- wrap numpy and cupy in matrix as np

- added type alias Mat for np.ndarray

src/rbm/stack_deep.py

@@ -1,14 +1,14 @@
-import numpy as np
 from status import Status
 from cd_train import cd_jens
 from stack import Stack, StackType
+from matrix import Mat, np
 
 class StackDeep(Stack):
 	def __init__(self, name: str, work_dir: str = '.'):
 		Stack.__init__(self, StackType.Deep, name, work_dir)
 
-	def batch_from(self, batch: np.ndarray, from_layer_id: int = 0):
-		_batch = np.matrix.copy(batch)
+	def batch_from(self, batch: Mat, from_layer_id: int = 0):
+		_batch = np.copy(batch)
 		for index, layer in enumerate(self.layers):
 			if index == from_layer_id:
 				break
@@ -16,27 +16,27 @@ class StackDeep(Stack):
 
 		return _batch
 
-	def train(self, batch: np.ndarray, status=Status()):
-		_batch = np.matrix.copy(batch)
+	def train(self, batch: Mat, status=Status()):
+		_batch = np.copy(batch)
 		for index, layer in enumerate(self.layers):
 			print(f"Train layer {index} for {layer.entity.params.num_epochs} epochs")
 			_batch = self.batch_from(batch, index)
 			layer.entity.train(_batch, cd_func=cd_jens, status=status)
 
 
-	def pass_up(self, visible: np.ndarray, from_layer_id: int = 0):
-		h = np.matrix.copy(visible)
+	def pass_up(self, visible: Mat, from_layer_id: int = 0):
+		h = np.copy(visible)
 		for layer in self.layers[from_layer_id:]:
 			h = layer.entity.gibbs_v_to_h(h)
 		return h
 
-	def pass_down(self, hidden: np.ndarray, from_layer_id: int = 0):
-		v = np.matrix.copy(hidden)
+	def pass_down(self, hidden: Mat, from_layer_id: int = 0):
+		v = np.copy(hidden)
 		for layer in list(reversed(self.layers))[from_layer_id:]:
 			v = layer.entity.gibbs_h_to_v(v)
 		return v
 
-	def pass_down_up(self, visible: np.ndarray, from_layer_id: int = 0):
+	def pass_down_up(self, visible: Mat, from_layer_id: int = 0):
 		h = self.pass_up(visible, from_layer_id)
 		v = self.pass_down(h)
 		return v