pyodide/benchmark/benchmarks/l2norm.py

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# http://stackoverflow.com/questions/7741878/how-to-apply-numpy-linalg-norm-to-each-row-of-a-matrix/7741976#7741976
# setup: import numpy as np ; N = 1000; x = np.random.rand(N,N)
# run: l2norm(x)
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# pythran export l2norm(float64[][])
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import numpy as np
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def l2norm(x):
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return np.sqrt(np.einsum('ij,ij->i', x, x))