9 Commits

Include renames

Author	SHA1	Message	Date
jono	9d967210fa	feat(ga): Overhaul GA for multi-root robustness and CPU performance ... ### 🚀 Performance (CPU) * Replaces `np.polyval` with a parallel Numba JIT function (`_calculate_ranks_numba`). * Replaces $O(N \log N)$ `np.argsort` with $O(N)$ `np.argpartition` in the GA loop. * Adds `numba` as a core dependency. ### 🧠 Robustness (Algorithm) * Implements Blend Crossover (BLX-$\alpha$) for better, extrapolative exploration. * Uses a hybrid selection model (top X% for crossover, 100% for mutation) to preserve root niches. * Adds `selection_percentile` and `blend_alpha` to `GA_Options` for tuning.	2025-10-30 11:31:00 -04:00
jonoandjono	4e46c11f83	feat(ga): Implement quality filtering and precision-based clustering (#19) ... The previous GA logic was returning the "top N" solutions, which led to test failures when the algorithm correctly converged on only one of all possible roots (e.g., returning 1000 variations of -1.0). This commit fixes the root-finding logic to correctly identify and return *all* unique, high-quality roots: 1. **feat(api):** Adds `root_precision` to `GA_Options`. This new parameter (default: 5) allows the user to control the number of decimal places for clustering unique roots. 2. **fix(ga):** Replaces the flawed "top N" logic in both `_solve_x_numpy` and `_solve_x_cuda`. The new process is: * Dynamically sets a `quality_threshold` based on the user's `root_precision` (e.g., `precision=5` requires a rank > `1e6`). * Filters the *entire* final population for all solutions that meet this quality threshold. * Rounds these high-quality solutions to `root_precision`. * Returns only the `np.unique()` results. This ensures the solver returns all distinct roots that meet the accuracy requirements, rather than just the top N variations of a single root. Reviewed-on: #19 Co-authored-by: Jonathan Rampersad <[email protected]> Co-committed-by: Jonathan Rampersad <[email protected]>	2025-10-27 19:26:50 +00:00
jonoandjono	c3b3513e79	feat(ga, api): Implement advanced GA strategy and refactor API for v0.4.0 (#16) ... This commit introduces a major enhancement to the genetic algorithm's convergence logic and refactors key parts of the API for better clarity and usability. - **feat(ga):** Re-implements the GA solver (CPU & CUDA) to use a more robust strategy based on Elitism, Crossover, and Mutation. This replaces the previous, less efficient model and is designed to significantly improve accuracy and convergence speed. - **feat(api):** Updates `GA_Options` to expose the new GA strategy parameters: - Renames `mutation_percentage` to `mutation_strength` for clarity. - Adds `elite_ratio`, `crossover_ratio`, and `mutation_ratio`. - Includes a `__post_init__` validator to ensure ratios are valid. - **refactor(api):** Moves `quadratic_solve` from a standalone function to a method of the `Function` class (`f1.quadratic_solve()`). This provides a cleaner, more object-oriented API. - **docs:** Updates the README, `GA_Options` doc page, and `quadratic_solve` doc page to reflect all API changes, new parameters, and updated usage examples. - **chore:** Bumps version to 0.4.0. Reviewed-on: #16 Co-authored-by: Jonathan Rampersad <[email protected]> Co-committed-by: Jonathan Rampersad <[email protected]>	2025-10-27 14:20:56 +00:00
jono	36f51ca67e	fix: Typo in test	2025-06-17 14:27:53 -04:00
jono	ee414ea0dc	feat: Added function * function multiplication	2025-06-17 14:26:26 -04:00
jono	3d2c724ad4	feat: Add nth derivative function and fix: typo derivitive->derivative	2025-06-17 14:06:45 -04:00
jono	4fe11a1c31	FIXED: typo in test file	2025-06-16 19:15:46 -04:00
jono	956c270762	FIXED: typo in test file	2025-06-16 19:08:51 -04:00
jono	2dc39b6ae3	Initial Commit	2025-06-16 18:40:06 -04:00