hugohadfield

6 ms·

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Show HN: Minimal image regression template for PyTorch(github.com)
2 points by hugohadfield 2y ago|0 comments
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by hugohadfield 2y ago
Yeah regularly sampled is the goal almost always, and great when its available! The main times I deal with non-uniformly sampled data is with jitter and missing data etc
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by hugohadfield 2y ago
hmm, I don't think I'm familiar with the kind of problems you might be thinking about. Care to share an example?
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by hugohadfield 2y ago
no problem!
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by hugohadfield 2y ago
I guess I'm not totally sure what the canonical way would be, probably convolution with the N'th derivative of a guassian smoothing kernal where the smoothing response is chosen by frequency analysis, or something along those line
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by hugohadfield 2y ago
Noise is added here: ``` # Generate noisy sinusoidal data with random time points np.random.seed(0) t = sorted(np.random.uniform(0.0, 10.0, 100)) noise_std = 0.01 y = np.sin(t) + noise_std * np.random.randn(len(t)) true_first_derivative = n
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by hugohadfield 2y ago
Thanks! So the example image is actually with both non-uniformly sampled measurements and noise :) works great for both/either
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by hugohadfield 2y ago
Thanks so much! Yeah this was also a key reason I like this approach. Quite often we end up with repeated values due to quantisation of signal or timing differences or whatever and we get exactly that problem you describe, either massive gr
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by hugohadfield 2y ago
You could almost certainly construct a convolutional kernal that computes smoothed derivatives of your function by the derivative of a gaussian smoothing kernal (that kind of technique is mostly used for images if I remember correctly ), in
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by hugohadfield 2y ago
Sounds like a fun project! I've not spent much time on ensemble KF but my mate Sam ( https://github.com/samDuffield/ ) did a lot of work in his PhD on them for high dimensional datasets. Is your dataset specifically
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by hugohadfield 2y ago
Glad you like it! This library will not generate a set of convolutional filter coefficients for you if that is what you are after, I'm sure it would be possible to do some fairly nasty maths to get out some kind of equivalent convoluti
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by hugohadfield 2y ago
No problem! Let's dream up a little use case: Imagine you have a speed sensor eg. on your car and you would like to calculate the jerk (2nd derivative of speed) of your motion (useful in a range of driving comfort metrics etc.). The sp
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by hugohadfield 2y ago
this is a perfect use case, let me know how it goes!
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Show HN: Automated smooth Nth order derivatives of noisy data(github.com)
146 points by hugohadfield 2y ago|45 comments
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Wide angle lens distortion correction from lines(hh409.user.srcf.net)
158 points by hugohadfield 2y ago|58 comments
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by hugohadfield 6y ago
High dimensional GAs are a relatively hot topic of research but something we haven't aimed to target with this library (mostly because its not our main research area). My personal preference for working with high d algebras would be St
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by hugohadfield 6y ago
Hi everyone, I'm Hugo one of the maintainers of this package, glad to see lots of interest! If anyone is interested in some of the applications of GA in robotics etc then check out the talk Eric and I gave at GAME2020 back in February
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by hugohadfield 7y ago
Oof my paragraph breaks really didn't come through there..
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by hugohadfield 7y ago
I agree with a lot of what you have said here about GAs ease of use for newcomers, people change notation continuously across papers and there is a lack of material to go from nice basic theory and examples to practical applications. I star