I have a n x 3 matrix E, a lot of means stocked in a d x 3 matrix M and a covariance matrix, say identity.
I want to compute, for each point in M, the mvnpdf(E[i,:],M(k,:),cov).
Basically, when I run mvnpdf(E,M(k,:),cov), I get a vector
[mvnpdf(E(1,:),M(k,:),cov)
mvnpdf(E(2,:),M(k,:),cov) etc]
I want to cat these vectors to get a matrix like:
[mvnpdf(E,M(1,:),cov), mvnpdf(E,M(2,:),cov), etc]
Is there any way to do that without a for loop?
This works on my machine, but see if it is what you are after:
Cov = eye(3);
C = arrayfun(#(x,y,z) mvnpdf(E,[x y z],Cov), M(1,:), M(2,:), M(3,:),'uni',false);
A = [C{:}]
Note: Consider not using cov as a variable because it is a MATLAB function.
EDIT: My original output M clobbered your input M. Please try again with original data!
Related
I'm new to MATLAB, and programming in general, and I am having difficulty accomplishing what I am sure is a very, very simple task:
I have a list of vectors v_i for i from 1 to n (n in some number), all of the same size k. I would like to create a vector v that is a "concatenation" (don't know if this is the correct terminology) of these vectors in increasing order: what I mean by this is that the first k entries of v are the k entries of v_1, the k+1 to 2k entries of v are the k entries of v_2 etc. etc. Thus v is a vector of length nk.
How should I create v?
To put this into context, here is function I've began writing (rpeakindex will just a vector, roughq would be the vector v I mentioned before):
function roughq = roughq(rpeakindex)
for i from 1 to size(rpeakindex) do
v_i = [rpeakindex(i)-30:1:rpeakindex(i)+90]
end
Any help is appreciated
Let's try two things.
First, for concatenating vectors there are a couple of methods here, but the simplest would be
h = horzcat(v_1, v_2);
The bigger problem is to enumerate all vectors with a "for" loop. If your v_n vectors are in a cell array, and they are in fact v{i}, then
h= [];
for j=1:n
h = horzcat(h, v{i});
end
Finally, if they only differ by name, then call them with
h=[];
for j=1:n
h= horzcat(h, eval(sprintf('v_%d',j));
end
Let the arrays (vectors) be:
v_1=1:10;
v_2=11:20;
v_3=21:30;
v_4=31:40;
and so on.
If they are few (e. g. 4), you can directly set then as input in the cat function:
v=cat(2,v_1,v_2,v_3,v_4)
or the horzcat function
v=horzcat(v_1,v_2,v_3,v_4)
otherwise you can use the eval function within a loop
v1=[];
for i=1:4
eval(['v1=[v1 v_' num2str(i) ']'])
end
Hope this helps.
Concatenating with horzcat is definitely an option, but since these vectors are being created in a function, it would be better to concatenate these vectors automatically in the function itself rather than write out horzcat(v1,v2,....vn) manually.
Given the function mentioned in the question, I would suggest something like this:
function v = roughq(rpeakindex)
v = zeros(121,length(rpeakindex)); %// create a 2D array of all zeros
for i = 1:size(rpeakindex)
v(:,i) = [rpeakindex(i)-30:1:rpeakindex(i)+90]; %// set result to ith column of v
end
v = v(:)'; %'//reshape v to be a single vector with the columns concatenated
end
Here's a simplified example of what's going on:
N = 3;
v = zeros(5,N);
for i = 1:N
v(:,i) = (1:5)*i;
end
v = v(:)';
Output:
v =
1 2 3 4 5 2 4 6 8 10 3 6 9 12 15
You may want to read up on MATLAB's colon operator to understand the v(:) syntax.
If you mean 2d matrix, you are using for holding vectors and each row hold vector v then you can simply use the reshape command in matlab like below:
V = [] ;
for i = 1:10
V(i,:) = randi (10,1 ,10) ;
end
V_reshpae = reshape (V, 1, numel(V)) ;
So I have the following matrices:
A = [1 2 3; 4 5 6];
B = [0.5 2 3];
I'm writing a function in MATLAB that will allow me to multiply a vector and a matrix by element as long as the number of elements in the vector matches the number of columns. In A there are 3 columns:
1 2 3
4 5 6
B also has 3 elements so this should work. I'm trying to produce the following output based on A and B:
0.5 4 9
2 10 18
My code is below. Does anyone know what I'm doing wrong?
function C = lab11(mat, vec)
C = zeros(2,3);
[a, b] = size(mat);
[c, d] = size(vec);
for i = 1:a
for k = 1:b
for j = 1
C(i,k) = C(i,k) + A(i,j) * B(j,k);
end
end
end
end
MATLAB already has functionality to do this in the bsxfun function. bsxfun will take two matrices and duplicate singleton dimensions until the matrices are the same size, then perform a binary operation on the two matrices. So, for your example, you would simply do the following:
C = bsxfun(#times,mat,vec);
Referencing MrAzzaman, bsxfun is the way to go with this. However, judging from your function name, this looks like it's homework, and so let's stick with what you have originally. As such, you need to only write two for loops. You would use the second for loop to index into both the vector and the columns of the matrix at the same time. The outer most for loop would access the rows of the matrix. In addition, you are referencing A and B, which are variables that don't exist in your code. You are also initializing the output matrix C to be 2 x 3 always. You want this to be the same size as mat. I also removed your checking of the length of the vector because you weren't doing anything with the result.
As such:
function C = lab11(mat, vec)
[a, b] = size(mat);
C = zeros(a,b);
for i = 1:a
for k = 1:b
C(i,k) = mat(i,k) * vec(k);
end
end
end
Take special note at what I did. The outer-most for loop accesses the rows of mat, while the inner-most loop accesses the columns of mat as well as the elements of vec. Bear in mind that the number of columns of mat need to be the same as the number of elements in vec. You should probably check for this in your code.
If you don't like using the bsxfun approach, one alternative is to take the vector vec and make a matrix out of this that is the same size as mat by stacking the vector vec on top of itself for as many times as we have rows in mat. After this, you can do element-by-element multiplication. You can do this stacking by using repmat which repeats a vector or matrices a given number of times in any dimension(s) you want. As such, your function would be simplified to:
function C = lab11(mat, vec)
rows = size(mat, 1);
vec_mat = repmat(vec, rows, 1);
C = mat .* vec_mat;
end
However, I would personally go with the bsxfun route. bsxfun basically does what the repmat paradigm does under the hood. Internally, it ensures that both of your inputs have the same size. If it doesn't, it replicates the smaller array / matrix until it is the same size as the larger array / matrix, then applies an element-by-element operation to the corresponding elements in both variables. bsxfun stands for Binary Singleton EXpansion FUNction, which is a fancy way of saying exactly what I just talked about.
Therefore, your function is further simplified to:
function C = lab11(mat, vec)
C = bsxfun(#times, mat, vec);
end
Good luck!
I have given a matrix with 485x1 elements. MatLab shall take the first 12 of them and make them a new matrix.
Then I have a variable named C and a function f(C).
MatLab shall take the first element of the new matrix and make it C. Then it shall perform f(C) and save the result as result1.
Then it should take the second element of the new matrix and make it C. Then it shall perform f(C) again and save the result as result2 and so on.
So in the end I need 12 result variables.
How can I program this?
This is question asks for very basic stuff. Like adressing the first 12 elements of a matrix. You might want to consider reading a few FAQs.
However a basic solution can look like this:
M = [1:485]'; %// a 485x1 matrix (vector)
newMatrix = M(1:12); %// newMatrix contains the first 12 elements of M (also vector)
result = cell(1,12); %// result as a cell
f = #(x) x+1; % a function f
for i = 1:12
C = newMatrix(i); % get each value from newMatrix, call it C
result{i} = f(C); % apply f() on c and store the result
end
It is not a good idea to generate variables result1, result2, ..., result12, you can read more about it here. Better use a cell for storing the variables, you can then adress the i-th result as result{i}.
I have a matrix K of dimensions n x n. I want to create a new block diagonal matrix M of dimensions N x N, such that it contains d blocks of matrix K as its diagonal.
I would have directly used M = blkdiag(K,K,K) etc. had d been smaller. Unfortunately, d is very large and I don't want to manually write the formula with d exactly same arguments for the blkdiag() function.
Is there any shorter, smarter way to do this?
you can use kron for that.
M = kron(X,Y)
returns the Kronecker tensor product of X and Y. The result is a large array formed by taking all possible products between the elements of X and those of Y. If X is m-by-n and Y is p-by-q, then kron(X,Y) is m*p-by-n*q. So in your case something like this will do:
M = kron(eye(L),K)
with L the # of blocks.
tmp = repmat({K},d,1);
M = blkdiag(tmp{:});
You should never use eval, or go into for loops unnecessarily.
Kron is a very elegant way.
Just wanted to share this as it also works.
The following should work:
d=5; K=eye(3); T = cell(1,d);
for j=1:d
T{j} =K;
end
M = blkdiag(T{:})
s = 'A,';
s = repmat(s,[1,n2]);
s = ['B=blkdiag(', s(1:end-1),');'];
eval(s);
It can be faster than using kron-eye.
A "for" loop may might help. Like:
M = k;
for i=1:N/n - 1
M=blkdiag(M,k);
end
In my code, I have to multiply a matrix A (dimensions 3x3) to a vector b1 (dimensions 3x1), resulting in C. So C = A*b1. Now, I need to repeat this process n times keeping A fixed and updating b to a different (3x1) vector each time. This can be done using loops but I want to avoid it to save computational cost. Instead I want to do it as matrix and vector product. Any ideas?
You need to build a matrix of b vectors, eg for n equal to 4:
bMat = [b1 b2 b3 b4];
Then:
C = A * bMat;
provides the solution of size 3x4 in this case. If you want the solution in the form of a vector of length 3n by 1, then do:
C = C(:);
Can we construct bMat for arbitrary n without a loop? That depends on what the form of all your b vectors is. If you let me know in a comment, I can update the answer.