In Swift, is there a way to input Lat/Long coordinates from MapKit, and translate those coordinates to a specified Projection, such as UTM84-17N (e.g. AutoCAD format)?
Well, I never did figure out how to do this using iOS/Swift, but I did discover a lisp code in both AutoCAD Map (two or three lines of code) and even better, in BricsCAD using the Spatial Manager extension (One single line of code).
For both, they involve three parameters: the source coordinates (in my case are in lat/long) the source projection code and the destination projection code.
The projection codes lists are provided in 'help.'
Actually, the company who owns Spatial Manager actually wrote the routine to fit into lisp for me when I posed the question to them; amazing!
So, there might be a manageable method that can be written for any platform.., who knows.
If anyone is interested in those lines of code to work in CAD, I'll look them up and post them.
By the way, a plug for BricsCAD: AutoCAD MAP from Autodesk, is now like almost $10,000 Canadian; BricsCAD is far less than $1000 for their best Pro option. NO BRAINER. AND, I find after being a guru in AutoCAD scripting for years, that BricsCAD is way better! AutoCAD is now so full of bloatware trinkets and useless stuff (similar to Windows' verification window pop ups) that it takes about 30 seconds just to load on a fast rig. And they have made things so much more difficult for old school users. Not to mention, the folks at BricsCAD are so incredibly helpful you feel like family with them!
...only one tiny flaw so far in going with them tho. They don't have a proper lisp editor (that I can find anyway), that lets you test the code line by line real time - like AutoCAD does...
I want to solve a problem similar to N-Queens one, but:
all chess pieces are available
user inputs how many pieces of what kind are to be placed (e.g. 3 rooks, 4 knights, 1 bishop)
I'm lying on the floor for some time now, but can't come up with how to adjust the backtracking algorithm for this purpose. I will be very grateful for any kind of help.
In principle, the same approach as in the classical N-Queen problem should work:
Find an empty, non-attacked square where you can place your next piece
Search the new position recursively (or if you already placed all your pieces, output the solution)
Take back the last placed piece and repeat (goto step 1) until you have tried all squares where you can place the next piece
The only difference to the classical N-Queens problem is that the different pieces have different attack patterns. And some common optimizations might no longer work. For instance, if you have pawns, it breaks symmetry as they only attack the squares in front of them. (Though you still have one symmetry axis even with pawns.)
I would expect the backtracking algorithm to be more efficient if you start with placing the pieces first that cover the most squares: first queens, then rooks and bishops, then knights and kings, and finally pawns.
I know there are ways to find synonyms either by using NLTK/pywordnet or Pattern package in python but it isn't solving my problem.
If there are words like
bad,worst,poor
bag,baggage
lost,lose,misplace
I am not able to capture them. Can anyone suggest me a possible way?
There have been numerous research in this area in past 20 years. Yes computers don't understand language but we can train them to find similarity or difference in two words with the help of some manual effort.
Approaches may be:
Based on manually curated datasets that contain how words in a language are related to each other.
Based on statistical or probabilistic measures of words appearing in a corpus.
Method 1:
Try Wordnet. It is a human-curated network of words which preserves the relationship between words according to human understanding. In short, it is a graph with nodes as something called 'synsets' and edges as relations between them. So any two words which are very close to each other are close in meaning. Words that fall within the same synset might mean exactly the same. Bag and Baggage are close - which you can find either by iteratively exploring node-to-node in a breadth first style - like starting with 'baggage', exploring its neighbors in an attempt to find 'baggage'. You'll have to limit this search upto a small number of iterations for any practical application. Another style is starting a random walk from a node and trying to reach the other node within a number of tries and distance. It you reach baggage from bag say, 500 times out of 1000 within 10 moves, you can be pretty sure that they are very similar to each other. Random walk is more helpful in much larger and complex graphs.
There are many other similar resources online.
Method 2:
Word2Vec. Hard to explain it here but it works by creating a vector of a user's suggested number of dimensions based on its context in the text. There has been an idea for two decades that words in similar context mean the same. e.g. I'm gonna check out my bags and I'm gonna check out my baggage both might appear in text. You can read the paper for explanation (link in the end).
So you can train a Word2Vec model over a large amount of corpus. In the end, you will be able to get 'vector' for each word. You do not need to understand the significance of this vector. You can this vector representation to find similarity or difference between words, or generate synonyms of any word. The idea is that words which are similar to each other have vectors close to each other.
Word2vec came up two years ago and immediately became the 'thing-to-use' in most of NLP applications. The quality of this approach depends on amount and quality of your data. Generally Wikipedia dump is considered good training data for training as it contains articles about almost everything that makes sense. You can easily find ready-to-use models trained on Wikipedia online.
A tiny example from Radim's website:
>>> model.most_similar(positive=['woman', 'king'], negative=['man'], topn=1)
[('queen', 0.50882536)]
>>> model.doesnt_match("breakfast cereal dinner lunch".split())
'cereal'
>>> model.similarity('woman', 'man')
0.73723527
First example tells you the closest word (topn=1) to words woman and king but meanwhile also most away from the word man. The answer is queen.. Second example is odd one out. Third one tells you how similar the two words are, in your corpus.
Easy to use tool for Word2vec :
https://radimrehurek.com/gensim/models/word2vec.html
http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf (Warning : Lots of Maths Ahead)
Another OpenCV question;
Without me having to implement 2 versions - can anyone enlighten me to what the differences are between cvPOSTIT and cvFindExtrinsicCameraParams2 and maybe the advantages of each.
The inputs and outputs appear to be the same.
From my experience, cvFindExtrinsicCameraParams2() works for coplanar points (so it is probably an implementation of http://dl.acm.org/citation.cfm?id=228149), while cvPOSIT() doesn't. But I am not 100% sure.
It appears that cvPOSIT() only exists in OpenCV's old C API and not in the new C++ API. Conversely, cvFindExtrinsicCameraParams2() is in both. While not a perfect indicator, my best guess is that they both implement the POSIT algorithm with minor modifications and the former exists only for legacy reasons.
Beyond that, your guess is good as mine. If you want a definitive answer, I suggest asking on the OpenCV mailing list.
I've used cvPOSIT already. It only works on 3D non-coplanar points on the object. Because it bases on the algorithm from "DAVIS, D. F. D. A. L. S. 1995. Model-Based Object Pose in 25 Lines of Code". So you will have to find a way around for coplanar features
With cvFindExtrinsicCameraParams2(), it also works on planar features, solve the transformation using cvFindHomography and then refine the result by levenberg-marquardt approximation. For non-coplanar points, the preprocessing is done by a different method DLT (Direct Linear Transformation) (not ".. 25 lines of Code" article anymore)
I'm not pretty sure about thier performance, which one is faster. As I know, ".. 25 lines of code" is very fast, and suitable for realtime vision up to now.
Although many of you will have a decent idea of what I'm aiming at, just from reading the title -- allow me a simple introduction still.
I have a Fortran program - it consists of a program, some internal subroutines, 7 modules with its own procedures, and ... uhmm, that's it.
Without going into much detail, for I don't think it's necessary at this point, what would be the easiest way to use MATLAB's plotting features (mainly plot(x,y) with some customizations) as an interactive part of my program ? For now I'm using some of my own custom plotting routines (based on HPGL and Calcomp's routines), but just as part of an exercise on my part, I'd like to see where this could go and how would it work (is it even possible what I'm suggesting?). Also, how much effort would it take on my part ?
I know this subject has been rather extensively described in many "tutorials" on the net, but for some reason I have trouble finding the really simple yet illustrative introductory ones. So if anyone can post an example or two, simple ones, I'd be really grateful. Or just take me by the hand and guide me through one working example.
platform: IVF 11.something :) on Win XP SP2, Matlab 2008b
The easiest way would be to have your Fortran program write to file, and have your Matlab program read those files for the information you want to plot. I do most of my number-crunching on Linux, so I'm not entirely sure how Windows handles one process writing a file and another reading it at the same time.
That's a bit of a kludge though, so you might want to think about using Matlab to call the Fortran program (or parts of it) and get data directly for plotting. In this case you'll want to investigate Creating Fortran MEX Files in the Matlab documentation. This is relatively straightforward to do and would serve your needs if you were happy to use Matlab to drive the process and Fortran to act as a compute service. I'd look in the examples distributed with Matlab for simple Fortran MEX files.
Finally, you could call Matlab from your Fortran program, search the documentation for Calling the Matlab Engine. It's a little more difficult for me to see how this might fit your needs, and it's not something I'm terribly familiar with.
If you post again with more detail I may be able to provide more specific tips, but you should probably start rolling your sleeves up and diving in to MEX files.
Continuing the discussion of DISLIN as a solution, with an answer that won't fit into a comment...
#M. S. B. - hello. I apologize for writing in your answer, but these comments are much too short, and answering a question in the form of an answer with an answer is ... anyway ...
There is the Quick Plot feature of DISLIN -- routine QPLOT needs only three arguments to plot a curve: X array, Y array and number N. See Chapter 16 of the manual. Plus only several additional calls to select output device and label the axes. I haven't used this, so I don't know how good the auto-scaling is.
Yes, I know of Quickplot, and it's related routines, but it is too fixed for my needs (cannot change anything), and yes, it's autoscaling is somewhat quircky. Also, too big margins inside the graf.
Or if you want to use the power of GRAF to setup your graph box, there is subroutine GAXPAR to automatically generate recommended values. -2 as the first argument to LABDIG automatically determines the number of digits in tick-mark labels.
Have you tried the routines?
Sorry, I cannot find the GAXPAR routine you're reffering to in dislin's index. Are you sure it is called exactly like that ?
Reply by M.S.B.: Yes, I am sure about the spelling of GAXPAR. It is the last routine in Chapter 4 of the DISLIN 9.5 PDF manual. Perhaps it is a new routine? Also there is another path to automatic scaling: SETSCL -- see Chapter 6.
So far, what I've been doing (apart from some "duck tape" solutions) is
use dislin; implicit none
real, dimension(5) :: &
x = [.5, 2., 3., 4., 5.], &
y = [10., 22., 34., 43., 15.]
real :: xa, xe, xor, xstp, &
ya, ye, yor, ystp
call setpag('da4p'); call metafl('xwin');
call disini(); call winkey('return');
call setscl(x,size(x),'x');
call setscl(y,size(y),'y')
call axslen(1680,2376) !(8/10)*2100 and 2970, respectively
call setgrf('name','name','line','line')
call incmrk(1); call hsymbl(3);
call graf(xa, xe, xor, xstp, ya, ye, yor, ystp); call curve(x,y,size(x))
call disfin()
end
which will put the extreme values right on the axis. Do you know perhaps how could I go to have one "major tick margin" on the outside, as to put some area between the curve and the axis (while still keeping setscl's effects) ?
Even if you don't like the built-in auto-scaling, if you are already using DISLIN, rolling your own auto-scaling will be easier than calling Fortran from MATLAB. You can use the Fortran intrinsic functions minval and maxval to find the smallest and largest values in the data, than write a subroutine to round outwards to "nice" round values. Similarly, a subroutine to decide on the tick-mark spacing.
This is actually not so easy to accomplish (and ideas to prove me wrong will be gladly appreciated). Or should I say, it is easy if you know the rough range in which your values will lie. But if you don't, and you don't know
whether your values will lie in the range of 13-34 or in the 1330-3440, then ...
... if I'm on the wrong track completely here, please, explain if you ment something different. My english is somewhat lacking, so I can only hope the above is understandable.
Inside a subroutine to determine round graph start/end values, you could scale the actual min/max values to always be between 1 and 10, then have a table to pick nice round values, then unscale back to the correct range.
--
Dump Matlab because its proprietary, expensive, bloated/slow and codes are not easy to parallelize.
What you should do is use something on the lines of DISLIN, PLplot, GINO, gnuplotfortran etc.