I know that to get 10 0's, one can do
A = zeros(10, 1);
To get 10 1's, one can do
A = ones(10, 1);
What about any arbitrary number? Say, I want 10 3's. I have come up with a way of doing it.
A = linspace(3, 3, 10);
Is this satisfactory? Is there a more elegant way of doing this?
Some alternatives:
Using repmat:
A = repmat(3, [5 7]); %// 5x7 matrix filled with the value 3
Using indexing:
A(1:m, 1:n) = x;
Following is a timing comparison between all proposed approaches. As you can see, #Dennis' solutions are the fastest (at least on my system).
Functions:
function y = f1(x,m,n) %// Dennis' "ones"
y = x*ones(m,n);
function y = f2(x,m,n) %// Dennis' "zeros"
y = x+zeros(m,n);
function y = f3(x,m,n) %// Luis' "repmat"
y = repmat(x,[m n]);
function y = f4(x,m,n) %// Luis' "dirty"
y(m,n) = 0;
y(:) = x;
function y = f5(x,m,n) %// Luis' "indexing"
y(1:m,1:n) = x;
function y = f6(x,m,n) %// Divakar's "matrix porod"
y = x*ones(m,1)*ones(1,n);
Benchmarking script for square arrays:
x = 3;
sizes = round(logspace(1,3.7,10)); %// max limited by computer memory
for s = 1:numel(sizes)
n = sizes(s);
m = sizes(s);
time_f1(s) = timeit(#() f1(x,m,n));
time_f2(s) = timeit(#() f2(x,m,n));
time_f3(s) = timeit(#() f3(x,m,n));
time_f4(s) = timeit(#() f4(x,m,n));
time_f5(s) = timeit(#() f5(x,m,n));
time_f6(s) = timeit(#() f6(x,m,n));
end
loglog(sizes, time_f1, 'r.-');
hold on
loglog(sizes, time_f2, 'g.:');
loglog(sizes, time_f3, 'b.-');
loglog(sizes, time_f4, 'm.-');
loglog(sizes, time_f5, 'c.:');
loglog(sizes, time_f6, 'k.:');
xlabel('Array size')
ylabel('Time')
legend('ones', 'zeros', 'repmat', 'dirty', 'indexing', 'matrix prod')
For column arrays: just change the following lines:
sizes = round(logspace(1,3.7,10)).^2; %// max limited by computer memory
n = 1;
m = sizes(s);
For row arrays:
sizes = round(logspace(1,3.7,10)).^2; %// max limited by computer memory
n = sizes(s);
m = 1;
Results for a dual-core CPU, 2-GB RAM, Windows Vista, Matlab R2010b:
Square arrays;
Column arrays;
Row arrays.
There are two basic ways to do this:
A = ones(10,1)*3
B = zeros(10,1)+3
The first one is most commonly used in examples, yet if I am not mistaken, the second one performs slightly better. All in all it is just a matter of taste.
Of course, if you have an existing matrix, there is another simple way:
C = zeros(10,1)
C(:) = 3;
And for completeness, the repmat solution as suggested by #Luis is also fine.
As an alternative, a matrix multiplication (which is supposed to be pretty fast on MATLAB) based method could be also be suggested for 2D or multidimensional array assignment work.
Thus, assuming m as rows, n as columns and x as the value to be assigned for all elements, the code would be -
y = x*ones(m,1)*ones(1,n);
Related
How can I delete all-zero pages from a 3D matrix in a loop?
I have come up with the following code, though it is not 'entirely' correct, if at all. I am using MATLAB 2019b.
%pseudo data
x = zeros(3,2,2);
y = ones(3,2,2);
positions = 2:4;
y(positions) = 0;
xy = cat(3,x,y); %this is a 3x2x4 array; (:,:,1) and (:,:,2) are all zeros,
% (:,:,3) is ones and zeros, and (:,:,4) is all ones
%my aim is to delete the arrays that are entirely zeros i.e. xy(:,:,1) and xy(:,:,2),
%and this is what I have come up with; it doesn't delete the arrays but instead,
%all the ones.
for ii = 1:size(xy,3)
for idx = find(xy(:,:,ii) == 0)
xy(:,:,ii) = strcmp(xy, []);
end
end
Use any to find indices of the slices with at least one non-zero value. Use these indices to extract the required result.
idx = any(any(xy)); % idx = any(xy,[1 2]); for >=R2018b
xy = xy(:,:,idx);
I am unsure what you'd expect your code to do, especially given you're comparing strings in all-numerical arrays. Here's a piece of code which does what you desire:
x = zeros(3,2,2);
y = ones(3,2,2);
positions = 2:4;
y(positions) = 0;
xy = cat(3,x,y);
idx = ones(size(xy,3),1,'logical'); % initialise catching array
for ii = 1:size(xy,3)
if sum(nnz(xy(:,:,ii)),'all')==0 % If the third dimension is all zeros
idx(ii)= false; % exclude it
end
end
xy = xy(:,:,idx); % reindex to get rid of all-zero pages
The trick here is that sum(xy(:,:,ii),'all')==0 is zero iff all elements on the given page (third dimension) are zero. In that case, exclude it from idx. Then, in the last row, simply re-index using logical indexing to retain only pages whit at least one non-zero element.
You can do it even faster, without a loop, using sum(a,[1 2]), i.e. the vectorial-dimension sum:
idx = sum(nnz(xy),[1 2])~=0;
xy = xy(:,:,idx);
I have an 8x8 matrix, e.g. A=rand(8,8). What I need to do is subset all 2x2 matrices along the diagonal. That means that I need to save matrices A(1:2,1:2), A(3:4,3:4), A(5:6,5:6), A(7:8,7:8). To better explain myself, the current version that I am using is the following:
AA = rand(8,8);
BB = zeros(8,2);
for i = 1:4
BB(2*i-1:2*i,:) = AA(2*i-1:2*i,2*i-1:2*i);
end
This works fine for small AA matrices and small AA submatrices, however as the size grows significantly (it can get up to even 50,000x50,000) using a for loop like the one above in not viable. Is there a way to achieve the above without the loop? I've thought of other approaches that could perhaps utilize upper and lower triangular matrices, however even these seem to need a loop at some point. Any help is appreciated!!
Here's a way:
AA = rand(8,8); % example matrix. Assumed square
n = 2; % submatrix size. Assumed to divide the size of A
mask = repelem(logical(eye(size(AA,1)/n)), n, n);
BB = reshape(permute(reshape(AA(mask), n, n, []), [1 3 2]), [], n);
This generates a logical mask that selects the required elements, and then rearranges them as desired using reshape and permute.
Here is an alternative that doesn't generate a full matrix to select the block diagonals, as in Luis Mendo' answer but instead directly generates the indices to these elements. It is likely that this will be faster for very large matrices, as creating the indexing matrix will be expensive in that case.
AA = rand(8,8); % example matrix. Assumed square
n = 2; % submatrix size. Assumed to divide the size of A
m=size(AA,1);
bi = (1:n)+(0:m:n*m-1).'; % indices for elements of one block
bi = bi(:); % turn into column vector
di = 1:n*(m+1):m*m; % indices for first element of each block
BB = AA(di+bi-1); % extract the relevant elements
BB = reshape(BB,n,[]).' % put these elements in the desired order
Benchmark
AA = rand(5000); % couldn't do 50000x50000 because that's too large!
n = 2;
BB1 = method1(AA,n);
BB2 = method2(AA,n);
BB3 = method3(AA,n);
assert(isequal(BB1,BB2))
assert(isequal(BB1,BB3))
timeit(#()method1(AA,n))
timeit(#()method2(AA,n))
timeit(#()method3(AA,n))
% OP's loop
function BB = method1(AA,n)
m = size(AA,1);
BB = zeros(m,n);
for i = 1:m/n
BB(n*(i-1)+1:n*i,:) = AA(n*(i-1)+1:n*i,n*(i-1)+1:n*i);
end
end
% Luis' mask matrix
function BB = method2(AA,n)
mask = repelem(logical(eye(size(AA,1)/n)), n, n);
BB = reshape(permute(reshape(AA(mask), n, n, []), [1 3 2]), [], n);
end
% Cris' indices
function BB = method3(AA,n)
m = size(AA,1);
bi = (1:n)+(0:m:n*m-1).';
bi = bi(:);
di = 0:n*(m+1):m*m-1;
BB = reshape(AA(di+bi),n,[]).';
end
On my computer, with MATLAB R2017a I get:
method1 (OP's loop): 0.0034 s
method2 (Luis' mask matrix): 0.0599 s
method3 (Cris' indices): 1.5617e-04 s
Note how, for a 5000x5000 array, the method in this answer is ~20x faster than a loop, whereas the loop is ~20 faster than Luis' solution.
For smaller matrices things are slightly different, Luis' method is almost twice as fast as the loop code for a 50x50 matrix (though this method still beats it by ~3x).
Here is the problem:
data = 1:0.5:(8E6+0.5);
An array of 16 million points, needs to be averaged every 10,000 elements.
Like this:
x = mean(data(1:10000))
But repeated N times, where N depends on the number of elements we average over
range = 10000;
N = ceil(numel(data)/range);
My current method is this:
data(1) = mean(data(1,1:range));
for i = 2:N
data(i) = mean(data(1,range*(i-1):range*i));
end
How can the speed be improved?
N.B: We need to overwrite the original array of data (essentially bin the data and average it)
data = 1:0.5:(8E6-0.5); % Your data, actually 16M-2 elements
N = 1e4; % Amount to average over
tmp = mod(numel(data),N); % find out whether it fits
data = [data nan(1,N-tmp)]; % add NaN if necessary
data2=reshape(data,N,[]); % reshape into a matrix
out = nanmean(data2,1); % get average over the rows, ignoring NaN
Visual confirmation that it works using plot(out)
Note that technically you can't do what you want if mod(numel(data),N) is not equal to 0, since then you'd have a remainder. I elected to average over everything in there, although ignoring the remainder is also an option.
If you're sure mod(numel(data),N) is zero every time, you can leave all that out and reshape directly. I'd not recommend using this though, because if your mod is not 0, this will error out on the reshape:
data = 1:0.5:(8E6+0.5); % 16M elements now
N = 1e4; % Amount to average over
out = sum(reshape(data,N,[]),1)./N; % alternative
This is a bit wasteful, but you can use movmean (which will handle the endpoints the way you want it to) and then subsample the output:
y = movmean(x, [0 9999]);
y = y(1:10000:end);
Even though this is wasteful (you're computing a lot of elements you don't need), it appears to outperform the nanmean approach (at least on my machine).
=====================
There's also the option to just compensate for the extra elements you added:
x = 1:0.5:(8E6-0.5);
K = 1e4;
Npad = ceil(length(x)/K)*K - length(x);
x((end+1):(end+Npad)) = 0;
y = mean(reshape(x, K, []));
y(end) = y(end) * K/(K - Npad);
reshape the data array into a 10000XN matrix, then compute the mean of each column using the mean function.
Imagine for instance we have the following functions:
f = #(n) sin((0:1e-3:1) .* n * pi);
g = #(n, t) cos(n .^ 2 * pi ^2 / 2 .* t);
h = #(n) f(n) * g(n, 0);
Now, I would like to be able to enter an array of values for n into h and return a sum of the results for each value of n.
I am trying to be efficient, so I am avoiding the novice for-loop method of just filling out a pre-allocated matrix and summing down the columns. I also tried using arrayfun and converting the cell to a matrix then summing that, but it ended up being a slower process than the for-loop.
Does anyone know how I might do this?
The fact is the "novice" for-loop is going to be competitively as fast as any other vectorized solution thanks to improvements in JIT compilation in recent versions of MATLAB.
% array of values of n
len = 500;
n = rand(len,1);
% preallocate matrix
X = zeros(len,1001);
% fill rows
for i=1:len
X(i,:) = h(n(i)); % call function handle
end
out = sum(X,1);
The above is as fast as (maybe even faster):
XX = cell2mat(arrayfun(h, n, 'UniformOutput',false));
out = sum(XX,1);
EDIT:
Here it is computed directly without function handles in a single vectorized call:
n = rand(len,1);
t = 0; % or any other value
out = sum(bsxfun(#times, ...
sin(bsxfun(#times, n, (0:1e-3:1)*pi)), ...
cos(n.^2 * t * pi^2/2)), 1);
I need a way of obtaining a Lx2 trajectory from a Nx2 array of points, i.e. a way of connecting those points into a single trajectory (for example, create a 10000x2 array of points from a 5x2 array of points). I have tried using interp1 and interp2 but either I don't fully understand them or they don't do what I need.
It sounds like you need to be using interp1 in a loop (i.e. to preserve original order) interpolating between each consecutive pair of points:
X = [10; 10.0001; 9; 48]; %// You can consider something like X = [10;10;9;48]; X=X+rand(size(X))*0.0001 instead of dealing with equal X values manually
Y = [10; 20; 50; 6];
m = 3333; %//num points between nodes
n = m*(length(X)-1);
Yi = zeros(n,1);
Xi = [];
for k = 1:length(X)-1
xi = linspace(X(k), X(k+1), m);
Xi = [Xi, xi];
Yi(((k-1)*m+1):k*m) = interp1(X(k:k+1), Y(k:k+1),xi);
end
plot(X,Y,'or');
hold on
plot(Xi,Yi);
To get a pentagon (not a W) try this looping code with these inputs:
X = [0.25; 0.75; 1; 0.5; 0; 0.25];
Y = [0; 0; 1; 1.8; 1; 0];
Result: