MATLAB VECTORIZE UPDATE
MATLAB VECTORIZE ARCHIVE
MATLAB VECTORIZE CODE

% Plotting using the awesome GRAMM-toolbox TAll = įprintf('finished dim1=%i,dim2=%i - took me %.2fs ',dim1,dim2,toc(tStart)) I did notice that allocating new matrices before doing the arithmetic expansion (vectorization) results in the same behaviour as bsxfun (but more lines of code) A = data(ix,:) Prompted by Anne Urai, I redid the analysis with multiplication & devision. I am able to vectorize in two dimensions using the transpose function, but for the third.

MATLAB VECTORIZE UPDATE

bsxfun is fastest if one needs to subset a medium sized array (n x m >100 x 1000), but see update below! Learn more about vectorization, multidimensional arrays MATLAB.

vectorization is fastest for small first dimension, then equally fast as bsxfun.

I subtract one row from either a subset (first 50 rows, dashed line) or all rows of an matrix with n= and m =. Therefore, I decided to systematically compare those. programming python Vectorization in Python np.vectorize example Vectorize. The same is true for different array sizes. Pyplot matplotlib.pyplot python plt.subplots figsize Subplot trong Matlab. While demonstrating this to my student, I noticed that subsetting an array has interesting effects on the performance differences. bsxfun: somewhat difficult to understand, I use it regularily, fast (often).arithmetic expansion: medium difficulty, should be general used, fast.

good old for-loop: Easy to understand, can be found everywhere, slow.

Compare bsxfun against vectorized arithmetic expansion in matlab against bsxfun The contenders

MATLAB VECTORIZE CODE

Show that for-loops are still quite slow in matlab. the very important concept of vectorization of code this is where a solid knowledge of linear algebra will be important. With the second link claiming for-loops in matlab are performing much better than before. We also recently had some discussion on vectorization in our research group.

MATLAB VECTORIZE ARCHIVE

To archive those and as an extended memory, I share them here. I also wrote the for-loop even more flexibly (read: as slow as I could think to) by using a list of dicts: years = 10 prem = 50 wit = 5 result = row.eom = row.From time to time I explain my students certain concepts. Here’s the non-vectorized version in Python: import numpy as np years = 10 bom = np.zeros(years*12) eom = np.zeros(years*12) for month in range(1, years*12): prem = 50 bom = eom + prem wit = 5 eom = bom - witĪnd here’s the vectorized version: import numpy as np years = 10 prem = 50 wit = 5 eom = np.arange(years*12)*prem - np.arange(years*12)*wit # and if you still want bom as an array: bom = eom + np.arange(years*12)*wit Now a lot of the function is not appear easily vectorizable. Basically a lot of inter-dependence emerges. Then you might have situations where IF WIT > 0, PREM = 0. In pseudocode, this is the ‘actuarial’ coding dilemma I ran into months back:ĮOM = 0 for months in years: PREM = 50 BOM = EOM + PREM WIT = 5 EOM = BOM – WITĪ simple example, but I think shows the BOM/EOM interdependence (there are a few other variables with a similar relationship.) You can’t vectorize BOM without knowing EOM, and you can’t vectorize EOM until you know BOM. I’m going to argue that this is a bad idea! Here’s a paraphrased email from a colleague: Still, a lot of numerical code is written in pure Python (no Cython, no Numba), for the flexibility. Seconds means we can iterate, try more options, and move faster. A lot of the work I do at MassMutual is fundamentally numerical computation, and the difference of a pipeline that takes hours, even minutes from one that takes seconds is a big deal. I’m kidding, because I haven’t done it, but I sure as heck could write it in MATLAB (at one point) or in Numpy or Julia now (I’ll stick to just magrittr and dplyr in R). Well, vectorizing the inner loop was about 4x faster, so now the footnote is a just a carbon footprint.įast numerical code is what makes machine learning even possible these days, though I’m not sure how many of the kids these days can write a QR decomposition in C. My favorite example of a vectorization is when a colleague shared his Lorenz 96 code with me, after writing a really cool paper about it that footnoted the massive amount of computation involved.

During my PhD at UVM, Professor Lakoba’s Numerical Analysis class was one of the most challenging courses I took and the deep knowledge of numerical code still sticks with me. I come from the world of MATLAB and numerical computing, where for loops are shorn and vectors are king.