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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/_glm/
H A Dlink.py41 def inverse(self, lin_pred): argument
54 def inverse_derivative(self, lin_pred): argument
73 def inverse(self, lin_pred): argument
74 return lin_pred
77 return np.ones_like(lin_pred)
89 def inverse(self, lin_pred): argument
90 return np.exp(lin_pred)
93 return np.exp(lin_pred)
105 def inverse(self, lin_pred): argument
106 return expit(lin_pred)
[all …]
H A Dglm.py39 lin_pred = _safe_lin_pred(X, coef)
40 y_pred = link.inverse(lin_pred)
41 d1 = link.inverse_derivative(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/tests/
H A Dtest_glm_weights.py387 mu = link().inverse(lin_pred)
460 lin_pred = 20 + exog.sum(1)
464 lin_pred = -2 + exog.sum(1)
488 lin_pred = np.clip(lin_pred, 1e-4, np.inf)
501 lin_pred = np.clip(lin_pred, 1e-3, np.inf)
596 lin_pred = (exog * beta).sum(axis=1)
627 lin_pred = (exog * beta).sum(axis=1)
658 lin_pred = (exog * beta).sum(axis=1)
690 lin_pred = (exog * beta).sum(axis=1)
723 lin_pred = (exog * beta).sum(axis=1)
[all …]
H A Dtest_glm.py1043 lin_pred = params[0] + params[1]*x
1044 pr = 1 / (1 + np.exp(-lin_pred))
1083 lin_pred = exog[:, 0] + exog[:, 1]**2
1084 prob = 1 / (1 + np.exp(-lin_pred))
1128 mu = link().inverse(lin_pred)
1225 lin_pred = 2 + exog.sum(1)
1231 lin_pred = np.clip(lin_pred, 1e-4, np.inf)
1239 lin_pred = np.clip(lin_pred, 1e-4, np.inf)
1342 lin_pred = 2 + exog.sum(1)
1349 lin_pred = np.clip(lin_pred, 1e-4, np.inf)
[all …]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/tests/results/
H A Delastic_net_generate_tests.py12 lin_pred = exog.sum(1) * 0.2 variable
13 exp_val = 1 / (1 + np.exp(-lin_pred))
20 lin_pred = exog.sum(1) * 0.2 variable
21 exp_val = np.exp(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/gam/
H A Dgeneralized_additive_model.py693 lin_pred = self.family.predict(mu)
695 lin_pred = np.dot(wlsexog, start_params) + self._offset_exposure
696 mu = self.family.fitted(lin_pred)
705 mu = self.family.fitted(lin_pred)
718 wlsendog = (lin_pred + self.family.link.deriv(mu) * (endog - mu)
723 lin_pred = np.dot(wlsexog, wls_results.params).ravel()
724 lin_pred += self._offset_exposure
725 mu = self.family.fitted(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/graphics/tests/
H A Dtest_regressionplots.py240 lin_pred = 4 + exog[:, 0] + 0.2 * exog[:, 1]**2
241 expval = np.exp(lin_pred)
291 lin_pred = 4 + exog[:, 1] + 0.2*exog[:, 2]**2
292 expval = np.exp(lin_pred)
327 lin_pred = 4 + exog[:, 1] + 0.2*exog[:, 2]**2
328 expval = np.exp(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/
H A Dgeneralized_linear_model.py435 lin_pred = np.dot(self.exog, params) + self._offset_exposure
436 expval = self.family.link.inverse(lin_pred)
632 lin_pred = self.predict(params, linear=True)
633 idl = self.family.link.inverse_deriv(lin_pred)
1184 lin_pred = self.family.predict(mu)
1186 lin_pred = np.dot(wlsexog, start_params) + self._offset_exposure
1187 mu = self.family.fitted(lin_pred)
1204 mu = self.family.fitted(lin_pred)
1217 lin_pred = np.dot(self.exog, wls_results.params)
1218 lin_pred += self._offset_exposure
[all …]
H A Dgeneralized_estimating_equations.py963 def mean_deriv(self, exog, lin_pred): argument
985 idl = self.family.link.inverse_deriv(lin_pred)
1008 lin_pred = np.dot(exog, params)
1010 lin_pred += offset_exposure
1012 idl = self.family.link.inverse_deriv(lin_pred)
2701 def mean_deriv(self, exog, lin_pred): argument
2720 expval = np.exp(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/tests/
H A Dtest_conditional.py172 lin_pred = np.dot(x, params) + group_effects
174 mean = 1 / (1 + np.exp(-lin_pred))
213 lin_pred = np.dot(x, params) + group_effects
215 mean = np.exp(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/
H A Dcount_model.py427 lin_pred = np.dot(exog, params_main[:self.exog.shape[1]]) + exposure + offset
457 return prob_main * np.exp(lin_pred)
459 return np.exp(lin_pred)
461 return lin_pred
463 return prob_main * np.exp(lin_pred) / (1 - prob_zero)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/
H A D_diagnostic_other.py262 lin_pred = res.predict(linear=True)
263 dm_incl = dm(x, lin_pred)
265 dm_excl = dm(x2, lin_pred)
/dports/math/py-spglm/spglm-1.0.8/spglm/
H A Dfamily.py161 def fitted(self, lin_pred): argument
177 fits = self.link.inverse(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/tests/
H A Dtest_predict_functional.py233 lin_pred = -1 + 0.5*x1**2 + (x3 == "B")
234 prob = 1 / (1 + np.exp(-lin_pred))
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/families/
H A Dfamily.py215 def fitted(self, lin_pred): argument
231 fits = self.link.inverse(lin_pred)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/duration/tests/
H A Dtest_phreg.py347 lin_pred = exog.sum(1)
348 elin_pred = np.exp(-lin_pred)
/dports/lang/clover/mesa-21.3.6/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()
/dports/graphics/libosmesa-gallium/mesa-21.3.6/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/gam/tests/
H A Dtest_gam.py87 lin_pred = np.dot(pol.basis, params)
89 expval = gaussian.link.inverse(lin_pred)
/dports/graphics/mesa-gallium-xa/mesa-21.3.6/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()
/dports/graphics/mesa-libs/mesa-21.3.6/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()
/dports/graphics/mesa-gallium-va/mesa-21.3.6/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()
/dports/graphics/mesa-gallium-vdpau/mesa-21.3.6/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()
/dports/graphics/mesa-dri-gallium/mesa-21.3.6/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()
/dports/graphics/mesa-devel/mesa-22.0-branchpoint-2059-ge8a63cf61ec/src/amd/compiler/
H A Daco_insert_NOPs.cpp261 for (unsigned lin_pred : block->linear_preds) { in handle_raw_hazard_internal() local
264 state, &state.program->blocks[lin_pred], nops_needed, reg, mask, true)); in handle_raw_hazard_internal()

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