/dports/biology/phyml/phyml-3.3.20200621/src/ |
H A D | main.c | 96 else io->n_trees = 1; in main() 98 if(io->n_trees == 0 && io->in_tree == 2) in main() 104 if((io->n_data_sets > 1) && (io->n_trees > 1)) in main() 106 io->n_data_sets = MIN(io->n_trees,io->n_data_sets); in main() 107 io->n_trees = MIN(io->n_trees,io->n_data_sets); in main() 128 for(num_tree=(io->n_trees == 1)?(0):(num_data_set);num_tree < io->n_trees;num_tree++) in main() 132 if(orig_random_input_tree == YES && io->n_trees > 1) in main() 366 if(io->n_trees > 1 && io->n_data_sets > 1) break; in main()
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H A D | tiporder.h | 43 void TIPO_Minimize_Tip_Order_Score(int n_trees, t_tree **list_tree, t_tree *ref_tree); 48 int TIPO_Untangle_Tree_List(int n_trees, t_tree **list_tree, t_tree *ref_tree);
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H A D | mixt.c | 1233 int n_trees; in MIXT_Record_All_Mixtures() local 1237 n_trees = 0; in MIXT_Record_All_Mixtures() 1245 n_trees++; in MIXT_Record_All_Mixtures() 1310 int n_trees; in MIXT_Reconnect_All_Mixtures() local 1315 n_trees = 0; in MIXT_Reconnect_All_Mixtures() 1320 n_trees++; in MIXT_Reconnect_All_Mixtures() 1335 int n_trees; in MIXT_Record_Has_Invariants() local 1339 n_trees = 0; in MIXT_Record_Has_Invariants() 1345 n_trees++; in MIXT_Record_Has_Invariants() 1359 int n_trees; in MIXT_Reset_Has_Invariants() local [all …]
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H A D | m4.c | 60 else io->n_trees = 1; in M4_main() 63 if((io->n_data_sets > 1) && (io->n_trees > 1)) in M4_main() 65 io->n_data_sets = MIN(io->n_trees,io->n_data_sets); in M4_main() 66 io->n_trees = MIN(io->n_trees,io->n_data_sets); in M4_main() 92 for(num_tree=(io->n_trees == 1)?(0):(num_data_set);num_tree < io->n_trees;num_tree++) in M4_main() 239 if(io->n_trees > 1 && io->n_data_sets > 1) break; in M4_main()
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/dports/math/py-optuna/optuna-2.10.0/optuna/importance/_fanova/ |
H A D | _evaluator.py | 63 self, *, n_trees: int = 64, max_depth: int = 64, seed: Optional[int] = None 66 n_trees=n_trees,
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H A D | _fanova.py | 42 n_trees: int, 51 n_estimators=n_trees,
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/dports/math/cgal/CGAL-5.3/include/CGAL/Classification/ETHZ/internal/random-forest/ |
H A D | common-libraries.hpp | 86 size_t n_trees; member 95 n_trees(100), in ForestParams() 108 ar & BOOST_SERIALIZATION_NVP(n_trees); in serialize() 121 I_Binary_write_size_t_into_uinteger32 (os, n_trees); in write() 133 I_Binary_read_size_t_from_uinteger32 (is, n_trees); in read()
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H A D | forest.hpp | 160 for (std::size_t i_tree = nb_trees; i_tree < nb_trees + params.n_trees; ++ i_tree) in train() 172 tbb::parallel_for(tbb::blocked_range<size_t>(nb_trees, nb_trees + params.n_trees), f); in train() 177 for (size_t i_tree = nb_trees; i_tree < nb_trees + params.n_trees; ++i_tree) in train() 180 …tf("Training tree %zu/%zu, max depth %zu\n", i_tree+1, nb_trees + params.n_trees, params.max_depth… in train()
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/dports/math/py-pynndescent/pynndescent-0.5.4/pynndescent/ |
H A D | pynndescent_.py | 645 n_trees=None, argument 662 if n_trees is None: 663 n_trees = 5 + int(round((data.shape[0]) ** 0.25)) 664 n_trees = min(32, n_trees) # Only so many trees are useful 668 self.n_trees = n_trees 759 n_trees, 1626 self.n_trees = int(np.round(self.n_trees / 3)) 1630 self.n_trees, 1814 n_trees=None, argument 1833 self.n_trees = n_trees [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/neighbors/ |
H A D | approximate_nearest_neighbors.py | 121 def __init__(self, n_neighbors=5, metric="euclidean", n_trees=10, search_k=-1): argument 123 self.n_trees = n_trees 132 self.annoy_.build(self.n_trees)
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/dports/math/py-pynndescent/pynndescent-0.5.4/pynndescent/tests/ |
H A D | test_pynndescent_.py | 246 n_trees=20, 268 n_trees=20, 299 n_trees=5, 317 n_trees=5, 335 n_trees=5, 352 n_trees=5,
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/dports/math/py-optuna/optuna-2.10.0/optuna/importance/ |
H A D | _mean_decrease_impurity.py | 44 self, *, n_trees: int = 64, max_depth: int = 64, seed: Optional[int] = None 49 n_estimators=n_trees,
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/dports/misc/py-xgboost/xgboost-1.5.1/demo/c-api/basic/ |
H A D | c-api-demo.c | 55 int n_trees = 10; in main() local 58 for (int i = 0; i < n_trees; ++i) { in main()
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/dports/misc/xgboost/xgboost-1.5.1/demo/c-api/basic/ |
H A D | c-api-demo.c | 55 int n_trees = 10; in main() local 58 for (int i = 0; i < n_trees; ++i) { in main()
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/dports/misc/py-xgboost/xgboost-1.5.1/src/tree/ |
H A D | updater_quantile_hist.h | 199 explicit Builder(const size_t n_trees, const TrainParam ¶m, in Builder() 201 : n_trees_(n_trees), param_(param), pruner_(std::move(pruner)), in Builder() 311 void SetBuilder(const size_t n_trees, std::unique_ptr<Builder<GradientSumT>>*, DMatrix *dmat);
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H A D | updater_quantile_hist.cc | 51 void QuantileHistMaker::SetBuilder(const size_t n_trees, in SetBuilder() argument 55 new Builder<GradientSumT>(n_trees, param_, std::move(pruner_), dmat)); in SetBuilder() 89 const size_t n_trees = trees.size(); in Update() local 92 this->SetBuilder(n_trees, &float_builder_, dmat); in Update() 97 SetBuilder(n_trees, &double_builder_, dmat); in Update()
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/dports/misc/xgboost/xgboost-1.5.1/src/tree/ |
H A D | updater_quantile_hist.h | 199 explicit Builder(const size_t n_trees, const TrainParam ¶m, in Builder() 201 : n_trees_(n_trees), param_(param), pruner_(std::move(pruner)), in Builder() 311 void SetBuilder(const size_t n_trees, std::unique_ptr<Builder<GradientSumT>>*, DMatrix *dmat);
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H A D | updater_quantile_hist.cc | 51 void QuantileHistMaker::SetBuilder(const size_t n_trees, in SetBuilder() argument 55 new Builder<GradientSumT>(n_trees, param_, std::move(pruner_), dmat)); in SetBuilder() 89 const size_t n_trees = trees.size(); in Update() local 92 this->SetBuilder(n_trees, &float_builder_, dmat); in Update() 97 SetBuilder(n_trees, &double_builder_, dmat); in Update()
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/dports/dns/knot3/knot-3.1.5/src/knot/updates/ |
H A D | changesets.c | 69 ch_it->trees[ch_it->n_trees++] = t; in changeset_iter_init() 74 assert(ch_it->n_trees); in changeset_iter_init() 508 if (--it->n_trees > 0) { in changeset_iter_next() 509 for (size_t i = 0; i < it->n_trees; i++) { in changeset_iter_next()
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H A D | changesets.h | 46 size_t n_trees; /*!< Their count. */ member
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/dports/dns/knot3-lib/knot-3.1.5/src/knot/updates/ |
H A D | changesets.c | 69 ch_it->trees[ch_it->n_trees++] = t; in changeset_iter_init() 74 assert(ch_it->n_trees); in changeset_iter_init() 508 if (--it->n_trees > 0) { in changeset_iter_next() 509 for (size_t i = 0; i < it->n_trees; i++) { in changeset_iter_next()
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H A D | changesets.h | 46 size_t n_trees; /*!< Their count. */ member
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/_hist_gradient_boosting/ |
H A D | gradient_boosting.py | 792 n_trees = len(predictors_of_ith_iteration) 800 if n_trees == 1: 801 log_msg += "{} tree, {} leaves, ".format(n_trees, n_leaves) 803 log_msg += "{} trees, {} leaves ".format(n_trees, n_leaves) 804 log_msg += "({} on avg), ".format(int(n_leaves / n_trees))
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/dports/math/py-openTSNE/openTSNE-0.6.1/openTSNE/ |
H A D | nearest_neighbors.py | 493 n_trees = 5 + int(round((data.shape[0]) ** 0.5 / 20)) 512 n_trees=n_trees,
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/dports/devel/pytype/pytype-2021.9.9/pytype/typeshed/stubs/annoy/annoy/ |
H A D | __init__.pyi | 37 def build(self, n_trees: int, n_jobs: int = ...) -> Literal[True]: ...
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