Updated lines 654-669 with: LaTeX formulation vis-a-vis proper delimiters ‘$$'

Updated lines 488 and 619 with: iterations=[0, 2, 8, 10]

Updated lines 488 and 619 with: iterations=[0, 25, 75, 190])

Updated lines 488 and 619 with: iterations=[0, 2, 8,13]

Updated lines 488 and 619 with: (iterations=[0,2,8,12])

Updated indentation on line 535 with: X, Y = np.meshgrid(x_range, y_range)

Updated with plot_positions, plot_3d_meshgrid_contour methods, optimize function leveraging the functionality spawning from the methods, and created the gr.components.Image functionality fostering the visualizations populating

Updated with hashtag removal on lines 537, 521, and 514

Updated with removal of hashtag on line 536. And hashtag removal on line 511

Updated with hashtags on lines 536-537

Updated with hashtags on the dispersion_plot and dispersion_heatmap_plot. Updated lines 505 and 508 with: best_positions_array = np.load('best_positions_array.npy') , plot_contour_and_wolves = gwo.plot_contour_and_wolves(best_positions_array)

Updated line 505 with: best_positions_1D = np.load('best_positions.npy’). And line 508 with: plot_contour_and_wolves = gwo.plot_contour_and_wolves(best_positions_1D)

Updated line 521 with: return [plot_contour_and_wolves, dispersion_plot, dispersion_heatmap_plot], best_fitness_text, best_positions_text, best_fitness_npy, best_positions_npy, dispersion_text

Updated line 521 with: return [plot_contour_and_wolves_plot, dispersion_plot, dispersion_heatmap_plot], best_fitness_text, best_positions_text, best_fitness_npy, best_positions_npy, dispersion_text

Updated lines 344-346 with: best_positions_1D = np.load('best_positions.npy') wolf_positions = best_positions_1D

Updated the optimize method with removal of contour_plot functionality. Updated plot_contour_and_wolves method lines 344-346 with: best_positions_loaded_array = np.load('best_positions_array.npy') wolf_positions = best_positions_loaded_array. And modified the optimize function with: # Load best_positions_loaded_array best_positions_loaded_array = np.load('best_positions_array.npy') # Plot the contour_and_wolves plot_contour_and_wolves = gwo.plot_contour_and_wolves(best_positions_loaded_array) Removed the contour_plot from gwo.optimize also only the other two variables remain

Updated lines 490-515 with: def optimize(npart, ndim, max_iter): # Initialize the GWO algorithm with the provided parameters gwo = GWO(obj=obj, npart=npart, ndim=ndim, max_iter=max_iter, init=init, bounds=bounds) best_positions, best_fitness, contour_plot = gwo.optimize() # Convert best_fitness and best_positions to NumPy arrays if necessary best_fitness_npy = np.array(best_fitness) best_positions_npy = np.array(best_positions) # Calculate dispersion dispersion = gwo.Dispersion() dispersion_text = f"Dispersion: {dispersion}" # Plot the dispersion over time dispersion_plot = gwo.plot_dispersion() # Plot the dispersion heatmap dispersion_heatmap_plot = gwo.plot_dispersion_heatmap(x_range=(-6,6), y_range=(-6,6)) # Format the output strings best_fitness_text = f"Best Fitness: {best_fitness_npy}" best_positions_text = f"Best Positions: {best_positions_npy}" # Return the images and text return [contour_plot, dispersion_plot, dispersion_heatmap_plot], best_fitness_text, best_positions_text, best_fitness_npy, best_positions_npy, dispersion_text

Updated line 506 with: contour_plot = gwo.plot_contour_and_wolves(best_positions_array)

Updated lines 343-346 with: # Ensure wolf_positions is a 2D array wolf_positions = np.array(wolf_positions) if wolf_positions.ndim == 1: wolf_positions = wolf_positions.reshape(-1, 1)

Updated lines 204-234 with: def optimize(self): """ Run a full optimization and return the best positions and fitness values. This method is designed to be used with Gradio. """ # Initialize the swarm self.Initialize() # Lists to store the best positions and fitness values at each step best_positions = [] best_fitness = [] # Main loop while not self.Done(): self.Step() # Perform an optimization step # Update best_positions and best_fitness with the current best values best_positions.append(self.gbest[-1]) best_fitness.append(self.gpos[-1]) # Convert the list of best positions to a NumPy array best_positions_array = np.array(best_positions) # Get the contour plot as a PIL Image contour_plot = self.plot_contour_and_wolves(best_positions_array) # Print the best positions and fitness found print("Best Positions:", best_positions) print("Best Fitness:", best_fitness) # Return the best positions and fitness after the optimization return best_positions, best_fitness, contour_plot

Updated lines 340-341 with: best_positions_npy = np.load(‘best_positions.npy’) [removed] and wolf_positions = best_positions_npy [removed]. Generating a PIL Image population deriving from gr.components.Image()

Ignore previous notation. Updated lines 488, 495, 499, 502 and 505 with: gwo . Also, udated line 516 with: return [contour_plot, dispersion_plot, dispersion_heatmap_plot], best_fitness_text, best_positions_text, best_fitness_npy, best_positions_npy, dispersion_text

Updated lines 488, 499, 502, 505 with: # Initialize the GWO algorithm with the provided parameters gwo = GWO(obj=obj, npart=npart, ndim=ndim, max_iter=max_iter, init=init, bounds=bounds)

Updated lines 486-487 with: # Initialize the GWO algorithm with the provided parameters gwo = GWO(obj=obj, npart=npart, ndim=ndim, max_iter=max_iter, init=init, bounds=bounds).

Updated lines 483 and 485 with: def optimize(npart, ndim, max_iter): and best_positions, best_fitness = gwo.optimize()

Updated lines 483-516 with: new optimize function modifications

Updated lines 515-517 with: # Load the best fitness and positions from .npy files, best_fitness_npy = np.load('best_fitness.npy’), best_positions_npy = np.load('best_positions.npy')

Updated line 517 with: removal of best_fitness_npy, best_positions_npy,

Updated line 414 with: dispersion_values = np.array([self.Dispersion() for _ in positions]) [plot_dispersion_heatmap] method

Updated line 4 with: from matplotlib.colors import LogNorm Updated lines 453-479 with: def plot_dispersion_heatmap(self, x_range, y_range, resolution=100): # Create a grid of points within the specified range x = np.linspace(*x_range, resolution) y = np.linspace(*y_range, resolution) X, Y = np.meshgrid(x, y) positions = np.vstack([X.ravel(), Y.ravel()]).T # Calculate the dispersion for each position in the grid dispersion_values = np.array([self.Dispersion(pos) for pos in positions]) Z = dispersion_values.reshape(X.shape) # Plot the dispersion heatmap plt.figure(figsize=(10, 8)) plt.pcolormesh(X, Y, Z, cmap='viridis', norm=LogNorm()) plt.colorbar(label='Dispersion') # Set plot title and labels plt.title('Dispersion Heatmap') plt.xlabel('x') plt.ylabel('y') # Convert the plot to a PIL Image and return it buf = BytesIO() plt.savefig(buf, format='png') buf.seek(0) plt.close() # Close the figure to free up memory return Image.open(buf)

Updated lines 393-403 with: def Dispersion(self): """Calculate the dispersion of the swarm""" # Ensure self.gpos is a NumPy array if not isinstance(self.gpos, np.ndarray): self.gpos = np.array(self.gpos) # Now self.gpos should be a NumPy array, so we can calculate the dispersion x, y = self.gpos[:, 0], self.gpos[:, 1] dx = x.max() - x.min() dy = y.max() - y.min() return (dx + dy) / 2.0

Updated lines 388-392 with: def Dispersion(self): # """Calculate the dispersion of the swarm""" dispersion = np.std(self.gpos[:, 0]) + np.std(self.gpos[:, 1]) return dispersion

Updated line 340 with: best_positions_npy = np.load('best_positions.npy’). Updated line 358 with: contour = plt.contour(X, Y, Z, levels=20, cmap='magma’). Updated line 455 with: contour_plot = gwo.plot_contour_and_wolves(best_positions)

Create app.py

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initial commit