Feb 13, 2021 · Isn't the answer to How to use np.vectorize? usually "Don't. It just pretends to be a vectorized function but is just a loop with a different name"?

Oct 4, 2009 · Is it a good idea to vectorize the code? What are good practices in terms of when to do it? What happens underneath?

Jan 3, 2009 · The v4 series of the gcc compiler can automatically vectorize loops using the SIMD processor on some modern CPUs, such as the AMD Athlon or Intel Pentium/Core chips. How …

Jul 19, 2024 · I am trying to Import and vectorize data from Azure Blob Storage to Azure AI Search. I have two embedding models deployed in Azure AI Studio - text-embedding-3-small …

The purpose of np.vectorize is to transform functions which are not numpy-aware (e.g. take floats as input and return floats as output) into functions that can operate on (and return) numpy arrays.

Sep 14, 2009 · Many CPUs have "vector" or "SIMD" instruction sets which apply the same operation simultaneously to two, four, or more pieces of data. Modern x86 chips have the SSE …

Sep 13, 2017 · As the title, I'd like to know how to define a vectorized function in R. Is it just by using a loop in the function? Is this method efficient? And what's the best practice ?

Oct 3, 2013 · Bear in mind that np.vectorize doesn't really give any performance benefit over a plain list comprehension - you'll still end up looping in Python rather than C

Feb 5, 2016 · What is the most efficient way to map a function over a numpy array? I am currently doing: import numpy as np x = np.array ( [1, 2, 3, 4, 5]) # Obtain array of square ...

Jul 9, 2014 · Let's say we have the following function: def f(x, y): if y == 0: return 0 return x/y This works fine with scalar values. Unfortunately when I try to use numpy arrays for x and y the