Hey Serafim, In this line https://github.com/scikit-learn/scikit-learn/blob/1495f69242646d239d89a5713982946b8ffcf9d9/sklearn/cluster/k_means_.py#L303 you can see that a randomstate object is constructed and that object is passed in the for loop that you are referring to, not the integer value that was passed in the function.
Cheers, Chris On Mon, 5 Aug 2019 20:58 serafim loukas, <seral...@hotmail.com> wrote: > 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 >
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