Vectorizing a simple accumulation?

Question

Ephedyn 2012-9-20

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/48642-vectorizing-a-simple-accumulation

I have something like a sparse array, whose first member is always nonzero, and I want to replace each zero element with the nearest non-zero element right before it. For example:

matrixData = [1.3; 0; 0; 0; 4.2; 0; 0; 1.5; 0; 0; 0; 0];

should become

matrixData = [1.3; 1.3; 1.3; 1.3; 4.2; 4.2; 4.2; 1.5; 1.5; 1.5; 1.5; 1.5];

I am currently using a loop:

emptyRows = (matrixData ==0);
for i = 2:length(matrixData)
    if emptyRows(i)
        matrixData(i) = matrixData(i-1);
    end
end

This is the performance bottleneck on my function, and it becomes very slow as I deal with extremely long arrays, and I can't think of a way to speed it up. (Can't parallelize it because the elements are non-independent.) Is there a way to vectorize this using accumarray or anything similar?

Thanks!

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Answer 1

Sean de Wolski 2012-9-20

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https://ww2.mathworks.cn/matlabcentral/answers/48642-vectorizing-a-simple-accumulation#answer_59446

编辑：Sean de Wolski 2012-9-20

在 MATLAB Online 中打开

matrixData = [1.3; 0; 0; 0; 4.2; 0; 0; 1.5; 0; 0; 0; 0];
idxk = find(matrixData);
idxr = cumsum(logical(matrixData));
matrixData = matrixData(idxk(idxr));

One of many ways...

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Ephedyn 2012-9-20

Thanks a lot! This solved my problem and is amazingly powerful. I wish I could accept both yours and Jan's answers for credit, as I had use for both.

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Answer 2

Jan 2012-9-20

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/48642-vectorizing-a-simple-accumulation#answer_59447

编辑：Jan 2012-9-20

在 MATLAB Online 中打开

If your vectors are really large, try a Mex function:

#include "mex.h"
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) {
  mwSize n, i;
  double *X, q, *Y;
  n = mxGetNumberOfElements(prhs[0]);
  plhs[0] = mxCreateDoubleMatrix(n, 1, mxREAL);
    X = mxGetPr(prhs[0]);
    Y = mxGetPr(plhs[0]);
    q = mxGetNaN();
    for (i = 0; i < n; i++) {
      if (X[i] != 0.0) {
        q = X[i];
      }
      Y[i] = q;
    }
    return;
  }

The M-version needs some large temporary arrays:

t1 = find(matrixData)
t2 = logical(matrixData))
t3 = cumsum(t2)
t4 = idxk(idxr)

Therefore the C-method should have a great advantage.

This function can be parallelized: Use two additional inputs as inital and final index. Skip the inital phase until the 2st non-zero is found instead of inserting NaNs. Proceed after the final index until the next non-zero element as long a the vector length is not exceeded. This should scale very well with the number of cores.

Depending on the processor, this could be faster than the IF method:

int m;
for (i = 0; i < n; i++) {
  m    = (X[i] == 0);
  q    = X[i] * m + q * (m - 1);
  Y[i] = q;
}

[EDITED] No, avoiding the IF is some percent slower. Some percent faster:

for (i = 0; i < n; i++) {
  if (X[i] == 0) {
     Y[i] = Y[i - 1];
  } else {
     Y[i] = X[i];
  }
}

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Ephedyn 2012-9-20

As above, I ended up implementing your solution in the production code though I had to debug in the command window (the actual function is a bit more complicated) using Sean's response. I'll really like to give my deepest gratitude to both of you and wish I could give both credit for answering my question. Thanks aplenty!

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