SMO classifier MacOS Intel vs. ARM architecture

Hello all,

I had my students run an experiment using the SMO classifier on an XOR dataset and we are getting strange results.
I and 2 of my students have a Windows computer, 2 students have a Mac with Intel processor, and 2 students have a Mac with ARM processors.
The windows on Windows and Mac Intel are identical. The results on Mac ARM are different. We ran an experiment with the following classifiers:
C = 10, Polynomial kernel, exponent = 6
C = 100, Polynomial kernel, exponent  = 4
C = 0.1, Polynomial kernel, exponent  = 10
C = 500, RBF kernel, gamma = 0.01
C = 50, RBF kernel, gamma = 10
Then we ran a corrected t-test on the percent_correct measure. 
On Windows and Mac Intel, we get 81.65, 89.75, 64.00, 62.25, 95.85 for the 5 classifiers.
On Mac ARM, we get 81.60, 89.75, 64.05, 63.25, 95.80 for the 5 classifiers.
I understand that the difference might be due to the different processors. 

However, the visualization of the boundaries for the worst classifier is very different. I asked the students to use the boundary visualizer for the worst classifier.
I don't know how to add pictures to this post, so I am sharing 2 screenshots on my google drive.
Mac Intel screenshot: https://drive.google.com/file/d/1_cKuLj3oD4eD_k7fF2ZyxDkBRk66LSqE/view?usp=sharing
Mac ARM screenshot: https://drive.google.com/file/d/1zKxPoUMAVGmVSaxMEqVuwY6Jv_0-k7wT/view?usp=sharing
As you can see, the Mac ARM screenshot is completely wrong. It shows a classification that is much better than 63.25%.

The students experimented a little bit and compared results and different computers and it seems that it has to do with the low value (around 0.01) of gamma for the RBF filter. Can somebody look into the code and see what is going on.
Thanks in advance!!

Marie-Pierre
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