da_result enums - 5.2 English - 68552

Enumeration defining which result to extract from a handle.

Values:

enumerator da_rinfo#

General information array, containing a variety of metrics. See each solver’s documentation for further information, since each solver stores different information in this array.

enumerator da_linmod_coef#

Optimal fitted coefficients produced by the last call to a linear regression solver.

enumerator da_linmod_dual_coef#

Optimal fitted dual coefficients produced by the last call to a linear regression solver. Only available for CG solver and when number of columns is greater or equal to number of rows.

enumerator da_pca_scores#

Matrix of scores computed by the PCA API.

enumerator da_pca_variance#

The variance explained by each component computed by the PCA API.

enumerator da_pca_principal_components#

Matrix of principal components computed by the PCA API.

enumerator da_pca_total_variance#

The total variance of the data matrix supplied to the PCA API.

enumerator da_pca_column_means#

Column means of the data matrix supplied to the PCA API.

enumerator da_pca_column_sdevs#

Column standard deviations of the data matrix supplied to the PCA API.

enumerator da_pca_u#

The matrix \(U\) from the singular value decomposition \(A = U\Sigma V^T\), computed as part of a PCA computation.

enumerator da_pca_sigma#

The nonzero diagonal entries of \(\Sigma\) from the singular value decomposition \(A = U\Sigma V^T\), computed as part of a PCA computation.

enumerator da_pca_vt#

The matrix \(V^T\) from the singular value decomposition \(A = U\Sigma V^T\), computed as part of a PCA computation.

enumerator da_kmeans_cluster_centres#

Matrix of cluster centres computed in k-means clustering.

enumerator da_kmeans_labels#

Labels of samples in the data matrix used to compute k-means clustering.

enumerator da_dbscan_labels#

Labels of samples in the data matrix used to compute DBSCAN clustering.

enumerator da_dbscan_n_clusters#

The number of clusters found in DBSCAN clustering.

enumerator da_dbscan_n_core_samples#

The number of core samples found in DBSCAN clustering.

enumerator da_dbscan_core_sample_indices#

Indices of core samples in the data matrix used to compute DBSCAN clustering.

enumerator da_nn_radius_neighbors_count#

Array containing the number of radius neighbors for each query point. The last element contains the total number of radius neighbors found for all query points.

enumerator da_nn_radius_neighbors_offsets#

Array containing the offsets to the arrays of indices or the array of distances used to locate the radius neighbors for each query point. For query points where no radius neighbors were found, the offsets will be the same.

enumerator da_nn_radius_neighbors_indices#

Array containing the indices of the radius neighbors for each query point. The offsets are required to locate the neighbors for each query point.

enumerator da_nn_radius_neighbors_distances#

Array containing the distances of the radius neighbors for each query point. The offsets are required to locate the neighbors for each query point.

enumerator da_nn_radius_neighbors_indices_index#

Index of the radius neighbors for a given query point. The query is passed as the first element of the array on input.

enumerator da_nn_radius_neighbors_distances_index#

Index of the radius neighbors distances for a given query point. The query is passed as the first element of the array on input.

enumerator da_svm_n_support_vectors#

Overall number of support vectors.

enumerator da_svm_n_support_vectors_per_class#

Number of support vectors per each class.

enumerator da_svm_idx_support_vectors#

Indexes of support vectors.

enumerator da_svm_n_iterations#

Number of iterations. In this context it counts the number of SMO subproblems solved, for each classifier.

enumerator da_svm_support_vectors#

Support vectors.

enumerator da_svm_bias#

Constant in decision function.

enumerator da_svm_probaA#

Array of parameters A for each binary classifier when probability estimates are enabled.

enumerator da_svm_probaB#

Array of parameters B for each binary classifier when probability estimates are enabled.

enumerator da_svm_dual_coef#

Weights assigned to each support vector, reflecting their importance in defining the optimal decision boundary.