Explanation of cellfun()

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Marvin Eckert 2020-5-20

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/527468-explanation-of-cellfun

评论： Marvin Eckert 2021-11-25

采纳的回答： Rik

在 MATLAB Online 中打开

Explaination of cellfun() call with @(data):

Hi everyone,

I have a rather stupid question I think, but I do not understand a specific call of the cellfun().

My question came up when I was working with the MATLAB example: Similarity-Based Remaining Useful Life Estimation. There is a function applied to each cell of a cell array, for example in line 34:

trainDataNormalized = cellfun(@(data) regimeNormalization(data, centers, centerstats), ...
    trainData, 'UniformOutput', false);

[...] line 176: (for info)

function data = regimeNormalization(data, centers, centerstats)

For me the content of the cellfun() and its function is clear, exept of the expression @(data). Cellfun() applies the function regimeNormalization individual to each cell of the cell array trainData.

Looking in the doku of cellfun() they call a funktion like this and leave out the additional function like above. Which I think I undestand, see below.

A = cellfun(@mean,C)
p = cellfun(@plot,X,Y);

But in the documentation they also do this, which is exactly like my problem, but the explaination is not sufficient for me. (MATLAB advanced beginner) What is the expression @(x) mean?

B = cellfun(@(x) x(1:3),str,'UniformOutput',false)

Does cellfun() accessing one cell of the cell array temporarily store the data inside the cell in the variable x?

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Answer 1

Rik 2020-5-20

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https://ww2.mathworks.cn/matlabcentral/answers/527468-explanation-of-cellfun#answer_434183

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cellfun will apply your function to the contents of you cell array. There are 3 ways to specify the function:

as a char array (only a handfull of functions, but they are faster when called like this)\
with a function handle, e.g. @mean
with an anonymous function

That last option is what you see here.

%instead of this
function output=MyFun(in1,in2)
output=in1.*in2;
end
%you do
MyFun=@(in1,in2) in1.*in2;

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Rik 2021-11-24

在 MATLAB Online 中打开

Actually the answer is yes.

Cellfun in this case is only hiding the loop.

%the first step is to create an anonymous function with the input variable
%called data. The values of the variables centers and centerstats are
%captured with their current value.
anon_function=@(data) regimeNormalization(data, centers, centerstats);
%the second step is to execute the anonymous function for each cell and
%store the result in a cell array.
trainDataNormalized = cellfun(anon_function, ...
    trainData, 'UniformOutput', false);

The cellfun call is equivalent to this:

trainDataNormalized=cell(size(trainData));
for n=1:numel(trainData)
    trainDataNormalized{n}=anon_function(trainData{n});
end

The loop will probably be marginally faster.

It is the anonymous function that is evaluated for every cell. Inside that anonymous function there is a call to regimenNormalization, which is called with the data in each cell, but also with two other variables that were captured when the anonymous function was created.

Marvin Eckert 2021-11-25