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| # utils.py | |
| import torch | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from gmm import GaussianMixtureModel | |
| def initialize_gmm(mu_list, Sigma_list, pi_list): | |
| mu = torch.tensor(mu_list, dtype=torch.float32) | |
| Sigma = torch.tensor(Sigma_list, dtype=torch.float32) | |
| pi = torch.tensor(pi_list, dtype=torch.float32) | |
| return GaussianMixtureModel(mu, Sigma, pi) | |
| def generate_grid(dx): | |
| x_positions = np.arange(-10, 10.5, 0.5) | |
| y_positions = np.arange(-10, 10.5, 0.5) | |
| fine_points = np.arange(-10, 10 + dx, dx) | |
| ones_same_size = np.ones_like(fine_points) | |
| vertical_lines = [np.stack([x*ones_same_size, fine_points], axis=1) for x in x_positions] | |
| horizontal_lines = [np.stack([fine_points, y*ones_same_size], axis=1) for y in y_positions] | |
| grid_points = np.concatenate(vertical_lines + horizontal_lines, axis=0) | |
| return torch.tensor(grid_points, dtype=torch.float32) | |
| def generate_contours(dtheta): | |
| angles = np.linspace(0, 2 * np.pi, int(2 * np.pi / dtheta)) | |
| std_normal_contours = np.concatenate([np.stack([r * np.cos(angles), r * np.sin(angles)], axis=1) for r in range(1, 4)], axis=0) | |
| return torch.tensor(std_normal_contours, dtype=torch.float32) | |
| def transform_std_to_gmm_contours(std_contours, mu, Sigma): | |
| gmm_contours = [] | |
| for k in range(mu.shape[0]): | |
| L = torch.linalg.cholesky(Sigma[k]) | |
| gmm_contours.append(mu[k] + torch.matmul(std_contours, L.T)) | |
| return torch.cat(gmm_contours, dim=0) | |
| def generate_intermediate_points(gmm, grid_points, std_normal_contours, gmm_samples, normal_samples, T, N): | |
| gmm_contours = transform_std_to_gmm_contours(std_normal_contours, gmm.mu.squeeze(), gmm.Sigma) | |
| intermediate_points_gmm_to_normal = gmm.flow_gmm_to_normal(gmm_samples.clone(), T, N) | |
| contour_intermediate_points_gmm_to_normal = gmm.flow_gmm_to_normal(gmm_contours.clone(), T, N) | |
| grid_intermediate_points_gmm_to_normal = gmm.flow_gmm_to_normal(grid_points.clone(), T, N) | |
| intermediate_points_normal_to_gmm = gmm.flow_normal_to_gmm(normal_samples.clone(), T, N) | |
| contour_intermediate_points_normal_to_gmm = gmm.flow_normal_to_gmm(std_normal_contours.clone(), T, N) | |
| grid_intermediate_points_normal_to_gmm = gmm.flow_normal_to_gmm(grid_points.clone(), T, N) | |
| return (intermediate_points_gmm_to_normal, contour_intermediate_points_gmm_to_normal, grid_intermediate_points_gmm_to_normal, | |
| intermediate_points_normal_to_gmm, contour_intermediate_points_normal_to_gmm, grid_intermediate_points_normal_to_gmm) | |
| def plot_samples_and_contours(samples, contours, grid_points, title): | |
| fig, ax = plt.subplots(figsize=(8, 6)) | |
| ax.scatter(grid_points[:, 0], grid_points[:, 1], alpha=0.5, c='black', s=1, label='Grid Points') | |
| ax.scatter(contours[:, 0], contours[:, 1], alpha=0.5, s=3, c='blue', label='Contours') | |
| ax.scatter(samples[:, 0], samples[:, 1], alpha=0.5, c='red', label='Samples') | |
| ax.set_title(title) | |
| ax.set_xlabel("x1") | |
| ax.set_ylabel("x2") | |
| ax.grid(True) | |
| ax.legend(loc='upper right') | |
| ax.set_xlim(-5, 5) | |
| ax.set_ylim(-5, 5) | |
| ax.set_aspect('equal', adjustable='box') | |
| plt.close(fig) | |
| return fig, ax | |