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Best fitness result NaN #6
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Hello, i think this is error somewhere. If you give me your input data, i'll try to reproduce this bug and try to fix it. |
I cannot replicate NaN error anymore. I noticed that scaling is highly important too. Here comes the tolerance issue. Below I do not display simplified polygons, as I do not know which property could give me the simplified polygon (Please advice which property I should search for). Below I compare different tolerances. By tolerance. I mean that I scale polygons before outputting to nesting application and when nesting is finished I scale back to original scale. Random seed number stays the same in all examples. Scaling 100 times results in some shapes not nested as they are probably average to integers and becomes bigger shapes than sheets. And then nested shapes overlap: It is quite obvious why it happens because polygons gets simplified or rounded to integers (correct me if i am wrong) But ordinary user do not have clue about the scale and which tolerance to choose. |
Thank you, for this report. I'll check it out. |
The code uses float in parts of the nest which can result in overflows so in my branch I flipped to double, just kicks the can down the road though. If you keep scaling up then of course the issue could reappear, as you've found. You could get some mileage by reducing ClipperScale but if you go too far you'll very quickly lose precision in the nest. |
Thank for the reply. Other question: do you know how to nest long linear elements, they usually nest very poorly? |
Only noticed after I replied that the question was 3 years old! Sorry for that, glad it helped. You're still using regularly? I had a similar problem; some long thin parts that nest fairly well with each other leaving gaps that could be filled by small parts if they were fit later, after the long thin ones have placed already. I've attempted two routes;
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Hi,
I would like to ask if this actually an error.
The software produces correct result. But when I have lots of elements to nest the sum of area becomes so large that instead of large number it displays NaN.
Does such result produces an error internally? Or yield incorrect genetic algorithm result?
Would it be possible to scale down internally?
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