[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_linear.py

@@ -0,0 +1,67 @@
+from rbm.matrix import Mat, np
+from rbm.entity import Entity, EntityParams, TrainingParams
+from model.model import Model
+from image.sub_image import normalize
+
+WORK_DIR = "../../results"
+USE_OPTIMIZER = True
+N_VIS = 3000
+N_CASES = 1000
+class LinearModel(Model):
+	def __init__(self, name: str, work_dir: str = '.', do_gaussian_hidden=False):
+		super().__init__(name, work_dir)
+
+		if do_gaussian_hidden:
+			# Hidden gaussian
+			self.unit1 = Entity((N_VIS, 64), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=True),
+								TrainingParams(learning_rate=0.01, momentum=0.9, num_epochs=1000))
+		else:
+			# Hidden binary
+			self.unit1 = Entity((N_VIS, 1000), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False),
+								TrainingParams(learning_rate=0.005, momentum=0.9, num_epochs=1000, mini_batch_size=1000))
+
+	def forward(self, x: Mat):
+		x = self.unit1.forward(x)
+		return x
+
+	def reconstruct(self, x: Mat):
+		x = self.unit1.reconstruct(x)
+		return x
+
+class _SmallLinearModel(Model):
+	def __init__(self):
+		super().__init__("linear_pytest", "results")
+		self.unit1 = Entity((64, 32), EntityParams(do_gaussian_visible=True),
+		                    TrainingParams(learning_rate=0.005, momentum=0.9, num_epochs=100))
+
+	def forward(self, x: Mat):
+		return self.unit1.forward(x)
+
+	def reconstruct(self, x: Mat):
+		return self.unit1.reconstruct(x)
+
+
+def test_linear():
+	model = _SmallLinearModel()
+	model.init(0.1)
+	batch = normalize(np.random.randn(50, 64, dtype=np.float64))
+	model.train(batch)
+	for pattern in batch:
+		recon = model.reconstruct(model.forward(pattern))
+		assert recon.size == pattern.size
+
+
+def main():
+	model = LinearModel("linear", "results")
+	model.init(0.1)
+	model.load()
+	training_batch = np.random.randn(N_CASES, N_VIS, dtype=np.float64)
+	training_batch_norm = normalize(training_batch)
+	model.train(training_batch_norm)
+	model.save()
+	for pattern in training_batch:
+		model.reconstruct(model.forward(pattern))
+
+
+if __name__ == "__main__":
+	main()