/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/ |
H A D | _split.py | 281 "%s of type %s was passed." % (n_splits, type(n_splits)) 283 n_splits = int(n_splits) 302 self.n_splits = n_splits 443 n_splits = self.n_splits 444 fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=int) 1065 n_splits = self.n_splits 1553 n_splits=n_splits, 1563 self.n_splits = n_splits 1699 n_splits=n_splits, 1795 n_splits=n_splits, [all …]
|
H A D | _search.py | 803 n_splits = cv_orig.get_n_splits(X, y, groups) 834 n_splits, n_candidates, n_candidates * n_splits 846 split_progress=(split_idx, n_splits), 861 elif len(out) != n_candidates * n_splits: 865 "splits, got {}".format(n_splits, len(out) // n_candidates) 886 all_candidate_params, n_splits, all_out, all_more_results 939 self.n_splits_ = n_splits 943 def _format_results(self, candidate_params, n_splits, out, more_results=None): 957 array = np.array(array, dtype=np.float64).reshape(n_candidates, n_splits) 959 for split_idx in range(n_splits):
|
/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/model_selection/ |
H A D | plot_cv_indices.py | 32 n_splits = 4 variable 98 def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10): argument 130 yticklabels = list(range(n_splits)) + ["class", "group"] 132 yticks=np.arange(n_splits + 2) + 0.5, 136 ylim=[n_splits + 2.2, -0.2], 148 cv = KFold(n_splits) 149 plot_cv_indices(cv, X, y, groups, ax, n_splits) 171 plot_cv_indices(cv(n_splits), X, y, uneven_groups, ax, n_splits) 206 this_cv = cv(n_splits=n_splits) 208 plot_cv_indices(this_cv, X, y, groups, ax, n_splits)
|
H A D | plot_nested_cross_validation_iris.py | 77 inner_cv = KFold(n_splits=4, shuffle=True, random_state=i) 78 outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)
|
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/tests/ |
H A D | common.py | 13 def __init__(self, n_splits=4, n_samples=99): argument 14 self.n_splits = n_splits 16 self.indices = iter(KFold(n_splits=n_splits).split(np.ones(n_samples))) 24 return self.n_splits
|
H A D | test_split.py | 69 n_splits = 2 102 n_splits, 103 n_splits, 108 n_splits, 553 n_splits = 3 555 cv = KFold(n_splits=n_splits, shuffle=False) 651 n_splits = 5 652 sgkf = StratifiedGroupKFold(n_splits=n_splits) 653 gkf = GroupKFold(n_splits=n_splits) 1129 n_splits = 3 [all …]
|
H A D | test_search.py | 912 n_splits = 3 992 n_splits = 3 1118 n_splits = 3 1153 n_splits = 3 1407 n_splits = 3 1422 cv = StratifiedKFold(n_splits=n_splits) 1736 n_splits = 5 1750 cv=KFold(n_splits=n_splits), 1771 cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), 1807 cv=KFold(n_splits=n_splits, shuffle=True), [all …]
|
H A D | test_validation.py | 1164 n_splits = 3 1174 estimator = MockImprovingEstimator(n_samples * ((n_splits - 1) / n_splits)) 1187 cv=KFold(n_splits=n_splits), 1213 cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), 1429 cv = KFold(n_splits=3) 1634 n_splits = 5 1643 cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), 1657 cv=KFold(n_splits=n_splits, shuffle=True), 1671 cv=KFold(n_splits=n_splits), 1995 kfold3 = KFold(n_splits=3) [all …]
|
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/covariance/ |
H A D | _shrunk_covariance.py | 242 n_splits = int(n_features / block_size) 249 for i in range(n_splits): 250 for j in range(n_splits): 256 beta_ += np.sum(np.dot(X2.T[rows], X2[:, block_size * n_splits :])) 257 delta_ += np.sum(np.dot(X.T[rows], X[:, block_size * n_splits :]) ** 2) 258 for j in range(n_splits): 260 beta_ += np.sum(np.dot(X2.T[block_size * n_splits :], X2[:, cols])) 261 delta_ += np.sum(np.dot(X.T[block_size * n_splits :], X[:, cols]) ** 2) 263 np.dot(X.T[block_size * n_splits :], X[:, block_size * n_splits :]) ** 2 267 np.dot(X2.T[block_size * n_splits :], X2[:, block_size * n_splits :])
|
/dports/biology/minimap2/minimap2-2.24/ |
H A D | splitidx.c | 33 mm_idx_t *mm_split_merge_prep(const char *prefix, int n_splits, FILE **fp, uint32_t *n_seq_part) in mm_split_merge_prep() argument 39 if (n_splits < 1) return 0; in mm_split_merge_prep() 41 for (i = 0; i < n_splits; ++i) { in mm_split_merge_prep() 55 for (i = 0; i < n_splits; ++i) { in mm_split_merge_prep() 61 for (i = j = 0; i < n_splits; ++i) { in mm_split_merge_prep() 74 void mm_split_rm_tmp(const char *prefix, int n_splits) in mm_split_rm_tmp() argument 79 for (i = 0; i < n_splits; ++i) { in mm_split_rm_tmp()
|
/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/semi_supervised/ |
H A D | plot_self_training_varying_threshold.py | 44 n_splits = 3 56 scores = np.empty((x_values.shape[0], n_splits)) 57 amount_labeled = np.empty((x_values.shape[0], n_splits)) 58 amount_iterations = np.empty((x_values.shape[0], n_splits)) 65 skfolds = StratifiedKFold(n_splits=n_splits)
|
/dports/math/polymake/polymake-4.5/apps/polytope/src/ |
H A D | split_compatibility_graph.cc | 29 const Int n_splits = SplitEquations.rows(); in split_compatibility_graph() local 31 Graph<> S(n_splits); in split_compatibility_graph() 34 for (Int s1 = 0; s1 < n_splits; ++s1) { in split_compatibility_graph() 35 for (Int s2 = s1+1; s2 < n_splits; ++s2) { in split_compatibility_graph()
|
H A D | split_polyhedron.cc | 33 Int n_splits = splits.rows(); in split_polyhedron() local 35 SparseMatrix<Scalar> facets(n_splits,n+1); in split_polyhedron() 36 for (Int j = 0; j < n_splits; ++j) { in split_polyhedron()
|
H A D | splits_in_subdivision.cc | 31 const Int n_splits = splits.rows(); in splits_in_subdivision() local 34 for (Int j = 0; j < n_splits; ++j) { in splits_in_subdivision()
|
/dports/databases/grass7/grass-7.8.6/vector/v.in.ogr/ |
H A D | clean.c | 17 int line_split, n_splits; in convert_osm_lines() local 21 n_splits = 0; in convert_osm_lines() 73 n_splits++; in convert_osm_lines() 106 n_splits++; in convert_osm_lines() 117 if (n_splits) in convert_osm_lines() 118 G_verbose_message(_("Number of OSM line splits: %d"), n_splits); in convert_osm_lines()
|
/dports/science/py-hiphive/hiphive-0.7.1/hiphive/fitting/ |
H A D | cross_validation.py | 84 n_splits: int = 10, 98 self._n_splits = n_splits 103 n_splits=self.n_splits, random_state=seed, 192 info['n_splits'] = self.n_splits 211 kwargs['n_splits'] = self.n_splits 223 def n_splits(self) -> int: member in CrossValidationEstimator
|
/dports/math/pspp/pspp-1.4.1/src/language/data-io/ |
H A D | matrix-data.c | 131 size_t n_splits = 0; in preprocess() local 168 n_splits++; in preprocess() 169 matrices = xrealloc (matrices, sizeof (double*) * n_splits); in preprocess() 170 matrices[n_splits - 1] = xmalloc (sizeof_matrix); in preprocess() 211 (matrices[n_splits-1])[col + mformat->n_continuous_vars * row] = e; in preprocess() 215 (matrices[n_splits-1]) [row + mformat->n_continuous_vars * col] = e; in preprocess() 248 n_splits = 0; in preprocess() 272 n_splits++; in preprocess() 356 dest_val->f = (matrices[n_splits - 1]) [col + mformat->n_continuous_vars * row]; in preprocess() 369 for (i = 0 ; i < n_splits; ++i) in preprocess() [all …]
|
/dports/databases/grass7/grass-7.8.6/vector/v.clean/ |
H A D | split.c | 62 int n_splits; in split_lines() local 74 n_splits = split_line(Map, type, Points, Cats, Err, split_distance); in split_lines() 76 if (n_splits) in split_lines() 79 n_splits_total += n_splits; in split_lines()
|
/dports/science/py-chainer/chainer-7.8.0/chainerx_cc/chainerx/routines/ |
H A D | activation.cc | 30 std::vector<Array> ExtractGates(const Array& x, int64_t n_splits, int64_t axis) { in ExtractGates() argument 34 shape_vec.emplace_back(n_splits); in ExtractGates() 35 shape_vec.emplace_back(static_cast<int64_t>(x.shape()[1] / n_splits)); in ExtractGates() 37 shape_vec.emplace_back(static_cast<int64_t>(x.shape()[1] / n_splits)); in ExtractGates() 38 shape_vec.emplace_back(n_splits); in ExtractGates() 45 std::vector<Array> x_split = Split(x_r, n_splits, axis); in ExtractGates()
|
/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/ensemble/ |
H A D | plot_gradient_boosting_oob.py | 83 def cv_estimate(n_splits=None): argument 84 cv = KFold(n_splits=n_splits) 90 val_scores /= n_splits
|
/dports/misc/py-xgboost/xgboost-1.5.1/demo/guide-python/ |
H A D | sklearn_examples.py | 20 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 31 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 42 kf = KFold(n_splits=2, shuffle=True, random_state=rng)
|
/dports/misc/xgboost/xgboost-1.5.1/demo/guide-python/ |
H A D | sklearn_examples.py | 20 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 31 kf = KFold(n_splits=2, shuffle=True, random_state=rng) 42 kf = KFold(n_splits=2, shuffle=True, random_state=rng)
|
/dports/x11-fm/nautilus/nautilus-41.1/src/ |
H A D | nautilus-batch-rename-utilities.c | 102 gint i, n_splits; in batch_rename_replace() local 128 n_splits = g_strv_length (splitted_string); in batch_rename_replace() 130 for (i = 0; i < n_splits; i++) in batch_rename_replace() 134 if (i != n_splits - 1) in batch_rename_replace() 267 gint i, n_splits; in batch_rename_replace_label_text() local 290 n_splits = g_strv_length (splitted_string); in batch_rename_replace_label_text() 292 for (i = 0; i < n_splits; i++) in batch_rename_replace_label_text() 299 if (i != n_splits - 1) in batch_rename_replace_label_text()
|
/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/tutorial/statistical_inference/ |
H A D | model_selection.rst | 62 >>> k_fold = KFold(n_splits=5) 108 - :class:`KFold` **(n_splits, shuffle, random_state)** 110 - :class:`StratifiedKFold` **(n_splits, shuffle, random_state)** 112 - :class:`GroupKFold` **(n_splits)** 128 - :class:`ShuffleSplit` **(n_splits, test_size, train_size, random_state)**
|
/dports/math/py-optuna/optuna-2.10.0/optuna/integration/ |
H A D | sklearn.py | 258 n_splits = self.cv.get_n_splits(self.X, self.y, groups=self.groups) 259 estimators = [clone(estimator) for _ in range(n_splits)] 261 "fit_time": np.zeros(n_splits), 262 "score_time": np.zeros(n_splits), 263 "test_score": np.empty(n_splits), 267 scores["train_score"] = np.empty(n_splits)
|