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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Fri May 20 15:18:56 2022 | |
| @author: dinesh | |
| """ | |
| import numpy as np | |
| import math | |
| def kMedoids(D, k, tmax=100): | |
| # determine dimensions of distance matrix D | |
| m, n = D.shape | |
| np.fill_diagonal(D, math.inf) | |
| if k > n: | |
| raise Exception('too many medoids') | |
| # randomly initialize an array of k medoid indices | |
| M = np.arange(n) | |
| np.random.shuffle(M) | |
| M = np.sort(M[:k]) | |
| # create a copy of the array of medoid indices | |
| Mnew = np.copy(M) | |
| # initialize a dictionary to represent clusters | |
| C = {} | |
| for t in range(tmax): | |
| # determine clusters, i. e. arrays of data indices | |
| J = np.argmin(D[:,M], axis=1) | |
| for kappa in range(k): | |
| C[kappa] = np.where(J==kappa)[0] | |
| # update cluster medoids | |
| for kappa in range(k): | |
| J = np.mean(D[np.ix_(C[kappa],C[kappa])],axis=1) | |
| j = np.argmin(J) | |
| Mnew[kappa] = C[kappa][j] | |
| np.sort(Mnew) | |
| # check for convergence | |
| if np.array_equal(M, Mnew): | |
| break | |
| M = np.copy(Mnew) | |
| else: | |
| # final update of cluster memberships | |
| J = np.argmin(D[:,M], axis=1) | |
| for kappa in range(k): | |
| C[kappa] = np.where(J==kappa)[0] | |
| np.fill_diagonal(D, 0) | |
| # return results | |
| return M, C |