Permalink
Cannot retrieve contributors at this time
Name already in use
A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
qubo-nn/qubo_nn/problems/tsp.py
Go to fileThis commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
executable file
52 lines (43 sloc)
1.83 KB
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
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 |