Does predict function work in parallel when predicting k-nearest neighbour?

Hi ,

I have a k-nearest neighbour classifier which I have trained with fitcknn. I am wondering, when predicting labels on the model using predicit does it work in parallel?

I have tested using predict in a for loop and parfor loop. The simple for loop performs a bit faster which makes me think there is some optimisation and built in parallelisation that the predict function is taking advantage of. However, the documentation makes no reference to this, and I thought MATLAB always runs in a single thread unless specifically using a parallel pool? In both cases, I am supplying the predict function with a vector of 1000 rows of test data at a time. Although my test dataset is a million rows, I am doing this so I can get see progress while the program is making predictions.

So basically:

  1. Does the predict function use parallisation on k-nn models?

NOTE:- provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

Perhaps you are mistaken. Most high level tools in MATLAB do not directly, intentionally use parallel processing, splitting the problem up. It is the lower level computations that do so, where you see the gains. And you can check when that is happening. It is quite easy to create a situation where MATLAB will use all the CPU power you have available. For example, just form a matrix multiply between two very large matrices. MATLAB passes this operation to the BLAS, lower level routines that can intelligently use multiple cores to do the work more efficiently. So if you watch a CPU monitor when that happens, suddenly your computer will get very busy. But it was not really MATLAB that did the parallel split, it was done more deeply under the hood.

Another case where I frequently see this happen is in a large computation where I might want to do a powermod operation, where I choose to compute mod(b^n,p) for huge values of n, and for many millions of numbers in the vector p. (Did you know there are roughly 51 million primes less than 1e9? I do know that.) My system fan kicks on immediately when I do these computations, with all CPUs running flat out. But again, it is not a high level MATLAB code that will decide when to parallelize code, but low level routines that see a process that can be split efficiently when that makes sense.




Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Technical Source

Simple! That is me, a simple person. I am passionate about knowledge and reading. That’s why I have decided to write and share a bit of my life and thoughts to.