/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/tests/ |
H A D | test_logistic.py | 496 assert_array_almost_equal(lr1.coef_, lr2.coef_) 500 assert_array_almost_equal(lr1.coef_, lr3.coef_) 595 assert_array_almost_equal(lr.coef_, lr_cv.coef_) 672 assert_array_almost_equal(lr.coef_, lr_str.coef_) 674 assert_array_almost_equal(lr_cv.coef_, lr_cv_str.coef_) 698 assert_array_almost_equal(clfs.coef_, clf.coef_) 763 assert_allclose(clf.coef_[2][np.newaxis, :], clf1.coef_) 791 assert clf.coef_.shape == clf_multi.coef_.shape 1103 assert_allclose(ref_i.coef_, clf_i.coef_, rtol=1e-2) 1324 assert_array_almost_equal(lr_cv.coef_, lr.coef_) [all …]
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H A D | test_coordinate_descent.py | 714 assert_array_almost_equal(clf2.coef_, clf.coef_) 809 assert_array_almost_equal(clf.coef_[0], clf.coef_[1]) 912 assert_almost_equal(clf.coef_, clf1.coef_[0]) 924 assert_almost_equal(clf.coef_, clf1.coef_[0]) 998 assert_allclose(clf1.coef_, clf2.coef_) 1195 assert_array_equal(clf.coef_, clf_float.coef_) 1313 assert_array_almost_equal(clf2.coef_, clf.coef_) 1474 assert_allclose(reg1.coef_, reg2.coef_) 1532 assert_allclose(reg_sw.coef_, reg.coef_) 1705 assert_allclose(ridge.coef_, enet.coef_) [all …]
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H A D | test_quantile.py | 82 assert_allclose(model.coef_[0], coef, atol=1e-2) 84 assert model.coef_[0] >= 1 85 assert model.coef_[0] <= 10 96 assert_allclose(huber.coef_, quant.coef_, atol=1e-1) 168 assert_allclose(model.coef_, coef, rtol=0.6) 174 model_coef = np.r_[model.intercept_, model.coef_] 191 assert_allclose(model.coef_, res.x[1:]) 220 assert_allclose(model2.coef_, a * model1.coef_, rtol=1e-5) 225 assert_allclose(model2.coef_, -a * model1.coef_, rtol=1e-5) 232 assert_allclose(model2.coef_, model1.coef_ + g_coef, rtol=1e-6) [all …]
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H A D | test_huber.py | 37 assert_almost_equal(huber.coef_, lr.coef_, 3) 78 huber_coef = huber.coef_ 88 assert_array_almost_equal(huber.coef_ / scale, huber_coef / scale) 95 huber_coef = huber.coef_ 102 assert_array_almost_equal(huber.coef_ / scale, huber_coef / scale) 109 assert_array_almost_equal(huber_sparse.coef_ / scale, huber_coef / scale) 120 assert_array_almost_equal(huber_sparse.coef_, huber.coef_) 166 assert_array_almost_equal(huber.coef_, sgdreg.coef_, 1) 174 huber_warm_coef = huber_warm.coef_.copy() 179 assert_array_almost_equal(huber_warm.coef_, huber_warm_coef, 1) [all …]
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H A D | test_sparse_coordinate_descent.py | 23 clf.coef_ = [1, 2, 3] 26 assert clf.sparse_coef_.toarray().tolist()[0] == clf.coef_ 40 assert_array_almost_equal(clf_dense.coef_, clf_sparse.coef_) 50 assert_array_almost_equal(clf.coef_, [0]) 69 assert_array_almost_equal(clf.coef_, [1]) 83 assert_array_almost_equal(clf.coef_, [0.45454], 3) 108 assert_array_almost_equal(clf.coef_, [1]) 122 assert_array_almost_equal(clf.coef_, [0.45454], 3) 196 assert_almost_equal(s_clf.coef_, d_clf.coef_, 5) 231 assert np.sum(s_clf.coef_ != 0.0) == n_informative [all …]
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H A D | test_sgd.py | 288 assert_array_almost_equal(clf3.coef_, clf.coef_) 294 assert_array_almost_equal(clf3.coef_, clf2.coef_) 332 assert_array_equal(clf.coef_, clf2.coef_) 505 assert_array_equal(clf1.coef_, clf2.coef_) 964 assert_almost_equal(clf1.coef_, clf2.coef_) 1201 assert clf.coef_[0] == clf.coef_[1] 1513 assert_allclose(clf3.coef_, clf.coef_) 1519 assert_allclose(clf3.coef_, clf2.coef_) 1539 assert_array_equal(clf.coef_, clf2.coef_) 1772 assert_array_almost_equal(est_en.coef_, est_l1.coef_) [all …]
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H A D | test_omp.py | 157 assert omp.coef_.shape == (n_features,) 159 assert np.count_nonzero(omp.coef_) <= n_nonzero_coefs 162 assert omp.coef_.shape == (n_targets, n_features) 166 coef_normalized = omp.coef_[0].copy() 169 assert_array_almost_equal(coef_normalized, omp.coef_) 173 assert np.count_nonzero(omp.coef_) <= n_nonzero_coefs 174 assert omp.coef_.shape == (n_features,) 178 assert omp.coef_.shape == (n_targets, n_features) 250 assert_array_almost_equal(ompcv.coef_, gamma_) 255 assert_array_almost_equal(ompcv.coef_, omp.coef_) [all …]
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H A D | test_least_angle.py | 75 res = y - np.dot(X, coef_) 95 res = y - np.dot(X, coef_) 246 output_2 = clf.fit(X, y).coef_ 268 coef_lars_ = lars.fit(X, y).coef_ 300 err = linalg.norm(clf1.coef_ - clf2.coef_) 414 lars_coef_ = lars.coef_ 465 estimator.coef_, 654 err = linalg.norm(clf1.coef_ - clf2.coef_) 865 assert np.mean((est.coef_ - est_jitter.coef_) ** 2) > 0.1 908 assert model.coef_.dtype == dtype [all …]
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H A D | test_ridge.py | 300 assert_almost_equal(ridge.coef_, ols.coef_) 304 assert_almost_equal(ridge.coef_, ols.coef_) 539 assert_allclose(gcv_ridge.coef_, loo_ridge.coef_, rtol=1e-3) 569 assert_allclose(gcv_ridge.coef_, loo_ridge.coef_, rtol=1e-3) 641 assert_allclose(gcv_ridge.coef_, kfold.coef_, rtol=1e-3) 1004 assert_array_almost_equal(reg.coef_, rega.coef_) 1017 assert_almost_equal(reg1.coef_, reg2.coef_) 1027 assert_almost_equal(reg1.coef_, reg2.coef_) 1034 assert_almost_equal(reg1.coef_, reg2.coef_) 1304 assert np.allclose(dense_ridge.coef_, sparse_ridge.coef_) [all …]
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H A D | test_theil_sen.py | 169 assert np.abs(lstq.coef_ - w) > 0.9 172 assert_array_almost_equal(theil_sen.coef_, w, 1) 180 assert np.abs(lstq.coef_ - w - c) > 0.5 183 assert_array_almost_equal(theil_sen.coef_, w + c, 1) 194 assert norm(lstq.coef_ - w) > 1.0 197 assert_array_almost_equal(theil_sen.coef_, w, 1) 241 assert_array_almost_equal(theil_sen.coef_, w, 1) 250 assert_array_almost_equal(theil_sen.coef_, lstq.coef_, 9) 265 assert norm(lstq.coef_ - w) > 1.0 270 assert_array_almost_equal(theil_sen.coef_, w, 1) [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/ |
H A D | _bayes.py | 311 coef_old_ = np.copy(coef_) 383 coef_ = np.linalg.multi_dot( 387 coef_ = np.linalg.multi_dot( 393 return coef_, rmse_ 625 coef_ = np.zeros(n_features) 657 return coef_ 667 coef_ = update_coeff(X, y, coef_, alpha_, keep_lambda, sigma_) 681 coef_[~keep_lambda] = 0 700 coef_old_ = np.copy(coef_) 708 coef_ = update_coeff(X, y, coef_, alpha_, keep_lambda, sigma_) [all …]
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H A D | _base.py | 369 self.coef_ = self.coef_ / X_scale 370 self.intercept_ = y_offset - np.dot(X_offset, self.coef_.T) 471 if sp.issparse(self.coef_): 472 self.coef_ = self.coef_.toarray() 503 self.coef_ = sp.csr_matrix(self.coef_) 685 self.coef_, self._residues = optimize.nnls(X, y) 691 self.coef_, self._residues = map(np.vstack, zip(*outs)) 707 self.coef_ = out[0] 715 self.coef_ = np.vstack([out[0] for out in outs]) 719 self.coef_ = self.coef_.T [all …]
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H A D | _coordinate_descent.py | 610 coef_, 634 coef_, 1027 coef_ = self.coef_ 1029 coef_ = coef_[np.newaxis, :] 1067 self.coef_ = coef_[0] 1070 self.coef_ = coef_ 1076 self.coef_ = np.asarray(self.coef_, dtype=X.dtype) 1719 self.coef_ = model.coef_ 2430 self.coef_ = np.asfortranarray(self.coef_) # coef contiguous in memory 2437 self.coef_, [all …]
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H A D | _stochastic_gradient.py | 72 est.coef_ = coef.reshape(1, -1) 208 self.coef_ = coef_init 336 coef = est.coef_.ravel() 345 coef = est.coef_[i] 665 self.coef_ = None 1479 self.coef_ = None 1613 coef = self.coef_ 1661 self.coef_ = coef 2174 coef = self.coef_ 2223 self.coef_ = coef [all …]
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/dports/math/xlife++/xlifepp-sources-v2.0.1-2018-05-09/src/term/ |
H A D | SymbolicTermMatrix.cpp | 64 coef_=S.coef_; in SymbolicTermMatrix() 88 coef_=S.coef_; in operator =() 186 {S.coef_*=c; return S;} in operator *() 189 {S.coef_*=c; return S;} in operator *() 192 {S.coef_/=c; return S;} in operator /() 204 if (coef_.imag()==0) out<< coef_.real(); in asString() 205 else out<<coef_; in asString() 270 if (S.coef_.imag()==0) R*=S.coef_.real(); in multMatrixVector() 271 else R*=S.coef_; in multMatrixVector() 294 if(S.coef_.imag()==0) R*=S.coef_.real(); in multVectorMatrix() [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/cluster/ |
H A D | plot_feature_agglomeration_vs_univariate_selection.py | 74 coef_ = clf.best_estimator_.steps[-1][1].coef_ variable 75 coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_) variable 76 coef_agglomeration_ = coef_.reshape(size, size) 85 coef_ = clf.best_estimator_.steps[-1][1].coef_ variable 86 coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_.reshape(1, -1)) variable 87 coef_selection_ = coef_.reshape(size, size)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/linear_model/ |
H A D | plot_huber_vs_ridge.py | 50 coef_ = huber.coef_ * x + huber.intercept_ variable 51 plt.plot(x, coef_, colors[k], label="huber loss, %s" % epsilon) 56 coef_ridge = ridge.coef_ 57 coef_ = ridge.coef_ * x + ridge.intercept_ variable 58 plt.plot(x, coef_, "g-", label="ridge regression")
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H A D | plot_lasso_and_elasticnet.py | 62 np.where(enet.coef_)[0], 63 enet.coef_[enet.coef_ != 0], 70 np.where(lasso.coef_)[0], 71 lasso.coef_[lasso.coef_ != 0],
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H A D | plot_lasso_dense_vs_sparse_data.py | 40 % linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_) 68 % linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/_glm/tests/ |
H A D | test_glm.py | 192 assert_allclose(glm.coef_, coef, rtol=1e-12) 210 coef = glm.coef_.copy() 215 assert_allclose(glm.coef_, coef, rtol=1e-12) 220 assert_allclose(glm.coef_, coef, rtol=1e-12) 227 coef1 = glm.coef_.copy() 229 assert_allclose(glm.coef_, coef1, rtol=1e-12) 243 assert_allclose(glm1.coef_, glm2.coef_) 272 assert_allclose(res.coef_, coef, rtol=2e-6) 305 assert_allclose(glm1.coef_, glm2.coef_, rtol=1e-5) 366 assert glm.coef_.shape == (X.shape[1],) [all …]
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/dports/math/symengine/symengine-0.8.1/symengine/ |
H A D | mul.cpp | 11 : coef_{coef}, dict_{std::move(dict)} in Mul() 82 hash_combine<Basic>(seed, *coef_); in __hash__() 92 if (is_a<Mul>(o) and eq(*coef_, *(down_cast<const Mul &>(o).coef_)) in __eq__() 108 int cmp = coef_->__cmp__(*s.coef_); in compare() 293 or (neq(*m->coef_, *one) and neq(*m->coef_, *minus_one))) { in dict_add_term_new() 466 new_coef = pow(coef_, exp); in power_num() 480 if (coef_->is_negative()) { in power_num() 486 } else if (coef_->is_positive() and not coef_->is_one()) { in power_num() 502 imulnum(coef, tmp->coef_); in power_num() 516 if (not coef_->is_one()) { in get_args() [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/svm/tests/ |
H A D | test_sparse.py | 62 assert sparse.issparse(sparse_svm.coef_) 63 assert_array_almost_equal(dense_svm.coef_, sparse_svm.coef_.toarray()) 130 svm.SVC(kernel="linear", probability=True, random_state=0).fit(X, y).coef_ 135 coef_sorted = sparse_svc.coef_ 158 coef_unsorted = unsorted_svc.coef_ 190 assert_array_almost_equal(clf.coef_, sp_clf.coef_.toarray()) 203 dec = safe_sparse_dot(iris.data, clf.coef_.T) + clf.intercept_ 209 dec = np.dot(X, clf.coef_.T) + clf.intercept_ 246 assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4) 254 assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4) [all …]
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/dports/science/mpqc/mpqc-2.3.1/src/lib/chemistry/molecule/ |
H A D | coor.cc | 412 coef_.resize(n); in SumIntCoor() 415 s.get(coef_[i]); in SumIntCoor() 427 int n = coef_.size(); in save_data_state() 432 s.put(coef_[i]); in save_data_state() 440 return coef_.size(); in n() 464 coef_[l] = coef; in add() 480 int n = coef_.size(); in normalize() 485 norm += coef_[i] * coef_[i]; in normalize() 486 if (fabs(biggest) < fabs(coef_[i])) biggest = coef_[i]; in normalize() 491 coef_[i] = coef_[i]*norm; in normalize() [all …]
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/dports/science/mpqc/mpqc-2.3.1/src/lib/chemistry/qc/mbpt/ |
H A D | mp2extrap.cc | 63 coef_[0] = 0.184090909090909; in MP2BasisExtrap() 64 coef_[1] = -1.551515151515153; in MP2BasisExtrap() 65 coef_[2] = 2.367424242424244; in MP2BasisExtrap() 133 val += coef_[i] * mbpt2[i]->corr_energy(); in compute() 144 coef_[i], mbpt2[i]->corr_energy()) in compute() 161 gradientvec.accumulate(coef_[i] * mbpt2[i]->corr_energy_gradient()); in compute()
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/dports/science/py-hiphive/hiphive-0.7.1/hiphive/fitting/ |
H A D | fit_methods.py | 157 results['parameters'] = lasso.coef_ 202 results['parameters'] = lassoCV.coef_ 242 x_new = lasso.coef_ / weights 315 results['parameters'] = ridge.coef_ 339 results['parameters'] = brr.coef_ 472 results['parameters'] = ardr.coef_ 530 self.coef_ = None 535 self.coef_ = results['parameters'] 543 return np.dot(A, self.coef_) 654 parameters = ompcv.coef_ [all …]
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