- wrap numpy and cupy in matrix as np

- added type alias Mat for np.ndarray

src/rbm/matrix.py

@@ -1,25 +1,36 @@
-import numpy as np
 import time
+from typing import TypeAlias
+
+USE_CUDA = 1
+if USE_CUDA:
+	import cupy as np
+	Mat: TypeAlias = np.array
+	def convert(src: Mat):
+		return np.asnumpy(src)
+else:
+	import numpy as np
+	Mat: TypeAlias = np.array
+	def convert(src: Mat):
+		return src
 
 np.random.seed(int(time.monotonic()))
-
-def uniform(shape: tuple, mu: float = 0.5, std: float = 1.0) -> np.ndarray:
+def uniform(shape: tuple, mu: float = 0.5, std: float = 1.0) -> Mat:
 	return std * (np.random.rand(shape[0], shape[1]) + mu - 0.5)
 
-def gaussian(shape: tuple, mu: float = 0.0, std: float = 1.0) -> np.ndarray:
+def gaussian(shape: tuple, mu: float = 0.0, std: float = 1.0) -> Mat:
 	return std * (np.random.randn(shape[0], shape[1]) + mu)
 
-def sample(src: np.ndarray) -> np.ndarray:
+def sample(src: Mat) -> Mat:
 	return (src > uniform(src.shape)).astype(float)
 
-def prob(src: np.ndarray) -> np.ndarray:
+def prob(src: Mat) -> Mat:
 	return 1.0 / (1 + np.exp(-src))
 
-def rms_error(d_err: np.ndarray):
+def rms_error(d_err: Mat):
 	d_err_squared = d_err * d_err
 	return np.sum(d_err_squared, 1) / d_err_squared[1]
 
-def rms_error_accu(d_err: np.ndarray):
+def rms_error_accu(d_err: Mat):
 	d_err_squared = d_err * d_err
 	s =  np.sum(d_err_squared) / d_err.size
 	return s