Hi,
Based on the question, it can be understand that parfor is working slow for your code.
There are a few reasons why the code you provided may be slow:
- Using find and indexing with logical operations: In the line find_idx = find(potential_rep_idx == unique_idx(i)), you are using the find function with a logical operation to index into the potential_rep_idx vector. This creates a temporary logical vector, which can be memory-intensive and slow for large arrays.
- Using mode function inside a loop: The mode function is being used inside a loop, which can be inefficient for large datasets. It is generally better to use vectorized operations instead of loops whenever possible.
- Creating a new table in each iteration of the loop: Inside the loop, a new table is being created in each iteration using array2table. This can be memory-intensive and slow for large datasets.
To improve the performance of the code, you can consider the following:
- Avoid using find and logical indexing: Instead of using find and logical indexing, you can use the ismember function to directly find the indices of the unique values in the potential_rep_idx vector.
- Use vectorized operations instead of loops: You can use the splitapply function to split the table into groups based on the values in the potential_rep_idx vector, apply the mode function to each group, and then combine the results into a single table. This can be much more efficient than using a loop.
- Avoid creating a new table in each iteration of the loop: Instead of creating a new table in each iteration of the loop, you can preallocate a matrix or cell array to store the results and then convert it to a table after the loop is finished.
Hope it helps,
Raghav Bansal
