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Merge pull request #86 from collinleiber/ddc
add DDC + video datasets
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| from sklearn.base import TransformerMixin, BaseEstimator | ||
| import numpy as np | ||
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| class ZNormalizer(TransformerMixin, BaseEstimator): | ||
| """ | ||
| Normalize a data set by calculating (data - mean) / std. | ||
| In general, two strategies are sensible to normalize a data set. | ||
| Either use all features simultaneously for the normalization or normalize each feature separately. | ||
| In the case of image data, a feature-wise transformation usually corresponds to a channel-wise transformation. | ||
| If this normalizer should be applied to RGB image data, the color channels should be in the first dimension, known as CHW representation. | ||
| Parameters | ||
| ---------- | ||
| feature_or_channel_wise : bool | ||
| Specifies if all data should be used for the normalization or if a feature-/channel-wise normalization should be applied (default: False) | ||
| Attributes | ||
| ---------- | ||
| shape : list | ||
| Shape of the data set with which this normalizer has been fitted | ||
| mean : np.ndarray or int | ||
| Mean value(s) of the data set | ||
| std : np.ndarray or int | ||
| Standard deviation value(s) of the data set | ||
| """ | ||
|
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| def __init__(self, feature_or_channel_wise: bool = False): | ||
| self.feature_or_channel_wise = feature_or_channel_wise | ||
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| def fit(self, X: np.ndarray, y: np.ndarray = None) -> 'ZNormalizer': | ||
| """ | ||
| Compute the mean and std values regarding the input data set. | ||
| Parameters | ||
| ---------- | ||
| X : np.ndarray | ||
| the given data set | ||
| y : np.ndarray | ||
| the labels (can be ignored) | ||
| Returns | ||
| ------- | ||
| self : ZNormalizer | ||
| this instance of the ZNormalizer | ||
| """ | ||
| self.shape = list(X.shape) | ||
| self.shape[0] = -1 | ||
| if not self.feature_or_channel_wise or (X.ndim > 2 and 3 not in self.shape): | ||
| # In case of not feature_or_channel_wise or grayscale images (2d or 3d) | ||
| self.std = np.std(X) | ||
| self.mean = np.mean(X) | ||
| elif self.feature_or_channel_wise and (X.ndim == 2 or (X.ndim in [4, 5] and X.shape[1] == 3)): | ||
| # In case of tabular data or RGB 2D or 3D images | ||
| self.std = np.array([np.std(X[:, j]) for j in range(self.shape[1])]) | ||
| self.mean = np.array([np.mean(X[:, j]) for j in range(self.shape[1])]) | ||
| else: | ||
| raise Exception( | ||
| "Your combination of feature_or_channel_wise={0} and X.ndim={1} is not working for the transformation".format( | ||
| self.feature_or_channel_wise, X.ndim)) | ||
| return self | ||
|
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||
| def transform(self, X: np.ndarray) -> np.ndarray: | ||
| """ | ||
| Transform the given data set using the fitted mean and std values. | ||
| Parameters | ||
| ---------- | ||
| X : np.ndarray | ||
| the given data set | ||
| Returns | ||
| ------- | ||
| X_out : np.ndarray | ||
| The transformed data set | ||
| """ | ||
| assert list(X.shape)[1:] == self.shape[ | ||
| 1:], "The shape of the input data does not match the fitted transformation. Shape must be {0}".format( | ||
| self.shape) | ||
| X_out = X.astype(float) | ||
| if not self.feature_or_channel_wise or (X.ndim > 2 and 3 not in self.shape): | ||
| # In case of not feature_or_channel_wise or grayscale images if feature_or_channel_wise | ||
| X_out = (X_out - self.mean) / self.std | ||
| elif self.feature_or_channel_wise and X.ndim in [2, 4, 5]: | ||
| # In case of tabular data or RGB 2D or 3D images | ||
| for j in range(self.shape[1]): | ||
| X_out[:, j] = (X_out[:, j] - self.mean[j]) / self.std[j] | ||
| else: | ||
| raise Exception( | ||
| "Your combination of feature_or_channel_wise={0} and X.ndim={1} is not working for the transformation".format( | ||
| self.feature_or_channel_wise, | ||
| X.ndim)) | ||
| return X_out | ||
|
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| def inverse_transform(self, X: np.ndarray) -> np.ndarray: | ||
| """ | ||
| Invert the transformation by applying (data * std) + mean. | ||
| Parameters | ||
| ---------- | ||
| X : np.ndarray | ||
| the given data set | ||
| Returns | ||
| ------- | ||
| X_out : np.ndarray | ||
| The transformed data set | ||
| """ | ||
| assert list(X.shape)[1:] == self.shape[ | ||
| 1:], "The shape of the input data does not match the fitted transformation. Shape must be {0}".format( | ||
| self.shape) | ||
| X_out = X.astype(float) | ||
| if not self.feature_or_channel_wise or (X.ndim > 2 and 3 not in self.shape): | ||
| # In case of not feature_or_channel_wise or grayscale images if feature_or_channel_wise | ||
| X_out = X_out * self.std + self.mean | ||
| elif self.feature_or_channel_wise and X.ndim in [2, 4, 5]: | ||
| # In case of tabular data or RGB 2D or 3D images | ||
| for j in range(self.shape[1]): | ||
| X_out[:, j] = X_out[:, j] * self.std[j] + self.mean[j] | ||
| else: | ||
| raise Exception( | ||
| "Your combination of feature_or_channel_wise={0} and X.ndim={1} is not working for the transformation".format( | ||
| self.feature_or_channel_wise, | ||
| X.ndim)) | ||
| return X_out | ||
|
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| def z_normalization(X: np.ndarray, feature_or_channel_wise: bool = False) -> np.ndarray: | ||
| """ | ||
| Wrapper for the ZNormalizer. | ||
| It automatically executes: X_transform = ZNormalizer(feature_or_channel_wise).fit_transform(X) | ||
| Parameters | ||
| ---------- | ||
| X : np.ndarray | ||
| the given data set | ||
| feature_or_channel_wise : bool | ||
| Specifies if all data should be used for the normalization or if a feature-/channel-wise normalization should be applied (default: False) | ||
| Returns | ||
| ------- | ||
| X_transform : np.ndarray | ||
| The transformed data set | ||
| """ | ||
| znorm = ZNormalizer(feature_or_channel_wise) | ||
| X_transform = znorm.fit_transform(X) | ||
| return X_transform |
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