From 6a75e0b33b8a8f2c9c6a5e9f94e7c3ed3b9c2ed9 Mon Sep 17 00:00:00 2001
From: Jens Ahrensfeld <jens@jayfield.org>
Date: Wed, 3 Jun 2026 22:14:34 +0200
Subject: [PATCH] added more Rnn helber

---
 src/stack/rnn_helper.py | 35 +++++++++++++++++++----------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/src/stack/rnn_helper.py b/src/stack/rnn_helper.py
index a27ee56..6d0b586 100644
--- a/src/stack/rnn_helper.py
+++ b/src/stack/rnn_helper.py
@@ -1,27 +1,39 @@
-import numpy as np
+from rbm.matrix import Mat, np
 
 VOCAB = ' .!?ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
 _CH2IDX = {ch: i for i, ch in enumerate(VOCAB)}
 
+def vec2idx(vec: Mat) -> int:
+	index = np.argmax(vec)
+	return int(index)
+
+def idx2vec(index: int) -> np.ndarray:
+	result = np.zeros([1, len(VOCAB)])
+	result[:, index] = 1
+	return result
+
 def ch2idx(ch: str) -> int:
-	return _CH2IDX[ch]
+	idx = _CH2IDX[ch]
+	return idx
 
 def idx2ch(idx: int) -> str:
 	return VOCAB[idx]
 
+def vocab_size():
+	return len(VOCAB)
 
 def concat(v: np.ndarray, c: np.ndarray) -> np.ndarray:
-	return np.concatenate([v, c])
+	return np.concatenate([v, c], axis=1)
 
 def split(vc: np.ndarray, v_size: np.ndarray|int) -> tuple[np.ndarray, np.ndarray]:
 	n = len(v_size) if isinstance(v_size, np.ndarray) else v_size
 	return vc[:n], vc[n:]
 
-def shift_right(m: np.ndarray) -> np.ndarray:
-	return np.hstack([np.zeros((m.shape[0], 1)), m[:, :-1]])
+def shift_right(m: np.ndarray, amount: int = 1) -> np.ndarray:
+	return np.hstack([np.zeros((m.shape[0], amount)), m[:, :-amount]])
 
-def shift_left(m: np.ndarray) -> np.ndarray:
-	return np.hstack([m[:, 1:], np.zeros((m.shape[0], 1))])
+def shift_left(m: np.ndarray, amount: int = 1) -> np.ndarray:
+	return np.hstack([m[:, amount:], np.zeros((m.shape[0], amount))])
 
 def shift_up(m: np.ndarray) -> np.ndarray:
 	return np.vstack([m[1:, :], np.zeros((1, m.shape[1]))])
@@ -29,12 +41,3 @@ def shift_up(m: np.ndarray) -> np.ndarray:
 def shift_down(m: np.ndarray) -> np.ndarray:
 	return np.vstack([np.zeros((1, m.shape[1])), m[:-1, :]])
 
-def clamp_one_hot(src_dst: np.ndarray) -> np.ndarray:
-	index = np.argmax(src_dst)
-	result = np.zeros_like(src_dst)
-	result[index] = 1
-	return result
-
-class Rnn:
-	def __init__(self):
-		pass