tune label fitter: epochs, momentum, encoding, decode threshold

- reduce num_epochs to 5000 and add momentum=0.9
- switch to OneHot encoding in Label constructor
- call fitter.init(0.1) before load for weight initialisation
- use train_labels as test set, lower hard-decode threshold to 0.5
- improve print output

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/label/label.py

@@ -13,7 +13,7 @@ class Label:
 	class Fitter(Model):
 		def __init__(self, dim: tuple[int,int], work_dir: str = '.'):
 			super().__init__(f"label-{dim[0]}x{dim[1]}", work_dir)
-			self.unit1 = Entity(dim, EntityParams(), TrainingParams(num_epochs=10000, do_rao_blackwell=False))
+			self.unit1 = Entity(dim, EntityParams(), TrainingParams(num_epochs=5000, do_rao_blackwell=False, momentum=0.9))
 
 		def forward(self, x: Mat) -> Mat:
 			return self.unit1.forward(x)
@@ -90,9 +90,10 @@ if __name__ == "__main__":
 	voc_size = 100
 
 	# Create Labeler
-	enc = Label(voc_size, 16, work_dir="../../results")
+	enc = Label(voc_size, 16, work_dir="../../results", encoding=Label.EncodingType.OneHot)
 
 	# Load
+	enc.fitter.init(0.1)
 	enc.fitter.load()
 
 	# Train
@@ -102,17 +103,18 @@ if __name__ == "__main__":
 	# Save fitter state
 	enc.fitter.save()
 
+	# Test
 	# Encode test labels
-	test_labels = np.array([1, 23, 99, 37, 55, 7, 31, 10, 19, 70])
-	encoded, binary_labels = enc.encode(train_labels)
+	test_labels = train_labels
+	encoded, binary_labels = enc.encode(test_labels)
 
 	# Decode
 	decoded_soft = enc.decode(encoded)
-	decode_hard = (decoded_soft > 0.9).astype(int)
+	decode_hard = (decoded_soft > 0.5).astype(int)
 
 	# Convert soft state into labels
 	labels_reconst = enc.vec2label(decoded_soft)
-	print(labels_reconst)
+	print(f"Reconst: {labels_reconst}")
 
 	print(f"Soft Error: {np.sum((decoded_soft - binary_labels) ** 2)}")
 	print(f"Hard Error: {np.sum((decode_hard - binary_labels) ** 2)}")