python - Implementing K-Medoids in numpy: the medoids selection step -


as author of this question i'm trying implement k-medoids using numpy. i'm more interested in how implement medoids-individuation step (second step in [2]), consisting in selecting, cluster cluster, sample minimizes sum of distances other samples belonging same cluster. let assume have same structures described in [1]:

# number of samples n_samples = 5 # distance square matrix d = np.array([[ 0.,  3.04959014,  4.74341649,  3.72424489,  6.70298441],               [ 3.04959014,  0.        ,  5.38516481,  4.52216762,  6.16846821],               [ 4.74341649,  5.38516481,  0.        ,  1.02469508,  8.23711114],               [ 3.72424489,  4.52216762,  1.02469508,  0.        ,  7.69025357],               [ 6.70298441,  6.16846821,  8.23711114,  7.69025357,  0.        ]]) # medoids medoids = np.array([0, 3]) # cluster membership array cl = np.array([0, 0, 1, 1, 0])

i can't implement using numpy ... can me?

[edit] current best solution is:

for c in range(number_of_clusters):     ind = np.where(cl == c)[0]     m = np.argmin(d[np.ix_(ind, ind)].sum(axis=0))     medoids[c] = ind[m]


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