How to add specific rows that meet a condition?

Question

Thomas 2016-4-15

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编辑： Roger Stafford 2016-4-16

Hi,

I have two column vectors dim 98x1 one is the unique values of an array (A) and the other is the percentage that these values occur (B). What I am trying to get is to sum the percentile values that are between 0-1 of the unique values and then 1-2 and so on. So that I have the percentile values of occurrence for everything =<1, =<2, ..., =<26 Here is an example of what I am trying to do,

 %
    A = [0.1 0.5 1.0 1.2 1.6 2.0]' % The unique values
    B = [50.3 3.0 2.5 2.4 1.8 1.4]' % The percentile values
    C = [55.8 5.6] % The percentile values that are in the range specified

So far I have been working with the following loop

%
  for i = 1:size(A,1)
     for j = 0:25
        if A <= i & A >= j
        C(i,j) = sum(B(j,i);
        end
     end
   end

Not entirely sure I I am going the right way about this. If anyone has any advice it would be greatly appreciated.

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Answer 1

Wayne King 2016-4-15

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在 MATLAB Online 中打开

How about just

[sum(B(A<=1)) sum(B(A>1 & A<=2))]

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Thomas 2016-4-15

在 MATLAB Online 中打开

Thanks this works a treat, however, I am trying to incorporate it into a for loop to complete multiple iterations but I'm having difficulty getting it to move to the next column. I've put it in like this,

%
for i = 1:26
 for j = 2:27
    C = [sum(B(A<=1)) sum(B(A>i & A<=j))]
 end
end

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Answer 2

Roger Stafford 2016-4-15

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https://ww2.mathworks.cn/matlabcentral/answers/279169-how-to-add-specific-rows-that-meet-a-condition#answer_218032

编辑：Roger Stafford 2016-4-15

在 MATLAB Online 中打开

   N = 1:26;
   C =sum(bsxfun(@times,bsxfun(@le,A,N),B));

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Thomas 2016-4-15

This adds the first values together in the range of 0-1 but for 1-2 it adds the first set of values. Is there anyway to separate between each range?

Roger Stafford 2016-4-16

编辑：Roger Stafford 2016-4-16

@Thomas: My apologies. Forget that solution I gave you. My head was not on straight this morning. There is a valid method using 'histc' and 'accumarray', but you might as well use obvious methods with for-loops for this unless your data is very large. (Let me know if you want to know the 'histc-accumarray' method.)

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