Nima Sarajpoor

@meghjoshii **Just sharing my thoughts:** To the best of my knowledge, K-Means updates its cluster centroids throughout the fitting process, and, in each iteration, it calculates the distance between observations...

I believe you can get that from your fitted model. You may want to take a look at the document: https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.TimeSeriesKMeans.html And look for inertia

@meghjoshii * Approach 1: create a git branch and modify the module so it can return the last distance matrix at the end of process, which is the distance matrix...

@ajanadj Hi...I am just interested in what you mentioned. Do you know how this can be done for simple tabular data with `n` samples and `p` features? I am trying...

I am not sure...this is based on what I read in a scipy PR: https://github.com/scipy/scipy/pull/14813 > tslearn/metrics/cysax.pyx:1: in init tslearn.metrics.cysax So, would you mind trying this? ``` # in tslearn/metrics/cysax.pyx...

@YannCabanes Do you mind if I go through the changed files and review them, and share with you my thoughts? I think it would help me improve my "review" skill...

> Yes, you can review my codes. Any help is welcome. Cool > we will try to replace the cython file by python files in which we will use numba....

@YannCabanes Great effort! Sure.

> Interesting. Would you be able to benchmark execution speeds before and after your suggestion? Sure I can. Just to confirm, the `python setup.py install` should suffice when I am...

Performance Code: ``` # in function tslearn/metrics/dtw_variants.py # test on function `njit_accumulated_matrix_from_dist_matrix` # input: seed = 0 np.random.seed(seed) D = np.random.rand(10000, 10000) mask = np.full_like(D, 1, dtype=bool) njit_accumulated_matrix_from_dist_matrix(D, mask) #...