94 feat generalized transform norm and distance fct #95
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musslick
requested changes
Sep 9, 2024
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| def norms(arr: np.ndarray) -> np.ndarray: | ||
| """ | ||
| Calculate the norms along the first axis |
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Would be good to explain here in the doc string: The norm of what exactly? E.g., "norm of an nd array). Also, is it norm (singular) or norms (plural)?
| return np.array([np.linalg.norm(np.ravel(row)) for row in arr]) | ||
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| def distances(arr_1: np.ndarray, arr_2: np.ndarray) -> np.ndarray: |
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Perhaps consider renaming this function to reflect this is a euclidean distance? E.g., numpy does it the following way:
dst = distance.euclidean(a, b)
| import numpy as np | ||
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| def norms(arr: np.ndarray) -> np.ndarray: |
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Perhaps consider renaming this to "norm" for singlular, also to mirror the naming convention in numpy (numpy.linalg.norm)
| return np.array(_lst) | ||
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| def to_array_flatten(arr: Union[pd.DataFrame, pd.Series, np.ndarray]) -> np.ndarray: |
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Here, we could either consider having a separate function flatten which can be combined with the to_array function. Alternatively, we could call it to_flat_array?
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Description
Add generalized transform and metrics for multidementional entries in cells of pd.Dataframes.
Remarks (Optional)
Allthough this makes life a bit harder for collaboraters (they are discouraged to use np metrics directly since theses don't work on nested arrays), I can not think of a better way to do this if we want to keep our package modular and support other formats then single number cell entries