Creating training parameters

neonrvm_param structure deals with training convergence conditions and initial values.

🚀 C/C++

Use neonrvm_create_param function to create one:

int neonrvm_create_param(neonrvm_param** param,
                         double alpha_init, double alpha_max, double alpha_tol,
                         double beta_init, double basis_percent_min, size_t iter_max)

➡️ Parameters

⬆️ [out] param: Pointer which it points to will be set to a freshly allocated structure.
⬇️ [in] alpha_init: Initial value for alpha. Must be a positive and small number.
⬇️ [in] alpha_max: Basis functions associated with alpha value beyond this limit will be purged. Must be a positive and big number.
⬇️ [in] alpha_tol: Training session will end if changes in alpha values gets lower than this value. Must be a positive and small number.
⬇️ [in] beta_init: Initial value for beta. Must be a positive and small value.
⬇️ [in] basis_percent_min: Training session will end if percentage of useful basis functions during current training session gets lower than this value. Must be a value in [0.0, 100.0] range.
⬇️ [in] iter_max: Training session will end if training loop iteration count goes beyond this value. Must be a positive and non-zero number.

⬅️ Returns

NEONRVM_SUCCESS: After successful execution.
NEONRVM_INVALID_Px: When facing erroneous parameters.

Once you are done with neonrvm_param structure and finished training process, you should call neonrvm_destroy_param to free up allocated memory.

int neonrvm_destroy_param(neonrvm_param* param)

➡️ Parameters

⬇️ [in] param: Memory allocated for this structure will be released.

⬅️ Returns

NEONRVM_SUCCESS: After successful execution.
NEONRVM_INVALID_Px: When facing erroneous parameters.

🐍 Python

A new Param instance should be created:

class Param(alpha_init: float, alpha_max: float, alpha_tol: float,
            beta_init: float, basis_percent_min: float, iter_max: int)

➡️ Parameters

⬇️ [in] alpha_init: Initial value for alpha. Must be a positive and small number.
⬇️ [in] alpha_max: Basis functions associated with alpha value beyond this limit will be purged. Must be a positive and big number.
⬇️ [in] alpha_tol: Training session will end if changes in alpha values gets lower than this value. Must be a positive and small number.
⬇️ [in] beta_init: Initial value for beta. Must be a positive and small value.
⬇️ [in] basis_percent_min: Training session will end if percentage of useful basis functions during current training session gets lower than this value. Must be a value in [0.0, 100.0] range.
⬇️ [in] iter_max: Training session will end if training loop iteration count goes beyond this value. Must be a positive and non-zero number.

⬅️ Returns

A new Param instance.