Multirom #249
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Multirom #249
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mtezzele
reviewed
Apr 16, 2024
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| # ddd | ||
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| # if self.n_database == 1 and self.n_reduction == 1: | ||
| # self.train_full_database = self.database | ||
| # self.reduction.fit(self.database.snapshots_matrix.T) | ||
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| # elif self.n_database == 1 and self.n_reduction > 1: | ||
| # self.train_full_database = self.database | ||
| # for reduction in self.reduction: | ||
| # reduction.fit(self.database.snapshots_matrix.T) | ||
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| # elif self.n_database > 1 and self.n_reduction == 1: | ||
| # self.train_full_database = self.database | ||
| # self.reduction = [ | ||
| # (k, copy.deepcopy(self.reduction)) | ||
| # for k in self.train_full_database | ||
| # ] | ||
| # print(self.reduction) | ||
| # for reduction, database in zip(self.reduction, self.train_full_database): | ||
| # self.reduction[reduction].fit(self.train_full_database[database].snapshots_matrix.T) | ||
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| # elif self.n_database > 1 and self.n_reduction > 1: | ||
| # raise NotImplementedError | ||
| # else: | ||
| # raise RuntimeError |
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| # print(self.predict_reduced_database) | ||
| # print(self.predict_reduced_database._pairs) | ||
| # print(self.predict_reduced_database._pairs[0]) | ||
| # print(self.predict_reduced_database._pairs[0][1].values) | ||
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| print(self.predict_reduced_database.parameters_matrix) | ||
| print(self.approximation.predict( | ||
| self.predict_reduced_database.parameters_matrix)) |
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| # print(self.predict_reduced_database) | ||
| # print(self.predict_reduced_database._pairs) | ||
| # print(self.predict_reduced_database._pairs[0]) | ||
| # print(self.predict_reduced_database._pairs[0][1].values) |
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| # print(self.predict_reduced_database.snapshots_matrix) | ||
| # print(self.reduction.inverse_transform( | ||
| # self.predict_reduced_database.snapshots_matrix.T).T) |
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| # ddd | ||
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| # if self.n_database == 1 and self.n_reduction == 1: | ||
| # self.train_full_database = self.database | ||
| # self.reduction.fit(self.database.snapshots_matrix.T) | ||
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| # elif self.n_database == 1 and self.n_reduction > 1: | ||
| # self.train_full_database = self.database | ||
| # for reduction in self.reduction: | ||
| # reduction.fit(self.database.snapshots_matrix.T) | ||
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| # elif self.n_database > 1 and self.n_reduction == 1: | ||
| # self.train_full_database = self.database | ||
| # self.reduction = [ | ||
| # (k, copy.deepcopy(self.reduction)) | ||
| # for k in self.train_full_database | ||
| # ] | ||
| # print(self.reduction) | ||
| # for reduction, database in zip(self.reduction, self.train_full_database): | ||
| # self.reduction[reduction].fit(self.train_full_database[database].snapshots_matrix.T) | ||
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| # elif self.n_database > 1 and self.n_reduction > 1: | ||
| # raise NotImplementedError | ||
| # else: | ||
| # raise RuntimeError |
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| # print(self.database) | ||
| # print(self.reduction) | ||
| # print(self.approximation) | ||
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| # from itertools import product | ||
| # element_keys = product( | ||
| # self.database.keys(), | ||
| # self.reduction.keys(), | ||
| # self.approximation.keys() | ||
| # ) | ||
| # self.roms = { | ||
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| # tuple(key): { | ||
| # 'database': copy.deepcopy(self.database[key[0]]), | ||
| # 'reduction': copy.deepcopy(self.reduction[key[1]]), | ||
| # 'approximation': copy.deepcopy(self.approximation[key[2]]) | ||
| # } | ||
| # for key in element_keys | ||
| # } | ||
| # print(self.roms) | ||
| # self._full_database = copy.deepcopy(self.database) | ||
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| # # FULL-ORDER PREPROCESSING here | ||
| # for plugin in self.plugins: | ||
| # plugin.fom_preprocessing(self) | ||
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| # self.fit_reduction() | ||
| # # self.reduction.fit(self._full_database.snapshots_matrix.T) | ||
| # # print(self.reduction.singular_values) | ||
| # # print(self._full_database.snapshots_matrix) | ||
| # reduced_snapshots = self.reduction.transform( | ||
| # self._full_database.snapshots_matrix.T).T | ||
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| # self._reduced_database = Database( | ||
| # self._full_database.parameters_matrix, reduced_snapshots) | ||
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| # # REDUCED-ORDER PREPROCESSING here | ||
| # for plugin in self.plugins: | ||
| # plugin.rom_preprocessing(self) | ||
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| # self.approximation.fit( | ||
| # self._reduced_database.parameters_matrix, | ||
| # self._reduced_database.snapshots_matrix) |
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| # def test_constructor_error(self): | ||
| # with self.assertRaises(TypeError): | ||
| # Database(np.eye(5)) |
| ]) | ||
| rom.fit() | ||
| pred = rom.predict(db._pairs[0][0].values) | ||
| print(pred) |
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