what does it mean by index exceed matrics dimension

%function for calculate the RMSE
function RMSE = rmse(data, k)
%here size of data and k must be same
%initially remove records with NaNs in data and k
dd = ~isnan(data) & ~isnan(k);
%for data
data = data(dd);
%for k
k = k(dd);
%compute RMSE
RMSE = sqrt(sum((data(:) - k(:)).^2) / numel(data));
end

[Walter Roberson]

In R2016b or later, you could get that error in the line

data = data(dd);

in the circumstance that the input data and k were not the same size, especially if one of them was a row and the other one was a column.

[Stephen23]

"what does it mean by index exceed matrics dimension"

Imagine that you define a vector with four elements in it:

M = [1,2,3,4];

And then you try to access the one millionth element:

M(1000000)

Does that vector have a one-millionth element? No, it does not. Trying to access an element that does not exist is an error. That is what MATLAB is telling you: your index exceeds the size of the array that you are indexing into.

Answer 1

[ANKUR KUMAR]

"index exceed matrix dimension" means that the index which you are trying of extract from matrix is out of the size of matrix. For example,

     >> A=rand(4,6)

 A =

    0.1622    0.1656    0.6892    0.2290    0.5383    0.1067
    0.7943    0.6020    0.7482    0.9133    0.9961    0.9619
    0.3112    0.2630    0.4505    0.1524    0.0782    0.0046
    0.5285    0.6541    0.0838    0.8258    0.4427    0.7749

The size of A is 4*6. Now, if you try to extract 2,7 element from A, then it will show the error that

>> A(2,7)
Index exceeds matrix dimensions.

Answer 2

[KALYAN ACHARJYA]

As per @Walter Sir Apart from this, there is no error like Index exceeds matrix dimensions.

Regarding Index exceeds matrix dimensions @Ankur well explained

Be Ensure that data and k having the same size, it must be, a k is resultant from data

data original and k is the precessed, definitely, both have the same size.

I have tested the code an example, no error

function RMSE=rmse(data,k);
%here size of data and k must be same
%initially remove records with NaNs in data and k
dd=~isnan(data) & ~isnan(k);
%for data
data=data(dd);
%for k
k=k(dd);
%compute RMSE
RMSE=sqrt(sum((data(:)-k(:)).^2)/numel(data));
end

And called the function

data=rgb2gray(imread('test.png'));
k=imnoise(data,'gaussian');
result=rmse(data,k);