I've noticed that spdiag is deprecated, but not speye. Why is this? Both diag and eye generate diagonal matrices, so shouldn't speye be unnecessary as well (or at least exactly as unnecessary as spdiag)?
You must be using an old version of Octave if that function is deprecated. It has already been removed in the last 5 releases (all 5 minor versions since 3.6.0).
Anyway, to address your question spdiag() does not return a sparse matrix, it does exactly the same as diag(). To get a sparse diagonal matrix use spdiags().
Related
When coding PyTorch in torch.nn.utils I see two functions, clip_grad_norm and clip_grad_norm_.
I want to know the difference so I went to check the documentation but when I searched I only found the clip_grad_norm_ and not clip_grad_norm.
So I'm here to ask if anyone knows the difference.
Pytorch uses the trailing underscore convention for in-place operations. So the difference is that the one with an underscore modifies the tensor in place and the other one leaves the original tensor unmodified and returns a new tensor.
In my CUDA code I am using cusparse<t>gtsv() function (more precisely, cusparseZgtsv and cusparseZgtsvStridedBatch ones).
In the documentaion it is said, that this function solves the equation A*x=alpha *B. My question is - what is alpha? I didn't find it as an input parameter. I have no idea how to specify it. Is it always equals to 1?
I performed some testing (solved some random systems of equations where tridiagonal matrices were always diagonally dominant and checked my solution using direct matrix by vector multiplication).
It looks like in the current version alpha = 1 always, so one can just ignore it. I suspect that it will be added as an input parameter in future releases.
I am all the time trying to determine how to reverse engineer this checksum. It should be a simple one, it is just a checksum for a firmware version of a device. Here are 5 hex-strings:
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
01854000ff1301050600323436393031343430344d45373435304430373132313239ffffffffffffffffffffffffffffffffffffffffffffffffffffff30303132343639303031333133303031485534355f4543455f4456445f535f4e00443520302e3120ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffeb57
It looks like the last 2 bytes (4 hex-letters) are the checksum. I marked the differences in black.
Is anyone able to find out the algorithm, how the checksum is created? I tried already many things to find it out, but either I did it wrong or it didn´t work.
My guess:
checksum(data) = CRC16-CCITT(data) XOR 0x6155
(which may be equivalent to another standard CRC16, I don't know)
See here for an online demo
Well it can be just about anything.. there are multiple implementation of crc, check for example these, I would apply those crcs on the data and compare their outputs to what you have ..
I am using an old version of Stata, Stata 9, and I am trying to use the clock() function to convert some dates.
gen double Sgytime = clock (surgerystartdatetime, "dmyhm").
Stata says clock not found. Help please.
The function clock() was introduced in Stata 10. That is documented for all to see at http://www.stata.com/help.cgi?whatsnew9to10
Your options are to find a version of Stata that is 10 or higher, to write your own commands (not functions) to handle date-time data, or to use the user-written command ntimeofday published in the Stata Journal. search ntimeofday will indicate a download source. Note that the latter command does not work as clock() does, and in general Stata 9 just does not recognise date-time variables as such.
A larger question is that you are evidently getting ideas from material written for later versions of Stata, but the help and manuals for Stata 9 are the only completely reliable guide to what functions are available to you.
I think you may have an extra space between clock and the first parenthesis:
. display %tc clock("5-12-1998 11:15", "MDY hm")
12may1998 11:15:00
. display %tc clock ("5-12-1998 11:15", "MDY hm")
clock not found
I am assuming that in the search for lost time you have already verified that Stata (and not STATA) 9 has the clock function by looking at the documentation.
Does any one know what the cache map hash function that generates a hash from a URL that mozilla firefox uses? I have allready found this http://benlast.livejournal.com/29164.html where it gives a pretty good example code, unfortunately I think it is outdated and not used anymore in the current version of Firefox.
Any help would be much appreciated.
You can see the hash algorithm used by Firefox here: http://hg.mozilla.org/mozilla-central/file/09935ede3c77/netwerk/cache/nsDiskCacheDevice.cpp#l272. It is pretty simple but rather different from the one you linked to (I checked and the change happened between Firefox 3.5 and Firefox 3.6). Essentially, it seems to treat the URL as a sequence of unsigned integers (little-endian, padded with zeros as necessary) and adds them up to the variables a, b and c (first and forth number to a, second and fifth to b, third and sixth to c). The three variables are then combined in function hashmix().
The initial value for a and b is 0x9e3779b9, the value for c is somewhat more complicated - it comes in as parameter. All callers use the default value of this parameter however which is 0 (see nsDiskCache.h).
I guess that http://burtleburtle.net/bob/hash/evahash.html might provide more info on the algorithm used.