Home
last modified time | relevance | path

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

/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/
H A Ddistributed_estimation.py94 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 …]
H A D_constraints.py252 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')
H A D_screening.py217 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)
H A Dmodel.py612 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)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/tests/
H A Dtest_distributed_estimation.py234 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 …]
H A Dtest_data.py734 fit_kwds = getattr(self, 'fit_kwds', {})
736 res = mod.fit(**fit_kwds)
806 cls.fit_kwds = {'disp': False}
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/imputation/
H A Dmice.py225 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 …]
H A Dbayes_mi.py240 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))
/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/
H A Ddistributed_estimation.py61 fit_kwds={"alpha": 0.2})
73 fit_kwds={"alpha": 0.2})
86 fit_kwds={"alpha": 0.2})
105 fit_kwds={"alpha": 0.2})
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/tests/
H A Dtest_constrained.py151 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 …]
/dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/
H A Ddistributed_estimation.ipynb84 " 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",
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/gam/
H A Dgeneralized_additive_model.py758 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)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/tests/
H A Dtest_glm.py1575 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 …]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/
H A Dpower.py371 fit_kwds = self.start_bqexp[key]
375 val, res = brentq_expanding(func, full_output=True, **fit_kwds)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/
H A Dgeneralized_linear_model.py1382 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')
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/exponential_smoothing/
H A Dbase.py141 def fit_constrained(self, constraints, start_params=None, **fit_kwds): argument
166 res = self.fit(start_params, **fit_kwds)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/imputation/tests/
H A Dtest_mice.py333 imp.set_imputer('x1', 'x2 + y', fit_kwds={'alpha': 1, 'L1_wt': 0})
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/
H A Ddiscrete_model.py1148 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')
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/
H A Dmlemodel.py741 def fit_constrained(self, constraints, start_params=None, **fit_kwds): argument
766 res = self.fit(start_params, **fit_kwds)