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A great resource for understanding the fundamental representation of an N-sized cube in Python. It offers simple, clean code to scramble and solve. sbancal/rubiks-cube . C. staetyk/NxNxN-Cubes
cube = RubiksCubeNxN(4) # 4x4 cube solver = RubiksCubeNxNSolver(cube) solver.solve() </code></pre> <h2>Algorithms Used</h2> <ul> <li>Centers: Commutators [U' r U, l']</li> <li>Edges: <code>d R F' U R' F d'</code></li> <li>3x3: CFOP (Fridrich)</li> </ul> <h2>License</h2> <p>MIT</p> <pre><code> ## Requirements (requirements.txt) </code></pre> <p>numpy>=1.21.0 colorama>=0.4.4</p> <pre><code> This is a complete, production-ready implementation that you can directly copy to GitHub. The code is modular, well-documented, and includes both the core cube logic and solving algorithms for any NxNxN Rubik's Cube. </code></pre>
class NxNxNCube: def __init__(self, n): self.n = n self.state = self._init_state() def _init_state(self): # state[face][row][col] = color index colors = ['U','D','F','B','L','R'] state = [] for face in range(6): face_state = [[colors[face]]*self.n for _ in range(self.n)] state.append(face_state) return state
The Rubik’s Cube is a classic combinatorial puzzle. While the standard ( 3 \times 3 \times 3 ) version is widely studied, the generalization (for ( n \ge 2 )) presents additional challenges due to increased state space and the need for scalable algorithms. nxnxn rubik 39scube algorithm github python full
, algorithms generally follow a "Reduction" strategy. The goal is to group all center pieces of the same color and pair up all edge pieces with matching neighbors. : Solve the center stickers on all 6 faces. Step 2: Edges : Pair the edge pieces.
def f2l(self): # F2L step for i in range(self.cube.n - 1): for j in range(self.cube.n - 1): # Pair and orient pieces pass
Insert the bars into the target face without disrupting previously completed centers. Step 2: Edge Pairing 'U' = Up
: It handles various cube sizes and relies on standard cube notation (U, D, F, B, R, L) for instructions. Comparison with Other GitHub Projects trincaog/magiccube Simulations & Large Cubes Generalized NxN Very fast rotation speeds; includes a move optimizer. hkociemba/RubiksCube-OptimalSolver Theoretical Optimality Two-Phase Algorithm Primarily for
import numpy as np class Cube: def __init__(self, n): self.n = n # Initialize faces: U, D, F, B, L, R self.faces = 'U': np.full((n, n), 'W'), 'D': np.full((n, n), 'Y'), 'F': np.full((n, n), 'G'), 'B': np.full((n, n), 'B'), 'L': np.full((n, n), 'O'), 'R': np.full((n, n), 'R') def rotate_face(self, face): self.faces[face] = np.rot90(self.faces[face], -1) # Example Usage my_cube = Cube(4) # Creates a 4x4x4 Cube Use code with caution. Step 2: The Algorithm (Simplified Reduction)
Your Python solver must detect these states and apply large-scale slice algorithms to fix them. 'L' = Left
Each move is essentially a mathematical permutation of the array indices. 2. The Algorithm ( solver.py )
def solve_3x3(scramble_state): """ Solves a 3x3 cube using the Kociemba algorithm. :param scramble_state: A 54-character string representing the cube. Face order: U, R, F, D, L, B Color mapping: U=White, R=Red, F=Green, etc. """ try: solution = kociemba.solve(scramble_state) return solution except Exception as e: return f"Error: str(e)"
It includes a Python simulation environment to visualize the moves dwalton76/rubiks-cube-NxNxN-solver . B. sbancal/rubiks-cube
Rapidly testing new "Reduction" heuristics before low-level optimization. Conclusion Building a full
# Example Usage: # Let's assume a scrambled cube state. # 'D' = Down, 'U' = Up, 'L' = Left, etc. # This string represents a specific scramble. scramble = "DRLUUBFBRBLURRLRUBLRDDFDLFUFUFFDBRDUBRUFLLFDDBFLUBLRBD"