I'm trying to write a linear index for 3D matrix. Is there a formula to determine what is the linear index of (i,j,k) th element in a matrix with (nx,ny,nz) dimensions?
Is there any difference whether I'm using C of FORTRAN or something else?
I searched for similar questions but nothing was founded .
Thanks for any guide.
Actually, Fortran is column major order. That means that when linearly indexing a multidimensional array the first index grows faster, i.e.
ind(i,j,k) = i + (j-1)*nx + (k-1)*ny*nx
where I assume indexing from 1. The function ind gives an index the element (i,j,k) would have when looking at the same as a one-dimensional array (e.g., in sequence association).
Most other languages, including C derivatives, use the row-major order, so that the last index grows the fastest. They also index from 0.
ind(i,j,k) = k + j*nz + i*ny*nz
There are also other differences — the multidimensional arrays are actually arrays of arrays (similar to pointers to pointers) in C.
Related
In julia, one can pre-allocate an array of a given type and dims with
A = Array{<type>}(undef,<dims>)
example for a 10x10 matrix of floats
A = Array{Float64,2}(undef,10,10)
However, for array of array pre-allocation, it does not seem to be possible to provide a pre-allocation for the underlying arrays.
For instance, if I want to initialize a vector of n matrices of complex floats I can only figure this syntax,
A = Vector{Array{ComplexF64,2}}(undef, n)
but how could I preallocate the size of each Array in the vector, except with a loop afterwards ? I tried e.g.
A = Vector{Array{ComplexF64,2}(undef,10,10)}(undef, n)
which obviously does not work.
Remember that "allocate" means "give me a contiguous chunk of memory, of size exactly blah". For an array of arrays, which is really a contiguous chunk of pointers to other contiguous chunks, this doesn't really make sense in general as a combined operation -- the latter chunks might just totally differ.
However, by stating your problem, you make clear that you actually have more structural information: you know that you have n 10x10 arrays. This really is a 3D array, conceptually:
A = Array{Float64}(undef, n, 10, 10)
At that point, you can just take slices, or better: views along the first axis, if you need an array of them:
[#view A[i, :, :] for i in axes(A, 1)]
This is a length n array of AbstractArrays that in all respects behave like the individual 10x10 arrays you wanted.
In the cases like you have described you need to use comprehension:
a = [Matrix{ComplexF64}(undef, 2,3) for _ in 1:4]
This allocates a Vector of Arrays. In Julia's comprehension you can iterate over more dimensions so higher dimensionality is also available.
I am talking about a zero-indexed matrix of integers denoted by a pointer to pointer, i.e.
int **mat;
Then what is the correct way to represent the mat[m][n] element? Is it
*(*(mat+m)+n)
or is it
*(*(mat+n)+m)
Also, visually speaking, between m and n, which one is the row index or which one is the column index? Or do terms like row and column make any sense here? I am sure I have some conceptual gap here, and some help will be great.
The expression
mat[m][n]
is parsed as
(mat[m])[n]
which is equivalent to
(*(mat + m))[n]
which is in turn equivalent to
*(*(mat + m) + n)
so your initial guess is correct.
As for which of these mean rows and which of these mean columns - in some sense, this is up to you to decide. You're the one creating the array and you can assign it any semantics that you'd like.
On the other hand, if you create a 2D array like this:
int mat[A][B];
then in memory this will be laid out as
[0, 0][0, 1][0, 2]...[0, B-1][1, 0][1, 1][1, 2]... ... [A-1][B-1]
Because of locality of reference, if you read across this in the order shown above (do all of mat[0], then all of mat[1], etc.) than it is to iterate in the reverse order (do mat[0][0], then mat[1][0], then mat[2][0], etc.). In that sense, it's common to treat 2D arrays as having the first component select a row and the second select a column, since that more naturally aligns with how the memory is laid out.
This is a simplified version of the project I am doing. I can get around this using other methods. I was just wondering, is it possible to do this in matlab ?
I want to store a 1*2 vector [100,100] to the (1,1) entry of a given matrix a. The following is the code.
a=zeros(2,2);
a(1,1)=[100,100];
Then I get Subscripted assignment dimension mismatch error.
I could use cell array instead. But there are not so many handy functions (like tril) for cell array compared with matrix. So, I was just wondering, does anyone know how to handle this situation or this is just a trivial case not need to mention at all. Many thanks for your time and attention.
You can use 3-d matrix instead of 2-d matrix if you already know the length of vector.
a = zeros (2,2,2) ;
a(1,1,:) = [100, 100] ;
or
a = [];
a (1,1,:) = [100,100];
In above example, you have to take care of indexing by yourself and matrix a can be in arbitrary dimensions.
Say I have an array of integers A such that A[i] = j, and I want to "invert it"; that is, to create another array of integers B such that B[j] = i.
This is trivial to do procedurally in linear time in any language; here's a Python example:
def invert_procedurally(A):
B = [None] * (max(A) + 1)
for i, j in enumerate(A):
B[j] = i
return B
However, is there any way to do this functionally (as in functional programming, using map, reduce, or functions like those) in linear time?
The code might look something like this:
def invert_functionally(A):
# We can't modify variables in FP; we can only return a value
return map(???, A) # What goes here?
If this is not possible, what is the best (most efficient) alternative when doing functional programming?
In this context are arrays mutable or immutable? Generally I'd expect the mutable case to be about as straightforward as your Python implementation, perhaps aside from a few wrinkles with types. I'll assume you're more interested in the immutable scenario.
This operation inverts the indices and elements, so it's also important to know something about what constitutes valid array indices and impose those same constraints on the elements. Haskell has a class for index constraints called Ix. Any Ix type is ordered and has a range implementation to make an ordered list of indices ranging from one specified index to another. I think this Haskell implementation does what you want.
import Data.Array.IArray
invertArray :: (Ix x) => Array x x -> Array x x
invertArray arr = listArray (low,high) newElems
where oldElems = elems arr
newElems = indices arr
low = minimum oldElems
high = maximum oldElems
Under the hood listArray uses zipWith and range to associate indices in the specified range to the listed elements. That part ought to be linear time, and so is the one-time operation of extracting elements and indices from an array.
Whenever the sets of the input arrays indices and elements differ some elements will be undefined, which for better or worse blow up faster than Python's None. I believe you could overcome the undefined issue by implementing new Ix a instances over the Maybe monad, for instance.
Quick side-note: check out the invPerm example in the Haskell 98 Library Report. It does something similar to invertArray, but assumes up front that input array's elements are a permutation of its indices.
A solution needing mapand 3 operations:
toTuples views an the array as a list of tuples (i,e) where i is the index and e the element in the array at that index.
fromTuples creates and loads an array from a list of tuples.
swap which takes a tuple (a,b) and returns (b,a)
Hence the solution would be (in Haskellish notation):
invert = fromTuples . map swap . toTuples
Is it possible to apply the idea of array in C to MATLAB
For example, if we have
Double array[10];
and if we want to assign a value we write for example
Array[5]=2;
Is there any way to write equivalent one in MATLAB ?
I'm not sure what you mean by "Is it possible to apply the idea of array in C to MATLAB". An array is just a 1D list of numbers (or other data types). MATLAB primarily is designed to work with matrices (MATLAB is short for Matrix laborartory) and an array or vector is simply a special case of a matrix. So I guess the answer to your question is yes, if I have understood correctly.
To initialise arrays or matrices in MATLAB we use zeros or ones:
>> array = zeros(1,5)
array =
0 0 0 0 0
We can then index elements of the array in the same way as C:
>> array(3) = 3
array =
0 0 3 0 0
Note, however, that MATLAB array indexing is one based whereas C arrays are zero based.
This article describes matrix/array indexing in MATLAB.
You can define your own class, override the [] operator.
I described the mechanism in Here
Since it is a custom function, you might as well change the 1-based indexing to 0-based indexing.
Regarding the constructor, I doubt that you can do it.
Anyhow, why would you want to do it?
You will confuse all of the Matlab users, and cause havoc.
When in Rome, do as Romans do.
Yes you can. Arrays are used in C and MATLAB and they can be used for the same functions. Except, please keep in mind the array-indexing of C and MATLAB are different.
The first element of a C array has an index of zero. i.e. in X = [10 20 30 40], x[0] will return 10. But in MATLAB, this will give an error. To access the number 10, you have to use the expression x[1] in MATLAB.
There is no indexing operator []. You must use () for indexing array.
If you write
x = 1:10;
x[2]
then you'll get the following error
x[2]
|
Error: Unbalanced or unexpected parenthesis or bracket.