class kmeanstf.kmeanstf.KMeansTF(n_clusters=8, init='k-means++', n_init: int = 10, max_iter=300, tol=0.0001, verbose=0, random_state=None)

implements k-means/k-means++

For full desription of methods see base class BaseKMeansTF

  • n_clusters (int) – The number of clusters to form as well as the number of centroids to generate.
  • init ('k-means++','random' or array) – method of initialization
  • n_init (int) – number of runs with different initializations (default 10)
  • max_iter (int) – Maximum number of Lloyd iterations for a single run of the k-means algorithm.
  • tol (float) – Relative tolerance with regards to inertia to declare convergence.
  • verbose (int) – Verbosity mode.
  • random_state (int) – None, or integer to seed the random number generators of python, numpy and tensorflow
  • cluster_centers (array, [n_clusters, n_features]) – Coordinates of cluster centers. If the algorithm stops before fully converging (see tol and max_iter), these will not be consistent with labels_.
  • labels (array, shape(n_samples)) – Labels of each point, i.e. index of closest centroid
  • inertia (float) – Sum of squared distances of samples to their closest cluster center.
  • n_iter (int) – Number of iterations run.
__init__(n_clusters=8, init='k-means++', n_init: int = 10, max_iter=300, tol=0.0001, verbose=0, random_state=None)

Initialize self. See help(type(self)) for accurate signature.


__init__([n_clusters, init, max_iter, tol, …]) Initialize self.
fit(X) Compute k-means clustering.
fit_predict(X) Compute cluster centers and predict cluster index for each sample.
get_errs_and_utils(X[, centroids]) Get error and utility values wrt.
get_gaussian_mixture([n, d, g, sigma]) generate test data from Gaussian mixture distribution
get_history() Get collected history data of performed run of fit().
get_log([abbr]) Get statistics of performed run of fit()
get_params() Get params used to define class
get_system_status([do_print]) print tensorflow version and availability of GPUs.
predict(X) Predict the closest cluster each sample in X belongs to.
self_test([X, n_clusters, n_init, n, d, g, …]) self-testing routine
set_random_seed(seed) setting random seed for tensorflow, python and numpy