The title sums it up. I have seen some mentions in the RatFun and SpectralMeasures package but I am mostly looking for a function analogous to the ones found in the Sympy module for Python.
Edit: Thanks everyone for your answers and comments. I was looking for the discrete version of a dirac delta. At first I was confused and wanted to look for a "function" but I actually was just looking for the Kronecker delta which exhibits the following behavior:
delta(x) = 1 if x=0
delta(x) = 0 otherwise
I quickly realized that I didn't even need a function, I just had to take care of the differences in the argument. Thanks again!
There is a Dirac type in MeasureTheory.jl, but unsure if it fits your needs.
The OP basically wants the Kronecker delta function
[https://en.wikipedia.org/wiki/Kronecker_delta]
which is trivial in julia:
delta(n) = n == 0
Related
I'm going through the code on a sample solution to my first Ruby Quiz (The Solitaire Cipher), and ran across this little nugget:
def move_down( index )
if index == #deck.length - 1
#deck[1..1] = #deck[index], #deck[1]
#deck.pop
else
...
end
end
The person who wrote this solution apparently used the multiple assignment in the second line to insert #deck[index] into the position before #deck[1]. Why not just use this?
#deck.insert(1, #deck[index])
Is there a difference?
OK, now I see what you mean. Sure, they will give the same result. I guess it just would be matter of choosing which style is more clear for you, or how you think your code would be easier to understand and in consequence more maintainable.
If your question is about which method is more "performant" I don't know that answer and I don't think it evens matters, as ruby is not meant to be performant but to be expressive.
I am currently trying to write a code to solve a non linear system of equations. I am using the functions of the gsl library, more specifically the multiroot_fdf_solver.
My problem is that it currently doesn't want to converge. More specifically, I have the following behavior:
-if my initial conditions are close to the result, the gsl_multiroot_fdf_solver_iterate does not update the parameters at all. I tried to display the results on the different steps, and I have for all the parameters dx = NaN (I think this quite srange), the status of gsl_multiroot_fdf_solver_iterate is "success" and the status of gsl_multiroot_test_residual is "the iteration has not converged yet"
-the parameters are only updated if my initial conditions are really far from the expected result. Obvisously in this case it does not converge to the right values.
I have already checked multiple times the expression of my function and my Jacobian, and they seem good.
I have to precise that my Jacobian (and my system as well) are quite complicated expression with many trigonometric function.
Would you have any idea of what it could be? Is it possible that if the expression of the Jacobian is too complicated, it has troubles to compute it?
Thank you in advance for your answers, I am really stucked at this point.
I have a few variables and here they are, three variables "R1, R2 and R3" each have a size of [40 x 1].
I have a fourth variable U of the same dimension. For every U(i) I need to search for an optimum value within R1(i), R2(i) and R3(i) which would return a single value solution. I intend to plot the optimum value against U9i).I have been trying to wrap my head around the knnsearch function but no luck.
Any one out there who could please help??
Thanks
Well when I can't wrap my head around something, I don't come here first.
A lot of people forget this one because we are online, but read a book on the topic. Have your code open so when you see something in the book, test it out.
Draw out any type of diagram. I call these "Napkin Diagrams", because I write it on anything, even a napkin.
I play with code until my keyboard has no letters left on it, then I keep plugging away until the keys fall off
Explore the language API's
Check for public repositories that you can play with
Google, is okay for a quick reference, but google will not teach you anything other than how to google
I talk my code over with myself all the time, people think I'm nuts, but so do I . . It actually works sometimes.
Then if I still can't get it, I come here with a list of things that I have tried, sample code that has not worked, etc.
I used to hate when people told me this, but that was the best thing anyone could have done for me so I tend to do the same now" Thinking about coding is a big part, but u have to get done wht u can. Then we all know what level u are at. Plus it being the end of semester a lot of these types of questions are homework...
Thinking is good, now turn those thoughts into a conceptual design . It's okay to be wrong in this stage, its all just conceptual
If I understood correctly, this might be what you need:
RR = [R1(:) R2(:) R3(:)];
d = bsxfun(#minus,RR, U(:));
[m mi] = min(abs(d),[],2);
answer = RR(:,mi);
first - put the three vectors into a single matrix:
RR = [R1(:) R2(:) R3(:)];
next, take the difference with U: bsxfun is ideal for this kind of thing
d = bsxfun(#minus,RR, U(:));
Now find the minimum absolute difference for each row:
[m mi] = min(abs(d),[],2);
The corresponding indices should allow you to find the "best fit"
answer = RR(:,mi);
I had to do some mind reading to get to this 'answer', so feel free to correct my misunderstanding of your problem!
update if you just need the highest of the three values, then
val = max([R1(:) R2(:) R3(:)]');
plot(U, val);
should be all you need...
I'm performing gaussian mixture model classification, and based on that, used "mvnpdf" function in MATLAB.
As far as I know the function returns a multi variate probability density for the data points or elements passed to it.
However I'm trying to recreate it on C and I assumed that mvnpdf is the regular Gaussian distribution (clearly it is not) because the results don't match.
Does anyone know how "mvnpdf" works ? Because I haven't been able to find documentation on it .
The documentation for mvnpdf is here
if you are looking for the exact code just put a break point at the point where you call it and see how it works
Okay I actually found a decent link that explains in detail what's happening inside .
This might be a better link to look at - http://octave.sourceforge.net/statistics/function/mvnpdf.html
I want to write a strategy to evaluate items in an array in parallel. The old strategies had parArr to do this (see here). But this is not found in the new Control.Parallel.Strategies module.
E.g.
parallel list evaluation: map f myList `using` parList rdeepseq
I would want to be able to do something like: amap f myArr `using` parArr rdeepseq, where amap is from Data.Array.Base and applies a function to each of the elements (sequentially).
The following seems to work but I wonder if it is doing it right, and want to know how I could define my own parArr.
This works: amap ((+1) `using` rpar) $ Array.array (0,4) [(0,10),(1,20),(2,30),(3,40),(4,50)]
For a previous question, I wrote a parallel evaluation strategy for the vector package. That should be a good place to start. You can see the code on hackage in the vector-strategies package.
I don't have time to give a full answer - perhaps I'll edit this later. Feel free to comment with extra questions and direction.
Apart from all the good advice given: The reason that there is no parArr anymore is simply that it has been replaced by the more general parTraversable. Just say:
amap f myArr `using` parTraversable rdeepseq
That should give you the behavior you asked for.