algorithms.clustering.utils¶
Module: algorithms.clustering.utils
¶
Functions¶
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nipy.algorithms.clustering.utils.
kmeans
(X, nbclusters=2, Labels=None, maxiter=300, delta=0.0001, verbose=0, ninit=1)¶ kmeans clustering algorithm
Parameters: X: array of shape (n,p): n = number of items, p = dimension :
data array
nbclusters (int), the number of desired clusters :
Labels = None array of shape (n) prior Labels. :
if None or inadequate a random initilization is performed.
maxiter=300 (int), the maximum number of iterations before convergence :
delta: float, optional, :
the relative increment in the results before declaring convergence.
verbose: verbosity mode, optionall :
ninit: int, optional, number of random initalizations :
Returns: Centers: array of shape (nbclusters, p), :
the centroids of the resulting clusters
Labels : array of size n, the discrete labels of the input items
J (float): the final value of the inertia criterion :
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nipy.algorithms.clustering.utils.
voronoi
(x, centers)¶ Assignment of data items to nearest cluster center
Parameters: x array of shape (n,p) :
n = number of items, p = data dimension
centers, array of shape (k, p) the cluster centers :
Returns: z vector of shape(n), the resulting assignment :