feat: first training attempt

It still sucks. I'm sure the the feature embedding is terribly wrong.

src/sleep_detection/sleep_epoch_classifer.py

@@ -0,0 +1,40 @@
+from pathlib import Path
+
+import torch
+from loguru import logger
+from torch import Tensor
+from torch.nn import Linear
+from torch.nn import Module
+from torch.nn import ReLU
+from torch.nn import Sequential
+
+
+class SleepEpochClassifier(Module):
+    """Per-epoch sleep stage classifier.
+
+    Input: a fixed-size feature vector summarizing one epoch's context window
+           (e.g. motion mean/max, HR mean/variance, time-of-night, previous
+           predicted label — whatever features you settle on).
+    Output: logits over sleep stage classes for that single epoch.
+
+    No recurrence/attention — sequence smoothing (majority vote / HMM)
+    happens as a separate post-processing step, not inside this network.
+    """
+
+    def __init__(self, n_features: int, n_classes: int, hidden_size: int = 16):
+        super().__init__()
+        self.net = Sequential(
+            Linear(n_features, hidden_size),
+            ReLU(),
+            Linear(hidden_size, n_classes),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        # x shape: (batch, n_features) -> returns (batch, n_classes) logits
+        return self.net(x)
+
+    def load(self, input_file: Path) -> list | None:
+        payload = torch.load(input_file, weights_only=True)
+        state_dict = payload.get("model_state_dict", payload)
+        self.load_state_dict(state_dict)
+        return payload.get("label_mapping")