[refactor] move tests to tests/, add pytest functions and main() entrypoints

- Moved src/tests/ → tests/
- Added test_* functions with assertions to script-style test files
- Added main() to each so IDEs offer it as a separate run target from pytest
- Fixed cupy_test.py: remove spurious x_gpu += x_cpu, fix duplicate xlabel

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tests/test_rnn.py

@@ -0,0 +1,99 @@
+"""Tests for StackRnn — shared-weights and unrolled (own-weights) modes."""
+import numpy as _np_cpu
+from stack.rnn import StackRnn
+from rbm.matrix import np, convert
+from rbm.entity import EntityParams, TrainingParams
+from rbm.status import Status
+
+SENSORY_SIZE = 8
+H_SIZE       = 4
+T            = 16    # sequence length
+NUM_SEQ      = 10    # sequences in the batch
+WORK_DIR     = "../../results"
+
+_PARAMS = TrainingParams(learning_rate=0.05, momentum=0.5, num_epochs=5,
+                         do_rao_blackwell=True)
+
+
+def _make_sequences() -> _np_cpu.ndarray:
+    rng  = _np_cpu.random.RandomState(0)
+    base = (rng.rand(NUM_SEQ, SENSORY_SIZE) > 0.5).astype(_np_cpu.float64)
+    return _np_cpu.stack([base] * T, axis=1)   # (NUM_SEQ, T, SENSORY_SIZE)
+
+
+def test_rnn_shared():
+    """Shared-weights mode: one entity reused at every time step."""
+    seqs = _make_sequences()
+    rnn  = StackRnn("test_rnn_shared", WORK_DIR)
+    rnn.append(StackRnn.make_layer("layer0", SENSORY_SIZE, H_SIZE,
+                                   EntityParams(), _PARAMS))
+    rnn.state_init(0.01)
+
+    assert rnn.is_shared
+    assert rnn.sensory_size() == SENSORY_SIZE
+    assert rnn.h_size()       == H_SIZE
+
+    rnn.train(seqs)
+
+    rnn.reset(batch_size=1)
+    for t in range(T):
+        h = rnn.step(np.array(seqs[0, t][None, :]))
+        assert h.shape == (1, H_SIZE), f"bad shape at t={t}"
+
+    recon = rnn.reconstruct(h)
+    assert recon.shape == (1, SENSORY_SIZE)
+
+    print(f"Original : {convert(np.array(seqs[0, -1][None, :]))}")
+    print(f"Recon    : {convert(recon)}")
+    print("test_rnn_shared: [passed]")
+
+
+def test_rnn_unrolled():
+    """Unrolled mode: T entities, one per time step, each with own weights."""
+    seqs = _make_sequences()
+    rnn  = StackRnn("test_rnn_unrolled", WORK_DIR)
+    for layer in StackRnn.make_unrolled(T, SENSORY_SIZE, H_SIZE,
+                                        EntityParams(), _PARAMS):
+        rnn.append(layer)
+    rnn.state_init(0.01)
+
+    assert not rnn.is_shared
+    assert rnn.num_layers() == T
+
+    rnn.train(seqs)
+
+    # Inference — _t advances through all T entities
+    rnn.reset(batch_size=1)
+    for t in range(T):
+        h = rnn.step(np.array(seqs[0, t][None, :]))
+        assert h.shape == (1, H_SIZE), f"bad shape at t={t}"
+        assert rnn._t == t + 1
+
+    recon = rnn.reconstruct(h)
+    assert recon.shape == (1, SENSORY_SIZE)
+
+    # next_entity wraps around modularly
+    rnn._t = T + 3
+    assert rnn.next_entity() is rnn.from_index(3).entity
+
+    print("test_rnn_unrolled: [passed]")
+
+
+def test_rnn_save_load():
+    seqs = _make_sequences()
+    rnn  = StackRnn("test_rnn_sl", WORK_DIR)
+    for layer in StackRnn.make_unrolled(T, SENSORY_SIZE, H_SIZE,
+                                        EntityParams(), _PARAMS):
+        rnn.append(layer)
+    rnn.state_init(0.01)
+    rnn.train(seqs)
+    rnn.state_save()
+    rnn.state_load()
+    print("test_rnn_save_load: [passed]")
+
+
+if __name__ == "__main__":
+    test_rnn_shared()
+    test_rnn_unrolled()
+    test_rnn_save_load()
+    print("All RNN tests passed.")