diff --git a/TODOs b/TODOs index 37323b0afc38b07af3b2bfaee4148ad649b9f699..5cd7e5142fe4b456f917214be8133d07ab5fc9d1 100644 --- a/TODOs +++ b/TODOs @@ -20,12 +20,17 @@ coscine_watchdog/coscine_client.py:185: # TODO use update file rath then uplo coscine_watchdog/coscine_client.py:220: ("datatype", "Datatype is missing"), # , # TODO coscine_watchdog/coscine_client.py:221: # ('dataset_type', 'Dataset type is missing') # TODO docs/index.rst:4:.. TODO change to two (or more) main pages + metadata. one on data generation one on ai. +dynamics_learning/benchmark_number_of_runs.py:211: # TODO load ITA model instead of dummy model +dynamics_learning/benchmark_number_of_runs.py:240: # TODO load LLT Data +dynamics_learning/benchmark_number_of_runs.py:241: # TODO load ITA model +dynamics_learning/benchmark_number_of_runs.py:242: # TODO set hyperparameters for LLT model based on ITA model with known hyperparameters +dynamics_learning/benchmark_number_of_runs.py:256: # TODO load LLT Data +dynamics_learning/benchmark_number_of_runs.py:257: # TODO load foundation model +dynamics_learning/benchmark_number_of_runs.py:258: # TODO set hyperparameters for LLT model based on foundation model dynamics_learning/dynamics_learning/testing/__init__.py:256: # somewhere FIXME: Daten scheinen zu kurz zu sein? dynamics_learning/dynamics_learning/testing/__init__.py:640: # somewhere FIXME: Daten scheinen zu kurz zu sein? -dynamics_learning/dynamics_learning/training/__init__.py:169: ) # TODO implement another naming schema, as soon as a custom application profiles for the models is available in coscine. -dynamics_learning/dynamics_learning/training/__init__.py:175: f"{decision_metric}_val_loss" # TODO implement custom coscine application profile for the models -dynamics_learning/foundation_model.py:75: test_dataset.download() # FIXME -dynamics_learning/foundation_model.py:77: model_analysis(test_dataset, model) # FIXME +dynamics_learning/dynamics_learning/training/__init__.py:265: ) # TODO implement another naming schema, as soon as a custom application profiles for the models is available in coscine. +dynamics_learning/dynamics_learning/training/__init__.py:271: f"{decision_metric}_val_loss" # TODO implement custom coscine application profile for the models based on MITM base profile franka_lock_unlock/README.rst:1:.. TODO describe docker compose up for individual component src_jan/ROS/package.xml:16: <license>TODO</license> src_jan/ROS/panda_auto_dynamics.py:5:# TODO DOCSTRING DOCUMENTATION