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Searched refs:X_val (Results 1 – 19 of 19) sorted by relevance

/dports/math/armadillo/armadillo-10.7.1/include/armadillo_bits/
H A Dop_clamp_meat.hpp338 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()
H A DSpSubview_col_list_meat.hpp348 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 =()
H A Dspop_misc_meat.hpp345 const eT X_val = (*X_it); in apply() local
353 (*vals_mem) = X_val; ++vals_mem; in apply()
/dports/math/R-cran-RcppArmadillo/RcppArmadillo/inst/include/armadillo_bits/
H A Dop_clamp_meat.hpp338 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()
H A DSpSubview_col_list_meat.hpp348 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 =()
H A Dspop_misc_meat.hpp345 const eT X_val = (*X_it); in apply() local
353 (*vals_mem) = X_val; ++vals_mem; in apply()
/dports/math/lis/lis-2.0.30/src/fortran/amg/
H A Dlis_s_solver_AMGCG.F9080 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
H A Dlis_m_solver_AMGCG.F90181 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
/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/
H A Dpermutation_importance.rst40 >>> 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)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/
H A D_gb.py503 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)
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neural_network/
H A D_multilayer_perceptron.py591 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))
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/ensemble/_hist_gradient_boosting/
H A Dgradient_boosting.py295 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)
H A D_gradient_boosting.pyx25 isn't usable for e.g. X_val).
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/deep-embedded-clustering/
H A Ddec.py85 X_val = X[sep:]
94 logging.log(logging.INFO, "Autoencoder Validation error: %f"%ae_model.eval(X_val))
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/deep-embedded-clustering/
H A Ddec.py85 X_val = X[sep:]
94 logging.log(logging.INFO, "Autoencoder Validation error: %f"%ae_model.eval(X_val))
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/
H A D_stochastic_gradient.py61 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)
/dports/math/py-optuna/optuna-2.10.0/
H A DREADME.md78 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)
H A DPKG-INFO86 … 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)
/dports/math/py-optuna/optuna-2.10.0/optuna.egg-info/
H A DPKG-INFO86 … 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)