ENH: optimize memory for euclidean-distance estimation#6
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ENH: optimize memory for euclidean-distance estimation#6
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I made also a similar improvement in compute_var_cov_pt function inside compute_DCBC_pt. |
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The computation of the euclidean distance can be very memory intensive if a and b are large. This is because the matrix dist has a shape of (N,N), where N is the number of samples (e.g., cortical nodes), and its size grows quadratically with N. In long analysis pipelines, the system can run in insufficient memory, killing the process. Here, there's an updated version of the euclidean_distance function that delivers identical results compared with the original one.