I'm wondering what tensor flow uses to perform row reduction. Specifically when I call tf.linalg.inv what algorithm runs? Tensorflow is open source so I figured that it would be easy enough to find but I find myself a little lost in the code base. If I could just get a pointer to the implementation of the aforementioned function that would be great. If there is a name for the Gauss Jordan elimination implementation they used that would be even better.
https://github.com/tensorflow/tensorflow
The op uses LU decomposition with partial pivoting to compute the inverses.
For more insighton tf.linalg.inv algorithm please refer to this link: https://www.tensorflow.org/api_docs/python/tf/linalg/inv
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If you wish to experiment with something similar please refer to this stackoverflow link here
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I am newbie to Opencv. I am trying stereosgbm compute function in gpu. I did not find any cuda porting as of now from opencv.
I want to know the difference between semi-global matching and semi-global block matching. But i did not find any difference.
Any help is appreciated.
Thanks.
StereoSGBM is OpenCV's implementation of Hirschmüller's original SGM algorithm. The implementation is a bit different from the original algorithm design.
The original algorithm uses pixel-wise aggregation cost, while StereoSGBM allows matching blocks. If the block size is set to 1, it's the same as working on pixels.
Mutual information cost function is not implemented in StereoSGBM.
OpenCV's SGBM focuses on speed. Therefore, by default, StereoSGBM is single-pass (i.e. it calculates the matching cost in fewer directions). You can set mode=StereoSGBM::MODE_HH to calculate the cost in all 8 directions.
StereoSGBM also implements sub-pixel estimation proposed by Birchfield
et al.
I suggest reading OpenCV's documentation about StereoSGBM via this link https://docs.opencv.org/3.4.1/d2/d85/classcv_1_1StereoSGBM.html This document describes the main differences between OpenCV's implementation (SGBM) and original SGM.
If you are interested, E. Dall’Asta, R. Roncella's paper, A COMPARISON OF SEMI-GLOBAL AND LOCAL DENSE MATCHING ALGORITHMS FOR SURFACE RECONSTRUCTION, discussed the differences between OpenCV's SGBM implementation and Hirschmüller's SGM.
Hope this answer helps you.
I am trying to find the equation I would need to use in order to implement a Least Squares Kernel classifier for a dataset with N samples of feature length d. I have the kernel equation k(x_i, x_j) and I need the equation to pug it into to get the length-d vector used to classify future data. No matter where I look/google, Although there are dozens of powerpoints and pdfs that seem to give me almost what I'm looking for, I can't find a resource which can give me a straight answer.
note: I am not looking for the programming-language tool that computes this for me such as lsqlin, but the mathematical formula.
Least Squares Kernel SVM (what I assume your actually asking about) is equivalent to Kernelized Ridge Regression. This is the simplest what to implement it, and the solution can be found here, assume you have the appropriate background.
I would like to get some helpful instructions about how to use the Q-learning algorithm with function approximation. For the basic Q-learning algorithm I have found examples and I think I did understand it. In case of using function approximation I get into trouble. Can somebody give me an explanation through a short example how it works?
What I know:
Istead of using matrix for Q-values we use features and parameters.
Make approximation with the linear combination of feauters and parameters.
Update the parameters.
I have checked this paper: Q-learning with function approximation
But I cant find any useful tutorial how to use it.
Thanks for help!
To my view, this is one of the best references to start with. It is well written with several pseudo-code examples. In your case, you can simplify the algorithms by ignoring eligibility traces.
Also, to my experience and depending on your use case, Q-Learning might not work very well (sometimes it needs huge amounts of experience data). You can try Fitted-Q value for example, which is a batch algorithm.
Hi I need to perform a Singular Value Decomposition on large dense square matrices using Map Reduce.
I have already checked the Mahout project but what they provide is a TSQR algorithm
http://arbenson.github.io/portfolio/Math221/AustinBenson-math221-report.pdf .
The problem is that I want the full rank and this method does not work in such case.
The Distributed Lanczos SVD implementation they were using before it does not suit my case as well.
I found that the TWO-SIDED JACOBI SCHEME could be used for such purpose but I did not manage to find any available implementation.
Does anybody know if and where I can find a reference code?
If it may help - look to spark lib (mlib). It had implementation. You can use it, or looking at it you can make your own.
https://spark.apache.org/docs/latest/mllib-dimensionality-reduction.html
I am trying to write a function that produces a single solution to an underrepresented system of equations (e.g. the matrix that describes the system is wider than it is tall). In order to do this, I have been looking in the LAPACK documentation for a way to row-reduce a matrix to it's reduced-echelon form, similar to the function rref() in both Mathematica and TI calculators. The closest I came across was http://software.intel.com/en-us/forums/intel-math-kernel-library/topic/53107/ this tiny thread. This thread, however, seems to imply that simply taking the "U" upper triangular matrix (and dividing each row by the diagonal) is the same as the reduced echelon form of a matrix, which I do not believe to be the case. I could code up rref() myself, but I do not believe I could achieve the performance LAPACK is famous for.
1) Is there a better way to simply get any one specific solution to an underrepresented system?
2) If not, is there a way for LAPACK to row-reduce a matrix?
Thanks!
One often used method for this is the least square solution, see lapack's sgelsx