I have looked it up and there are some posts, for example this one, that suggest to use torch.gesv but I can't seem to find it in the PyTorch documentation.
Pytorch provides the function torch.solve, which behaves like numpy.linalg.solve. It will output the solution of the the linear system and the LU factorization that has been used to compute it.
More information and example codes here.
Pytorch now has a linalg part very similar to NumPy. So you can do torch.linalg.solve and expect the same behavior as NumPy.
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I'm looking for function for linear interpolation in tensorflow similar to np.interp(..)
I'm aware that tensorflow is able to receive any numpy function and apply it on tensors but
np.interp is only activated on single object and as far as I checked couldn't be broadcasted.
so is there any efficient way to apply it using tensoflow ?
Thank you
I know this is a late answer, but google brought me here, so my answer might be useful for others.
You can use the interp_regular_1d_grid from tensorflow probability.
It works in a similar fashion as numpy.interp(), but consult the documentation for exact functionality.
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
Title says it all. Is there a convenient function in pytorch that can do something like np.trapz(y, x) (integrating over the the points in x and y via trapezoidal rule)?
Since pytorch version 1.2 a native trapz function has been introduced.
There is no built-in tool for that, but it should not be difficult to implement it yourself, especially using the numpy code as a guideline.
I'm learning python to make models these days. I read the documentation of scipy.optimize.fmin. It also recommends scipy.optimize.minimize. It seems that scipy.optimize.minimize is a more advanced method. Real wonder what's the difference between these two.
scipy.optimize.minimize is a high-level interface that lets you choose from a broad range of solvers, one of which is Nelder–Mead. scipy.optimize.fmin is a special solver using Nelder–Mead. For a specific subdocumentation of minimize with Nelder–Mead, see here.
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