def global_src_features(X, D, sparse_coder, n_class_atoms, n_jobs=1): """ return the features for each datapoint which are the approximation errors of the datapoint encoded over each sub-dictionary in D """ E = global_error(X, D, sparse_coder, n_class_atoms, n_jobs=n_jobs) Z_final = norm_cols(E) return Z_final
def local_src_features(X, D, sparse_coder, n_class_atoms, n_jobs=1): n_samples = X.shape[1] n_classes = len(n_class_atoms) data = [X] args = [D, n_class_atoms, sparse_coder] Z_final = run_parallel(func=local_error, data=data, args=args, batched_args=None, result_shape=(n_classes, n_samples), n_batches=100, mmap=False, msg="building global SRC features", n_jobs=n_jobs) Z_final = norm_cols(Z_final) return Z_final