testing

hsmm/toy_data.py

@@ -1,48 +1,33 @@
 import torch
 import numpy as np
 
-def generate_toy_data(n_samples=50, seq_len=300, n_clusters=10, dim=5):
+def generate_toy_data(n_samples=500, seq_len=300, n_clusters=10, dim=5):
     """
     Generates sequences where the hidden states are clusters in D-dimensional space.
-    
-    Args:
-        n_samples: Number of audio 'files'
-        seq_len: Length of each file
-        n_clusters: Number of states (phonemes)
-        dim: Number of features (e.g. 30 for Wav2Vec PCA, 5 for testing)
     """
     data_list = []
     
-    # 1. Generate Random Cluster Centers
-    # We multiply by 10 to ensure they are far apart in space
-    # Shape: (10, 5)
+    # Cluster Centers (Spread out)
     centers = np.random.randn(n_clusters, dim) * 10.0
-    
     print(f"Generated {n_clusters} cluster centers in {dim}D space.")
-    print(f"Example Center 0: {np.round(centers[0], 2)}")
     
     for _ in range(n_samples):
         seq = []
         state = np.random.randint(0, n_clusters)
-        
         t = 0
         while t < seq_len:
-            # Random duration
             dur = np.random.randint(10, 30)
             
-            # 2. Generate Segment
-            # Shape: (Duration, Dim)
-            # Center[state] + Gaussian Noise
-            noise = np.random.randn(dur, dim) # Standard normal noise
+            # Segment: Center + Noise
+            noise = np.random.randn(dur, dim) 
             segment = noise + centers[state]
             seq.append(segment)
             
-            # 3. Transition (No self-loops)
+            # Transition (No self-loops)
             next_state = state
             while next_state == state:
                 next_state = np.random.randint(0, n_clusters)
             state = next_state
-            
             t += dur
             
         full_seq = np.concatenate(seq)[:seq_len]