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Alex Rivera
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How can a list of vectors be elegantly normalized, in NumPy? Here is an example that does not work: from numpy import * vectors = array([arange(10), arange(10)]) # All x's, then all y's norms = apply_along_axis(linalg.norm, 0, vectors) # Now, what I was expecting would work: print vectors.T / norms # vectors.T has 10 elements, as does norms, but this does not work The last operation yields "shape mismatch: objects cannot be broadcast to a single shape". How can the normalization of the 2D vectors in vectors be elegantly done, with NumPy? Edit : Why does the above not work while adding a dimension to norms does work (as per my answer below)?
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