How to Roll Matrix Rows Independently Using Advanced Indexing?

Rolling Matrix Rows Independently with Advanced Indexing

In the realm of data manipulation, it's often necessary to perform complex operations on multi-dimensional arrays. One such scenario involves rolling the rows of a matrix independently, based on provided shift values.

Given an input matrix A and an array of shift values r, the task is to apply the roll operation to each row of A using the corresponding shift from r. The desired result is:

[[0 0 4]
 [1 2 3]
 [0 5 0]]

Advanced indexing provides an elegant solution to this challenge. By leveraging negative shift values and advanced array slicing techniques, you can efficiently implement the roll operation as follows:

<code class="python">rows, column_indices = np.ogrid[:A.shape[0], :A.shape[1]]

# Always use a negative shift, so that column_indices are valid.
# Alternative: r %= A.shape[1]
r[r < 0] += A.shape[1]
column_indices = column_indices - r[:, np.newaxis]

result = A[rows, column_indices]</code>

In this approach, ogrid generates a grid of indices corresponding to the rows and columns of A. By manipulating the column indices based on the negative shift values, the roll operation is effectively applied to each row. This method offers a highly efficient solution for rolling matrix rows independently, avoiding the need for loops.

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