = self.initial_solution()\n",
"\n",
" self.log(\"Starting annealing.\")\n",
" while self.T >= self.stopping_temperature and self.iteration < max_it:\n",
" candidate = list(self.cur_solution)\n",
" l = random.randint(1, self.N - 1)\n",
" i = random.randint(0, self.N - l)\n",
" candidate[i : (i + l)] = reversed(candidate[i : (i + l)])\n",
" self.accept(candidate)\n",
" self.T *= self.alpha\n",
" self.iteration += 1\n",
"\n",
" self.fitness_list.append(self.cur_fitness)\n",
"\n",
" self.log(f\"Best fitness obtained: {self.best_fitness}\")\n",
" improvement = 100 * (self.fitness_list[0] - self.best_fitness) / (self.fitness_list[0])\n",
" self.log(f\"Improvement over greedy heuristic: {improvement : .2f}%\")\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'\n",
"tsp_coords = np.array(load_data(tsp_problem))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import timeit\n",
"start = timeit.default_timer()\n",
"\n",
"# Your Model Running\n",
"\n",
"model = MyModel()\n",
"best_solution, fitness_list = TSP(tsp_problem, model)\n",
"\n",
"# Your Model End\n",
"\n",
"stop = timeit.default_timer()\n",
"print()\n",
"print('[*] Running for: {time:.1f} seconds'.format(time=(stop - start)))\n",
"\n",
"print()\n",
"print (\"Best Fitness:\\t\", fitness(best_solution, tsp_coords))\n",
"print (\"Best Solution:\\t\", best_solution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test All Dataset"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"./template/data/medium/pcb442.tsp\n",
"./template/data/medium/a280.tsp\n",
"./template/data/hard/dsj1000.tsp\n",
"./template/data/simple/att48.tsp\n",
"./template/data/simple/ulysses16.tsp\n",
"./template/data/simple/st70.tsp\n"
]
}
],
"source": [
"for root, _, files in os.walk('./template/data'):\n",
" if(files):\n",
" for f in files:\n",
" print(str(root) + \"/\" + f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for root, _, files in os.walk('./template/data'):\n",
" if(files):\n",
" for f in files:\n",
" # Get input file name\n",
" tsp_file = str(root) + '/' + str(f)\n",
" log(tsp_file)\n",
"\n",
" # Your Model\n",
" model = MyModel()\n",
"\n",
" # Run TSP\n",
" TSP(tsp_file, model)\n",
"\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": null,
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