/dports/math/armadillo/armadillo-10.7.1/include/armadillo_bits/ |
H A D | op_clamp_meat.hpp | 338 const eT& X_val = X_mem[i]; in apply_direct() local 340 T val_real = std::real(X_val); in apply_direct() 341 T val_imag = std::imag(X_val); in apply_direct() 491 const eT& X_val = X_mem[i]; in apply_direct() local 493 T val_real = std::real(X_val); in apply_direct() 494 T val_imag = std::imag(X_val); in apply_direct()
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H A D | SpSubview_col_list_meat.hpp | 348 const eT X_val = (*X_mem); ++X_mem; in operator =() local 350 if(X_val != eT(0)) in operator =() 353 access::rw(Y.values [count]) = X_val; in operator =()
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H A D | spop_misc_meat.hpp | 345 const eT X_val = (*X_it); in apply() local 353 (*vals_mem) = X_val; ++vals_mem; in apply()
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/dports/math/R-cran-RcppArmadillo/RcppArmadillo/inst/include/armadillo_bits/ |
H A D | op_clamp_meat.hpp | 338 const eT& X_val = X_mem[i]; in apply_direct() local 340 T val_real = std::real(X_val); in apply_direct() 341 T val_imag = std::imag(X_val); in apply_direct() 491 const eT& X_val = X_mem[i]; in apply_direct() local 493 T val_real = std::real(X_val); in apply_direct() 494 T val_imag = std::imag(X_val); in apply_direct()
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H A D | SpSubview_col_list_meat.hpp | 348 const eT X_val = (*X_mem); ++X_mem; in operator =() local 350 if(X_val != eT(0)) in operator =() 353 access::rw(Y.values [count]) = X_val; in operator =()
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H A D | spop_misc_meat.hpp | 345 const eT X_val = (*X_it); in apply() local 353 (*vals_mem) = X_val; ++vals_mem; in apply()
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/dports/math/lis/lis-2.0.30/src/fortran/amg/ |
H A D | lis_s_solver_AMGCG.F90 | 80 REAL (kind=kreal) :: R_val,B_val,w,X_val,R_norm,GR_norm local 188 X_val=0.0 191 X_val= X_val+P % V(i) * coarser_X(inod) 193 X(j)=X(j)+X_val
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H A D | lis_m_solver_AMGCG.F90 | 181 REAL (kind=kreal) :: R_val,B_val,w,X_val,R_norm,GR_norm local 505 X_val=0.0 508 X_val= X_val+HIERARCHICAL_DATA(nth_lev+1) % P % V(i) * coarser_X(inod) 510 X(j)=X(j)+X_val
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/ |
H A D | permutation_importance.rst | 40 >>> X_train, X_val, y_train, y_val = train_test_split( 44 >>> model.score(X_val, y_val) 53 >>> r = permutation_importance(model, X_val, y_val, 89 ... model, X_val, y_val, n_repeats=30, random_state=0, scoring=scoring)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/ |
H A D | _gb.py | 503 X, X_val, y, y_val, sample_weight, sample_weight_val = train_test_split( 523 X_val = y_val = sample_weight_val = None 592 X_val, 616 X_val, argument 646 y_val_pred_iter = self._staged_raw_predict(X_val, check_input=False)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neural_network/ |
H A D | _multilayer_perceptron.py | 591 X, X_val, y, y_val = train_test_split( 601 X_val = None 661 self._update_no_improvement_count(early_stopping, X_val, y_val) 706 def _update_no_improvement_count(self, early_stopping, X_val, y_val): argument 709 self.validation_scores_.append(self.score(X_val, y_val))
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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 | 295 X_train, X_val, y_train, y_val = train_test_split( 308 X_val, 323 X_val = y_val = sample_weight_val = None 342 if X_val is not None: 343 X_binned_val = self._bin_data(X_val, is_training_data=False)
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H A D | _gradient_boosting.pyx | 25 isn't usable for e.g. X_val).
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/deep-embedded-clustering/ |
H A D | dec.py | 85 X_val = X[sep:] 94 logging.log(logging.INFO, "Autoencoder Validation error: %f"%ae_model.eval(X_val))
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/deep-embedded-clustering/ |
H A D | dec.py | 85 X_val = X[sep:] 94 logging.log(logging.INFO, "Autoencoder Validation error: %f"%ae_model.eval(X_val))
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/ |
H A D | _stochastic_gradient.py | 61 def __init__(self, estimator, X_val, y_val, sample_weight_val, classes=None): argument 66 self.X_val = X_val 74 return est.score(self.X_val, self.y_val, self.sample_weight_val)
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/dports/math/py-optuna/optuna-2.10.0/ |
H A D | README.md | 78 X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0) 81 y_pred = regressor_obj.predict(X_val)
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H A D | PKG-INFO | 86 … X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0) 89 y_pred = regressor_obj.predict(X_val)
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/dports/math/py-optuna/optuna-2.10.0/optuna.egg-info/ |
H A D | PKG-INFO | 86 … X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0) 89 y_pred = regressor_obj.predict(X_val)
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