Comments (2)
Numerical differentiation can be implemented as linear convolution. In Matlab for instance, diff(X) is basically the same as conv(X,[1,-1]). If you know the sample interval dt, it would be diff(X)/dt and conv(X,[1,-1]/dt)
diffnc (as originally implemented by Stefan Schaal in Matlab, see the comments) works the same, but also smooths the signal by taking both neighbouring samples into account, i.e. conv(X,[1,0,-1]/2*dt)
diffnc is essentially a "minimal" version of this
https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter
with m=3 and k=1.
In Python it would perhaps be more sensible to use numpy.gradient. But I copied diffnc from previous Matlab code I was using to ensure the result would be the same.
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I got it. Thank you
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Related Issues (20)
- Implement robot_demo_traj HOT 3
- Refactor Parameterizable in C++ HOT 2
- Making C++/Python implementation of Dmp interchangeable HOT 1
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- Implement DmpWithGainSchedules in v2 HOT 2
- Implement DmpContextual in v2 HOT 2
- Replace travis
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- Provide demo descriptions
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- Generalization of dmp learning for multiple trajectory HOT 3
- About Code Running HOT 1
- May I ask a theoretical question HOT 3
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- a question about your paper HOT 4
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