| 133 | |
| 134 | |
| 135 | class LogZ_Normalizer(NormalizationMethod): |
| 136 | def __init__(self, mean=None, std=None, **kwargs): |
| 137 | super().__init__(**kwargs) |
| 138 | self.z_normalizer = Z_Normalizer(mean, std) |
| 139 | |
| 140 | def normalize(self, data, *args, **kwargs): |
| 141 | normalized_data = np.log(data + 1e-5) |
| 142 | normalized_data = self.z_normalizer.normalize(normalized_data) |
| 143 | return normalized_data |
| 144 | |
| 145 | def inverse_normalize(self, normalized_data): |
| 146 | data = self.z_normalizer.inverse_normalize(normalized_data) |
| 147 | data = np.exp(data) - 1e-5 |
| 148 | return data |
| 149 | |
| 150 | def stored_values(self): |
| 151 | return self.z_normalizer.stored_values() |
| 152 | |
| 153 | def change_input_type(self, new_type): |
| 154 | self.z_normalizer.change_input_type(new_type) |
| 155 | |
| 156 | def apply_torch_func(self, fn): |
| 157 | self.z_normalizer.apply_torch_func(fn) |
| 158 | |
| 159 | def __call__(self, data, *args, **kwargs): |
| 160 | normalized_data = np.log(data + 1e-5) |
| 161 | return self.z_normalizer(normalized_data) |
| 162 | |
| 163 | |
| 164 | class MinMax_LogNormalizer(NormalizationMethod): |