Comment by brahbrah | Hacker News Reader

Comment by brahbrah | Hacker News Reader

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So in addition to what akasaka said (another thumbs up for line profiler from me, great tool) this isn’t a problem with linalg.norm being slow. It’s plenty fast, but calling it thousands of separate times in a Python loop will be slow. This is more just about learning how to vectorize properly. If you’re working in numpy land and you’re calling a numpy function in a loop that’s iterating over more than a handful of items, chances are you’re not vectorizing properly

I realized that I should say more specifically about numpy usage.

If you see a piece of code like this, it rings the bell that the person has no idea what he/she is doing:

Bad Pattern:

    my_list = np.array(xxx)
    summed = []
    for row in my_list:
        summed.append(np.sum(row))

Worst Pattern:

    my_list = np.array(xxx)
    
    def get_summed(arr):
        return np.sum(arr)

    summed = []
    for i in range(len(my_list)):
        summed.append(
            get_summed(my_list[i])
        )

Perfered:

    # np.array(xxx) is redundant
    summed = np.sum(xxx, axis=1)

Same applies to all numpy operations.

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