fix(ga): Suppress divide-by-zero warning in NumPy solver

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.

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 # --- Core Metadata ---
 name = "polysolve"
-version = "0.4.0"
+version = "0.4.1"
 authors = [
   { name="Jonathan Rampersad", email="jonathan@jono-rams.work" },
 ]

src/polysolve/__init__.py

@@ -295,7 +295,8 @@ class Function:
             y_calculated = np.polyval(self.coefficients, solutions)
             error = y_calculated - y_val
             
-            ranks = np.where(error == 0, np.finfo(float).max, np.abs(1.0 / error))
+            with np.errstate(divide='ignore'):
+                ranks = np.where(error == 0, np.finfo(float).max, np.abs(1.0 / error))
             
             # Sort solutions by fitness (descending)
             sorted_indices = np.argsort(-ranks)
@@ -345,7 +346,8 @@ class Function:
         # After all generations, do one last ranking to find the best solutions
         y_calculated = np.polyval(self.coefficients, solutions)
         error = y_calculated - y_val
-        ranks = np.where(error == 0, np.finfo(float).max, np.abs(1.0 / error))
+        with np.errstate(divide='ignore'):
+            ranks = np.where(error == 0, np.finfo(float).max, np.abs(1.0 / error))
         sorted_indices = np.argsort(-ranks)
         
         # Get the top 'sample_size' solutions the user asked for