removed warning
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7562e7c85f
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@ -277,7 +277,7 @@ def l1_norm(prevalences: ArrayLike) -> np.ndarray:
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"""
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"""
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n_classes = prevalences.shape[-1]
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n_classes = prevalences.shape[-1]
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accum = prevalences.sum(axis=-1, keepdims=True)
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accum = prevalences.sum(axis=-1, keepdims=True)
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prevalences = np.true_divide(prevalences, accum, where=accum > 0)
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prevalences = np.true_divide(prevalences, accum, where=accum > 0, out=None)
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allzeros = accum.flatten() == 0
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allzeros = accum.flatten() == 0
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if any(allzeros):
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if any(allzeros):
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if prevalences.ndim == 1:
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if prevalences.ndim == 1:
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