[new] set as template
This commit is contained in:
parent
a7dbaffe06
commit
352639f91b
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@ -30,11 +30,11 @@
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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.load_data import log\n",
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"from utils.visualize_tsp import plotTSP\n",
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"from utils.tsp import TSP\n",
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"from utils.tsp import TSP_Bench\n",
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"from utils.tsp import TSP_Bench_ALL"
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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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@ -291,7 +291,7 @@
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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):\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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@ -322,7 +322,7 @@
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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(tsp_file, MyRandomModel, max_it=100)"
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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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@ -361,7 +361,7 @@
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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):\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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@ -390,7 +390,7 @@
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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"
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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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@ -408,7 +408,7 @@
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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(tsp_file, MyDFSModel)"
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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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@ -447,7 +447,7 @@
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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):\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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@ -481,7 +481,7 @@
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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"
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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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@ -503,7 +503,7 @@
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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(tsp_file, MyBFSModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyBFSModel)"
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]
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},
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{
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@ -560,7 +560,7 @@
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" distances.append(self.dist(node, i))\n",
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" return [x for _,x in sorted(zip(distances, MST))]\n",
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"\n",
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" def fit(self, max_it):\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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@ -595,7 +595,7 @@
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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"
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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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@ -613,7 +613,7 @@
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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(tsp_file, MyDPDModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyDPDModel)"
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]
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},
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{
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@ -661,7 +661,7 @@
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" distances.append(self.dist(node, i))\n",
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" return [x for _,x in sorted(zip(distances, MST))]\n",
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"\n",
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" def fit(self, max_it):\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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@ -700,7 +700,7 @@
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" self.best_solution = MST_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"
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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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@ -718,7 +718,7 @@
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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(tsp_file, MyDPBModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyDPBModel)"
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]
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},
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{
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@ -759,7 +759,7 @@
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"\n",
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" self.log(\"Nothing to initialize in your model now\")\n",
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"\n",
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" def fit(self, max_it):\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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@ -793,7 +793,7 @@
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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(tsp_file, MyModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyModel)"
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]
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},
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{
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@ -811,21 +811,13 @@
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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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"tsp_path = './template'\n",
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"for root, _, files in os.walk(tsp_path + '/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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"tsp_path = './template/data'"
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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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@ -867,7 +859,7 @@
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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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"scrolled": false
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},
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"outputs": [],
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"source": [
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@ -1019,7 +1011,7 @@
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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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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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@ -30,11 +30,11 @@
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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.load_data import log\n",
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"from utils.visualize_tsp import plotTSP\n",
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"from utils.tsp import TSP\n",
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"from utils.tsp import TSP_Bench\n",
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"from utils.tsp import TSP_Bench_ALL"
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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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@ -280,8 +280,8 @@
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"source": [
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"import math\n",
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"import random\n",
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"import numpy as np\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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@ -293,7 +293,7 @@
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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):\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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@ -302,6 +302,7 @@
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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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@ -325,7 +326,7 @@
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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(tsp_file, MyRandomModel, max_it=100)"
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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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@ -373,7 +374,7 @@
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" distances.append(self.dist(node, i))\n",
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" return [x for _,x in sorted(zip(distances, MST))]\n",
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"\n",
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" def fit(self, max_it):\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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@ -440,7 +441,7 @@
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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(tsp_file, MyUCSModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyUCSModel)"
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]
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},
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{
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@ -486,7 +487,7 @@
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" distances.append(self.dist(node, i))\n",
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" return [x for _,x in sorted(zip(distances, MST))]\n",
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"\n",
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" def fit(self, max_it):\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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@ -538,7 +539,7 @@
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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"
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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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@ -556,7 +557,7 @@
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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(tsp_file, MyASModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyASModel)"
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]
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},
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{
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@ -682,7 +683,7 @@
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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(tsp_file, MyHillClimbModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyHillClimbModel)"
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]
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},
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{
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@ -710,7 +711,6 @@
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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 MyModel(Model):\n",
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" def __init__(self):\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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" self.log(\"Nothing to initialize in your model now\")\n",
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"\n",
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" def fit(self, max_it):\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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@ -757,7 +758,7 @@
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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(tsp_file, MyModel)"
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"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyModel)"
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]
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},
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{
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@ -773,21 +774,13 @@
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"metadata": {},
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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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"tsp_path = './template'\n",
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"for root, _, files in os.walk(tsp_path + '/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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"tsp_path = './template/data'"
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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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"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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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" con: array([], dtype=float64)\n",
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" fun: -17.999999976555795\n",
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" message: 'Optimization terminated successfully.'\n",
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" nit: 4\n",
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" slack: array([5.8 , 0.00000002, 0. ])\n",
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" status: 0\n",
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" success: True\n",
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" x: array([4.19999999, 1.2 , 0. , 0. , 0. ])\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"res = linprog(c, A_ub=A, b_ub=b, bounds=[x0_bounds, x1_bounds, x2_bounds, x3_bounds, x4_bounds])\n",
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"\n",
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" con: array([ 0. , -4.56053464])\n",
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" fun: -0.6099904044751114\n",
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" message: 'The algorithm terminated successfully and determined that the problem is infeasible.'\n",
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" nit: 4\n",
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" slack: array([], dtype=float64)\n",
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" status: 2\n",
|
||||
" success: False\n",
|
||||
" x: array([0.2893146 , 0.75265113, 0.60865936])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"res = linprog(c, A_eq=A, b_eq=b, bounds=[x0_bounds, x1_bounds, x2_bounds,])\n",
|
||||
"\n",
|
||||
|
|
@ -180,7 +150,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -194,26 +164,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" con: array([], dtype=float64)\n",
|
||||
" fun: -8.999999998556428\n",
|
||||
" message: 'Optimization terminated successfully.'\n",
|
||||
" nit: 4\n",
|
||||
" slack: array([4., 0., 4.])\n",
|
||||
" status: 0\n",
|
||||
" success: True\n",
|
||||
" x: array([3., 0.])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"res = linprog(c, A_ub=A, b_ub=b, bounds=[x0_bounds, x1_bounds])\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -30,11 +30,11 @@
|
|||
"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\n",
|
||||
"from utils.tsp import TSP_Bench\n",
|
||||
"from utils.tsp import TSP_Bench_ALL"
|
||||
"\n",
|
||||
"from tsp import TSP_Bench_ONE\n",
|
||||
"from tsp import TSP_Bench_PATH\n",
|
||||
"from tsp import TSP_Bench_ALL"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -289,7 +289,7 @@
|
|||
" \"\"\"\n",
|
||||
" super().init(nodes)\n",
|
||||
"\n",
|
||||
" def fit(self, max_it):\n",
|
||||
" def fit(self, max_it=1000):\n",
|
||||
" \"\"\"\n",
|
||||
" Put your iteration process here.\n",
|
||||
" \"\"\"\n",
|
||||
|
|
@ -320,7 +320,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MyRandomModel, max_it=100)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyRandomModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -355,10 +355,13 @@
|
|||
"\n",
|
||||
" self.iteration = 0\n",
|
||||
"\n",
|
||||
" def init(self, nodes, *args):\n",
|
||||
" def init(self, nodes):\n",
|
||||
" super().init(nodes)\n",
|
||||
" \n",
|
||||
" T, stopping_temperature, alpha = args\n",
|
||||
" # Set hyper-parameters\n",
|
||||
" T = -1\n",
|
||||
" stopping_temperature = -1\n",
|
||||
" alpha = 0.99\n",
|
||||
"\n",
|
||||
" self.T = math.sqrt(self.N) if T == -1 else T\n",
|
||||
" self.alpha = 0.995 if alpha == -1 else alpha\n",
|
||||
|
|
@ -409,7 +412,7 @@
|
|||
" 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):\n",
|
||||
" def fit(self, max_it=1000):\n",
|
||||
" \"\"\"\n",
|
||||
" Execute simulated annealing algorithm.\n",
|
||||
" \"\"\"\n",
|
||||
|
|
@ -444,18 +447,6 @@
|
|||
"tsp_file = './template/data/simple/ulysses16.tsp'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set hyper-parameters\n",
|
||||
"T = -1\n",
|
||||
"stopping_T = -1\n",
|
||||
"alpha = 0.99"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -464,7 +455,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MySAModel, T, stopping_T, alpha, max_it=1000)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MySAModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -504,13 +495,13 @@
|
|||
" 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):\n",
|
||||
" def fit(self, max_it=1000):\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",
|
||||
|
|
@ -541,7 +532,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MyModel)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -557,7 +548,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for root, _, files in os.walk('./template/data'):\n",
|
||||
"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)"
|
||||
|
|
@ -577,15 +569,6 @@
|
|||
" fig.suptitle(title, fontsize=20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tsp_path = './template/data'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -605,18 +588,6 @@
|
|||
"plot_results(best_solutions, times, \"Random Model\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set hyper-parameters\n",
|
||||
"T = -1\n",
|
||||
"stopping_T = -1\n",
|
||||
"alpha = 0.8"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -626,7 +597,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Simulated Annealing\")\n",
|
||||
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MySAModel, T, stopping_T, alpha, max_it=1000)"
|
||||
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MySAModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -671,7 +642,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.3"
|
||||
"version": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -30,11 +30,11 @@
|
|||
"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\n",
|
||||
"from utils.tsp import TSP_Bench\n",
|
||||
"from utils.tsp import TSP_Bench_ALL"
|
||||
"\n",
|
||||
"from tsp import TSP_Bench_ONE\n",
|
||||
"from tsp import TSP_Bench_PATH\n",
|
||||
"from tsp import TSP_Bench_ALL"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -289,7 +289,7 @@
|
|||
" \"\"\"\n",
|
||||
" super().init(nodes)\n",
|
||||
"\n",
|
||||
" def fit(self, max_it):\n",
|
||||
" def fit(self, max_it=1000):\n",
|
||||
" \"\"\"\n",
|
||||
" Put your iteration process here.\n",
|
||||
" \"\"\"\n",
|
||||
|
|
@ -320,7 +320,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MyRandomModel, max_it=100)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyRandomModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -405,11 +405,22 @@
|
|||
" self.distance += self.edges[self.tour[i]][self.tour[(i + 1) % self.num_nodes]].weight\n",
|
||||
" return self.distance\n",
|
||||
"\n",
|
||||
" def init(self, nodes, *args):\n",
|
||||
" def init(self, nodes):\n",
|
||||
" super().init(nodes)\n",
|
||||
" mode, colony_size, elitist_weight, min_scaling_factor, alpha, beta, rho, pheromone_deposit_weight, initial_pheromone, labels = args\n",
|
||||
"\n",
|
||||
" self.mode = mode[0]\n",
|
||||
" # Set hypter-parameters\n",
|
||||
" mode=['ACS']\n",
|
||||
" colony_size=10\n",
|
||||
" elitist_weight=1.0\n",
|
||||
" min_scaling_factor=0.001\n",
|
||||
" alpha=1.0\n",
|
||||
" beta=3.0\n",
|
||||
" rho=0.1\n",
|
||||
" pheromone_deposit_weight=1.0\n",
|
||||
" initial_pheromone=1.0\n",
|
||||
" labels = None\n",
|
||||
"\n",
|
||||
" self.mode = str(mode[0]) \n",
|
||||
" self.colony_size = colony_size\n",
|
||||
" self.elitist_weight = elitist_weight\n",
|
||||
" self.min_scaling_factor = min_scaling_factor\n",
|
||||
|
|
@ -490,25 +501,6 @@
|
|||
"tsp_file = './template/data/simple/ulysses16.tsp'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set hypter-parameters\n",
|
||||
"mode='ACS', \n",
|
||||
"colony_size=10\n",
|
||||
"elitist_weight=1.0\n",
|
||||
"min_scaling_factor=0.001\n",
|
||||
"alpha=1.0\n",
|
||||
"beta=3.0\n",
|
||||
"rho=0.1\n",
|
||||
"pheromone_deposit_weight=1.0\n",
|
||||
"initial_pheromone=1.0\n",
|
||||
"labels = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -517,7 +509,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MyACOModel, mode, colony_size, elitist_weight, min_scaling_factor, alpha, beta, rho, pheromone_deposit_weight, initial_pheromone, labels, max_it=1000, timeout=300)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyACOModel, timeout=300)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -546,13 +538,13 @@
|
|||
" 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):\n",
|
||||
" def fit(self, max_it=1000):\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",
|
||||
|
|
@ -583,7 +575,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MyModel)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -599,7 +591,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for root, _, files in os.walk('./template/data'):\n",
|
||||
"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)"
|
||||
|
|
@ -619,15 +612,6 @@
|
|||
" fig.suptitle(title, fontsize=20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tsp_path = './template/data'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -647,25 +631,6 @@
|
|||
"plot_results(best_solutions, times, \"Random Model\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set hypter-parameters\n",
|
||||
"mode='ACS', \n",
|
||||
"colony_size=10\n",
|
||||
"elitist_weight=1.0\n",
|
||||
"min_scaling_factor=0.001\n",
|
||||
"alpha=1.0\n",
|
||||
"beta=3.0\n",
|
||||
"rho=0.1\n",
|
||||
"pheromone_deposit_weight=1.0\n",
|
||||
"initial_pheromone=1.0\n",
|
||||
"labels = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -673,7 +638,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Ant Colony Optimization\")\n",
|
||||
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyACOModel, mode, colony_size, elitist_weight, min_scaling_factor, alpha, beta, rho, pheromone_deposit_weight, initial_pheromone, labels, max_it=100, timeout=600)"
|
||||
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyACOModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -709,7 +674,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.3"
|
||||
"version": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -30,11 +30,11 @@
|
|||
"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\n",
|
||||
"from utils.tsp import TSP_Bench\n",
|
||||
"from utils.tsp import TSP_Bench_ALL"
|
||||
"\n",
|
||||
"from tsp import TSP_Bench_ONE\n",
|
||||
"from tsp import TSP_Bench_PATH\n",
|
||||
"from tsp import TSP_Bench_ALL"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -289,7 +289,7 @@
|
|||
" \"\"\"\n",
|
||||
" super().init(nodes)\n",
|
||||
"\n",
|
||||
" def fit(self, max_it):\n",
|
||||
" def fit(self, max_it=1000):\n",
|
||||
" \"\"\"\n",
|
||||
" Put your iteration process here.\n",
|
||||
" \"\"\"\n",
|
||||
|
|
@ -320,7 +320,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MyRandomModel, max_it=100)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyRandomModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -345,6 +345,11 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import math\n",
|
||||
"import random\n",
|
||||
"from model.base_model import Model\n",
|
||||
"from random import randint, sample\n",
|
||||
"\n",
|
||||
"class Gene: # City\n",
|
||||
" def __init__(self, name, lat, lng):\n",
|
||||
" self.name = name\n",
|
||||
|
|
@ -417,19 +422,7 @@
|
|||
" if route.fitness > fittest.fitness:\n",
|
||||
" fittest = route\n",
|
||||
"\n",
|
||||
" return fittest"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import math\n",
|
||||
"import random\n",
|
||||
"from model.base_model import Model\n",
|
||||
"from random import randint, sample\n",
|
||||
" return fittest\n",
|
||||
"\n",
|
||||
"class MyGAModel(Model):\n",
|
||||
" def __init__(self):\n",
|
||||
|
|
@ -495,7 +488,7 @@
|
|||
" def selection(self, population, competitors_n):\n",
|
||||
" return Population(sample(population.individuals, competitors_n)).get_fittest()\n",
|
||||
"\n",
|
||||
" def fit(self, max_it=20):\n",
|
||||
" def fit(self, max_it=100):\n",
|
||||
" \"\"\"\n",
|
||||
" Execute simulated annealing algorithm.\n",
|
||||
" \"\"\"\n",
|
||||
|
|
@ -543,7 +536,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Genetic Algorithm\")\n",
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_problem, MyGAModel, max_it=200)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_problem, MyGAModel, timeout=300)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -602,13 +595,13 @@
|
|||
" 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):\n",
|
||||
" def fit(self, max_it=1000):\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",
|
||||
|
|
@ -639,7 +632,7 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_solution, fitness_list, time = TSP_Bench(tsp_file, MyModel)"
|
||||
"best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -655,7 +648,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for root, _, files in os.walk('./template/data'):\n",
|
||||
"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)"
|
||||
|
|
@ -675,15 +669,6 @@
|
|||
" fig.suptitle(title, fontsize=20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tsp_path = './template/data/medium'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -712,7 +697,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Genetic Algorithm\")\n",
|
||||
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyGAModel, max_it=100, timeout=600)"
|
||||
"best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyGAModel)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -724,13 +709,6 @@
|
|||
"plot_results(best_solutions, times, \"Genetic Algorithm\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -755,7 +733,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.3"
|
||||
"version": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@ docker build -t com2014-tsp .
|
|||
|
||||
## Windows
|
||||
|
||||
docker container run -v $PWD/requirements.txt:/tsp/requirements.txt -v $PWD/output/:/output/ com2014-tsp
|
||||
docker container run -v $PWD/output/:/output/ com2014-tsp
|
||||
|
||||
## Linux
|
||||
|
||||
docker container run -v $PWD/requirements.txt:/tsp/requirements.txt -v $PWD/output/:/output/ com2014-tsp
|
||||
docker container run -v $PWD/output/:/output/ com2014-tsp
|
||||
|
|
|
|||
Loading…
Reference in New Issue