mlpy.cluster.vq.kmeans¶
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mlpy.cluster.vq.
kmeans
(x, k, n_iter=None, thresh=None, mean=None, fn_plot=None, return_assignment=False, return_err_hist=False, verbose=False)[source]¶ Hard cluster data using kmeans.
Parameters: x : array_like, shape (n, dim)
List of dim-dimensional data points. Each row corresponds to a single data point.
k : int
The number of clusters to fit.
n_iter : int, optional
Number of iterations to perform. Default is 100.
thresh : float, optional
Convergence threshold. Default is 1e-3.
mean : array_like, shape (ncomponents,), optional
Initial guess for the cluster centers.
fn_plot : callable, optional
A plotting callback function.
return_assignment : bool, optional
Whether to return the assignments or not. Default is False.
return_err_hist : bool, optional
Whether to return the error history. Default is False.
verbose : bool, optional
Controls if debug information is printed to the console. Default is False.
Returns: ndarray or tuple :
The cluster centers and optionally the assignments and error history.
Examples
>>> from mlpy.cluster.vq import kmeans