To solve a problem I need manhattan distances between all the vectors. I tried sklearn.metrics.pairwise_distances but the size was too large, so in order to decrease memory footprint I used scipy.spatial.distance.pdist to get the condensed 1D matrix of distances.
I used below formula:
index = diagonalShape*(diagonalShape-1)/2 - (diagonalShape-i)*(diagonalShape-i-1)/2 + j - i - 1
to calculate the index of the 1D matrix to get the distance value of ij.
I've observed that for many entries the distances are different form scipy and sklearn. Why this is so when the formula used for calculating cityblock distances is same for both the libraries?
