- wrap numpy and cupy in matrix as np

- added type alias Mat for np.ndarray

src/rbm/layer.py

@@ -1,9 +1,9 @@
-import numpy as np
 from params import RbmParams
 from state import RbmState
 from status import Status
 from cd_train import cd_jens
 from entity import Entity
+from matrix import Mat, np
 
 class Layer:
 	def __init__(self, name: str, shape: tuple[int, int, int, int], params: RbmParams):
@@ -43,7 +43,7 @@ def xor():
 	layer.load()
 
 	# Prepare training data
-	training_batch = np.array([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
+	training_batch = Mat([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
 
 	# Train layer
 	layer.entity.train(training_batch, cd_jens, Status())
@@ -52,7 +52,7 @@ def xor():
 	layer.save()
 
 	# Test with test data
-	test_batch = np.array([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)
+	test_batch = Mat([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)
 	for pattern in test_batch:
 		h = layer.entity.gibbs_v_to_h(pattern)
 		v = layer.entity.gibbs_h_to_v(h)