Instructions to use cahlen/ramsey-r55-cuda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use cahlen/ramsey-r55-cuda with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("cahlen/ramsey-r55-cuda") - Notebooks
- Google Colab
- Kaggle
| """CPU-only verification test for Ramsey R(5,5) Simulated Annealing Search""" | |
| print("Testing ramsey-r55-cuda...") | |
| from itertools import combinations | |
| def count_monochromatic_k5(n, adj_set): | |
| """Count monochromatic K_5 in 2-coloring. adj_set = set of edges colored red.""" | |
| count = 0 | |
| for combo in combinations(range(n), 5): | |
| edges = list(combinations(combo, 2)) | |
| red = sum(1 for e in edges if e in adj_set) | |
| if red == 10 or red == 0: # all red or all blue | |
| count += 1 | |
| return count | |
| # K_4: no K_5 subgraph possible, so count=0 for any coloring | |
| print(f" K_4 all-red: {count_monochromatic_k5(4, set(combinations(range(4),2)))} K_5 (expect 0)") | |
| assert count_monochromatic_k5(4, set(combinations(range(4),2))) == 0 | |
| # K_5 all-red: exactly 1 monochromatic K_5 | |
| all_edges = set(combinations(range(5), 2)) | |
| print(f" K_5 all-red: {count_monochromatic_k5(5, all_edges)} K_5 (expect 1)") | |
| assert count_monochromatic_k5(5, all_edges) == 1 | |
| # K_6 all-red: C(6,5)=6 monochromatic K_5 | |
| all_edges_6 = set(combinations(range(6), 2)) | |
| print(f" K_6 all-red: {count_monochromatic_k5(6, all_edges_6)} K_5 (expect 6)") | |
| assert count_monochromatic_k5(6, all_edges_6) == 6 | |
| print(f"\n3/3 tests passed") | |