Getting the training results

After successful completion of training process, training results including useful basis function indices and their associated weights can be queried using neonrvm_get_training_stats and neonrvm_get_training_results functions.

You should first get the useful basis functions count, and then allocate enough memory for the basis function indices and weights vectors so neonrvm can fill them for you.

🚀 C/C++

int neonrvm_get_training_stats(neonrvm_cache* cache, size_t* basis_count, bool* bias_used)

➡️ Parameters

  • ⬇️ [in] cache: Contains intermediate variables and training results.
  • ⬆️ [out] basis_count: Value pointed to will be set to the number of useful basis functions. (Includes bias too if it was found useful)
  • ⬆️ [out] bias_used: Value pointed to will be set to true if bias was useful during training.

⬅️ Returns

  • NEONRVM_SUCCESS: After successful execution.
  • NEONRVM_INVALID_Px: When facing erroneous parameters.
int neonrvm_get_training_results(neonrvm_cache* cache, size_t* index, double* mu)

➡️ Parameters

  • ⬇️ [in] cache: Contains intermediate variables and training results.
  • ⬆️ [out] index: Vector with enough room for useful basis function indices. Last element contains SIZE_MAX if bias was found to be useful.
  • ⬆️ [out] mu: Vector with enough room for useful basis function weights. Last element contains bias weight, if bias was found to be useful.

⬅️ Returns

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

🐍 Python

A single function call is enough:

def get_training_results(cache: Cache)

➡️ Parameters

  • ⬇️ [in] cache: Contains intermediate variables and training results.

⬅️ Returns

  • index: numpy.ndarray: Vector of useful basis function indices. Last element contains SIZE_MAX if bias was found to be useful.
  • mu: numpy.ndarray: Vector of useful basis function weights. Last element contains bias weight, if bias was found to be useful.
  • basis_count: int: Number of useful basis functions. (Includes bias too if it was found useful)
  • bias_used: bool: Whether bias was useful during training.