defining better parameters

Non relevant update

project_1.py

@@ -105,7 +105,7 @@ def create_dataloader(data, batch_size=64, shuffle=True):
     data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
     return data_loader
 
-def visualize_trends(data, segment_names, num_plots=3):
+def visualize_trends(data, segment_names, num_plots=3, save_path=None):
     # Visualize random trends/segments
     num_samples, _, _ = data.shape
     for _ in range(num_plots):
@@ -114,6 +114,9 @@ def visualize_trends(data, segment_names, num_plots=3):
         plt.imshow(data[idx].numpy())
         plt.title(f"Segment: {segment_names[idx]}")
         plt.colorbar()
+        if save_path:
+            subject_save_path = os.path.join(save_path, f"trends_visualization_segment_{segment_names}.png")
+            plt.savefig(subject_save_path)
         plt.show()
 
 def perform_pca(data, num_components=2):
@@ -169,7 +172,7 @@ def visualize_correlation_matrix(data, segment_names, subject_mode=True, num_sub
             plt.title(f"Correlation Matrix for {num_subjects_to_visualize} Subjects {subject}")
 
             if save_path:
-                subject_save_path = os.path.join(save_path, f"correlation_matrix_subject_group{subject}.png")
+                subject_save_path = os.path.join(save_path, f"correlation_matrix_subject_group_{subject}.png")
                 plt.savefig(subject_save_path)
 
             plt.show()
@@ -312,7 +315,7 @@ def main():
 
     data_path, labels_path, saving_path = get_data_paths(data_format, is_linux=is_linux, is_hpc=is_hpc)
 
-    train_data, segment_names, labels = load_data(data_path, labels_path, dataset_size=20, train=True, data_format=data_format)
+    train_data, segment_names, labels = load_data(data_path, labels_path, dataset_size=10, train=True, data_format=data_format)
     # test_data, _, _ = load_data(data_path, labels_path, dataset_size=30, train=False)
 
     train_dataloader = create_dataloader(train_data)
@@ -327,12 +330,11 @@ def main():
 
     # Visualize the correlation matrix
     visualize_correlation_matrix(train_data, segment_names, subject_mode=True, num_subjects_to_visualize=None, save_path=saving_path)
-    visualize_correlation_matrix(train_data, segment_names, subject_mode=False, num_subjects_to_visualize=None, save_path=saving_path)
+    # visualize_correlation_matrix(train_data, segment_names, subject_mode=False, num_subjects_to_visualize=None, save_path=saving_path)
     # visualize_correlation_matrix(train_data, segment_names, subject_mode=False, num_subjects_to_visualize=None, save_path=saving_path)
 
     # Perform MFVI for your data
     K = 4  # Number of clusters
-    n_optimization_iterations = 100
     miu_mfvi, pi_mfvi, resp_mfvi = perform_mfvi(train_data, K, n_optimization_iterations=1000, convergence_threshold=1e-5, run_until_convergence=False)
 
     # Calculate clustering metrics for MFVI