Dear Sklearn community,
I have a simple question concerning the implementation of KMeans clustering algorithm. Two of the input arguments are the “n_init” and “random_state”. Consider a case where “n_init=10” and “random_state=0”. By looking at the source code (https://github.com/scikit-learn/scikit-learn/blob/1495f69242646d239d89a5713982946b8ffcf9d9/sklearn/cluster/k_means_.py#L187), we have the following: for it in range(n_init): # run a k-means once labels, inertia, centers, n_iter_ = kmeans_single( X, sample_weight, n_clusters, max_iter=max_iter, init=init, verbose=verbose, precompute_distances=precompute_distances, tol=tol, x_squared_norms=x_squared_norms, random_state=random_state) My question is: Why the results are not going to be the same for all `n_init` iterations since `random_state` is fixed? Bests, Makis
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
