kmeanstf.kmeanstf.KMeansTF¶
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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
Parameters: - 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
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.
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__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.
Methods
__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