multidimensional-array

Why is numpy’s einsum faster than numpy’s built in functions?

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Lets start with three arrays of dtype=np.double. Timings are performed on a intel CPU using numpy 1.7.1 compiled with icc and linked to intel’s mkl. A AMD cpu with numpy 1.6.1 compiled with gcc without mkl was also used to verify the timings. Please note the timings scale nearly linearly with system size and are not due to the small overhead incurred in the numpy functions if statements these difference will show up in microseconds not milliseconds:

Vectorized NumPy linspace for multiple start and stop values

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I need to create a 2D array where each row may start and end with a different number. Assume that first and last element of each row is given and all other elements are just interpolated according to length of the rows In a simple case let’s say I want to create a 3X3 array with same start at 0 but different end given by W below: