72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
import math
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import random
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from model.base_model import Model
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class MyUCSModel(Model):
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def __init__(self):
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super().__init__()
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def init(self, nodes):
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"""
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Put your initialization here.
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"""
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super().init(nodes)
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def getMST(self, node):
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MST = []
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distances = []
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for i in range(0, self.N):
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if i != node:
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MST.append(i)
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distances.append(self.dist(node, i))
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return [x for _,x in sorted(zip(distances, MST))]
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def fit(self, max_it=1000):
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"""
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Put your iteration process here.
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"""
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UCS_solutions = []
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UCS_losses = []
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for i in range(0, self.N):
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solution = [i]
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UCS_solutions.append(solution)
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UCS_losses.append(math.inf)
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MSTs = []
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for i in range(0, self.N):
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MSTs.append([-1] * self.N)
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# Breadth First: Set each city as starting point, then go to next city simultaneously
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min_loss = math.inf
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while(len(UCS_solutions[ UCS_losses.index(min(UCS_losses)) ]) != self.N):
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unvisited_list = list(range(0, self.N))
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min_loss = min(UCS_losses)
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# For each search path
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for i in range(0, self.N):
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if UCS_losses[i] == min_loss:
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cur_city = UCS_solutions[i][-1]
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unvisited_list = list( set(range(0, self.N)) - set(UCS_solutions[i]) )
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if MSTs[cur_city][0] == -1:
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MST = self.getMST(cur_city)
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MSTs[cur_city] = MST
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for j in MSTs[cur_city]:
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if(j in unvisited_list):
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UCS_solutions[i].append(j)
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N = len(UCS_solutions[i])
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cur_fit = 0
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for k in range(len(UCS_solutions[i])):
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coord_0, coord_1 = self.coords[UCS_solutions[i][k % N]], self.coords[UCS_solutions[i][(k + 1) % N]]
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cur_fit += math.sqrt((coord_0[0] - coord_1[0]) ** 2 + (coord_0[1] - coord_1[1]) ** 2)
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UCS_losses[i] = cur_fit
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# if(UCS_losses[i] < min_loss):
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# min_loss = UCS_losses[i]
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break
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self.best_solution = UCS_solutions[ UCS_losses.index(min(UCS_losses)) ]
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self.fitness_list.append(self.fitness(self.best_solution))
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return self.best_solution, self.fitness_list
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