/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/ |
H A D | distributed_estimation.py | 94 if fit_kwds is None: 98 return mod.fit_regularized(**fit_kwds).params 120 if fit_kwds is None: 124 return mod.fit(**fit_kwds).params 250 if fit_kwds is None: 254 alpha = fit_kwds["alpha"] 256 if "L1_wt" in fit_kwds: 257 L1_wt = fit_kwds["L1_wt"] 360 fit_kwds=fit_kwds, 506 if fit_kwds is None: [all …]
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H A D | _constraints.py | 252 start_params=None, fit_kwds=None): argument 310 if fit_kwds is None: 311 fit_kwds = {} 337 res_constr = mod_constr.fit(start_params=start_params, **fit_kwds) 344 def fit_constrained_wrap(model, constraints, start_params=None, **fit_kwds): argument 376 fit_kwds=fit_kwds) 382 cov_type = fit_kwds.get('cov_type', 'nonrobust')
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H A D | _screening.py | 217 disp=False, fit_kwds=None): argument 257 fkwds = fit_kwds if fit_kwds is not None else {} 258 fit_kwds = {'maxiter': 200, 'disp': False} 259 fit_kwds.update(fkwds) 268 res_pen = mod_pen.fit(**fit_kwds) 298 **fit_kwds) 355 **fit_kwds)
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H A D | model.py | 612 return_auxiliary=False, k_params=None, **fit_kwds): argument 658 fit_kwds['start_params'] = start_params[keep_index_p] 666 res_constr = mod_constr.fit(**fit_kwds)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/tests/ |
H A D | test_distributed_estimation.py | 234 fit_kwds={"alpha": 0.5}) 239 fit_kwds={"alpha": 0.5}) 244 fit_kwds={"alpha": 0.5}) 251 fit_kwds={"alpha": 0.5}) 257 fit_kwds={"alpha": 0.5}) 263 fit_kwds={"alpha": 0.5}) 282 fit_kwds={"alpha": 0.5}) 287 fit_kwds={"alpha": 0.5}) 292 fit_kwds={"alpha": 0.5}) 299 fit_kwds={"alpha": 0.5}) [all …]
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H A D | test_data.py | 734 fit_kwds = getattr(self, 'fit_kwds', {}) 736 res = mod.fit(**fit_kwds) 806 cls.fit_kwds = {'disp': False}
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/imputation/ |
H A D | mice.py | 225 self.fit_kwds = defaultdict(lambda: dict()) 360 if fit_kwds is not None: 361 self.fit_kwds[endog_name] = fit_kwds 518 fit_kwds = self._process_kwds(self.fit_kwds[vname], ix) 520 return endog, exog, init_kwds, fit_kwds 937 fit_kwds = self._boot_kwds(fit_kwds, rix) 944 self.models[vname].fit_regularized(**fit_kwds)) 1139 init_kwds=None, fit_kwds=None): 1148 self.fit_kwds = fit_kwds if fit_kwds is not None else {} 1186 self.fit_kwds.update({"start_params": start_params}) [all …]
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H A D | bayes_mi.py | 240 formula=None, fit_args=None, fit_kwds=None, xfunc=None, 272 if fit_kwds is None: 275 fit_kwds = f 276 self.fit_kwds = fit_kwds 324 result = model.fit(*self.fit_args(da), **self.fit_kwds(da))
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
H A D | distributed_estimation.py | 61 fit_kwds={"alpha": 0.2}) 73 fit_kwds={"alpha": 0.2}) 86 fit_kwds={"alpha": 0.2}) 105 fit_kwds={"alpha": 0.2})
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/tests/ |
H A D | test_constrained.py | 151 fit_kwds={'method': 'bfgs', 'disp': 0}) 188 fit_kwds={'method': 'newton', 212 fit_kwds={'method': 'newton', 264 fit_kwds={'method': 'bfgs', 'disp': 0}) 287 fit_kwds={'method': 'newton', 341 fit_kwds={'atol': 1e-10}) 367 fit_kwds={'atol': 1e-10}) 464 cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'atol': 1e-10}) 529 cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'atol': 1e-10, 552 mod.fit_constrained(R, q, fit_kwds={'method': 'bfgs'}) [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/ |
H A D | distributed_estimation.ipynb | 84 " zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={\"alpha\": 0.2}\n", 108 " zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={\"alpha\": 0.2}\n", 132 " zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={\"alpha\": 0.2}\n", 162 " zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={\"alpha\": 0.2}\n",
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/gam/ |
H A D | generalized_additive_model.py | 758 method='basinhopping', **fit_kwds): argument 840 kwds.update(fit_kwds) 847 kwds.update(fit_kwds) 851 fit_res = optimize.minimize(fun, start_params, **fit_kwds)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/tests/ |
H A D | test_glm.py | 1575 fit_kwds = dict(method='newton') 1578 family=sm.families.Poisson()).fit(**fit_kwds) 1579 fit_kwds = dict(method='newton', start_params=start_params) 1581 family=sm.families.Poisson()).fit(**fit_kwds) 1597 fit_kwds = dict(cov_type='HC0') 1600 family=sm.families.Poisson()).fit(**fit_kwds) 1601 fit_kwds = dict(cov_type='HC0', start_params=start_params) 1603 family=sm.families.Poisson()).fit(**fit_kwds) 1620 fit_kwds = dict(cov_type='cluster', cov_kwds={'groups': gid, 'use_correction':False}) 1627 family=sm.families.Poisson()).fit(**fit_kwds) [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/ |
H A D | power.py | 371 fit_kwds = self.start_bqexp[key] 375 val, res = brentq_expanding(func, full_output=True, **fit_kwds)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/ |
H A D | generalized_linear_model.py | 1382 def fit_constrained(self, constraints, start_params=None, **fit_kwds): argument 1428 fit_kwds=fit_kwds) 1433 cov_type = fit_kwds.get('cov_type', 'nonrobust')
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/exponential_smoothing/ |
H A D | base.py | 141 def fit_constrained(self, constraints, start_params=None, **fit_kwds): argument 166 res = self.fit(start_params, **fit_kwds)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/imputation/tests/ |
H A D | test_mice.py | 333 imp.set_imputer('x1', 'x2 + y', fit_kwds={'alpha': 1, 'L1_wt': 0})
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/ |
H A D | discrete_model.py | 1148 def fit_constrained(self, constraints, start_params=None, **fit_kwds): argument 1194 fit_kwds=fit_kwds) 1204 cov_type = fit_kwds.get('cov_type', 'nonrobust')
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/ |
H A D | mlemodel.py | 741 def fit_constrained(self, constraints, start_params=None, **fit_kwds): argument 766 res = self.fit(start_params, **fit_kwds)
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