Does somebody know about a entropy minimization implementation in python?
I want to use this to correct inhomogeneity in histology images.
Reference about the algorithm I am talking about.
I suspect that you'll have to hack something together yourself, maybe using PIL and pyentropy.
Related
I read OpenCV.fisheye official manual, and I'm confused about the use of calibrate() and undistortImage() functions.
I know that both are used to fix some distortion in an image but, it looks too similar to me.
The docs I referred to are:
https://docs.opencv.org/trunk/db/d58/group__calib3d__fisheye.html
https://hackaday.io/project/12384-autofan-automated-control-of-air-flow/log/41862-correcting-for-lens-distortions
If you know, explain the difference between them, please share it... :)
calibrate() returns you your camera's intrinsic parameters / distortion coefficients which then can be passed into undistortImage() to correct for the lens warping.
is there a filter function of kalman in Python that works in the same way as the Kalman function of matlab?
[kest] = kalman(sys,Qn,Rn)
The idea is that the function receives as parameters a space of states and the respective weight matrices (it is to implement an LQR controller)
You can use the pyKalman library. See the sin example followed by the filter example.
It is not exactly like Matlab but it is easy enough to use.
I finally found the ovtave source code for the kalman filter and implemented it in python. Anyway, thank you very much
I wish to integrate a n-variate (bi-variate for simplicity) function using the scipy.integrate.nquad method. However, I would also like to use a fixed-order quadrature rule, as in scipy.integrate.fixed_quad, however I have not been able to find a way to do this.
Is this a known problem, and are there any clever ways of doing this?
Thanks in advance!
Are there functions in python that will fill out missing values in a matrix for you, by using collaborative filtering (ex. alternating minimization algorithm, etc). Or does one need to implement such functions from scratch?
[EDIT]: Although this isn't a matrix-completion example, but just to illustrate a similar situation, I know there is an svd() function in Matlab that takes a matrix as input and automatically outputs the singular value decomposition (svd) of it. I'm looking for something like that in Python, hopefully a built-in function, but even a good library out there would be great.
Check out numpy's linalg library to find a python SVD implementation
There is a library fancyimpute. Also, sklearn NMF
Does anyone know which optimization algorithm specifically is implemented in scipy.optimize.leastsq?
According to the documentation:
“leastsq” is a wrapper around MINPACK’s lmdif and lmder algorithms.
So (as #tillsten points out) that’s a Levenberg–Marquardt implementation.