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 <rampersad.jonathan@gmail.com>
Co-committed-by: Jonathan Rampersad <rampersad.jonathan@gmail.com>
The previous benchmark results showed that the GA was failing to find accurate roots (high MAE) for many polynomials. This was because the fixed default search range ([-100, 100]) was often incorrect, and the GA was searching in the wrong place.
This commit introduces a significantly more robust solution:
1. Adds a `_get_cauchy_bound` helper function to mathematically calculate a search radius that is guaranteed to contain all real roots.
2. Updates `_solve_x_numpy` and `_solve_x_cuda` with new logic:
* If the user provides a *custom* `min_range` or `max_range`, we treat them as an expert and use their specified range.
* If the user is using the *default* range, we silently discard it and use the smarter, automatically-calculated Cauchy bound instead.
This provides the best of both worlds: a powerful, smart default for most users and an "expert override" for those who need to fine-tune the search area.
The `_solve_x_numpy` method was correctly using `np.where(error == 0, ...)` to handle perfect roots. However, NumPy eagerly calculates `1.0 / error` for the entire array before applying the `where` condition, which was causing a `RuntimeWarning: divide by zero` when a perfect root was found.
This warning was harmless but created unnecessary console noise during testing and use.
This commit wraps the `ranks = ...` assignments in a `with np.errstate(divide='ignore'):` block to silence this specific, expected warning. The CUDA kernel is unaffected as its ternary operator already prevents this calculation.
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 <rampersad.jonathan@gmail.com>
Co-committed-by: Jonathan Rampersad <rampersad.jonathan@gmail.com>
