Prior to ‘like’ you would have to understand both the underlying type of the array (gpuArray, fi, distributed, etc.) and how to create an array of that type. Now you can simply write

function out = myAlgorithm(in)

myOnes = ones(size(in), ‘like’, in);
callInternalFunction(in, myOnes);
end

In this way the code you write can be resilient to many different types of numeric input, and if we design some new numeric type in the future (which supports ‘like’) your code will work with that new type as well!

B = zeros(900,900,class(A));

or

B = zeros(900,900,’double’);

you would see very different behavior. (On my computer, these syntaxes are both faster than ‘like’,'A’.)

Brett

I was also very impressed with the speed increase with ZEROS by using ‘like’.

function zeroslike

[t1, t2] = deal(0);
A = magic(2000);
for ii = 1:100
tic
B = zeros(900,900);
t1=t1+toc;
tic
C = zeros(900,900,’like’,A);
t2=t2+toc;
end

t1./t2

On my system I’m seeing >100x speedup.

B = zeros(100,100,'like',rgb);

than it is to write

B = zeros(100,100,class(rgb));

But digging in to the doc a bit, I see that Tom’s excitement probably stems more from the rest of what ‘like’ does than just from its class conversion capacity. In particular, ‘like’ reflects complexity (real vs complex) and sparsity, in addition to class. So if, for instance, I created a sparse, complex 3×3 matrix:

A = sparse(complex(rand(3)));

I can now readily create a matrix of zeros that is also sparse and complex. And, with an additional input, 3×3:

B = zeros(size(A),'like',A);

In this case, both A and B are _type double_; sparse matrices are required to be of that class. But one could also use ‘like’ to reflect “complex + single,” for example.

Cheers,
Brett

An obvious approach would be to reduce the number of unique colors.

Perhaps this was what the user thought about?