kmeanstf.kmeanstf.TunnelKMeansTF

class kmeanstf.kmeanstf.TunnelKMeansTF(n_clusters=8, init='random', n_init=1, max_iter=300, tol=0.0001, verbose=0, random_state=None, max_tunnel_iter=300, max_tunnel_moves_per_iter=100, criterion=1.0, local_trials=1, collect_history=False)

implements tunnel k-means

For full desription of methods see base class BaseKMeansTF

Parameters:
  • n_clusters (int) – The number of clusters to form as well as the number of centroids to generate.
  • init ('random', 'k-means++' or array) – method of initialization
  • n_init (int) – number of runs of the initial k-means phase with different initializations (default 1). Only one tunnel phase is performed even if n_init is larger than 1.
  • 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
  • max_tunnel_iter (int) – how many tunnel iterations to perform maximally
  • max_tunnel_moves_per_iter (int) – how many centroids to move maximally in one tunnel iteration
  • criterion (float) – inital required ratio error/utility (is increased adaptively)
  • local_trials (int) – how many time should each tunnel move be repeated with different random offset vector (1 or larger)
  • collect_history (bool) – collect historic information on inertia, criterion, tunnel moves, codebooks
Variables:
  • 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='random', n_init=1, max_iter=300, tol=0.0001, verbose=0, random_state=None, max_tunnel_iter=300, max_tunnel_moves_per_iter=100, criterion=1.0, local_trials=1, collect_history=False)

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

The Methods

__init__([n_clusters, init, n_init, …]) 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