mlxtend version: 0.13.0

Kmeans

Kmeans(k, max_iter=10, convergence_tolerance=1e-05, random_seed=None, print_progress=0)

K-means clustering class.

Added in 0.4.1dev

Parameters

  • k : int

    Number of clusters

  • max_iter : int (default: 10)

    Number of iterations during cluster assignment. Cluster re-assignment stops automatically when the algorithm converged.

  • convergence_tolerance : float (default: 1e-05)

    Compares current centroids with centroids of the previous iteration using the given tolerance (a small positive float)to determine if the algorithm converged early.

  • random_seed : int (default: None)

    Set random state for the initial centroid assignment.

  • print_progress : int (default: 0)

    Prints progress in fitting to stderr. 0: No output 1: Iterations elapsed 2: 1 plus time elapsed 3: 2 plus estimated time until completion

Attributes

  • centroids_ : 2d-array, shape={k, n_features}

    Feature values of the k cluster centroids.

  • custers_ : dictionary

    The cluster assignments stored as a Python dictionary; the dictionary keys denote the cluster indeces and the items are Python lists of the sample indices that were assigned to each cluster.

  • iterations_ : int

    Number of iterations until convergence.

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/classifier/Kmeans/

Methods


fit(X, init_params=True)

Learn model from training data.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • init_params : bool (default: True)

    Re-initializes model parameters prior to fitting. Set False to continue training with weights from a previous model fitting.

Returns

  • self : object

get_params(deep=True)

Get parameters for this estimator.

Parameters

  • deep : boolean, optional

    If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

  • params : mapping of string to any

    Parameter names mapped to their values.'

    adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py

    Author: Gael Varoquaux gael.varoquaux@normalesup.org

    License: BSD 3 clause


predict(X)

Predict targets from X.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

  • target_values : array-like, shape = [n_samples]

    Predicted target values.


set_params(params)

Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Returns

self

adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py

Author: Gael Varoquaux gael.varoquaux@normalesup.org

License: BSD 3 clause