commit e5cb97d2e527cdda8c5f7f09ecaccdf2720c5d7f
Author: jbkzi <jabra11.gpg@gmail.com>
Date:   Wed Jan 21 16:30:17 2026 +0100

    initial

diff --git a/hsmm/.python-version b/hsmm/.python-version
new file mode 100644
index 0000000..24ee5b1
--- /dev/null
+++ b/hsmm/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/hsmm/README.md b/hsmm/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/hsmm/hsmm_inference.py b/hsmm/hsmm_inference.py
new file mode 100644
index 0000000..31ccb2d
--- /dev/null
+++ b/hsmm/hsmm_inference.py
@@ -0,0 +1,71 @@
+import torch
+import torch.nn.functional as F
+
+def viterbi_decode(model, x):
+    """
+    Returns the optimal sequence of states (path).
+    """
+    with torch.no_grad():
+        T = x.shape[0]
+        N = model.n_states
+        D_max = model.max_dur
+        
+        # 1. Setup Probs
+        log_emit = model.compute_emission_log_probs(x)
+        log_trans = F.log_softmax(model.get_masked_transitions(), dim=1)
+        log_dur = F.log_softmax(model.dur_logits, dim=1)
+        log_pi = F.log_softmax(model.pi_logits, dim=0)
+        
+        # 2. Viterbi Tables
+        # max_prob[t, s] = Best log-prob ending at t in state s
+        max_prob = torch.full((T, N), -float('inf'), device=x.device)
+        # backpointers[t, s] = (previous_state, duration_used)
+        backpointers = {} 
+
+        # 3. Dynamic Programming
+        for t in range(T):
+            for d in range(1, D_max + 1):
+                if t - d + 1 < 0: continue
+                
+                # Emission sum for segment
+                seg_emit = log_emit[t-d+1 : t+1].sum(dim=0)
+                dur_prob = log_dur[:, d-1]
+                
+                if t - d + 1 == 0:
+                    # Init
+                    score = log_pi + dur_prob + seg_emit
+                    for s in range(N):
+                        if score[s] > max_prob[t, s]:
+                            max_prob[t, s] = score[s]
+                            backpointers[(t, s)] = (-1, d) # -1 is Start
+                else:
+                    # Transition
+                    prev_scores = max_prob[t-d] # (N,)
+                    # Find best transition for each target state s
+                    # (N, 1) + (N, N) -> (N, N)
+                    trans_scores = prev_scores.unsqueeze(1) + log_trans
+                    best_prev_score, best_prev_idx = trans_scores.max(dim=0) # (N,)
+                    
+                    current_score = best_prev_score + dur_prob + seg_emit
+                    
+                    for s in range(N):
+                        if current_score[s] > max_prob[t, s]:
+                            max_prob[t, s] = current_score[s]
+                            backpointers[(t, s)] = (best_prev_idx[s].item(), d)
+
+        # 4. Backtracking
+        best_end_state = torch.argmax(max_prob[T-1]).item()
+        path = []
+        curr_t = T - 1
+        curr_s = best_end_state
+        
+        while curr_t >= 0:
+            if (curr_t, curr_s) not in backpointers: break
+            prev_s, d = backpointers[(curr_t, curr_s)]
+            
+            # Append this state 'd' times
+            path = [curr_s] * d + path
+            curr_t -= d
+            curr_s = prev_s
+            
+        return path
\ No newline at end of file
diff --git a/hsmm/hsmm_model.py b/hsmm/hsmm_model.py
new file mode 100644
index 0000000..4bcb5b5
--- /dev/null
+++ b/hsmm/hsmm_model.py
@@ -0,0 +1,107 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+# --- JIT Compiled Forward Loop for Speed ---
+from typing import List
+
+@torch.jit.script
+def hsmm_forward_loop(T: int, N: int, D_max: int,
+                      log_emit: torch.Tensor,
+                      log_trans: torch.Tensor,
+                      log_dur: torch.Tensor,
+                      log_pi: torch.Tensor) -> torch.Tensor:
+    
+    # We use a List to store alpha steps. This avoids "in-place" errors.
+    # We initialize with dummy tensors to handle negative indexing if needed, 
+    # but logically we just append.
+    alpha_list: List[torch.Tensor] = []
+
+    for t in range(T):
+        # Initialize current step with -inf
+        current_alpha = torch.full((N,), -float('inf'), device=log_emit.device)
+        
+        for d in range(1, D_max + 1):
+            if t - d + 1 < 0: continue
+
+            # 1. Emission Score (Sum)
+            seg_emit = log_emit[t-d+1 : t+1].sum(dim=0)
+
+            # 2. Duration Score
+            dur_score = log_dur[:, d-1]
+
+            # 3. Transition Score
+            if t - d + 1 == 0:
+                # Init (t=0 or start of a duration)
+                score = log_pi + dur_score + seg_emit
+                current_alpha = torch.logaddexp(current_alpha, score)
+            else:
+                # Recursion: look back at alpha[t-d]
+                # In a list, alpha[t-d] is just alpha_list[t-d]
+                prev_alpha = alpha_list[t-d]
+                
+                trans_score = torch.logsumexp(prev_alpha.unsqueeze(1) + log_trans, dim=0)
+                score = trans_score + dur_score + seg_emit
+                current_alpha = torch.logaddexp(current_alpha, score)
+        
+        # Save this step to the history list
+        alpha_list.append(current_alpha)
+
+    # The result is the sum of the very last step
+    return torch.logsumexp(alpha_list[-1], dim=0)
+
+class GaussianHSMM(nn.Module):
+    def __init__(self, n_states, input_dim, max_dur=20):
+        super().__init__()
+        self.n_states = n_states
+        self.max_dur = max_dur
+        
+        # --- Parameters ---
+        # 1. Start Probabilities
+        self.pi_logits = nn.Parameter(torch.randn(n_states))
+        
+        # 2. Transition Matrix (Bigram)
+        # We manually mask the diagonal later to forbid self-transitions
+        self.trans_logits = nn.Parameter(torch.randn(n_states, n_states))
+        
+        # 3. Duration Model (Categorical weights for 1..max_dur)
+        self.dur_logits = nn.Parameter(torch.randn(n_states, max_dur))
+        
+        # 4. Emissions (Gaussian Means & LogVars)
+        self.means = nn.Parameter(torch.randn(n_states, input_dim))
+        self.log_vars = nn.Parameter(torch.zeros(n_states, input_dim))
+
+    def compute_emission_log_probs(self, x):
+        """ Calculates Gaussian Log-Likelihood: (T, N) """
+        # x: (T, Dim) -> (T, 1, Dim)
+        # means: (N, Dim) -> (1, N, Dim)
+        # log_prob = -0.5 * (log(2pi) + log_var + (x-mu)^2/var)
+        
+        diff = x.unsqueeze(1) - self.means.unsqueeze(0)
+        vars = self.log_vars.exp().unsqueeze(0)
+        log_vars = self.log_vars.unsqueeze(0)
+        
+        log_prob = -0.5 * (np.log(2 * np.pi) + log_vars + (diff**2) / vars)
+        return log_prob.sum(dim=-1) # Sum over feature dimensions
+
+    def get_masked_transitions(self):
+        """ Enforces A_ii = -inf (No self-transitions allowed in Bigram) """
+        mask = torch.eye(self.n_states, device=self.trans_logits.device).bool()
+        return self.trans_logits.masked_fill(mask, -float('inf'))
+
+    def forward(self, x):
+        """ Returns Negative Log Likelihood (Scalar Loss) """
+        T = x.shape[0]
+        
+        # 1. Precompute static probabilities
+        log_emit = self.compute_emission_log_probs(x)
+        log_trans = F.log_softmax(self.get_masked_transitions(), dim=1)
+        log_dur = F.log_softmax(self.dur_logits, dim=1)
+        log_pi = F.log_softmax(self.pi_logits, dim=0)
+        
+        # 2. Run JIT Loop
+        total_ll = hsmm_forward_loop(T, self.n_states, self.max_dur,
+                                     log_emit, log_trans, log_dur, log_pi)
+        
+        return -total_ll
diff --git a/hsmm/main.py b/hsmm/main.py
new file mode 100644
index 0000000..b144ff7
--- /dev/null
+++ b/hsmm/main.py
@@ -0,0 +1,80 @@
+import torch
+import torch.optim as optim
+import matplotlib.pyplot as plt
+from hsmm_model import GaussianHSMM
+from hsmm_inference import viterbi_decode
+from toy_data import generate_toy_data
+
+# --- Settings ---
+N_STATES = 10
+INPUT_DIM = 5     # Matches the 'dim' in generate_toy_data
+MAX_DUR = 50
+LR = 0.05
+EPOCHS = 20
+
+# In train():
+def train():
+    print("1. Generating Data...")
+    train_data = generate_toy_data(n_samples=30, seq_len=300, n_clusters=N_STATES, dim=INPUT_DIM)
+    
+    print("2. Initializing Model...")
+    model = GaussianHSMM(N_STATES, INPUT_DIM, MAX_DUR)
+    optimizer = optim.Adam(model.parameters(), lr=LR)
+    
+    print("3. Training Loop...")
+    loss_history = []
+    
+    for epoch in range(EPOCHS):
+        epoch_loss = 0
+        optimizer.zero_grad()
+        
+        # Batching: Gradient Accumulation
+        for seq in train_data:
+            loss = model(seq) # Forward pass
+            loss.backward()   # Backward pass
+            epoch_loss += loss.item()
+            
+        # Normalize gradients
+        for p in model.parameters():
+            if p.grad is not None:
+                p.grad /= len(train_data)
+        
+        optimizer.step()
+        loss_history.append(epoch_loss)
+        
+        if epoch % 5 == 0:
+            print(f"Epoch {epoch:02d} | NLL Loss: {epoch_loss:.2f}")
+
+    # --- Verification ---
+    print("\n4. Results:")
+    learned_means = model.means.detach().view(-1).numpy()
+    learned_means.sort()
+    print(f"True Means:    [-5.0, 0.0, 5.0]")
+    print(f"Learned Means: {learned_means}")
+    
+# --- Visualization Block in main.py ---
+    print("5. Visualizing Inference...")
+    test_seq = train_data[0]
+    predicted_path = viterbi_decode(model, test_seq)
+    
+    fig, ax = plt.subplots(2, 1, figsize=(12, 6), sharex=True)
+    
+    # Plot 1: The Multi-Dimensional Data (Transposed so Time is X-axis)
+    # This shows the "features" changing color as the state changes
+    ax[0].imshow(test_seq.numpy().T, aspect='auto', cmap='viridis', interpolation='nearest')
+    ax[0].set_title(f"Raw Data ({INPUT_DIM} Dimensions)")
+    ax[0].set_ylabel("Feature Dim")
+    
+    # Plot 2: The Inferred States
+    # Reshape path to (1, T) for imshow
+    path_img = np.array(predicted_path)[np.newaxis, :]
+    ax[1].imshow(path_img, aspect='auto', cmap='tab10', interpolation='nearest')
+    ax[1].set_title("Inferred HSMM States")
+    ax[1].set_ylabel("State ID")
+    ax[1].set_xlabel("Time (Frames)")
+    
+    plt.tight_layout()
+    plt.show()
+
+if __name__ == "__main__":
+    train()
diff --git a/hsmm/pyproject.toml b/hsmm/pyproject.toml
new file mode 100644
index 0000000..490da3d
--- /dev/null
+++ b/hsmm/pyproject.toml
@@ -0,0 +1,10 @@
+[project]
+name = "hsmm"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "matplotlib>=3.10.8",
+    "torch>=2.9.1",
+]
diff --git a/hsmm/toy_data.py b/hsmm/toy_data.py
new file mode 100644
index 0000000..634ddef
--- /dev/null
+++ b/hsmm/toy_data.py
@@ -0,0 +1,51 @@
+import torch
+import numpy as np
+
+def generate_toy_data(n_samples=50, 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)
+    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 = noise + centers[state]
+            seq.append(segment)
+            
+            # 3. 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]
+        data_list.append(torch.tensor(full_seq, dtype=torch.float32))
+        
+    return data_list
diff --git a/hsmm/uv.lock b/hsmm/uv.lock
new file mode 100644
index 0000000..3a70575
--- /dev/null
+++ b/hsmm/uv.lock
@@ -0,0 +1,691 @@
+version = 1
+revision = 3
+requires-python = ">=3.13"
+
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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