var(I(:)); Means?
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Hi, I have this problem
signal_var = var(I(:));
What's the meaning of the code below?
var(I(:));
Thanks a lot :)
9 个评论
Oleg Komarov
2012-5-5
You can execute the code piece by piece, just highlight for example I(:) and press F9.
Also, doc var.
Superb
2012-5-5
Oleg Komarov
2012-5-5
F9 executes the code.
Superb
2012-5-6
Walter Roberson
2012-5-6
The key depends upon the operating system. For Linux it is F5 .
Superb
2012-5-6
Image Analyst
2012-5-6
I didn't know that. By the way, in WIndows 7 F9 will evaluate the selection (e.g. the line of code you're on in the command history panel - just right click and see), while F5 says to run the whole m-file, or to continue running non-stop if you had stopped somewhere inside the m-file. So for Windows 7 they both work. Of course this has not much to do with the original question, which was answered twice below (but apparently unseen by Superb) in clear and explicit detail.
Walter Roberson
2012-5-6
Ah, I misread. I never used "evaluate selection".
Image Analyst
2012-5-6
I never do either.
回答（2 个）
the cyclist
2012-5-5
1 个投票
I(:) treats the array I as a column vector (instead of, say, maybe the MxN array that it was). The var() function takes the variance of that column vector. [See "help var" for details.]
(That assumes that you did not define var as a variable name earlier in the code. If var is a variable, then this code is indexing into that variable.)
Image Analyst
2012-5-5
1 个投票
Superb, normally var() will take the mean of each column of a 2D array and return a row vector where each element is the variance of the corresponding column. The programmer didn't want that - he wanted the variance of the entire array, not a column-by-column variance. So to do that you can use the (:) construct, which basically takes all the elements in an array (of any dimension) and strings them all together in a single 1D list of numbers (called a "column vector"). Once you've done that, then the var() function will operate on the whole array, not column-by-column. Now it will return a single number rather than a 1D row vector of the column variances. Does that explain it well?
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