[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

searches/README.md

@@ -118,18 +118,18 @@ The acceptance of worse solutions helps escape local minima, and this tendency d
 
 ### Advantages
 
-✓ Can escape local minima  
-✓ Simple to implement  
-✓ Widely applicable  
-✓ No gradient required  
-✓ Works with discrete and continuous problems  
+✓ Can escape local minima
+✓ Simple to implement
+✓ Widely applicable
+✓ No gradient required
+✓ Works with discrete and continuous problems
 
 ### Disadvantages
 
-✗ Requires careful parameter tuning  
-✗ No guarantee of finding global optimum  
-✗ Can be slow for very large problems  
-✗ Performance depends on cooling schedule  
+✗ Requires careful parameter tuning
+✗ No guarantee of finding global optimum
+✗ Can be slow for very large problems
+✗ Performance depends on cooling schedule
 
 ## Other Search Algorithms
 

searches/SIMULATED_ANNEALING_GUIDE.md

@@ -201,9 +201,9 @@ for rate in cooling_rates:
         cooling_rate=rate,
         max_iterations=1000
     )
-    
+
     best_state, best_cost = optimizer.optimize()
-    
+
     print(f"\nCooling rate: {rate}")
     print(f"  Final cost: {best_cost:.6f}")
     print(f"  Iterations: {optimizer.iteration}")
@@ -229,7 +229,7 @@ def portfolio_risk(weights):
     total = sum(weights)
     if abs(total - 1.0) > 0.01:
         return 1e6  # Penalty for invalid weights
-    
+
     # Simple risk calculation (in reality, use covariance matrix)
     risk = sum(w**2 * std**2 for w, (_, std) in zip(weights, stocks))
     return risk
@@ -290,7 +290,7 @@ print(f"Maximum profit: {max_profit:.2f}")
 
 ### Temperature Parameters
 
-- **Initial Temperature**: 
+- **Initial Temperature**:
   - Higher = more exploration, slower convergence
   - Typical range: 100 - 10000
   - Start high for complex problems
@@ -311,7 +311,7 @@ print(f"Maximum profit: {max_profit:.2f}")
 2. **Use slower cooling** (higher rate) for complex landscapes
 3. **Increase iterations** if not converging
 4. **Multiple runs** can help due to randomness
-5. **Monitor acceptance rate**: 
+5. **Monitor acceptance rate**:
    - Too high? Increase cooling rate
    - Too low? Increase initial temperature
 

searches/simulated_annealing_tsp.py

@@ -273,17 +273,23 @@ def _create_widgets(self):
         )
 
         ttk.Button(
-            control_frame, text="Generate Cities", command=self._generate_cities_from_input
+            control_frame,
+            text="Generate Cities",
+            command=self._generate_cities_from_input,
         ).grid(row=0, column=2, padx=5)
 
         # Parameters
-        ttk.Label(control_frame, text="Initial Temp:").grid(row=0, column=3, padx=(20, 5))
+        ttk.Label(control_frame, text="Initial Temp:").grid(
+            row=0, column=3, padx=(20, 5)
+        )
         self.temp_var = tk.StringVar(value="1000")
         ttk.Entry(control_frame, textvariable=self.temp_var, width=10).grid(
             row=0, column=4
         )
 
-        ttk.Label(control_frame, text="Cooling Rate:").grid(row=0, column=5, padx=(20, 5))
+        ttk.Label(control_frame, text="Cooling Rate:").grid(
+            row=0, column=5, padx=(20, 5)
+        )
         self.cooling_var = tk.StringVar(value="0.995")
         ttk.Entry(control_frame, textvariable=self.cooling_var, width=10).grid(
             row=0, column=6
@@ -299,7 +305,10 @@ def _create_widgets(self):
         self.start_button.pack(side=tk.LEFT, padx=5)
 
         self.pause_button = ttk.Button(
-            button_frame, text="Pause", command=self._pause_optimization, state=tk.DISABLED
+            button_frame,
+            text="Pause",
+            command=self._pause_optimization,
+            state=tk.DISABLED,
         )
         self.pause_button.pack(side=tk.LEFT, padx=5)
 
@@ -346,7 +355,8 @@ def _setup_plots(self):
     def _generate_random_cities(self, n: int):
         """Generate n random cities."""
         self.cities = [
-            City(random.uniform(0, 100), random.uniform(0, 100), f"C{i}") for i in range(n)
+            City(random.uniform(0, 100), random.uniform(0, 100), f"C{i}")
+            for i in range(n)
         ]
         self._plot_initial_cities()
 
@@ -478,13 +488,14 @@ def _update_plots(self):
         # Plot route
         self.ax_route.plot(x_coords, y_coords, "b-", alpha=0.6, linewidth=2)
         self.ax_route.scatter(
-            [city.x for city in self.cities], [city.y for city in self.cities],
-            c="red", s=100, zorder=2
+            [city.x for city in self.cities],
+            [city.y for city in self.cities],
+            c="red",
+            s=100,
+            zorder=2,
         )
 
-        self.ax_route.set_title(
-            f"Best Route (Distance: {route.total_distance():.2f})"
-        )
+        self.ax_route.set_title(f"Best Route (Distance: {route.total_distance():.2f})")
         self.ax_route.set_xlabel("X")
         self.ax_route.set_ylabel("Y")
         self.ax_route.set_aspect("equal")
@@ -533,7 +544,10 @@ def main():
     print("=" * 60)
 
     # Create 10 random cities
-    cities = [City(random.uniform(0, 100), random.uniform(0, 100), f"City{i}") for i in range(10)]
+    cities = [
+        City(random.uniform(0, 100), random.uniform(0, 100), f"City{i}")
+        for i in range(10)
+    ]
 
     print(f"\nSolving TSP for {len(cities)} cities...")
 

searches/simulated_annealing_with_gui.py

@@ -85,7 +85,9 @@ def _generate_neighbor(self) -> list[float]:
             zip(self.current_state, self.bounds)
         ):
             # Adaptive step size based on temperature
-            step_size = (max_val - min_val) * 0.1 * (self.temperature / self.initial_temp)
+            step_size = (
+                (max_val - min_val) * 0.1 * (self.temperature / self.initial_temp)
+            )
             new_val = current_val + random.uniform(-step_size, step_size)
             # Ensure within bounds
             new_val = max(min_val, min(max_val, new_val))
@@ -214,16 +216,22 @@ def _create_widgets(self):
             width=20,
         )
         problem_combo.grid(row=0, column=1, padx=5)
-        problem_combo.bind("<<ComboboxSelected>>", lambda e: self._load_default_problem())
+        problem_combo.bind(
+            "<<ComboboxSelected>>", lambda e: self._load_default_problem()
+        )
 
         # Parameters
-        ttk.Label(control_frame, text="Initial Temp:").grid(row=0, column=2, padx=(20, 5))
+        ttk.Label(control_frame, text="Initial Temp:").grid(
+            row=0, column=2, padx=(20, 5)
+        )
         self.temp_var = tk.StringVar(value="1000")
         ttk.Entry(control_frame, textvariable=self.temp_var, width=10).grid(
             row=0, column=3
         )
 
-        ttk.Label(control_frame, text="Cooling Rate:").grid(row=0, column=4, padx=(20, 5))
+        ttk.Label(control_frame, text="Cooling Rate:").grid(
+            row=0, column=4, padx=(20, 5)
+        )
         self.cooling_var = tk.StringVar(value="0.95")
         ttk.Entry(control_frame, textvariable=self.cooling_var, width=10).grid(
             row=0, column=5
@@ -247,7 +255,10 @@ def _create_widgets(self):
         self.start_button.pack(side=tk.LEFT, padx=5)
 
         self.pause_button = ttk.Button(
-            button_frame, text="Pause", command=self._pause_optimization, state=tk.DISABLED
+            button_frame,
+            text="Pause",
+            command=self._pause_optimization,
+            state=tk.DISABLED,
         )
         self.pause_button.pack(side=tk.LEFT, padx=5)
 
@@ -297,7 +308,9 @@ def _setup_plots(self):
 
         self.fig.tight_layout()
 
-    def _get_test_function(self, name: str) -> tuple[Callable, list[float], list[tuple]]:
+    def _get_test_function(
+        self, name: str
+    ) -> tuple[Callable, list[float], list[tuple]]:
         """
         Get test function, initial state, and bounds.
 

searches/test_simulated_annealing.py

@@ -151,9 +151,7 @@ def test_tsp_solver():
     # Optimal for square should be 4 (perimeter)
     print(f"  Expected optimal: 4.0")
     assert final_distance <= initial_distance, "Distance increased!"
-    assert (
-        abs(final_distance - 4.0) < 0.1
-    ), f"Not close to optimal: {final_distance}"
+    assert abs(final_distance - 4.0) < 0.1, f"Not close to optimal: {final_distance}"
     print("  ✓ Test passed!\n")
 
     # Test with more cities
@@ -177,7 +175,9 @@ def test_tsp_solver():
     print(f"\n  Initial distance: {initial_distance:.4f}")
     print(f"  Final distance: {final_distance:.4f}")
     print(f"  Improvement: {initial_distance - final_distance:.4f}")
-    print(f"  Improvement %: {100 * (initial_distance - final_distance) / initial_distance:.2f}%")
+    print(
+        f"  Improvement %: {100 * (initial_distance - final_distance) / initial_distance:.2f}%"
+    )
     print(f"  Iterations: {solver.iteration}")
 
     assert final_distance < initial_distance, "No improvement found!"