MATLAB has an excellent profiler, which can tell you how much time your code spends in each one of its functions. I particularly liked the way you laid out the number of conceptual problems that need to be solved in a given line of code. Inputs A and B must have. Thus the singleton dimensions have been expanded. Latest activity Answered by Rik Rik view profile. 
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This can often save huge amounts of memory over repmat, and maflab significantly speed up your code. Instead use sub2ind as follows:. I've noticed that a lot of good answers to Matlab questions on SO frequently use the function bsxfun. In other words, if:. Opportunities for recent engineering grads.
By using bsxfunone can do the same job neatly and easily in just a single line: Then using bsxfun or the new operators to perform any binary operation will expand those singleton dimensions to match the size of that dimension of the other array, giving an output with size: Mathwork's licensing matlwb lets you do this royalty free. Suppose we have a large numeric matrix and we want to apply a function to arbitrary sized blocks of it.
Crushing MATLAB Loop Runtimes with BSXFUN
I just try to understand how bsxfun works. Whenever a dimension of A or B is singleton equal to onebsxfun virtually replicates the array along that dimension to match the other array. As we'll see, the technique I'm going to describe is more restrictive than a general for-loop and thus represents your computation more concisely, which opens the door to more optimization sbxfun your algorithm and less chances for bugs in the long run.
Here are some examples. After the expansion, a binary function is applied elementwise on the two arrays. Timing the two loops with the tic and toc commands, we see that preallocating in this case speeds up the code by about 30 times.
Thank you for your answer. Extended Capabilities Tall Arrays Calculate with arrays that have more rows than fit in memory.
Apply element-wise operation to two arrays with implicit expansion enabled - MATLAB bsxfun
Prompted by your comment I now tried this: In recent versions, bsxfun is multithreaded and can take advantage of multiple cpus. By using bsxfunone can do the same job neatly and easily in just a single line:. In other languages, this is commonly referred to as broadcasting and happens automatically in Python numpy and Octave.
Use the cellfun function to apply this function to every one of the 12 elements in groupsi. Subtract the column mean from the corresponding column elements of a matrix A. Improving the question-asking experience. I'm guessing there are some circumstances where using bsxfun will be much faster than the "manual" approach. In my case, I use bsxfun because it avoids me to think about the column or row issues.
Bsxfun, introduced in version a, is now perhaps the most useful and important function for vectorizing your code. Since memory bandwidth is a valuable asset and often the bottleneck on todays architectures, you want to use it wisely and decrease the memory requirements of your code to improve the performance. I repeated the analysis with multiplication and division and the general shape of the results are identical.
For me, bsxfun is about 3 times faster than repmat. Tom Minka has some tips, and references to other people's tips, here. You must first specify the operation you wish to perform as a function handle.
However, we get the elements indexed by every combination of R, and C, which is not what we want. Note that if X is a matrix, it operates, counts along rows.
First group the row and column indices separately.
Select a Web Site Choose a web site to get translated content where available and see local events and offers. It is now recommended that you replace most uses of bsxfun with direct mqtlab to the functions and operators that support implicit expansion.
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