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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/
H A D_split.py281 "%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.py803 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 Dplot_cv_indices.py32 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 Dplot_nested_cross_validation_iris.py77 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 Dcommon.py13 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 Dtest_split.py69 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 Dtest_search.py912 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 Dtest_validation.py1164 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.py242 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 Dsplitidx.c33 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 Dplot_self_training_varying_threshold.py44 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 Dsplit_compatibility_graph.cc29 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 Dsplit_polyhedron.cc33 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 Dsplits_in_subdivision.cc31 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 Dclean.c17 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 Dcross_validation.py84 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 Dmatrix-data.c131 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 Dsplit.c62 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 Dactivation.cc30 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 Dplot_gradient_boosting_oob.py83 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 Dsklearn_examples.py20 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 Dsklearn_examples.py20 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 Dnautilus-batch-rename-utilities.c102 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 Dmodel_selection.rst62 >>> 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 Dsklearn.py258 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)

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