I have a AxBxC array where AXB are pointing to individual grids of a field that i sampled (like coordinates) and C corresponds to the layers underneath. Now I want to calculate the impact of certain activities on these individual points by multiplying it with a 2D matrix.
E.g.
x=5; %x-Dimensions of the sampled area
y=5; %y-Dimensions of the sampled area
z=3; %z-number of layers sampled
Area= zeros(x,y,z);
AreaN= zeros(x,y,z);
now I want to multiply every layer of a given point in X*Y with:
AppA=[0.4,0.4,0.2;0.4,0.5,0.1;0.1,0.2,0.7];
I tried:
for i=1:x
for j=1:y
AreaN(i,j,:)= AppA*Area(i,j,:);
end
end
Unfotunately I get the error:
Error using *
Inputs must be 2-D, or at least one input must be scalar.
To compute elementwise TIMES, use TIMES (.*) instead.
Any help to this is appreciated since I am not yet really familiar with matlab.
Correct Approach
I think, to correct your code, you need to convert that Area(i,j,:) to a column vector, which you can do with squeeze. Thus, the correct loop-based code would look something like this -
AreaN= zeros(x,y,z);
for i=1:x
for j=1:y
AreaN(i,j,:)= AppA*squeeze(Area(i,j,:));
end
end
Now, there are efficient no-loop/vectorized approaches that can be suggested here to get to the output.
Vectorized Approach #1
First approach could be with matrix multiplication and has to be pretty efficient one -
AreaN = reshape(reshape(Area,x*y,z)*AppA.',x,y,z)
Vectorized Approach #2
Second one with bsxfun -
AreaN = squeeze(sum(bsxfun(#times,Area,permute(AppA,[3 4 2 1])),3))
Vectorized Approach #2 Rev 1
If you would like to get rid of the squeeze in the bsxfun code, you need to use an extra permute in there -
AreaN = sum(bsxfun(#times,permute(Area,[1 2 4 3]),permute(AppA,[4 3 1 2])),4)
This would solve the matrix multiplication problem:
AreaN(i,j,:)= AppA*reshape(Area(i,j,:),3,[]);
You might want to consider using bsxfun to aviod loops.
Related
I am trying to perform a specific downsampling process. It is described by the following pseudocode.
//Let V be an input image with dimension of M by N (row by column)
//Let U be the destination image of size floor((M+1)/2) by floor((N+1)/2)
//The floor function is to emphasize the rounding for the even dimensions
//U and V are part of a wrapper class of Pixel_FFFF vImageBuffer
for i in 0 ..< U.size.rows {
for j in 0 ..< U.size.columns {
U[i,j] = V[(i * 2), (j * 2)]
}
}
The process basically takes pixel values on every other locations spanning on both dimensions. The resulting image will be approximately half of the original image.
On a one-time call, the process is relatively fast running by itself. However, it becomes a bottleneck when the code is called numerous times inside a bigger algorithm. Therefore, I am trying to optimize it. Since I use Accelerate in my app, I would like to be able to adapt this process in a similar spirit.
Attempts
First, this process can be easily done by a 2D convolution using the 1x1 kernel [1] with a stride [2,2]. Hence, I considered the function vImageConvolve_ARGBFFFF. However, I couldn't find a way to specify the stride. This function would be the best solution, since it takes care of the image Pixel_FFFF structure.
Second, I notice that this is merely transferring data from one array to another array. So, I thought vDSP_vgathr function is a good solution for this. However, I hit a wall, since the resulting vector of vectorizing a vImageBuffer would be the interleaving bits structure A,R,G,B,A,R,G,B,..., which each term is 4 bytes. vDSP_vgathr function transfers every 4 bytes to the destination array using a specified indexing vector. I could use a linear indexing formula to make such vector. But, considering both even and odd dimensions, generating the indexing vector would be as inefficient as the original solution. It would require loops.
Also, neither of the vDSP 2D convolution functions fit the solution.
Is there any other functions in Accelerate that I might have overlooked? I saw that there's a stride option in the vDSP 1D convolution functions. Maybe, does someone know an efficient way to translate 2D convolution process with strides to 1D convolution process?
I have a 4x4x1250 matrix in MATLAB. I want to find a way to move through the 4x4 matrices slice by slice in order to find the condition of the 4x4 matrices individually.
I don't want to do it in a loop because I want to do this on the GPU and would like it to be indexed.
I saw "squeeze", but I don't think it works for 3D arrays...
I kind of want to use arrayfun, but I don't know how to indicate the specific dimension that I'm interested in.
Any ideas?
Edit: I thought the details I gave are sufficient, nevertheless:
I have a matrix A, size 4x4x1250.
I am interested in the conditions of the 1250 4x4 matrices that make up A. So lets say B = A(:,:,1).
I want to calculate cond(B), but in reality I want 1250 of these calculations.
If I do arrayfun, I don't know how to specify the specific dimension of A along which to slice.
ARRAYFUN disregards the shape of the input, and operates in a purely element-wise fashion. There's also PAGEFUN on the GPU which operates on pages of an array - however, PAGEFUN only really offers an advantage if you're using one of the functions explicitly supported - otherwise it operates in an element-wise fashion.
I am using the mean function in MATLAB on a 4D matrix.
The matrix is a 32x2x20x7 array and I wish to find the mean of each row, of all columns and elements of 3rd dimension, for each 4th dimension.
So basically mean(data(b,:,:,c)) [pseudo-code] for each b, c.
However, when I do this it spits me out separate means for each 3rd dimension, do you know how I can get it to give me one mean for the above equation - so it would be (32x7=)224 means.
You could do it without loops:
data = rand(32,2,20,7); %// example data
squeeze(mean(mean(data,3),2))
The key is to use a second argument to mean, which specifies across which dimension the mean is taken (in your case: dimensions 2 and 3). squeeze just removes singleton dimensions.
this should work
a=rand(32,2,20,7);
for i=1:32
for j=1:7
c=a(i,:,:,j);
mean(c(:))
end
end
Note that with two calls to mean, there will be small numerical differences in the result depending on the order of operations. As such, I suggest doing this with one call to mean to avoid such concerns:
squeeze(mean(reshape(data,size(data,1),[],size(data,4)),2))
Or if you dislike squeeze (some people do!):
mean(permute(reshape(data,size(data,1),[],size(data,4)),[1 3 2]),3)
Both commands use reshape to combine the second and third dimensions of data, so that a single call to mean on the new larger second dimension will perform all of the required computations.
Does anyone know how to do array matrix multiplication in matlab? i.e. I have two 3 dimensional arrays consisting of sets of matrices in the first 2 dimensions and I would like to multiply each matrix in the first array with the corresponding one in the second array. So, i.e. if
A=randn(3,3);
B=cat(3,A,A);
I would like [[operation]] such that
B[[operation]]B = cat(3,A*A, A*A)
done in efficient vector form.
Many thanks in advance.
I have used MULTIPROD from the Mathworks FileExchange for N-D array multiplication before. It is basically an extension of bsxfun to N-D arrays, and works quite nicely (and fast) - although the interface is a bit cumbersome.
I want to have a time series of 2x2 complex matrices,Ot, and I then want to have 1-line commands to multiply an array of complex vectors Vt, by the array Ot where the position in the array is understood as the time instant. I will want Vtprime(i) = Ot(i)*Vt(i). Can anyone suggest a simple way to implement this?
Suppose I have a matrix, M(t), where the elements m(j,k) are functions of t and t is an element of some series (t = 0:0.1:3). Can I create an array of matrices very easily?
I understand how to have an array in Matlab, and even a two dimensional array, where each "i" index holds two complex numbers (j=0,1). That would be a way to have a "time series of complex 2-d vectors". A way to have a time series of complex matrices would be a three dimensional array. (i,j,k) denotes the "ith" matrix and j=0,1 and k=0,1 give the elements of that matrix.
If I go a head and treat matlab like a programming language with no special packages, then I end up having to write the matrix multiplications in terms of loops etc. This then goes towards all the matrix operations. I would prefer to use commands that will make all this very easy if I can.
This could be solved with Matlab array iterations like
vtprime(:) = Ot(:)*Vt(:)
if I understand your problem correctly.
Since Ot and Vt are both changing with time index, I think the best way to do this is in a loop. (If only one of Ot or Vt was changing with time, you could set it up in one big matrix multiplication.)
Here's how I would set it up: Ot is a complex 2x2xI 3D matrix, so that
Ot(:,:,i)
references the matrix at time instant i.
Vt is a complex 2xI matrix, so that
Vt(:,i)
references the vector at time instant i.
To do the multiplication:
for i = 1:I
Vtprime(:,i) = Ot(:,:,i) * Vt(:,i);
end
The resulting Vtprime is a 2xI matrix set up so that Vtprime(:,i) is the output at time instant i.