Nxnxn Rubik 39scube Algorithm Github Python Full [ Safe ◎ ]

import numpy as np from scipy.spatial import distance

def solve_cube(cube): pieces = explore_cube(cube) groups = group_pieces(pieces) permutations = generate_permutations(groups) solution = optimize_solution(permutations) return solution

def optimize_solution(permutations): # Optimize the solution solution = [] for permutation in permutations: moves = [] for i in range(len(permutation) - 1): move = (permutation[i], permutation[i + 1]) moves.append(move) solution.extend(moves) return solution nxnxn rubik 39scube algorithm github python full

The Python implementation of the NxNxN-Rubik algorithm is as follows:

# Example usage: cube = np.array([ [[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]], [[7, 7, 7], [8, 8, 8], [9, 9, 9]] ]) import numpy as np from scipy

solution = solve_cube(cube) print(solution) This implementation defines the explore_cube , group_pieces , generate_permutations , and optimize_solution functions, which are used to solve the cube.

In 2019, a team of researchers and cubers developed a new algorithm for solving the NxNxN Rubik's Cube. The algorithm, called "NxNxN-Rubik", uses a combination of mathematical techniques, including group theory and combinatorial optimization. The algorithm is capable of solving cubes of any size, from 3x3x3 to larger sizes like 5x5x5 or even 10x10x10. The algorithm is capable of solving cubes of

def explore_cube(cube): # Explore the cube's structure pieces = [] for i in range(cube.shape[0]): for j in range(cube.shape[1]): for k in range(cube.shape[2]): piece = cube[i, j, k] pieces.append(piece) return pieces