refactored

src/rbm/matrix.py

@@ -0,0 +1,26 @@
+import numpy as np
+import time
+
+rng = np.random.default_rng(int(time.monotonic()))
+
+def uniform(shape: tuple, mu: float = 0.5, std: float = 1.0) -> np.ndarray:
+	return std * (rng.uniform(size=shape) + mu - 0.5)
+
+def gaussian(shape: tuple, mu: float = 0.5, std: float = 1.0) -> np.ndarray:
+	return rng.normal(size=shape, loc=mu, scale=std)
+
+def sample(src: np.ndarray) -> np.ndarray:
+	return (src > uniform(src.shape)).astype(float)
+
+def prob(src: np.ndarray) -> np.ndarray:
+	return 1.0 / (1 + np.exp(-src))
+
+def rms_error(d_err: np.ndarray):
+	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):
+	d_err_squared = d_err * d_err
+	s =  np.sum(d_err_squared) / d_err.size
+	return s
+