- fixed v_to_ph parameter consideration

- matrix: use different random implementations

src/rbm/entity.py

@@ -62,7 +62,7 @@ class Entity:
 
 	def v_to_ph(self, v: np.ndarray) -> np.ndarray:
 		state = self.state.v_to_h(v)
-		if self.params.do_gaussian_visible:
+		if self.params.do_gaussian_hidden:
 			return state
 
 		return prob(state)

src/rbm/matrix.py

@@ -1,13 +1,13 @@
 import numpy as np
 import time
 
-rng = np.random.default_rng(int(time.monotonic()))
+np.random.seed(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)
+	return std * (np.random.rand(shape[0], shape[1]) + 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 gaussian(shape: tuple, mu: float = 0.0, std: float = 1.0) -> np.ndarray:
+	return std * (np.random.randn(shape[0], shape[1]) + mu)
 
 def sample(src: np.ndarray) -> np.ndarray:
 	return (src > uniform(src.shape)).astype(float)