| /dports/security/libmcrypt/libmcrypt-2.5.8/modules/modes/ |
| H A D | ecb.c | 28 #define _end_mcrypt ecb_LTX__end_mcrypt in n_classes()
33 #define _is_block_algorithm_mode ecb_LTX__is_block_algorithm_mode in n_classes()
75 _mcrypt_block_decrypt(akey, &cipher[j * blocksize]); in test_error_on_invalid_option()
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/ |
| H A D | _sag_fast.pyx.tp | 49 from ..utils._seq_dataset cimport SequentialDataset32, SequentialDataset64
74 {{for name_suffix, c_type, np_type in dtypes}}
93 out += exp(arr[i] - vmax)
94
95 return log(out) + vmax
137 Bishop, C. M. (2006). Pattern recognition and machine learning.
191 logsumexp_prediction)
192
193 # y is the indice of the correct class of current sample.
235 """Stochastic Average Gradient (SAG) and SAGA solvers.
[all …]
|
| /dports/math/octave-forge-statistics/statistics-1.4.3/inst/@cvpartition/ |
| H A D | repartition.m | 41 n_classes = C.n_classes; variable
54 n_classes = numel (n_per_class); variable
|
| H A D | cvpartition.m | 71 n_classes = 1; variable
88 n_classes = numel (n_per_class); variable
|
| /dports/misc/vxl/vxl-3.3.2/contrib/mul/clsfy/ |
| H A D | clsfy_random_forest.h | 43 unsigned n_classes() const override {return 1;} in n_classes() function
|
| H A D | clsfy_mean_square_1d.h | 50 unsigned n_classes() const override { return 1;} in n_classes() function
|
| H A D | clsfy_binary_threshold_1d.h | 46 unsigned n_classes() const override { return 1;} in n_classes() function
|
| H A D | clsfy_k_nearest_neighbour.h | 74 unsigned n_classes() const override {return 1;} in n_classes() function
|
| H A D | clsfy_binary_hyperplane.h | 61 unsigned n_classes() const override { return 1;} in n_classes() function
|
| H A D | clsfy_rbf_parzen.h | 100 unsigned n_classes() const override {return 1;} in n_classes() function
|
| H A D | clsfy_binary_1d_wrapper.h | 60 unsigned n_classes() const override { return 1u; } in n_classes() function
|
| H A D | clsfy_simple_adaboost.h | 95 unsigned n_classes() const override { return 1;} in n_classes() function
|
| H A D | clsfy_rbf_svm.h | 108 unsigned n_classes() const override {return 1;} in n_classes() function
|
| H A D | clsfy_binary_pdf_classifier.h | 90 unsigned n_classes() const override {return 1;} in n_classes() function
|
| H A D | clsfy_direct_boost.h | 105 unsigned n_classes() const override { return 1;} in n_classes() function
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/ |
| H A D | _iforest.py | 14 gen_batches,
15 get_chunk_n_rows,
|
| /dports/devel/liboil/liboil-0.3.17/testsuite/ |
| H A D | list_impls.c | 39 int n_classes; in main() local
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/tests/ |
| H A D | test_discriminant_analysis.py | 156 def test_lda_predict_proba(solver, n_classes): argument
482 def test_lda_dimension_warning(n_classes, n_features): argument
|
| /dports/math/py-networkx/networkx-2.6.3/networkx/algorithms/node_classification/ |
| H A D | hmn.py | 91 def _build_base_matrix(X, labels, n_classes): argument
|
| H A D | utils.py | 58 def _init_label_matrix(n_samples, n_classes): argument
|
| H A D | lgc.py | 95 def _build_base_matrix(X, labels, alpha, n_classes): argument
|
| /dports/misc/py-xgboost/xgboost-1.5.1/src/metric/ |
| H A D | auc.cc | 90 float MultiClassOVR(std::vector<float> const& predts, MetaInfo const& info, size_t n_classes) { in MultiClassOVR()
271 size_t n_classes = meta[1] / meta[0]; in Eval() local
331 size_t n_classes) { in GPUMultiClassAUCOVR()
|
| /dports/x11-toolkits/gtk40/gtk-4.4.1/gtk/ |
| H A D | gtkcssnodedeclaration.c | 31 guint n_classes; member
36 sizeof_node (guint n_classes) in sizeof_node()
310 guint *n_classes) in gtk_css_node_declaration_get_classes()
|
| /dports/misc/xgboost/xgboost-1.5.1/src/metric/ |
| H A D | auc.cc | 90 float MultiClassOVR(std::vector<float> const& predts, MetaInfo const& info, size_t n_classes) { in MultiClassOVR()
271 size_t n_classes = meta[1] / meta[0]; in Eval() local
331 size_t n_classes) { in GPUMultiClassAUCOVR()
|
| /dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/neighbors/ |
| H A D | plot_nca_illustration.py | 55 dist_embedded = np.einsum("ij,ij->i", diff_embedded, diff_embedded)
|