algorithms.clustering.utils¶
Module: algorithms.clustering.utils
¶
Functions¶
-
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
- Labelsarray of size n, the discrete labels of the input items
- J (float): the final value of the inertia criterion
-
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