[CheckpointStatus] - add checkpoint status handler; Model.train() accepts status param

status.py: add CheckpointStatus(save_fn, update_interval) — prints progress
and calls save_fn at every report interval to persist model state mid-training.
model.py: Model.train() now accepts an optional Status instance; defaults to
plain Status() if none provided.
test_faces_sub_image.py: use CheckpointStatus(model.save) during training.

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

src/tests/test_faces_sub_image.py

@@ -7,6 +7,7 @@ from rbm.model import Model
 from rbm.entity import Entity, EntityParams, TrainingParams
 from rbm.image import SubImage, normalize
 from rbm.matrix import Mat, np, convert
+from rbm.status import CheckpointStatus
 
 DATA_DIR  = '/media/jens/cifs/bilder/MachineVision/Caltech_WebFaces/'
 PATCH     = 32
@@ -214,7 +215,7 @@ if __name__ == '__main__':
 		print(f'Loading {args.n_images} training images (stride={STRIDE})...')
 		train_patches = load_patches(DATA_DIR, args.n_images)
 		print(f'Training on {train_patches.shape[0]} patches ({N_VIS}-dim each)')
-		model.train(train_patches)
+		model.train(train_patches, status=CheckpointStatus(save_fn=model.save))
 		model.save()
 
 	# Show learned weight filters