Here are the data structures with brief descriptions:
  LEARN_A_MODEL | This structure describes a "LEARN_A_MODEL" (a learn alpha model). This structure constains the dimentionality of the defined problems and the model parameters. It should always initialised with ccl_learn_alpha_model_alloc, and destroyed with ccl_learn_alpha_model_free |
| LEARN_MODEL_LW_PI | This structure describes learning a direct locally weighted linear policy model parameters LEARN_MODEL_LW_PI |
| LEARN_MODEL_LW_PI_WS | This structure describes workspace for directly learning the policy model parameters using locally weighted linear regression |
| LEARN_MODEL_PI | This structure describes learning a direct linear policy model parameters LEARN_MODEL_PI |
| LEARN_MODEL_PI_WS | This structure describes workspace for directly learning the policy model parameters using linear regression |
| LEARN_MODEL_WS | This structure describes workspace for directly learning the model parameters using linear regression |
| LEARN_NCL_MODEL | This structure describes a "LEARN_NCL_MODEL" (a learn ncl model). This structure constains the dimentionality of the defined problems and the model parameters. It should always initialised with ccl_learn_ncl_model_alloc, and destroyed with ccl_learn_ncl_model_free |
| MP_INV_WS | This structure describes the workspace memory of calculating matrix pseudo inverse MP_INV_WS |
| NHAT_Model | This structure defines the model parameters for NHAT_Model |
| NHAT_result | This structure defines the model parameters for NHAT_result |
| NHAT_search | This structure defines the searching parameters for NHAT_search |
| OBJ_WS | This structure defines the workspace variables for calculating objective functions |
| OPTION | This structure defines the OPTION for the optimizer |
| SOLVE_LM_WS | This structure defines the workspace variables for solving the non-linear LM optimization |
 SOLVE_NONLIN_WS | This structure defines the workspace variables for solving the non-linear LM optimization |
