Exemple #1
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 def test_BZ_ln(self):
     model = apc.Model()
     model.data_from_df(apc.loss_BZ(), data_format='CL')
     model.fit('log_normal_response', 'AC')
     model.simulate(repetitions=10)
     model.simulate(repetitions=10, fitted_values=model.fitted_values * 10)
     model.simulate(repetitions=10, sigma2=10)
Exemple #2
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 def test_BZ(self):
     model = apc.Model()
     model.data_from_df(apc.loss_BZ(), data_format='CL')
     model.fit('od_poisson_response', 'AC')
     model.forecast(method='n_poisson')
     fc = model.forecast(attach_to_self=False)
     self.assertEqual(fc['method'], 't_odp')
     model.forecast([0.9])
Exemple #3
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 def test_BZ(self):
     model = apc.Model()
     model.data_from_df(apc.loss_BZ(), data_format='CL')
     model.fit('od_poisson_response', 'APC')
     sub_models = [model.sub_model(per_from_to=(1977,1981)),
                   model.sub_model(per_from_to=(1982,1984)),
                   model.sub_model(per_from_to=(1985,1987))]
     f = apc.f_test(model, sub_models)
     self.assertEqual(round(f['F_stat'], 3), 1.855)
     self.assertEqual(round(f['p_value'], 3), 0.133)
Exemple #4
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 def test_BZ(self):
     r = apc.r_test(apc.loss_BZ(),
                    'gen_log_normal_response',
                    'APC',
                    R_stat='ls',
                    R_dist='wls_ql',
                    data_format='CL')
     self.assertEqual(round(r['R_stat'], 5), 113.92399)
     self.assertEqual(round(r['p_value'], 5), 0.01754)
     self.assertEqual(round(r['power_at_R'], 5), 0.86713)
Exemple #5
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    def test_BZ_gln(self):
        model = apc.Model()
        model.data_from_df(apc.loss_BZ(), data_format='CL')
        model.fit(family='gen_log_normal_response', predictor='APC')

        self.assertAlmostEqual(model.deviance, -287.459, 3)
        self.assertTrue(
            np.allclose(
                model.parameters.sum().values,
                np.array([11.83624119, 1.26201587, 312.66337107, 12.1751463])))
        self.assertAlmostEqual(model.fitted_values.sum(), 10214114.721, 3)
Exemple #6
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    def test_BZ(self):
        model = apc.Model()
        model.data_from_df(apc.loss_BZ(), data_format='CL')

        self.assertEqual(model.data_format, 'CL')
        self.assertEqual(model.I, 11)
        self.assertEqual(model.J, 11)
        self.assertEqual(model.K, 11)
        self.assertEqual(model.L, 0)
        self.assertEqual(model.n, 66)
        self.assertEqual(model.time_adjust, 0)
        self.assertAlmostEqual(model.data_vector.sum()['response'], 10221194.0,
                               3)
Exemple #7
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 def test_BZ(self):
     model = apc.Model()
     model.data_from_df(apc.loss_BZ(), data_format='CL')
     model.fit('od_poisson_response', 'APC')
     sub_models = [
         model.sub_model(per_from_to=(1977, 1981)),
         model.sub_model(per_from_to=(1982, 1984)),
         model.sub_model(per_from_to=(1985, 1987))
     ]
     bartlett = apc.bartlett_test(sub_models)
     self.assertEqual(round(bartlett['B'], 3), 1.835)
     self.assertEqual(round(bartlett['LR'], 3), 2.235)
     self.assertEqual(round(bartlett['C'], 3), 1.218)
     self.assertEqual(round(bartlett['m'], 3), 3)
     self.assertEqual(round(bartlett['p_value'], 3), 0.4)
Exemple #8
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    def test_BZ_ln(self):
        model = apc.Model()
        model.data_from_df(apc.loss_BZ())
        model.fit('log_normal_response', 'APC')
        model.identify()

        self.assertTrue(
            np.allclose(
                model.parameters_adhoc.sum().values,
                np.array([11.52208551, 1.92807476, 309.55066566, 16.5398655])))
        self.assertTrue(
            np.allclose(
                model.identify('sum_sum', attach_to_self=False).sum().values,
                np.array([-17.03573805, 5.27028714, 18.73830563,
                          19.19957677])))
Exemple #9
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 def test_BZ(self):
     model = apc.Model()
     model.data_from_df(apc.loss_BZ(), data_format='CL')
     model.fit('log_normal_response', 'AC')
     model.forecast()
     self.assertEqual(model.forecasts['method'], 't_gln')