| 91 | |
| 92 | |
| 93 | class MinMax_Normalizer(NormalizationMethod): |
| 94 | def __init__(self, min=None, max_minus_min=None, max=None, **kwargs): |
| 95 | super().__init__(**kwargs) |
| 96 | self.min = min |
| 97 | if min: |
| 98 | assert max_minus_min or max |
| 99 | self.max_minus_min = max_minus_min or max - min |
| 100 | |
| 101 | def normalize(self, data, axis=None, *args, **kwargs): |
| 102 | # self.min = np.min(data, axis=axis) |
| 103 | # self.max_minus_min = (np.max(data, axis=axis) - self.min) |
| 104 | return self(data) |
| 105 | |
| 106 | def inverse_normalize(self, normalized_data): |
| 107 | shapes = normalized_data.shape |
| 108 | if len(shapes) >= 2: |
| 109 | normalized_data = normalized_data.reshape(normalized_data.shape[0], -1) |
| 110 | data = normalized_data * self.max_minus_min + self.min |
| 111 | if len(shapes) >= 2: |
| 112 | data = data.reshape(shapes) |
| 113 | return data |
| 114 | |
| 115 | def stored_values(self): |
| 116 | return {'min': self.min, 'max_minus_min': self.max_minus_min} |
| 117 | |
| 118 | def __call__(self, data): |
| 119 | return (data - self.min) / self.max_minus_min |
| 120 | |
| 121 | |
| 122 | class LogNormalizer(NormalizationMethod): |
no outgoing calls
no test coverage detected