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 import numpy as np from qubo_nn.problems.problem import Problem class TSP(Problem): def __init__(self, cfg, dist_matrix, constraint, P=10): self.dist_matrix = dist_matrix self.constraint = constraint self.P = 10 def gen_qubo_matrix(self): n = len(self.dist_matrix) Q = np.zeros((n ** 2, n ** 2)) quadrants_y = list(range(0, n ** 2, n)) quadrants_x = quadrants_y[1:] + [quadrants_y[0]] # The diagonal positive constraints for start_x in quadrants_y: for start_y in quadrants_y: for i in range(n): Q[start_x + i][start_y + i] = 2 * self.constraint # The distance matrices for (start_x, start_y) in zip(quadrants_x, quadrants_y): for i in range(n): for j in range(n): if i == j: continue Q[start_x + i][start_y + j] = self.P * self.dist_matrix[j][i] Q[start_x + i][start_y + i] = 2 * self.constraint # The middle diagonal negative constraints for start_x in quadrants_x: for i in range(n): Q[start_x + i][start_x + i] = -2 * self.constraint for j in range(n): if i != j: Q[start_x + i][start_x + j] += 2 * self.constraint return Q @classmethod def gen_problems(self, cfg, n_problems, size=4, **kwargs): # TODO: 200 and 100 (contraint) is hardcoded !! problems = [] for _ in range(n_problems): dist_matrix = np.random.random(size=(size, size)) np.fill_diagonal(dist_matrix, 0) constraint = np.random.random() * 200 + 100 problems.append({"dist_matrix": dist_matrix.tolist(), "constraint": constraint}) return problems