Khue - 1 year ago 76

C++ Question

I have a function

`compute()`

a matrix

`C`

`X`

- For each column of C, if the sum of its positive elements are less than or equal to 1, then the column
`C_j`

of`X_j`

is the same as`X`

, except that the non-positive elements are set to zeros, i.e.`C_j`

(in Matlab language).`X_j = C_j(C_j > 0)`

- Otherwise where
`X_j = f(C_j)`

is some function. This part is not concerned in this question.`f`

What I have tried so far.

`MatrixXd compute(MatrixXd &C)`

{

// The matrix to return

MatrixXd X(C.rows(), C.cols());

// Thresholding C: if negative then set to 0

MatrixXd P = (C.array() > 0).select(C, 0);

// Compute the sum of each column of P (i.e. the

// sum of positive elements in C

MatrixXd S = P.colwise().sum();

// Now want to set all column X_j to P_j whenever S_j <= 1

// I don't know how to vectorize this code

// It's easy in Matlab: X(:, S <= 1) = P(:, S <= 1);

for(int j = 0; j < S.cols(); j++){

if(S(j) <= 1)

X.col(j) = P.col(j);

}

return X;

}

For each column of

`C`

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Answer Source

The shortest solution to your problem I found is this:

```
MatrixXd compute(const MatrixXd& C)
{
MatrixXd P = C.cwiseMax(0.0); // cheaper than (...).select(C,0)
RowVectorXd S = P.colwise().sum(); // colwise().sum() returns only one row
MatrixXd X = (S.array()<=1).replicate(C.rows(), 1).select(P, 0.0);
// compare by 1 --^ ^ ^
// replicate result of comparison ---/ |
// select P or 0 depending on comparison ----/
return X;
}
```

Notice that neither comparisons nor `select`

are currently (version 3.3rc2) vectorized in Eigen (but they eventually shall be, for any progress on that see http://eigen.tuxfamily.org/bz/show_bug.cgi?id=97).
That means that your current loop-implementation could actually be faster (which likely also depends on several other factors, like the size of your input).

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