added JayRnn

JayRnn.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+"""Character-level RNN-RBM (unrolled) — see docs/Rnn.drawio.png.
+
+Diagram recap
+    t=0:  v[0]={0}  v[1:N]=[' ',' ','J']  W[0]    → h
+    t=1:  v[0]=h₀   v[1:N]=[' ','J','A']  W[1]    → h
+    ...
+    t=M-1: v[0]=h_{M-2}  v[1:N]=['J','A','Y']  W[M-1]  → h
+
+v[0]   = recurrent context (previous hidden state, or zeros at t=0)
+v[1:N] = N_WIN one-hot-encoded characters concatenated (the sliding window)
+W[t]   = RBM weight matrix for time step t  (one per step → unrolled mode)
+"""
+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
+
+from rbm.entity import EntityParams, TrainingParams, Entity
+from rbm.matrix import np, Mat
+from model.model import Model
+from stack.rnn_helper import vocab_size, shift_left, concat, vec2idx, idx2ch, ch2idx, idx2vec
+from rbm.train import train
+from rbm.status import Status
+
+# ── Hyper-parameters ──────────────────────────────────────────────────────────
+TEXT    = "JAY IS COOL"   # the character sequence to learn (matches diagram example)
+WIN    = 3         # sliding-window width  (= N in the diagram)
+STRIDE = 1     	   # sliding-window step size
+UNITS  = 1
+H_SIZE = 64         # hidden units per RBM cell
+
+TRAIN_PARAMS = TrainingParams(
+	learning_rate    = 0.05,
+	momentum         = 0.9,
+	num_epochs       = 1000,
+	do_rao_blackwell = True,
+)
+#WIN=3
+#STRIDE=1
+#UNIT=3
+#WIN |   |
+#U0: "JAY IS COOL"
+#U1: "AY IS COOL "
+#U2: "Y IS COOL  "
+
+def vec2str(mat: Mat):
+	result = ''
+	for vec in mat:
+		result += idx2ch(vec2idx(vec))
+	return result
+
+def str2vec(ch_str: str) -> Mat:
+	result = Mat([len(ch_str), vocab_size()])
+	for i, ch in enumerate(ch_str):
+		vec = idx2vec(ch2idx(ch))
+		result[i, :] = vec
+	return result
+
+def to_window(text: str):
+	win_text = []
+	text_padded = text + ' '*WIN
+	for i in range(len(TEXT)):
+		win_text.append(text_padded[i:i+WIN])
+
+	return win_text
+
+def to_batch(_win_str: list[str]):
+	_batch = np.zeros([len(TEXT), WIN*vocab_size()])
+	for i, win in enumerate(_win_str):
+		_batch[i, :] = str2vec(win).flatten()
+	return _batch
+
+
+class RnnModel(Model):
+	def __init__(self, name: str, work_dir: str = '.'):
+		super().__init__(name, work_dir)
+
+		self.units: list[Entity] = []
+		for _ in range(UNITS):
+			unit = Entity((WIN*vocab_size() + H_SIZE, H_SIZE), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), TrainingParams(learning_rate=0.001, momentum=0.9, num_epochs=1000), enable_training=True)
+			self.units.append(unit)
+
+	def train(self, batch: Mat, status: Status = None):
+		c = np.zeros([len(batch), H_SIZE])
+		for i, unit in enumerate(self.units):
+			vc = concat(batch, c)
+			train(unit, vc, status)
+			c = unit.forward(vc)
+
+	def forward_step(self, v_curr: Mat):
+		c = np.zeros([1, H_SIZE])
+		z = np.zeros(v_curr.shape)
+		for i, unit in enumerate(self.units):
+			pass
+
+	def forward(self, x: Mat):
+		pass
+
+
+if __name__ == "__main__":
+	win_text = to_window(TEXT)
+	print(win_text)
+
+	batch = to_batch(win_text)
+	print(batch)
+
+	model = RnnModel(name='JayRnn', work_dir='results')
+	model.init(0.01)
+	model.load()
+	model.train(batch, status=Status())
+	model.save()
+