The short answer: the built-in function
arrayfun does exactly what your
map function does for numeric arrays:
>> y = arrayfun(@(x) x^2, 1:10) y = 1 4 9 16 25 36 49 64 81 100
However, these functions are often not necessary if you take advantage of vectorization, specifically using element-wise arithmetic operators. For the example you gave, a vectorized solution would be:
>> x = 1:10; >> y = x.^2 y = 1 4 9 16 25 36 49 64 81 100
Some operations will automatically operate across elements (like adding a scalar value to a vector) while others operators have a special syntax for element-wise operation (denoted by a
. before the operator). Many built-in functions in MATLAB are designed to operate on vector and matrix arguments using element-wise operations (often applied to a given dimension, such as
mean for example), and thus don't require map functions.
To summarize, here are some different ways to square each element in an array:
x = 1:10; % Sample array f = @(x) x.^2; % Anonymous function that squares each element of its input % Option #1: y = x.^2; % Use the element-wise power operator % Option #2: y = f(x); % Pass a vector to f % Option #3: y = arrayfun(f, x); % Pass each element to f separately
Of course, for such a simple operation, option #1 is the most sensible (and efficient) choice.